Acarbose is one of a group of oligosaccharides (complex carbohydrates) which inhibit enzymes of starch and disaccharide digestion. It was introduced in 1990 by Bayer AG, Germany under the trade name Glucobay. In the U.S. it is marketed under the name Precose. Other trade names are Glumida and Prandose. It is also available in generic.

Specifically, acarbose is called an alpa-glucoside inhibitor as it inhibits the action of alpha-amylase and alpha-glucoside enzymes which are both responsible for hydrolyzing complex carbohydrates in the small intestine. By delaying the absorption of glucose after meals, acarbose is therefore useful in controlling rises in blood glucose levels after meals for Diabetes mellitus type II therapy. Acarbose can be used when diet alone fails to control blood glucose levels. Insulin dependent patients can also sometimes have benefit from acarbose because of the reduction in the exaggerated rise in blood glucose level after a meal.

Acarbose was discovered by researchers at Bayer during a screening of the culture broth of Actinoplanes, a bacteria of the genera actinomycetes, which are organisms with characteristics of both bacteria and fungi, but considered bacteria.

Acarbose has been improperly marketed as a 'starch blocker' to accelerate fat loss for the general population, with little evidence of efficacy, as it does not normally induce weight loss or malabsorbtion.

General Adverse Side Effect of Acarbose

The major side effects of acarbose are flatulence, abdominal pain and discomfort, abdominal bloating (distension), and diarrhea. These can be severs enough to prevent acarbose from being used. Also possible are more severe effects such as liver impairment or toxicity with jaundice. Acrobose could be dangerous to those with an intestinal obstruction, malabsoprtion, inflammatory bowel disease, liver disease, colonic ulceration, and diabetic ketoacidosis. It has also been known to affect the sense of taste.

Acarbose should only be used under the superivision of a physician and for blood sugar control, in line with its documented use as an antibiabetic drug. Acarbose has no efficacy in fat loss.

by EricT

The term we use to describe the energy derived from foods is Calorie. In other words, the terms energy and Calorie, when applied to foods, are synonymous. One calorie is defined as the quantity of heat necessary to raise one kg (1 liter) of water 1°C. What we call a calorie, therefore, is actually a kilogram calorie or kilocalorie, which is abbreviated kcal. If a food contained 100 kcal, then the energy the food contained would increase the temperature of 100 liters of water by 1°C. A capital C is used here, in the word Calorie, to indicate the kilocalorie, since one calorie would actually be the amount of heat needed to raise the temperature of 1 gram of water by 1°C. For more on the calorie, and its problems, see Calorie Confusion.

The energy from foods is measured using direct calorimetry. The instrument used is called a bomb calorimeter. This is a metal container inside which the food sample is burned in a sealed and pressurized pure oxygen environment. The food is burned inside the reaction chamber which is ignited by an electrified fuse running through the top of the chamber. This ignites the oxygen and food inside the chamber, which, as it burns, gives off heat which is absorbed into the surrounding tank. This chamber is surrounded by a water bath which is well-insulated from any changes in temperature due to the outside environment. Therefore, the heat from the food in the reaction chamber also raises the temperature of the water to some extent, which is recorded by a highly accurate thermometer. The change in temperature of the water, once the food is combusted, is the heat of combustion or the thermal energy of the food, hence the caloric content. The average caloric content of a wide variety of foods has been determined by this method. This information can be used to derive an average caloric value for types of foods, and for macronutrients.

Bomb Calorimeter, older setup than
the one below.
image by Akshat Goel via wikimedia

However, the thermal energy, or gross energy value of the food, does not represent the actual net energy that a human being can derive from the food. One reason is that digestion is not one hundred percent efficient. Since one hundred percent of all foods cannot be digested and absorbed, not all the energy can be extracted from the food nutrients through metabolism. The efficiency of digestion for different foods is called the coefficient of digestibility. This is usually given as a percentage value. For example, meats and fish, on average, have 97% digestibility.

Bomb Calorimeter, more modern setup
than the one below.
image by Harbor1 via wikimedia

In addition to energy lost through digestion, some amount of energy is also lost during the process of metabolism. This is especially true of protein, since the body cannot oxidize the nitrogen component. The nitrogen must be combined with hydrogen to form urea, which is excreted in the urine. This loss of hydrogen, to deal with the nitrogen, represents a loss of energy, which averages about 19% of a protein molecule's energy lost.

Because of this energy loss, the average caloric content as determined through direct calorimetry and the net energy a human derives will differ.

Lipid Calories

Not all lipids (fats) contain the same amount of energy, as determined through calorimetry. One gram of beef or pork fat yields about 9.5kcal, and this is the average for one gram lipids from meat, fish, or eggs. One gram of butter fat yields about 9.27, and one gram of dairy fat gives about 9.25. Lipids from vegetables and fruits averages 9.30 kcal.

High Fat Foods

The average heat of combustion (bomb calorimeter) for lipid is generally given as 9.4 kcal per gram. The net energy (average calories) for humans is the same, at 9.4 kcal per gram of fat. This is usually rounded to 9 calories per gram.

Carbohydrate Calories

Gross energy from glucose is 3.74 kcal per gram, and 4.20 for starch. The average for carbohydrate is given as 4.2 kcal per gram gross energy, and is the net energy is for humans is the same. The actual amount of energy from any carbohydrate varies depending on the shape of the molecule. The calories from carbohydrate is usually rounded to 4 calories per gram.

Protein Calories

Energy from protein can depend on the amount of nitrogen the protein contains, as well as the digestibility of the food. The higher the nitrogen content, the lower the amount of energy that can be derived through human metabolism. Proteins from meat, eggs, beans, and corn have about 16% nitrogen. Protein from nuts and sees, and most grains (cereals) have a higher nitrogen content of around 18.9%. Protein from milk has a lower nitrogen content of about 15.7%.

The average heat of combustion for protein is 5.65 kcal per gram. The average net energy for humans is generally given as 4.2 kcal per gram, the same as for carbohydrate. This, again, is usually rounded to 4 calories per gram.

Alcohol Calories

Alcohol (ethanol) yields around 7 kcal per gram through direct calorimetry. The net energy for humans is usually the same. If, however, a large amount of alcohol is consumed, it is possible for less energy to be available to the body, which may be due to damage to the mitochondria in the liver cells.

by EricT

Turning over a new leaf, just for this post, at least, I decided to actually write about fat loss. People who read my articles regularly know that I do not hand out weight loss advice. But a fun subject, and one a knowledgeable feller like myself can tackle, is the "negative calorie" claim that has surfaced through the years. The thing about this claim is that it can seem logical at first glance, to someone with no in-depth knowledge of nutrition, and at the heart of it, there is a kernel of truth. For those without knowledge and those who wish to cash in on that market, a kernel of truth is all that is needed.

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Negative Calories and TEF

Negative Calories has to top the list of magical weight loss myths, because it would be the most amazing if it were true. This myth says that there are some foods that by virtue of their composition require more energy to chew and digest than they actually impart to the body, thus giving "negative calories" and helping to lose fat. Basically, you are burning calories by eating the food, so if you eat a lot of it, you'll lose weight!

It is actually pretty hard to believe, at present, that people swallowed this one, but they did. Oh, they really did! Celery was the king of negative calories. Other contenders are cabbage, lettuce, cucumbers, and Snicker bars.

But I did say there was a kernel of truth, didn't I? Well the idea is based on what is called the thermic effect of food (TEF). Of course, when someone comes up with a term like that, someone else always has to one-up them so there are other names for it like diet-induced thermogenesis (DIT), specific dynamic effect (SDE) of food, and specific dynamic activity (SDA) of food. You'll understand if I stick to thermic effect or "TEF" for short.

TEF is one of the three categories that total energy expenditure (TEE) is broken down into. The other two are your resting energy expenditure(REE) and your voluntary physical activity. You may also have heard the terms "basal energy expenditure" (BEE) or "basal metabolism rate" (BMR). See the section I added below for further explanation. For now, know that resting energy expenditure typically accounts for about 70 percent of TEE. Physical activity accounts for about 20 percent. That leaves just around 10 percent for the TEF. 1,2 In clinical settings, TEF is not measured, but rather estimated based on the equation:

TEE = (REE + EEPA) x 1.10

Again, TEE is total energy expenditure, REE is resting energy expenditure and EEPA is energy expended in physical activity. And the factor of 1.10 accounts for the TEF.2

The TEF is the energy required to process the food you eat. It takes some energy to chew it, digest it, transport it, absorb it, metabolize its nutrients, and to store some nutrients. You chew food. That takes a bit of energy. The muscles in your GI tract speed up their contractions, so that takes some energy. Digestive juices and enzymes are secreted, and that takes some energy as well.3 All this produces heat, thus a "thermic effect". To produce heat is to expend calories.

The thermic effect of food actually has another component. The energy required to process the food, as I've described above, is called the obligatory thermogenesis. The other component is facultative thermogenesis. This is the excess energy expended above the obligatory thermogenesis due to metabolic inefficiency. 2

This COSMED device is an indirect calorimetry device.
Indirect calorimetry is a way of estimating energy expenditure in
steady state conditions from the rate of oxygen consumption (Vo₂),
and the rate of carbon dioxide production (Vco₂).
Image courtesy of COSMED

So how much energy are we talking about, for the thermic effect? Well it's easily overestimated. Or even underestimated. That's the problem. See, we can only estimate it in the first place and the errors involved in that estimate are likely so large that they offset any small contribution to calories burned. As you can see, it is simply assumed to be 10% and only the resting and physical energy requirements are measured actively. The composition of the diet greatly affects the TEF. The TEF is greater with carbohydrate and protein ingestion than for fat, protein being the greatest. It should be noted that this energy cost is probably more related to the cost of storage than to the cost of processing and absorption, although there are probably other components as well.

Also, other dietary factors such as spicy foods, caffeine, and nicotine can increase it. So based on our ten percent, let's say that a person who takes in 2000 calories a day burns around 200 calories to process the food….plus or minus how much?2,3

Looking at celery, you can see, as with many myths, there is an element of truth at base. Celery is 95% water and the rest mostly cellulose, which is difficult for our body to break down. One stalk contains only six to ten calories. The chewing part, well, that couldn't possibly burn enough calories as you'd have to chew for an hour to burn 5 to 10 calories, at an estimate. Which means an hour worth of celery. The math is a bit staggering to me so I'll leave that alone. The actual digestion? Sure, by the time the body deals with celery it is probably fair to say that there is no appreciable net gain in calorie energy. If you did nothing but chew on celery all day..you may even end up with a calorie deficit. But of course, you would never do that and there is no way you could ever eat enough celery to make a difference in your caloric intake. Celery is not only hard to digest, it is a nutritional pauper. A celery diet would be a diet with an absolute lack of proper nutrition.

The whole concept of a "negative calorie" is absurd in the first place. A calorie, by which we usually mean a kilocalorie, is a distinct positive value: the quantity of heat needed to raise the temperature of 1 kg of water from 0 to 1°C. There can never be a negative calorie.

Although most of the negative calorie hoopla is limited to advertisements or articles in questionable publications like the Weekly World News, it finds its way into mainstream books, magazines, and websites all the time. Negative calorie foods are often billed as foods that help you or even cause you to lose weight. Some M.D. named Neal D. Barnard actually wrote a book called Foods that Cause You To Lose Weight: The Negative Calorie Effect. 4

There is something called a Negative Calorie Diet but I am not sure if that is one fad diet, several, or just a general term for any diet claiming to utilize this effect. I really do not think that investigating that nonsense further is worth a few more hours of my life. Also, there is the Cabbage Soup Diet, which is based on the supposed negative calorie value of cabbage and other vegetables put in a soup. Or something like that.

Clever Rules for Negative Calorie Counting

Anahad O'Connor, author of the "Really" column in the New York Times, in his book Never Shower in a Thunderstorm: Surprising Facts and Misleading Myths About Our Health and The World we Live In 5, relates some funny stories of readers who wrote into the Times with some clever rules for "negative calorie counting." Most of these are obviously jokes, but the author does not really say so. Thing is, I've heard people say things like this so at least some of them may not have been joking. Here are the highlights:

• With foods like steamed crabs, it takes more energy to get at the crab meat than the crab actually contains. Therefore, you have to factor in the calories used in getting to the food in the first place. Marylanders rejoice!

• The cold beer rule: Cold beer has negative calories since it takes more energy to bring the beer down to body temperature than there is calories in the beer. Given the typical calorie count of beer, you'd have to say this one with a grin on your face and your finger crossed behind your back.

• The body can't count, so count your food, not your calories. This one says that your body cannot count very high so eat in multiples and make sure to eat so many of each thing that your body reaches its counting limit. Anything over this limit has no calories. Apparently, anything over six silver-dollar pancakes, your body cannot recognize. If the person who wrote in this one wasn't joking, they have serious mental deficiencies.

• Anything you resist eating…you get to subtract from your calories for the day. That's right..the temptations you don't give in to are negative calories. If you turn down a big chunk of chocolate cake, that's 400 calories less at the end of the day. Just today, I refused to eat cake, M&M's, and Doritos. I also avoided two Dr. Peppers which were taunting me. That puts me at, I don't know, 1500 calories down?5

Ice Water and Negative Calories

Ice water gets a lot of credit for its negative calorie effects. You saw the cold beer thing above right? Well, beer contains calories. How much better to drink ice water, with no calories. Why, it takes so much energy to warm that cold water up to body temperature, you could cancel out a whole meal by drinking ice water! Really? Of course not. But you could cancel out some of it though, right?

Let's see. For each gram of ice water the body will have to use about 37 calories to raise the temperature to body temp. Hey, this is looking good! I think we're on to something. Now, if I eat 3000 calories, I just need to figure out how much ice water I need to drink. It shouldn't matter when I drink it; I can drink it throughout the day or all at once, whatever. So, a gram of water is equivalent to a cubic centimeter of water:

(37 cal/cc)(#cc) = (3000 x 10³ cal)

Okay, so I divide 37 into 3000, and I get 81 and then, that leaves me with #cc = 81 x 10""3"" cc. So…the answer is 81 liters, or 20 gallons. It will take 20 GALLONS of ICE water to burn those calories. Oops.

Well, I only need to burn off that extra 500 calories.

(37 cal/cc)(#cc) = 500 x 10³ cal).

That is so much better. You can do that with only 13.5 liters of ice water. That is about 3.5 gallons. You try it and let me know how it works out for you. By the way, chewing the ice cubes works even better. That takes 80 calories for each gram of ice. Nice! And cold.

I know you are impressed with my math. Sucker! I can't even help my son with his fourth grade math, half the time. I got help from Back-of-The-Envelope Physics by Clifford E. Swartz, which is the only kind of physics book I can understand, except the kind of physics in most science fiction books.6.

Chewing Your Way Thin

I mentioned calories burned by chewing above. A lot has been made, in books and articles, about a study published in the New England Journal of Medicine back in 2007 7, usually given as "a doctor from the Mayo Clinic," which found that chewing gum burns 11 calories per hour. Eh, so that's a lot of chewing, and I have TMJ problems, so I had to give up gum. But, it's not like there is nothing to this. You see, there are more things to consider besides just the calories burned by the chewing. Maybe you avoid a high calorie snack. Or maybe there is an appetite suppression, or it contributes to resting energy expenditure in some way. Tom Venuto blogged about this, and brought up another study that dove further, so you can find out more in Does Chewing Gum Help You Lose Weight?

Here's the thing: I don't know about you but I find people who constantly chew gum to be a bit repulsive. Sorry to any dedicated gum chewers reading this but, it's a little off-putting to have someone chewing their cud while talking to you. So, consider that if you try the chewing gum diet, you may not be able to sit with me at the cool kid's table.

Negative Calories and the Dieter's Paradox

The belief in negative calories is part of the kind of thinking involved in what Alexander Chernev calls "The Dieter's Paradox." The kinds of foods promoted to be negative calorie foods fall into the category of healthy foods, for the most part. People are being encouraged to eat more healthy foods. In fact, it is outright shouted by every health organization in the world. Eat more health foods! Eat more fruits and vegetables; everybody needs to! This has been going on for a while. Yet, there seems to have been no impact on the obesity epidemic. According to Chernov, "An important factor contributing to this obesity trend is the misguided belief about the relationship between a meal's healthiness and its impact on weight gain, whereby people erroneously believe that eating healthy foods in addition to unhealthy ones can decrease a meal's calorie count."

Chernov asserts that although lack of willpower to control consumptive behaviors is often given as the chief cause of the still rising proportion of overweight people, this is not the only factor. He argues that over-consumption "might also stem from people's misguided belief about the relationship between a meal's healthiness and its impact on weight gain."

And there may be something to that. He gives the example of adding a side salad to an unhealthy meal and believing that the healthy option added to the unhealthy meal decreases the calorie count of the meal. This "halo affect" of healthy foods is believed to extend not only to the nutrient quality of the meal but also to its effect on weight gain. A side salad 'cancels out' other calories because of the negative calorie bias.8

Have you seen this behavior? Have you exhibited it? I've seen it. Two big macs and a side salad, anyone? This belief in protective effects is just one part of the dieter's paradox. The paradox, in general, is that the most weight-conscious people, especially dieters, are more prone to making irrational decisions about food, and therefore more prone to weight gain!

In chapter 4 of the book, Leveraging Consumer Psychology for Effective Health Communications, Chernov and co-author Pierre Chandon name this negative calorie bias is one of several biases that cause people to make errors in their estimation of caloric intake. The negative calorie balance falls under a larger bias, called the halo bias. Another main category of underestimation bias is the averaging bias.

Chernov says the halo bias "refers to the tendency of a particular feature of the food, such as nutrition labels or marketing claims that it is healthy, to influence the overall estimation of the calorie content of the food item or of an entire meal." And "the averaging bias refers to people’s tendency to average the calorie content of combinations of healthy and unhealthy items."

The problem with the idea of the halo bias is that the higher the actual calorie count of a food is, the more people tend to underestimate it. In a quick evaluation of a clients problem with his middle-age paunch, for instance, I asked him to list out his meals for a few days. He, of course, didn't think he was eating much and had no idea where all the calories were coming from. Typically, you will find that your clients have been severely underestimating (informally) the calories actually contained in the highest calorie items they tend to eat. For this client, the big problem was the habit of getting take-out lunches from convenience stores and consuming some "large-ticket" items without a clue as to the huge amount of calories he was taking in. Accordingly, he was eating a "healthy" turkey sandwich, for instance, but was chasing it with a large (jumbo) sweetened bottled iced tea that topped 500 calories, a large snack pack of potato chips or other chips that easily did the same. He simply severely underestimated just how many calories were coming from the most calorie laden things in his lunches. Comparably, his typical breakfast and dinner was healthy and moderate. It was easy for him to drop up to 1000 calories a day by just changing a few lunch habits! Hardly a life-changing feat. But this was not the "halo-bias" it was just a basic problem: the more calories a food actually contains, the more our estimates will fall short.

So, Wansink and Chandon controlled for this in a study where they asked consumers to estimate the number of calories in two ten-ounce cups. One contained M&M's and the other contained granola. Guess what, you may not have guessed this but both of these have pretty much the same number of calories. The M&M's contain 1,380 calories and the granola contained 1,330 calories. What do you think happened? Of course, the participants underestimated the calories in the granola, by 28% while overestimating the calories in the M&M's by 9 percent. This is consistent with the perception that granola is a "healthy snack" and that M&M's are not.

The halo bias is also caused by food label claims and other marketing descriptions. So claims of "low fat" or "healthy" will cause people to underestimate the true calorie count. These general effects are not isolated to the over-weight and calorie conscious but are also present in normal-weight people. So there is a lot more to it, of course, than one bias.

Even though a persons chief goal is to control caloric intake, the way they categorize different types of food as "healthy" or "unhealthy" will cause them to underestimate the calories in a meal, based on this perception. In reality, the number of calories in a meal is nothing more than the calories contained in each food item added together. But people often do not rely on reality to help them choose a meal, instead they rely on impressions. Consider the example given by Chernov and Chandon. You are limiting calorie intake and you have a choice between a hamburger or a hamburger and a side salad. You choose the hamburger and side salad. This is inconsistent with your goals, logically, because the two-item meal contains more calories. But you chose the higher calorie option.

What is going on here? For one, you may have perceived the salad as imparting some "benefits" to your health, such as vitamins, fiber, etc. The addition of a beneficial and healthful food, then, caused you to categorize the meal with the hamburger AND salad as more healthful than just the hamburger. The idea that something is more helpful can cause you to underestimate its calories..even with the 'math' in plain site. Biases, remember, are largely unconscious. You "averaged out" the low calorie and healthy salad with the high calorie hamburger causing you to derive a lower calorie estimate, this is the averaging bias. These biases are interrelated, as you can see.

Now, you might be thinking, the salad thing is just the negative calorie bias! That would make sense, at first glance. A person might believe that the greens in the salad consumed more energy to process than they gave, so that they actually ended up subtracting a bunch of calories from the meal. Well, some people might believe this but the negative calorie thing is more a fad belief than a consistent bias. In fact, when asked to consider such foods alone, consumers will not tend to assume they have zero calories or negative calories, but will give an actual calorie estimate.

To test this, Chernev and Gal asked participants to estimate the calorie content of a cheeseburger alone, a broccoli salad alone, or a meal containing both. The average calories given for the cheeseburger was 761 calories. The average calories given for the broccoli salad was 67 calories. Okay, so you can see that the participants did not perceive the broccoli salad as having negative calories, when viewed alone, despite broccoli frequently being viewed as negative calorie food. They gave a positive estimate. when we look at the combined meal group, the one who evaluated the cheeseburger and broccoli salad together, we should expect to get a number that is a combination of both the cheeseburger and broccoli salad number, approximately 830 calories, right? Wrong. This is not what happened. Instead, the combined meal group estimated that the cheeseburger-broccoli combo had 583 calories. That's 178 calorie LESS than the cheeseburger alone group. What seems to have happened is the averaging bias and the halo bias. The broccoli's perceived health benefits contrived to produce a lower estimate for the combined meal.

Just to be sure, they asked another group to compare a cheeseburger and a cookie in the same way. The cookie had the effect we would expect, causing the combined meal to be perceived as the highest calorie option.9 To read more about these studies and biases, see the references below.

Basal Energy Expenditure, BMR, and Resting Energy Expenditure: What's the Difference?

As I explained above, one of the three main components, and really the main component, of your daily energy expenditure is your resting energy expenditure. Yet, you may not have come across this term before, and instead have been exposed to the terms basal energy expenditure and/or basal metabolic rate. Many times these terms are used interchangeably. Most fat loss experts on the internet tend to prefer the term basal metabolic rate, or BMR, for short.

You may have seen formulas, such as the Harris-Benedict Formula (Equation) that are used to determine your BMR. Well, unless you fast a lot and never get out of bed, this is a bit incorrect, in more ways than one. The terms "resting" and "basal" do not quite mean the same thing, when used in these terms.

Both resting and basal energy expenditure are estimates of the energy needed in a 24 hour period.
Basal refers to the minimal level of metabolism needed to keep your alive. Resting refers to Basal energy expenditure and is usually measured in a clinical setting, while lying at rest after a good sleep, after at least a 12 hour fast. Also, the environment is thermo-neutral so that the subject does not produce heat by shivering. The basal metabolic rate is measured and this is extrapolated to a 24 hour period. RMR is a a bit different and researchers usually measure this about 3 or 4 hours after a person eats or does significant physical activity. The RMR is measured and this is extrapolated to a 24 hour period. So, the RMR, and thus the REE, is slightly higher than the BMR and BEE. But, it's really not that significant.

As you probably guessed, the whole basal thing is a bit impractical, having to meet ideal conditions and all of that. It is okay to use these terms interchangeably but what we really want, technically speaking, are estimates of our resting energy expenditure, combined with our TEF and the energy used in physical activity.

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by EricT

The term anorexia nervosa comes from the Greek word for "lack of appetite" and a Latin word implying a nervous origin. It is a major emotional eating disorder and is characterized by three main criteria:

• Significant self-induced starvation, or near-starvation
• An extreme desire for thinness or being extremely afraid of becoming fat
• The presence of medical signs and symptoms resulting from starvation

The short-hand anorexia is often used for this condition but this term only denotes loss of appetite as a symptom, and can occur as a result of many medical conditions. It is important, then, to recognize that anorexia nervosa describes an emotional disturbance resulting in anorexia, and not just any extreme lack of appetite. The term anorexia may also be misleading in the early stages of the disorder, since lack of appetite rarely occurs early on.

Those with anorexia nervosa sometimes eat only minimal amounts of food, causing body weight to drop dangerously. They may perceive themselves to be fat, or be extremely afraid of becoming fat, even though they may look perfectly normal to everyone else or even be very thin or emaciated. This perception and fear is accompanied by depression. Signs of anorexia include obsessive exercise and calorie or fat-gram counting. Like bulimia nervosa, self-induced vomiting may occur. In fact, only half of those with the disorder will lose weight by drastically reducing calories alone. The other half will use extreme dieting along with binge eating and purging behaviors. Some will purge even after eating only small amounts of food. It is also possible for bulimia nervosa to occur as a separate, but concurrent, disorder. Signs of depression, anxiety, and irritability usually occur.

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Kaplan and Sadock's Synopsis of Psychiatry: Behavioral Sciences and Clinical Psychiatry

Many sufferers may use alcohol and other drugs to help cope with the psychological distress, anxiety, guilt, depression and shame. Also, appetite suppressant drugs such as diet pills, methamphetamines, cocaine, nicotine may be used. Diuretics may be abused to control "water weight" and laxative abuse is common as well.

Usually beginning in adolescence, anorexia nervosa is much more prevalent in females than males, by 10 to 20 times. It has been reported to occur in up to 4 percent of adolescent and young adult students and has been reported more frequently in recent years, with prepubertal onset becoming more common, although the most common age of onset is midteens (14 to 18 years) with about 5 percent occurring in the early 20's. It is estimated to occur in 0.5 to 1% of adolescent girls.

Other Signs and Symptoms

• Rapid weight loss occurring over several weeks or months
• Continual dieting even though the person is very thin or when weight is very low
• Intense fear of or preoccupation with gaining weight
• Often eating in secret or having other strange eating habits or rituals
• A seeming obsession with food, calories, or nutrition
• Very interested in cooking and the desire to cook large gourmet meals for other which they rarely consume themselves
• May often express that they "feel fat", regardless of actual weight
• Unable to objectively assess their own weight
• Overly self-critical, self-loathing, or perfectionist attitude
• self esteem tied into body shape or weight
• Frequent illness due to malnourishment
• Loose baggy clothing to hide the weight loss
• Social withdrawal
• In females, infrequent or irregular menstrual periods (it is important to note that endurance training in female athletes can also cause this "amenorrhea " and therefore this symptom alone should not be taken as a sign of anorexia nervosa)
• Sleep disturbances including insomnia, early morning waking, or oversleeping

Physical signs other than low body weight

• Raynaud's syndrome: tips of fingers and toes cold and red, or white and blue in color, due to poor circulation
• Irregular heartbeat
• Low blood pressure
• Low body temperature
• Lanuga hair: a fine layer of hair covering the body, grown to help create body heat in the absence of body fat and muscle
• Dry skin
• Brittle nails
• Thinning hair on scalp
• Caluses forming on hands
• Constipation or diarrhea
• Yellowing skin

Associated Disorders

• Bulimia nervosa
• Body dysmorphic syndrome
• Depression
• Social phobia
• Obsessive compulsive disorder

Mortality

Anorexia nervosa has the highest death rate of any eating disorder. Among those followed for a sufficient length of time, up to 20% die as a result of the disorder, with around 5-10% dying within 10 years of onset. Up to 30% of these deaths are suicides, and anorexics are 50 times more likely to commit suicide than the general population.

Treatment

Many people with anorexia nervosa will actively refuse treatment. As an ethical dilemna is inherit in treating a disorder that is an irrational drive to lose weight or remain thin when that person does not wish to be treated, initial assessment and treatment are often delayed for months or years. when treatment is undertaken, it is often due to the demands of loved-ones and the patients often drop out of treatment. When treatment is accepted, any components that involve increasing food intake, gaining weight or reducing physical activity may be resisted, even though psychotherapy and family therapy may be accepted. It is also not uncommon for treatment to be accepted, even in a inpatient setting, but the treatment protocols to be subverted in secret. Patients might throw out food, secretly use laxatives, or refuse certain treatments. When treatment is forced, an anorexic might use legal means to protect themselves from being treated.

Although the full legal and ethical considerations of treatment are beyond the scope of this explanation. One of the main ethical issues in the refusal of treatment is the issue of competence and the concept of autonomy. Even though family and friends find it difficult to watch their loved one engaged in behavior seen as foolish, destructive, and even deadly, it is generally agreed that patients who possess the competence to make treatment choices should be allowed to do so. So the question is whether someone suffering from anorexia nervosa has the capcitiy to understand and make rational treatment decisions. Determination of the right to refuse treatment is associated with foru main elements:

• the potential risk of the condition
• the likely benefit of treatment
• likely harm of treatment
• competence of the individual to make a reasonable medical decision

Although many anorexics might accept treatment during an emergency crisis precipitated by their condition, there is a difference between recognizing an emergency and identifying an impending emergency. Therefore, the potential risk component of competence is not as clear-cut and "obvious" as one might think. In fact, some deaths from anorexia nervosa occur after very little clinical warning, such as a sudden cardiac event. The following signs and symptoms could be used to determine the need for immediate medical attention:

• Rapid weight loss of greater than 15lbs in four weeks
• Seizures
• Fainting episodes
• Organic brain syndrome
• Slow heart rate of less than 40 beats per minute (bradycardia)
• Frequent chest pain from exercise
• Heart dysrhythmia
• Kidney dysfunction or low urine output of less than 400cc per day
• Excess loss of body fluid and a rise in blood sodium levels (i.e. volume depletion)
• muscle spasms caused by low blood calcium (tetany)
• rapid diminishing of exercise tolerance

Prognosis

Many anorexia nervosa patients recover, and sometimes recovery occurs spontaneously. Those who receive treatment in specialized anorexia nervosa programs seem more likely to recover than those treated in generalized programs. The results of treatment, however, cannot be reliable predicted at this time. It is clear that the results of short-term intervention such as refeeding and psychiatric treatment does improve quality of life for the short-term, but long-term prognosis is generally considered to be guarded. The earlier treatment is undertaken, the better the chances for recovery.

Athletes and Anorexia Nervosa

Female athletes, especially, can suffer from anorexia nervosa, especially in those sport which emphasize and require low-body weight, thinness, or a certain body image, such as ballet, gymnastics, figure skating, long distance running, and other endurance sports. However, disordered eating often occurs in serious athletes which is not necessarily anorexia nervosa or another eating disorder, but a unique but associated manifestation of ahtletics. This has been given its own term: anorexia athletica. For more information see Female Ahthletes - Eating Disorders.

Further Resources

National Alliance on Mental Illness. http:///www.nami.org
National Association of Anorexia Nervosa and Associated Disorders. http://www.anad.org
National Eating Disorders Association. Anorexia Nervosa. http://www.nationaleatingdisorders.org

This page contains affiliate links to Amazon.com. We have not been compelled in any way to place links to particular products and have received no compensation for doing so. We receive a very small commission only if you buy a product after clicking on one of these affiliate links.

by EricT

By Michelle May, M.D.

Portion sizes have increased dramatically over the last few decades, requiring us to be proactive in our effort to eat moderately. Traditional approaches to “portion control” such as weighing and measuring food are often ineffective for the long term because they are require too much time and energy and they are disconnected from our body’s needs.

A simpler and more practical approach to eating moderate portions is to use your innate hunger and fullness signals as your guide. Despite the super-sized servings we often encounter, we can create a habit of eating to the point of “just right” rather than “I can’t believe I ate the whole thing!” When the focus is on feeling good rather than being good, common sense will prevail.

Try these basic strategies to manage your intake based on awareness of your body’s natural cues, rather than using external rules:

How much food do I need?

Before you start eating, think about how you want to feel afterward. Do you want to feel content and comfortable, or stuffed and miserable?

• Imagining that your stomach is like a balloon inside your abdominal cavity. Gauge your hunger and fullness levels by picturing your balloon before, during, and after eating.
• Decide how full you want to feel when you are done eating; in other words, how full do you want your balloon to be?
• Estimate how much food it will take to get you to that level of fullness.
• Prepare, serve, or order that amount of food.
• If you serve or receive too much, move or remove the excess.
• Physically divide your food in half to create a “speed bump” – a reminder to re-assess your hunger and fullness level.

Check in during eating

• Calm yourself with a few deep breaths before eating. Remind yourself that it is just food.
• Remind yourself to stay focused on your food and how you are feeling.
• Pause for a couple of minutes when you reach your “speed bump” to notice how full you feel.
• Keep in mind that the feeling of fullness is often delayed and estimate how much more food, if any, it will take you to get to your desired fullness level.
• Re-center and calm yourself with a few deep breaths.
• Notice when your taste buds become less sensitive and food doesn’t hold your full attention.
• Pay attention to how full your balloon feels inside your belly.

Signal that you’re done

When you think you have had enough, let yourself (and others) know that you’re done eating:

• Put your napkin and fork over your plate on top of the food.
• Announce to someone at the table that you are done.
• Clear your plate and the table right away.
• Get up from the table.
• Chew a piece of gum or a mint or brush your teeth.
• Plan to take a walk or do something else you’ll look forward to.
• Remind yourself how you wanted to feel when you were done eating.
• Remember that you will feel even fuller in just a short while.

I can’t believe I ate the whole thing!

• If you’ve overeaten, don’t miss the lesson. What can you learn from this experience?
• Sit for a few moments to notice how you feel when you overeat—without judging or shaming yourself.
• Focus on the sensations so you’ll remember them the next time you’re tempted to overeat.
• Ask yourself, “Why did it happen?” For example: Was I too hungry? Did I tell myself it was a special occasion? Did I eat too fast? Was I distracted? Was I eating for emotional reasons? Was there too much food on my plate? Do I hate to waste food?
• Ask yourself, “What could I do differently next time?” For example, serve or order less food; pay attention while eating; ask for a to-go container before I start eating.
• Then, wait and see how long it takes for you to get hungry again. What you are hungry for now?

With practice, you’ll learn to trust your body to let you know how much to eat.

Michelle May, M.D. is a recovered yoyo dieter and the award-winning author of Eat What You Love, Love What You Eat. Download a copy of 101 Things to Do Besides Eat at www.AmIHungry.com.

by EricT

I've talked about the athlete fallacy many times. This fallacy is related to exercise guilt and the feeling that if you are not "going all the way" you are doing something wrong, wasting your time, may as well not bother, etc. and so on.

Also related to this idea, intrinsic to it really, is the idea that you must regularly go to the gym and engage in an exercise program or training plan in order to derive any health benefits from exercise. So, in other words, it takes a few weeks to a month to see any true benefit because that benefit is always from the cumulative results of regular exercise.

This is at least partly true. Of course exercise benefit is cumulative and more benefit, over time, will be seen from regular exercise that is based on some kind of progressive overload (to some extent).

Travis Saunders, who blogs regularly for the PLOS blog Obesity Panacea recently posted an article called 7 Myths About Physical Activity. Now, these were not your typical recycled-from-the-webernet-misinformation-mill myths but some surprising ones..some of which should be seen as quite liberating:

"One of the most amazing things about exercise is that the benefits of a single workout are seen within hours of the workout itself, and last up to 3 days after the workout is completed! For example, a single session of aerobic exercise has been shown to reduce important risk factors for diabetes and heart disease such as triglyceride levels, blood pressure, and insulin resistance, while also increasing HDL-cholesterol (the “good” cholesterol). Of course the benefits of exercise increase over time, but even a single workout can produce measurable improvements in important risk factors." - Saunders1

Saunders suggests reading this review by Paul Thompson, et al. What this review discusses is "acute" versus "chronic" response to exercise. Different responses, such as lower blood pressure and triglycerides/cholesterol may require different exposure times to different intensities of exercise and not everything is known about how much exercise is needed to produce these beneficial effects. It should also be clear, which is pointed out in the paper, that regular exercise over time results in greater exercise capacity and thus a greater acute effect. In other words, the more you work out, the harder and longer you will be able to work out and thus the more benefit you will get from it.

Although this may seem obvious, most people do not think of exercise as something beneficial that gets more beneficial over time. They think of it as requiring a long-term commitment before any benefit can be expected to be derived. This is simply not the case. For example, obese people are often told that they must focus on diet and lose weight before they even bother to exercise. They are essentially told they are "too unhealthy" to exercise. It should be clear that exercise will have immediate benefits for everyone, especially the obese, and that these benefits will simply become greater over time.

There are so many conflicting messages in exercise land. "Doing something is better than doing nothing," is perhaps the message that needs to be heard more often. Doing something regularly would be better. Doing something regularly and with a plan would be best.

Many of the messages concerning regular activity and exercise concerns fat loss. To lose weight "move more and eat less," we are told. It is rarely mentioned that "moving more" does not need to result in weight loss to positively impact our health. When it is mentioned this benefit is of the far-off kind. The message is that unless you sit down and analyze everything, consult a professional, make a complicated plan with a long-term goal, you will not be accomplishing anything. The mere process becomes so daunting and life-changing that many are discouraged and fearful. When they do begin, this discouragement and fear causes them to do too much too soon, resulting in fast burn out. The perpetual couch potato, is in large part, created by fear of failure which is due to being healthy seeming as hard to do as quantum physics!

The fitness and nutrition industries feed this failure. In large part this is due to the industry being bent on creating customers rather than performing a service. That is, the fitness and nutrition industry is not as "service oriented" as it could be. As Jamie Hale recently said "The overwhelming majority of people working in the fitness industry are not concerned with your health. They have other interests- making money and helping you look better."2

Too often do people suffer heart attacks or stroke and then start going out for walks or other moderate activity. It seems that nobody needs to be told that "something is better than nothing" after they have already seen the results of their ill-health. Yet, making a difference can be by doing something as simple as deciding to walk down to the store instead of drive to it or to take the stairs instead of the elevator.

The same things are true of resistance exercise and strength training. You do not need to be daunted and overwhelmed by the complexity of strength training to get some immediate and tangible benefits from it. You do not need a complicated long-term plan to get started. As a matter of fact, picking a handful of exercises, let's say body weight exercises, and simply playing around with them, getting a feel and deriving a baseline, can and will be of immediate benefit. Yet, many a trainee, if they were to tell a strength coach they were going to do this, would be admonished for it and told they didn't know what they were doing. You must pick a program and stick to it! I've complained about this before:

""Get off your butt and pick a program" is probably the worst thing I could say to someone who is daunted and intimidated by the overwhelming task of changing their sedentary lifestyle. Now a lot of the trainers and coaches out there may disagree with me. "Overwhelming?", they will say. "You just have to do it. You just have to stop being lazy and do it." These attitudes themselves are a big pet peeve of mine because most terse statements like these require all sorts of qualification to be useful."3

Don't let the loud mouth blow-hards scare you out of exercising and back onto the couch. For everybody that enters a marathon for the sole purpose of breaking out of their sedentary habits there are many more who would find this approach to be disastrous. So what is the message here? It is okay to "just" exercise. At least at first. Pick something you are interested in and like to do and start doing it. Just a little at first. Add to it if you like. As you begin to feel better you will most likely find making a more comprehensive plan of attack easier. You will also have more information to go on. Exercise has health benefits…period.

However, if you want to be able to stick to it, there must be some reward. I do not mean an intangible and hard to imagine award to be patiently cultivated for years to come. I mean some immediate reward. This is the reason that such a strategy can backfire. So, knowing that exercise of any kind can have immediate benefits does not mean we can make exercise a habit and make a lasting change in our life. Acute benefits are, of course, temporary!

As I have always recommended in the past, instead of making it your goal something vague like get moving, start exercising, get in shape, etc. try to think of something you would like to be better at. Set a performance related goal. It can be any number of things. Now, don't set unrealistic goals! Such as "win a marathon". Set easily achieved and personal goals like being able to run a mile, perform 5 pullups in a row, do a set of 10 pushups. Maybe you'd like to be a better swimmer? Hey, perhaps you'd just like to see if you can improve your back pain. You catch my drift. Even though you may start by the seat of your pants once you start to see some results your motivation to both organize your exercise and keep doing it will grow.

The message here is that you do not have to sign over your first-born to the training God's to see benefit from exercise. It is okay to "just do something". The trick is coming at it with an attitude that works.

Comments

by EricT

International Journal of Behavioral Nutrition and Physical Activity 2011

Past research has shown that promotional messages such as food advertising influence food consumption. However, what has gone largely unexplored is the effect of exercise advertising on food intake. This study experimentally tested the effects of exposure to exercise commercials on food intake at a lunch meal as compared to the effects of control commercials.

Prior to (71 women, 54 men) watched 8 commercials, either all related to exercise or fitness (n=67) or neutral products (i.e. car insurance) (n=58). The meal consisted of a pasta dish with tomato sauce, salad and chocolate pudding. The post-lunch questionnaire included questions about body mass index, exercise habits, motivation and dietary restraint.

Participants exposed to exercise commercials reduced their caloric intake by 21.7% relative to the control condition. Additionally, watching exercise messages increased the perceived healthiness and liking of the meal. Although exercise habits and intentions did not moderate the effect of commercial condition on food intake, we also found that this intake reduction was driven by participants with higher body mass index levels.

These results imply that exercise messages may serve as a reminder of the link between food and physical activity and affect food consumption. It also highlights the need for increased awareness that these messages have powerful influences not only on exercise behavior, but also on closely related behaviors such as eating.

Background

Many health benefits are associated with physical activity such as a positive mental health [1] and a lower risk of chronic diseases, for example coronary heart disease, diabetes, certain types of cancer and osteoporosis [2]. However, due to the obesity crisis and rapidly declining physical activity levels in many countries world wide, promoting physical activity has been identified as a major public health priority [3]. Both a reduction of food intake and an increase in physical exercise are seen as highly necessary and complementary routes in the battle against overweight and obesity [4]. Numerous initiatives, international guidelines and campaigns have emphasized the significant role physical activity plays in maintaining good health and preventing diseases. Unfortunately, these efforts have so far not translated into increased physical activity levels. The effectiveness of physical activity promotion has been the topic of extensive research [5, 6]. A key result of these studies is that although many people are aware of the health benefits of exercise, promotional media campaigns have little or no impact on exercise behaviour [7].

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Past research has shown that consumption decisions are influenced by promotional messages, such as food advertising [8, 9]. What has been largely unexplored is the effect that exercise commercials have on immediate food consumption and food evaluations. A recently conducted study showed that food consumption can be influenced by exercise promotion messages [10]. Albarracin, Wang and Leeper [10] conducted two laboratory experiments to study changes in food intake following exercise messages. In the first experiment, participants were either exposed to five exercise promotion posters or similar looking control posters with the supposed objective of rating the appeal and likely efficacy of the ads. Immediately after this task, participants evaluated twenty raisins in a taste test, with the instruction to eat as many as they wanted. Results show that recipients of the exercise message ate an average 18 calories relative to about 12 calories in the control condition. A similar significant increase in food consumption of about 20% was observed in a second experiment where participants were subliminally exposed to either action words (i.e. ‘active’ or ‘go’) versus control words (i.e. ‘pear’ or ‘moon’).

To examine the generalizability and stability of this effect, more research is needed, because exposure to this type of messages with a resulting increase in food intake could potentially backfire against people, particularly those who exercise to manage their weight. Although real-life commercials may vary in persuasiveness and mood arousal, we used exercise related commercials instead of exercise promotion posters used by Albarracin and colleagues [10] in our attempt to increase the external validity of the study. Therefore, the main research aim of the present study is to test the effects of exposure to exercise commercials on actual food intake. Besides testing the effect of exercise commercials on the intake of foods during a meal following watching exercise commercials, an additional aim of the present study is to examine how individuals’ characteristics (i.e. weight status, exercise habits and intentions) play a role.

Theoretical background

People typically process promotional messages, such as exercise commercials, with low levels of involvement and do not always cognitively elaborate on the meaning [11]. According to the research on priming, the effect of advertising on behaviour may even occur outside of people’s conscious awareness by subtly reminding them of the associations we should have of the products promoted. Priming or non-conscious activation of social knowledge structures has been extensively studied over the past decades [12].

Research on how people set and pursue their goals has shown that mental representations of goals can also be triggered by environmental cues without a consciously made choice such that subsequent behaviour is then guided by these goals [13, 14]. For example, priming hedonic consumption goals tends to intensify the desire for hedonic foods. Harris, Bargh and Brownell [8] present evidence of an automatic causal link between food advertising as a prime and greater snack consumption. They found that food advertising increased food consumption and these effects were not related to hunger or other conscious influences. Similarly, studies have shown that observing someone engaging in an action or imagining action leads to an increased tendency to perform that behaviour oneself [15, 16]. In explaining the behavioural effects of priming, the ideomotor theory proposes that behaviour follows automatically from the activation of relevant mental contents. In other words, merely thinking about doing something automatically makes it more likely that you will perform the action [17].

Basically, all kinds of psychological concepts and processes (e.g. stereotypes, traits, social norms, goals) can be primed or put into motion without conscious awareness [12]. Relevant in this study is the recently proposed model of general action and inaction goals by Albarracin and colleagues [18]. Action goals are goals with end states at the extreme of the continuum of activity level. Once set, people may search for and engage in variety of behaviours to reach the end state of high activity. General states of action and inaction can also be activated by primes. For example, action goals can be activated by exposure to action words (e.g. ‘act’), but as well by exposure to exercise promotion messages, as in the study examining the effect of exercise promotion posters [10, 18]. Albarracin and colleagues [10] state that motivational mechanisms associated with the use of action words and images are likely to be automatically unfold when exercise-promotion messages are presented. For example, words such as ‘active’ and ‘go’ may produce a generalized desire for motor output that can be satisfied with eating [10]. Hence, similar to the study of Albarracin and colleagues [10], exposure to exercise commercials may also trigger automatic eating of available foods. Therefore, our primary hypothesis is that people would have a significantly higher calorie intake after watching exercise commercials compared to watching neutral commercials.

Furthermore, we expect that the effect of exercise messages will be moderated by individuals’ weight status. That means, in stead of triggering the tendency to eat, people with higher body mass index levels will restrain themselves from eating too much. This expectation is based on findings in the literature on stereotype priming. Advertising typically strongly reflects current stereotypes in a society to bring the message to the public. There is considerable evidence that primed stereotypes elicit corresponding behaviour in the perceiver [16]. Two categories of effects of priming stereotypes can be distinguished. That is, people’s behaviour can become consistent with the primed stereotype (assimilation effect) or inconsistent with the primed stereotype (contrast effect) [17]. In their review of the literature, Wheeler and Petty [17] report that the majority of published experiments demonstrated that people assimilate their behaviour to the activated stereotype. Under some conditions, however, they make evoke contrast effects. Contrast effect are more likely to occur when the primed stereotype is very dissimilar, concrete and relevant in relation to self-perceptions [16].

Since the primed stereotype in a typical exercise commercial involves attractive, confident and skinny individuals, a contrast effect is more likely to be expected than an assimilation effect for people with higher body mass index (BMI) levels. Moreover, the primed stereotype in this type of commercial is self-relevant as many overweight individuals are usually dissatisfied with their weight and try to loose this weight. Exercise is associated with trying to loose weight, particularly for women [19]. This is consistent with the model of general action and inaction goals, which proposes that general action goals are tied to specific goals. Supporting this argument, lowering food intake is seen as the better way to achieve the goal of weight loss than physical activity alone or a combination of the two [20]. People realize that although exercise burns off calories, a lot of exercise is needed to burn off a relatively small number of calories [21]. Moreover, individuals trying to lose weight have not adopted regular physical activity as part of their weight loss practice [22]. An Australian study found that overweight young women were less likely than normal weight women to see leisure time physical activity as being feasible. It is likely that embarrassment, lack o confidence and physical discomfort are key factors explaining this [23]. In sum, the effects of activating the exercise node in memory of people with higher body mass index levels predict a contrast effect for caloric intake rather than the assimilation effect predicted for normal weight individuals.

In sum, we predict that exercise commercials increase caloric intake of a following meal. In contrast, we also expect that individuals with higher body mass index levels reduce their caloric intake. We test these predictions in a laboratory experiment in which we randomly assigned participants to two experimental conditions (exercise commercials versus neutral commercials).

Methods

Design

Participants were randomly assigned to either an exercise commercial condition or a control commercial condition. Participants watched commercials first and then served themselves food one by one. Two additional conditions were created which were watching a television cartoon after serving oneself food (so during consumption) or not watching a cartoon. The idea of this manipulation was that television watching might impact food intake. Identical procedures were followed during the experiment where participants watched a cartoon and the experiment where participants did not watch a cartoon while eating. The amounts of total food and individual foods consumed did not differ between the two experiments (all Fs<2, ps>0.24). Therefore, results from both conditions were combined to increase the statistical power of the analyses.

Participants

Participants were 128 Cornell University undergraduate students (73 women, 55 men) who participated in exchange for money or extra course credit. The study was approved by the Institutional Review Board.

Procedure and materials

Participants signed up for the study which was held during lunch break (between 11.30 am and 1.30 pm). Participants were welcomed and seated at a table. The first part of the experiment consisted of an advertising evaluation task in which participants were either exposed to a series of eight exercise commercials (4 minutes and 57 seconds in total) or a control condition of eight commercials (4 minutes and 59 seconds in total) that did not refer to foods or exercise (i.e. car insurance, home appliance, pet adoption program). Instead of public health campaign commercials, we selected commercials of exercise equipment (i.e. running shoes) and services (i.e. fitness centre, fitness program) because these commercial messages dominate the messages that people see regarding physical activity and public health campaign commercials are relatively infrequent [24].

Both men and women were featured in these commercials which were actively engaged in sports. Care was taken that no explicit weight management or weight loss message was included in the commercials. To shield the purpose of the manipulation, the evaluation of the commercials was presented as an advertising study. To support the cover story, after each pair of commercials, participants rated the commercials on some attributes and indicated which one they liked the most. They also responded to items which all started with ‘watching these commercials made me…’ followed by: happy, hungry, feel somewhat guilty, feel athletic, feel relaxed, feel active, healthy and in good shape. Each item is scored on a scale from 1 (strongly disagree) to 7 (strongly agree). A 7-point Likert type scale was included which asked participants to indicate the likelihood of going to the gym the coming week (1=very unlikely and 7=very likely). Gym was selected as this is a common form of physical activity among college students.

Immediately after the commercial evaluation task, participants were instructed to line up for the food buffet. The meal consisted of a pasta dish with a tomato sauce, a salad, and a chocolate pudding. Chocolate was selected as this is typically seen as an indulgent calorie rich food, which might activate self-control mechanisms. In addition, salad dressing (light or regular), cheese and drinks (Regular Coke, Diet Coke or water) were available. At the buffet table, pasta, salad and pudding were placed on scales. The scales were hidden for participants by large table cloths. All scales had longer cords leading to a digital display that was placed out of sight of participants. Research assistants noted the weight of the scale before a participant spooned food out of the bowl. When the participant finished spooning food out of the bowl, the weight was again noted. The total amount of food served was calculated by subtracting the second measurement from the first. After serving, the participant sat down at a table to eat. When done, the tray with leftovers was taken away and a second questionnaire was handed to the participants. Leftovers were measured out of sight of participants to calculate the amount of food consumed. No second servings were allowed. Salad dressing, type of drink and cheese taken with the meal were not weighted but research assistants identified the choices participants made.

A few times, research assistants were not able to note the one or more weights, for example because a participant handed the serving spoon to next participant without putting it down in the bowl. As a result, nine data points (3 concerning salad; 2 concerning pasta and 4 concerning pudding) were missing. Grams were converted into calories (pasta was 75 calories per 100 grams, salad was 14 calories per 100 grams and chocolate pudding was 149 calories per 100 grams).

Post-meal questionnaire

After eating their lunch, participants filled out a questionnaire with items related to their liking of the foods, their moods and several scales. We measured how participants felt on a four-item, seven-point mood scale [25], anchored by ‘sad/happy’, ‘bad mood/good mood’, ‘irritable/pleased’ and ‘depressed/cheerful’. We also included some mood descriptors from the PANAS scales [26]: active, strong, proud, guilty as they seem relevant in relation to our study objective.

As high self-esteem is associated with healthy behaviours such as physical activity [27], short-lived (i.e. state) changes in self-esteem were measured according the scale of Heatherton and Polivy [28]. Some items were adapted to better fit the purpose of this study (see Appendix 1 for items). Each item is scored on a scale from 1 (strongly disagree) to 7 (strongly agree). The items were preceded by the phrase ‘Answer these questions as they are true for you right now’.

Furthermore, participants self-reported their height and weight, which were used to calculate body mass index (BMI). As a manipulation check, it was asked how much effort participant put in the task, the time since they had their last food and how familiar, appealing and convincing they rated the shown commercials. Participants were asked about their physical activity habits by the Godin Leisure Time Exercise Questionnaire (GLTEQ) [29]. The three item GLTEQ assesses the frequency of performed strenuous exercise, moderated exercise and mild exercise for at least 15 minutes in a typical week. Total weekly leisure activity is calculated by summing up the products of the separate components as follows: Total exercise = (frequency of mild exercise × 3) + (frequency of moderate exercise × 5) + (frequency of strenuous exercise × 9). The GLTEQ has been found to have good 2-week test-retest reliability and construct validity [30].

As people’s motives to exercise vary [31, 32], exercise motivation was assessed by a modified version of the Exercise Motivation Scale [33]. A seven-point Likert-type response format ranging from ‘strongly disagree’ to ‘strongly agree’ was used for all items (see Appendix 1 for overview of items and reliability). Perceived exercise behavioural control was included to measure an individual’s self-confidence in being able to engage in physical activity, based on the scale of Armitage [34]. Dietary restraint was assessed as possible explanatory variable and measured with a 10-item scale from Polivy, Herman and Warsh [35]. reliability of this scale in the current study was a=0.73. The Restraint scores ranged from 4 to 29 with a mean of 14.02 (SD=5.5).

Data analysis

Using analyses of variance (ANOVAs) we first checked whether there were differences between commercial condition (exercise versus control) and the time since participant had last food, BMI, exercise habits, commercial evaluations and exercise motivation and exercise behavioural control to examine whether our randomization was successful. To test the effect of commercial condition on mood, food intake and lunch evaluations we used ANOVAs. Additional analyses were conducted to test for the interaction effects of commercial condition and possible moderator variables (i.e. weight status, dietary restraint, exercise habits, exercise intentions). Moderated multiple regression analysis was carried out. Simple slope analysis to compare non-standardized betas was done if a moderating effect was identified.

Results

Manipulation checks

Even though the experiment was announced to be not suitable for vegetarians (the pasta dish served contained chicken), two female participants indicated to be vegetarian and were removed from the dataset. The screening procedure led us also to remove one male participant whose exercise pattern deviated strongly from all other participants. His weekly exercise activity, measured by the GLTEQ deviated more than five standard deviations from the mean exercise score in the entire sample, suggesting that this participant could be a professional athlete, leaving 125 (71 women, 54 men) in the analysis. The average of the final sample was 20.5 years (SD=5.0) and a mean BMI of 22.7 kg/m2 (SD=3.3).

Time since last food intake did not differ between conditions. There were also no differences in the effort put into the task (all Fs<1). All commercials were equally familiar and appealing. However, participants found the control commercials more convincing (F(1,123)=10.54, p<0.01). There were also no differences in BMI and exercise habits as measured by the GLTEQ between the groups. In addition, there were no differences across conditions in the likelihood of going to the gym the coming week and ratings of exercise motivations and exercise behavioural control (all Fs<1).

Message effects on amount of foods consumed

When all three foods are considered together, results showed that participants consumed 299.5 grams of food on average (188.0 grams of pasta, 47.3 grams of salad, and 69.1 grams of pudding; standard deviations 97.8, 24.5, 56.7 and 119.3 for pasta, salad, pudding and total foods, respectively)(Table 1 omitted). After watching the exercise commercials, participants served themselves less foods in total (Table 2 omitted), resulting in a lower overall food and calories intake (F=5.54, p=0.02 and F=8.67, p<0.01). In particular, participants being exposed to exercise commercials reduced their caloric intake of the meal by 21.7% relative to the control condition. There were no differences across the two conditions in the type of drink and salad dressing participants took (both ps>0.22). Watching the control versus the exercise
commercials did not lead to other choices of putting cheese on the meal (p=0.36).

The correlation between the amount of food participants served themselves and the amount they consumed was high (r(120)=0.94, p<0.001). The average difference between what people served themselves and what they ate was low (M=40.6 grams), which implies that participants ate almost all the food they served themselves. As a result, all analyses of the amount consumed showed identical results as with the analyses of the amount served. Therefore, from now we only report the analyses of the amount of foods consumed.

Commercial effect on reported feelings, self-esteem, lunch evaluations and mood

Watching the exercise commercials made participants feel less relaxed, more athletic, healthy and in good shape than watching the control commercials (all ps<0.05). Participants’ mood was not affected, although there was a marginal effect on the dimension ‘sad-happy’ in that the exercise commercials made participants feel slightly happier (p=0.07). The exercise commercials also resulted in a marginally higher rating of feelings of hunger (p=0.09). Regarding the lunch evaluations, there was a significant difference in participant ratings of the healthiness of the meal between the two conditions (p=0.02). Participants in the exercise commercial condition rated the lunch as healthier compared to participants in the control condition. The food was also more liked in the exercise message condition, particularly the salad and salad dressing (all ps<0.05). Despite differences in overall energy intake, ratings on hunger and fullness (i.e. ‘at this moment I feel full’ and ‘I could not eat another bite’) did not differ by condition (p=0.39 and p=0.93 respectively). It was checked whether the vitality feelings after watching the exercise commercials and healthiness ratings of the lunch mediated the relation between commercial exposure and food intake, but this was not the case. It was also checked whether food intake mediated the relation between commercial exposure and healthiness ratings of the lunch, as it may be that the on average smaller portions consumed are perceived as healthier. However, this was also not the case. This suggests that effects of commercial exposure on feelings after watching the commercials and food intake involve different processes.

Weight status

A possible interaction between message condition and weight status was examined by using a moderated regression analysis, as BMI is a continuous variable [36, 37]. First, BMI scores were mean-centered to make the zero a meaningful value so that the intercept in the regression becomes interpretable. The key dependent variable was the total caloric intake. Condition was coded as a dummy variable equivalent to one if the participant was exposed to the exercise commercials and zero if the participant was exposed to the control condition. At first, a regression equation with only condition and BMI as predictors was run. In this model, the beta coefficient for BMI is not statistically significant (unstandardized beta=2.03, p=0.51), but condition is statistically significant (unstandardized beta=-59.40, p<0.01). In a second regression model, condition, mean-centered BMI and the interaction between condition and BMI were included as independent predictors. The beta coefficient of the interaction term (unstandardized beta=-14.95, p=0.02) is statistically significant from zero. The addition of this product term resulted in an R2 change of 0.045 (F(1,116)=5.96, p=0.02). This result shows the presence of a moderating effect explaining 4.5% of variance in caloric intake above and beyond the variance explained by only BMI and condition.

The significant interaction was investigated further with simple slope analysis to assess whether these slopes were different from zero. Results show that for participants with a higher BMI (1 SD above the mean) the slope was significant different from zero (ß=-108.57, t(116)=-3.82, p<0.001), but the simple slope for participants with lower BMI (1 SD below the mean) was not significant different from zero (ß=-8.67, t(116)=-0.30, p=0.77). To illustrate the effect of the interaction coefficient, Figure 1 below shows the differences between participants with low and high BMI at plus and minus one standard deviation from the mean of BMI. The analyses indicate that the main effect of commercial condition is driven by those participants with a higher BMI whose relative high food intake was significantly reduced after watching exercise commercials.

Figure 1: Commercials Watched Before Lunch and Total Food Intake

Another regression analysis of BMI on caloric intake among those participants in the control condition showed that participants with higher BMI levels had a significantly higher food intake (unstandardized beta=10.67, p=0.04). A similar regression analysis among participants in the exercise condition showed no significant effect of BMI on food intake. Additional bivariate correlations assessing the relationship between BMI and exercise motivations, perceived exercise behavioural control and GLTEQ showed no significant correlations (all ps>0.20).

Exercise variables and dietary restraint

Restrained eating was significantly positively correlated with BMI (r=0.23, p=0.01). A moderated regression analysis was done similar to the analysis above. Neither the main effect of restrained eating style and the two-way interaction between message condition and restrained eating style on caloric intake reached statistical significance (both ps>0.47).

Another pathway by which commercials relating to exercise may affect food consumption is through its influence on exercise intentions and motivations. Exercise habits could also moderate the relationship between commercial exposure and food intake. Both the exercise habits of participants as measured by the GLTEQ and their estimated likelihood of going to the gym the coming week were examined for their potential moderating impact. Mean GLTEQ exercise score was 54.91 (SD=28.43). The mean likelihood of going to the gym to have a workout was 4.42 (SD=2.30). The correlation between these two variables was significantly different from zero (r(125)=0.33, p<0.001). As in the previous analyses, a moderated regression analysis was carried out, which showed no significant interaction between commercial condition and mean-centered GLTEQ (p=0.92) on total calories consumed. As the GLTEQ specifically builds on recall of exercise habits in a typical week, it does not take into account whether participants have concrete plans to be physically active on the short term. Therefore, another moderated regression analysis was conducted with likelihood of going to the gym the coming week and commercial condition on total calories consumed. For this dependent variable, there was no main or interaction effect of likelihood of going to the gym (ps>0.46) for total calories consumed.

Discussion

This study was designed to understand more fully the effects of physical activity messages on food consumption. The present study demonstrated that exercise commercials can influence overall food intake. When participants were exposed to exercise commercials, their caloric intake was reduced in the meal that followed.

Although the food was liked more, and despite the marginally higher ratings of hunger just before lunch after watching exercise messages, participants restricted themselves, which resulted in lowered caloric intake of 21.7%. As BMI is positively correlated with energy intake [38], we found that higher BMI levels lead to a higher food intake after watching control commercials. Importantly, we found that the overall reduction in intake was driven by participants with a higher BMI whose relative high food intake in the control condition was significantly reduced after watching exercise commercials. This finding is particularly important as it helps to explain the difference between our findings and those of Albarracin, Wang and Leeper [10] who found an increase in raisin intake after watching exercise promotion messages.

Priming action goals, which is what seems to happen when individuals watch exercise commercials, may prompt a selection of active behaviours. However, which active behaviours are chosen may depend on other factors, such as the perceived ease and desirability of the action [18]. That is because people’s associations with a particular topic (e.g. exercise) are based on learned responses over time building on one’s history of reward and punishment with those stimuli [16]. These associative linkages between the activated concepts and related behaviours will lead to the automatic behavioural response. As indicated earlier, the perceived ease and desirability of exercise is lower for overweight individuals than for normal weight individuals [19, 20, 22]. The exercise commercials may have brought associations to mind which could explain the reduced intake.

It is possible that the exercise commercials reminded participants of the hassle of exercise and the time it takes to burn off small amounts of calories. Accordingly, they may have felt that physical activity is a poor strategy for weight management since the energy burned up is relatively small compared to the efforts put into it. It may also be that exercise advertising leads to increased self-criticism and in this way influences food intake. In the exercise commercials in this study, ideal bodies were portrayed which for men is to gain muscle and for women to have a toned and skinny body. These ideals may have distressed overweight participants in realizing that their bodies did not fit this ideal (a contrast effect). In this respect, our results resemble the findings of Smeesters, Mussweiler and Mandel [39] who found that individuals with high BMI levels ate less and wanted to diet and exercise more after being exposed to thin models in print advertisements. Research on priming has also shown that an influence on behaviour particularly arises when an individual can identify him or herself with performance in the activated domain [17]. Nevertheless, we found no difference in response to the commercials among participants who exercised little or a lot. It could be that our sample was too homogenous in their exercise habits.

The study of Albarracin and colleagues was presented to participants as a taste task and only a very small amount of raisins were given to participants (20 each). It could be that this amount was perceived as a snack instead of a meal as in our study. Whether a person perceives a food as a meal or a snack influences what and how much one eats [40]. Furthermore, the type of messages used differed substantially between studies. Commercials showing real people actively engaged in exercise are likely to evoke different responses than cartoon-type posters as used by Albarracin and colleagues. Additionally, raisins are typically promoted and perceived as a relatively healthy snack, in contrast to a complete pasta meal with a chocolate pudding as dessert. Overall, the results of both studies raise questions that cannot yet be answered. For example, it would be useful to examine response to exercise messages based on the perceived healthiness of the food.

All participants felt more active, healthy, athletic and in good shape after watching exercise commercials compared to control commercials. Furthermore, watching exercise commercials made participants perceive the lunch as healthier and higher in liking. Provencher, Polivy and Herman [41] showed that people ate about 35% more of a snack when it was regarded as healthy than when it was seen as unhealthy. In our study, even though the food was regarded as healthier after watching the exercise commercials, this did not result in an increase of food consumption.

It is important to address the limitations of the present study. First, the study used commercials related to exercise / physical activity, which might limit the generalizability of the findings. In contrast to Albarracin and colleagues [10], we did not control for mood and arousal effects of the commercials and the exercise commercials were seen as more persuasive by the participants. Commercials may differ from physical activity campaigns sponsored by governments in several ways, i.e. the use of more youthful and attractive actors, other story lines, so the effects of exercise commercials might differ from effects of physical activity campaigns. Furthermore, we only measured participants’ likelihood of going to the gym. In reality, participants may have had intentions to do other sports activities. Moreover, it is likely that the exercise commercials produced short-term reductions in food intake and that this reduction was compensated later. Hence, more research is needed to understand food consumption responses to messages related to exercise and physical activity over the long term. Physical activity is strongly promoted because of its key role in the prevention of weight gain and obesity. Previous research suggests that physical activity and weight control messages have been minimally adopted by the public [22]. While prior research has begun to show that people’s food consumption can be affected by messages about physical activity, a more thorough explanation has been lacking. Cues in the environment, such as advertising, can be enough to activate the goal of exercising. This activation may endorse related behaviours such as eating, because food intake seems tightly linked to exercise and fitness in the mind of people.

These results may have implications for the development of promotions to encourage physical activity. It is important to realize that exercise promotion messages are likely to influence more than only exercise behaviour. We found that people with higher body mass index levels are more likely to be influenced by exercise commercials. As overweight individuals are often the target of health promotion campaigns, it may be that exercise messages may lead to a food compensation mechanism and possibly even to less motivation to exercise, particularly when a promotion campaign gives people a feeling of pressure or obligation to be physically active, fit and healthy. Finally, health promotion messages aimed at increasing physical activity are unlikely to succeed unless developers fully understand the potential impact of the messages on the target audience. The results of this study provide a starting point for research into the effect of advertising promotion on food consumption, both on the short and long term.

Competing interests

The authors declare no competing interests.

Authors' contributions

In this study, EvK conducted literature review, data analysis and wrote the
manuscript. EvK and MS collected the data. MS, BW and EvK all participated in the
design and coordination of the study. MS and BW helped to draft the manuscript and
provided advice on data analysis. All authors read and approved the final manuscript.

Acknowledgements

This research was supported by a Marie Curie International Outgoing Fellowship
within the 7th European Community Framework Programme.

Appendix 1

Exercise motivation

Scales used to measure exercise motivation, based on a modified version of
Buckworth and colleagues’ Exercise Motivation Scale [33].
‘Effort-competence’ exercise motivation (a=0.85)

1. I think I am pretty good at physical activity
2. I put a lot of effort into physical activity
3. I am pretty skilled at the level of physical activity that I do
4. I haven’t tried very hard to do well at physical activities(R)
‘Interest-enjoyment’ exercise motivation (a=0.94)
1. I enjoy participating in physical activity very much
2. physical activity is fun to do
3. Physical activity does not hold my attention at all (reverse)
4. I think that physical activity is quite enjoyable
‘Appearance’ exercise motivation (a=0.74)
1. I exercise to control my weight so that I look good for others
2. I don’t want to look weak, so I try to work out a lot
3. I exercise so that I will not look too fat or flabby
4. people who are physically active are more attractive than those who are not
‘Competition-social’ exercise motivation (a=0.87)
1. I play sports to win
2. My friends tell me that I am good at physical activities
3. I like a little friendly competition with my friends when I work out with them
4. My family and friends are proud of my achievements in my physical activities

Perceived exercise behavioural control

Perceived exercise behavioural control, measured according to Armitage [34] (a =
0.91).

1. I am capable to participate in regular physical activity
2. I am confident that I am able to participate in regular physical activity
3. I believe I have the ability to participate in regular physical activity

Self-esteem

Short-lived changes in self-esteem, adapted from Heatherthon and Polivy [28].
Physical performance satisfaction (a=0.84)

1. I have an active life
2. I feel good about myself
3. I am confident about my physical abilities
4. I feel healthy
Body and weight satisfaction (a=0.74).
1. I am pleased with my appearance
2. I am dissatisfied with my weight (R)
3. I am satisfied with the way my body looks
- 21

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© 2011 van Kleef et al. ; licensee BioMed Central Ltd.
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

by EricT

This is an old post of my reaction to the infamous 2009 Time Magazine Article, "Why Exercise Won't Make You Thin". Even though this has been blogged on by so many others and it is old news I wanted to re-post with a little rewrite because 1) my reaction was quite different than the reaction of the "fitness industry" at large, 2) I think the general reaction says more about the fitness industry than it does about fat loss and 3) it is a good example of the type of disordered priorities that is prevalent in the fat loss world.

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So, again, I said "Get over the Time Magazine Article" Why? Because it's beside the point. The fitness industry will not lose a buck because of an article…even in Time. So really, all you fitness professionals can stop being so very precious. Isn't this about helping people and not your own self concept?

Originally I was content to link a post by Michelle May that aligns with my attitude about the article "Why Exercise Won't Make You Thin". She called her post Why I don't care about the Time article... Same with me, you see. I didn't care then and I don't care now.

It's All in the Details?

How about I rename the article? Why Details Won't Make You Thin. Because that is all anybody is arguing. Details. Physiological details quoted from research studies won't make you thin. Hello.

Everybody says that in order to succeed you must make "fitness" a part of your lifestyle. Yet nobody seems to know what the term lifestyle means any more than there can be any clear definition of fitness. Just words. It is just vague psycho-babble. And since there is nowhere else to go they dwell on details.

Which is the problem. Too many people are starting with the details because they are being led to believe that is where perfection lies. Starting with the specific is just so very wrong, folks. You will never see the big picture. Always start with the general before proceeding to the specifics.

Because if you think you can succeed in such a goal as fat loss with compulsively detail oriented thinking just think about where COMPULSIVE behaviors have gotten you.

Let me be clear here. I am NOT saying that the answer is to start with the big picture and slowly move toward obsessing about tiny details. Obsession is obsession and no matter how long it takes you to arrive there it is still an unhealthy grasping.

I actually just read a post in a "health" blog about dwelling on details the message of which seemed to be "most of you better not be obsessing over details. There's only a few of us purists who can handle it!

Hey, exercise won't make you thin. A hammer won't make you thin. I typed it in and nothing exploded. Tools don't make you thin. It's the work you do with them that produces results. And it is possible to do good work with the latest computer driven woodworking tools OR the most primitive chisel as long as you know how to use the tool…and you have a steady vision of that which you wish to accomplish.

Getting upset because someone insults the brand of tool you use is silly. Can they question the results you get? That is the real question. And despite the all-fired vehemence of the "industry" over this article, there are thousands of plodders trying to work off a few hundred calories here or there that really should be honing in their diet and they really WOULD get better results - and more healthful ones - if they did so.

Exercise can be one key to long term body weight maintenance. Exercise can be the key to long term failure to maintain healthy body weight.

But if anyone thinks that a Time magazine article is going to empty out the mile long row of treadmills at the local YMCA then I ask, where have you been? And while it is true that exercise, in and of itself, can improve health outcomes without weight loss, obesity is still a force to be reckoned with.

Instead of getting defensive perhaps the fitness industry should be asking why there is a mile long row of treadmills at the local YMCA and why there are people obsessively pounding away on them in the face of NO results. Perhaps the fitness industry should worry about its clients and not about itself.

I have developed a rule that I use everyday. I simply ask myself a question and answer it as honestly as I can. With no judgment. I just look at it and give the the first natural answer that comes.

"Did the things I accomplished today have more value to others than they did to me?"

That is what I want to make happen. I don't ask myself that because I think it is a moral obligation or because it "sets me above". I ask myself that because that will mean that I do MORE rather than less. Self profit means that we seek to do that which is most expedient. It's a cost-benefit analysis. That one simple rule guarantees that I do not seek the expedient route and thus what I do has more value in general, including to myself. But I am not as interested in building a career as I am in growing a human being. As long as I do that, why should I care about a Time magazine article?

When the fitness industry gets all upset about an article in a magazine that they feel differs or contradicts them, from my perspective, it seems as if they are operating under the delusion that people care, ultimately about their knowledge of science or obscure physiological facts. Their self-perception about their "knowledge" is a big part of their self-concept. Threaten someone's self concept and you will get piss and vinegar every time.

Many people will listen to you based on their perception of your knowledge base. For a while at least until that knowledge fails them.

Knowledge in that way is a double-edged sword because in today's fitness world many individuals use knowledge before experience. And many more use data dumps as a smokescreen to cover a lack of many of the essential components that separate a person with knowledge from a person with the capacity to genuinely make a difference in another person's life.

On the other hand others dwell on experience and undermine the importance of knowledge.

There would not be a question of the relative value of each if the fitness industry instead focused on THE most important part of the equation. How much you care.

In the end, what keeps people coming back to you is showing them that you give a shit. That it is not about YOU. It just so happens that giving a shit is what helps us thrust ego aside and become better at what we do. More knowledgeable, more reasonable, more experienced, and more successful. And when I say successful, I mean making a difference in people lives, not selling the most product. If you can do both those things successfully, then more power to you. But if your number one priority isn't to help others, then you are in the wrong field.

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by EricT

By Jean-Philippe Chaput¹, Lars Klingenberg², Mads Rosenkilde³, Jo-Anne Gilbert⁴, Angelo Tremblay⁵, and Anders Sjödin⁶

Journal of Obesity 2011

Emerging literature highlights the need to incorporate physical activity into every strategy intended to prevent weight gain as well as to maintain weight loss over time. Furthermore, physical activity should be part of any plan to lose weight. The stimulus of exercise provides valuable metabolic adaptations that improve energy and macronutrient balance regulation. A tight coupling between energy intake and energy expenditure has been documented at high levels of physical exercise, suggesting that exercise may improve appetite control. The regular practice of physical activity has also been reported to reduce the risk of stress-induced weight gain. A more personalized approach is recommended when planning exercise programs in a clinical weight loss setting in order to limit the compensatory changes associated to exercise-induced weight loss. With modern environment promoting overeating and sedentary behavior, there is an urgent need for a concerted action including legislative measures to promote healthy active living in order to curb the current epidemic of chronic diseases.

Introduction

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In the field of obesity research, physical exercise has been traditionally considered as a strategy to burn calories. However, physical exercise is much more than that. It is a stimulus that, when properly managed, contributes to a significant improvement in energy and macronutrient balance regulation and to global body functioning, that is, a precise regulation of body homeostasis [5]. It thus seems appropriate to propose that an active lifestyle can influence energy balance and body fat to a much greater extent than what is generally perceived by health professionals. To reach this outcome, exercise should ideally be performed regularly and on a permanent basis.

The main preoccupation of this conceptual paper is to discuss the critical role of physical activity in body weight regulation. The paper should not be perceived as an exhaustive literature review and critical analysis of the exercise–body weight connection, but rather an attempt to emphasize why and how physical exercise should be part of any plan to achieve body weight stability and overall health. Although the results of exercise programs designed to reduce body weight are generally considered disappointing [6] (see Figure 1), we still believe that exercise is an important player in obesity prevention and management. For the purpose of this paper, the general term “regular exercise” refers to the population-at-large consensus message that accumulation of 30?min of moderate intensity activity such as brisk walking, on at least 5 days of the week, can provide important health benefits.

Figure 1: Weight loss related to an exercise intervention, a diet intervention, and a diet + exercise intervention. The magnitude of weight loss due to physical activity is additive to caloric restriction, but physical activity is generally insufficient by itself to bring about clinically significant weight loss, that is, a decrease of 5% or more in body weight. Figure adapted from Wing [6].

Physical Exercise: More Than a Calorie-Burning Agent

There has been increasing evidence over the past decades of the importance of physical exercise in maintaining cardiovascular health and preventing diseases [7]. In recent years the list of beneficial effects has continued to grow. It has been shown that physically active individuals are less likely to develop stroke [8], some forms of cancer [9], type 2 diabetes [10], obesity [11], osteoporosis [12], sarcopenia [13], and loss of function and autonomy [14]. Evidence is also accumulating that exercise has profound benefits for brain function, including improvements in learning and memory as well as in preventing and delaying loss of cognitive function with aging or neurodegenerative disease [15]. The knowledge gained from this large body of evidence is highlighting the crucial role of exercise to health and well-being, and it underscores the need to pay serious attention to this area of public health [7].

When sedentary individuals undertake exercise, the activity provides a massive stimulus with widespread physiological implications. The precise metabolic regulation brought about by exercise is expressed at many levels of regulatory processes, be it by stimulating the effect of key enzymes, by increasing cell sensitivity to numerous hormones, by facilitating substrate transport through membranes, by influencing cell receptors in a tissue-specific manner, and much more [5]. With the generalized sedentariness observed in modern societies, the human body needs to compensate for the lack of exercise stimulation to maintain energy and macronutrient balance. Fat gain and the metabolic syndrome are unfortunately the price to pay to maintain this balance [16].

The physiological perception of obesity considers fat gain as a biological adaptation that ultimately permits the person gaining weight to reach a new homeostatic state [17]. Some of the adaptations to this state of positive energy balance include an increase in fat oxidation [18], sympathetic nervous system activity [19], insulinemia at euglycemia [20], and leptinemia [21], all of which promoting over time the reequilibration of energy balance. However, fat gain cannot fully replace the positive impact conferred by a healthy lifestyle. The problem related to fat gain as a physiological compensation to sedentariness is that it cannot occur with the same metabolic efficiency as exercise. Specifically, fat gain relies more on increased concentration of substrates (e.g., free fatty acids) and hormones (e.g., insulin and leptin) to reequilibrate energy balance, which likely underlies the occurrence of the metabolic syndrome. These observations reinforce the relevance of adhering to healthy diet and physical activity habits in order to maintain body weight stability rather than relying on the overuse of regulatory systems soliciting the effects of hyperinsulinemia on the control of energy intake and expenditure.

In the context of weight management, it is more and more recognized that exercise should be encouraged and the emphasis on weight loss reduced [22–24]. This is concordant with the evidence that cardiorespiratory fitness is a more powerful predictor of cardiovascular and mortality risk than body weight [25, 26]. The culture of focusing on body weight as the sole indicator of success is misleading because exercise without weight loss has been reported to be associated with marked reductions in abdominal fat and increases in skeletal muscle mass [27]. Moreover, body weight per se does not seem to be the most important risk factor for obesity comorbidities [28–30]. It is nevertheless understandable that many people feel disappointed by the poor weight loss success of exercise.

According to King et al. [31], the general perception that exercise is futile for weight management is damaging, and a more transparent and positive attitude to the health benefits of exercise is required. Therefore, there is a need to promote physical exercise and to prevent it being undervalued by the community and by public health professionals [25, 31].

Exercise-Induced Negative Energy Balance

The ability of exercise to induce an overall body energy deficit or to prevent a positive energy balance within a given period of time depends on its energy cost, its ability to modify postexercise energy metabolism, and the postexercise compensation in energy intake [5]. Furthermore, the resulting exercise-related energy balance is influenced by exercise modalities (type, duration, frequency, and intensity) as well as the nutritional context surrounding its practice.

Beyond the energy cost of exercise, early studies have shown that an increase in energy metabolism can persist for many hours following the exposure to the exercise stimulus [32, 33]. Many years later, we confirmed this observation by demonstrating that resting metabolic rate was greater in endurance-trained individuals compared to the level predicted by their body weight [34]. Taken together, the energy cost of physical activity and its related increase in postexercise metabolic rate should normally induce a significant body weight loss if no compensation in energy intake occurs over time.

In the 1980s, Flatt [35] proposed the RQ?:?FQ concept according to which variations in energy balance correspond to those in macronutrient balance. In fact, since the regulation of carbohydrate and protein balance is precise, this concept ultimately implies that energy balance is equivalent to fat balance. Thus, the capacity of exercise to induce an energy deficit would depend on the ability to increase lipid oxidation above lipid intake. Importantly, this concept also emphasizes the impact of variations of body fat mass on fat oxidation. For instance, in the study of Schutz et al. [36], a change of 10?kg in fat mass was related to a change of 20?g in daily lipid oxidation in the same direction. If these results are applied to the context of a weight-reducing program, this would mean that 40 to 60?min of light to moderate exercise might be necessary to compensate for the decrease in fat oxidation resulting from a 10?kg fat mass loss. Our research experience with female elite swimmers agrees with this observation since a two-month interruption of regular intensive training resulted in a 4?kg fat gain corresponding to a positive energy balance of about 600?kcal/day [37]. Thus, one of the main clinical implications of the RQ?:?FQ concept is that regular physical activity seems to be necessary to compensate for the weight loss-induced decrease in fat oxidation and thus prevent weight regain in a context where fat intake would be unchanged. This is consistent with data reported by Ewbank et al. [38] who found that exobese regular exercisers regained much less body weight compared to less active subjects.

The RQ?:?FQ concept also helps in understanding the effects of exercise modalities on body weight. As previously reviewed in [39], increasing exercise duration is related to some accentuation of weight loss and ultimately to the occurrence of a plateau. Once again, fat loss over time has a sufficient influence on weight regulation to compensate for the stimulating effects of exercise.

Our research has also been oriented towards the evaluation of exercise intensity per se on energy balance and body weight. In this case, the key question is whether, “calorie for calorie,” an increase in exercise intensity is sufficient to modify the spontaneous coupling between energy intake and expenditure. The first answer to this question was provided by the Canada Fitness Survey which showed that after statistical adjustment for the energy cost of leisure time activities, subcutaneous adiposity was lower in individuals reporting the practice of vigorous physical activities [40]. Subsequently, the comparison of two exercise training programs revealed that for a given energy expenditure of exercise, subcutaneous fat loss was greater after a high intensity intermittent exercise program compared to a more conventional endurance training program [41]. This study also showed that high intensity exercise induced a more pronounced enhancing effect on the oxidative potential of skeletal muscle. Furthermore, experiments performed under standardized laboratory conditions confirmed the effects of exercise intensity, be it on postexercise spontaneous energy intake or energy expenditure/fat oxidation. Indeed, after having performed a 500?kcal-exercise of either low or high intensity, the postexercise compensation in ad libitum energy intake was lower when exercise intensity was high [42]. We repeated the same experimental strategy to measure the effect of vigorous exercise on postexercise energy metabolism. Specifically, high intensity exercise accentuated postexercise resting VO2 and fat oxidation which was, however, abolished by beta blockade [43]. This finding is relevant regarding the RQ?:?FQ concept [35], because it demonstrates the involvement of beta adrenergic stimulation as a mechanism underlying the stimulating effect of vigorous physical activity on fat oxidation.

In summary, the experience of many decades of investigation on the impact of physical activity on body weight shows that the exercise stimulus can influence energy balance. This effect is more pronounced when prolonged vigorous exercise is performed but clinical experience indicates that some individuals may be unable to take in charge such a physical demand. According to the RQ?:?FQ concept, one must also keep in mind that independently of the features of the exercise regimen, metabolic adaptations occurring with fat loss will progressively attenuate the anorexigenic and thermogenic effects of prolonged vigorous activity up to complete resistance to further lose fat. The obvious corollary of this observation is that exercise will thus have to be maintained in a reduced obese state to prevent further weight regain.

Physical Exercise Improves the Accuracy by Which Energy Intake Is Matched with Energy Expenditure

Several decades ago, Mayer et al. [44] evaluated the association between caloric intake, body weight, and physical work in an industrial male population in West Bengal (India). The mill workers covered a wide range of physical exercise, from sedentary to very hard work. The authors observed that caloric intake was greater in workers exposed to a greater labor demand, but only for moderate-to-high levels of physical exercise. In contrast, energy intake and energy expenditure were uncoupled in sedentary individuals. Indeed, energy intake was greater in sedentary workers compared to those performing light and medium work. These data are suggestive of a disruption in the accuracy by which energy intake is matched with energy expenditure at low levels of physical exercise and might explain, at least in part, why it is so difficult to prevent weight regain in sedentary individuals after a weight loss intervention. Conversely, a tight coupling between energy intake and energy expenditure has been documented at high levels of physical exercise [45, 46].

Furthermore, physical exercise has been shown to improve energy compensation in response to covert preload energy manipulation [47–49]. Indeed, active individuals seem more able to distinguish between preloads by adequately adjusting energy intake at a subsequent meal, denoting a better short-term appetite control. In contrary, sedentary individuals generally show a deficient homeostatic feedback control of hunger and satiety end are unable to distinguish between a low- and high-energy preload, and have similar energy intake at a subsequent meal [47–49]. The “long-term” effects of physical exercise on energy compensation in response to covertly manipulated preloads have also been recently investigated [50]. The authors observed an improved appetite control after a 6-week moderate intensity exercise program in normal weight sedentary individuals, with a more sensitive eating behavior in response to previous energy intake. In addition, results from a recent randomized crossover study showed that acute exercise significantly increased postprandial levels of PYY, GLP-1, and pancreatic polypeptide in normal weight adults, suggesting that exercise can trigger physiological changes in hormone secretion, which could help in appetite control [51]. The transitory increase in the plasma levels of satiety hormones reported in the latter study may help to explain the short-term suppression of hunger observed after exercise, a phenomenon that is known as “exercise-induced anorexia.” Thus, it seems appropriate to say that exercisers display a better appetite control in general than their less active counterparts. However, short-term satiety data cannot directly be extrapolated to long-term appetite control, because adaptations may occur.

Critical Role of Physical Activity in the Long-Term Weight Regulation

The role of physical activity on the long-term prevention of weight gain or maintenance of weight loss has been assessed in numerous studies in the literature. Most recently, cross-sectional data from 7 European countries in the EPIC-PANACEA survey indexed a total of 125?629 men and 280?190 women into four categories according to self-reported physical activity practice and found that physical activity was inversely associated with BMI and waist circumference [52]. Prospective cohort studies investigating the relationship between obesity and levels of physical activity over time are fairly consistent. Most studies found that people who are physical active on a regular basis are less likely to gain weight [53–56]. Interestingly, Drøyvold et al. [56] found that subjects reporting exercise of higher intensities were less likely to gain weight than those reporting low intensity exercises, even after adjusting for baseline BMI and age. Furthermore, Kimm et al. [57] showed that a decline in physical activity in adolescence was related to increases in BMI and skin fold thickness over time. Given the risk of adolescence overweight and obesity for later development of obesity, these findings underscore the importance of physical activity in long-term weight regulation. In contrast, Petersen et al. [58] did not find a relationship between long-term physical activity participation and development of obesity. The study rather suggested that obesity may lead to physical inactivity.

The above-mentioned findings are important in establishing associations between long-term weight regulation and physical activity; however, randomized controlled trials are needed to investigate the causal relationship between these two factors. Few studies have been conducted in this area. Donnelly et al. [59] studied weight changes in response to a 16-month supervised exercise trial (45?min/day, 5?days/week) in overweight young men and women. They found that exercise produced ~5?kg weight loss in male exercisers compared to controls. Whereas in women controls the weight gain was ~3?kg, the exercisers remained weight stable. Slentz et al. [60] studied the effects of different exercise volumes and intensities over 8 months on body weight and body fat distribution in middle-aged men and women with mild-to-moderate hypertension. Without a reduction in caloric intake, loss of both body mass and fat mass occurred in a dose-dependent manner in regards to exercise volume and intensity. Furthermore, the controls gained weight throughout the study period. In the Look AHEAD trial, a total of 5145 men and women with type 2 diabetes were studied [61]. Greater self-reported physical activity was the strongest correlate of weight reduction, followed by clinically significant endpoints such as treatment attendance and meal replacements.

In general, physical activity has not been regarded as the most effective strategy for obtaining weight loss. Several systematic reviews have showed that a lower weight loss can be expected by physical activity alone compared to caloric restriction [62–64]. However, many methodological issues, such as doses of physical activity, assessment of energy balance and energy intake, and variations in baseline variables (e.g., age, weight, and percentage of body fat) have to be considered when interpreting these findings. In recent years, several well-controlled studies that carefully matched energy deficits by either caloric restriction or physical activity have shown that weight loss through exercise can be achieved [65–67]. Furthermore, weight loss induced by exercise seems to reduce total and ectopic body fatness to a greater extent than caloric restriction [65–67], which is a finding of high clinical importance.

In more aggressive weight loss strategies, physical activity also plays an important role. Evans et al. [68] studied gastric bypass surgery patients at months 3, 6, and 12 postsurgery. Patients reporting participation in at least 150?min/week of moderate-to-high intensity exercise had greater weight loss at 6 and 12 months postsurgery.

The US National Weight Control Registry, published in 2008, reports that those who are successful at maintaining weight loss (individuals maintaining a 13.6?kg weight loss for more than 1 year) are an extremely physically active group, despite a large variance in individual levels of physical activity [69]. These findings have been confirmed by Jakicic et al. [70], who studied obese women randomly assigned to 1 of 4 groups based on physical activity energy expenditure (1000 versus 2000?kcal/week) and intensity (moderate versus vigorous) with a concomitant decrease in daily dietary energy intake (-1200? to -1500?kcal/day). Despite no difference in weight loss at 6 and 24 months between the groups, post hoc analyses showed that individuals sustaining a loss of 10% or more of initial body weight at 24 months reported performing more physical activity (1835?kcal/week or 275?min/week) compared to those sustaining a weight loss of less than 10% of initial body weight.

The reasons for this association between high levels of physical activity and successful maintenance of weight loss in the long term are not fully understood; however, it is probably related to the maintenance of resting metabolic rate or total daily energy expenditure. Redman et al. [71] randomized overweight subjects to either a low calorie diet (~900?kcal/day), caloric restriction of 25% of daily energy requirements, or 12.5% caloric restriction plus 12.5% increase in energy expenditure by structured exercise. The authors observed that 6 months of caloric restriction resulted in a metabolic adaptation characterized by a reduction in free-living energy expenditure that is larger than what can be explained by changes in body weight and body composition. Furthermore, there was a reduction in free-living activity thermogenesis after caloric restriction which was prevented when caloric restriction was combined to exercise.

As mentioned previously, another explanation for the association between high levels of physical exercise and successful maintenance of weight loss in the long term pertains to the better coupling between energy intake and energy expenditure, thereby facilitating the maintenance of energy balance [46]. Finally, high levels of physical activity are associated with better adherence to energy-restricted diets [64]. All together, the emerging scientific literature highlights the need to incorporate physical activity into every strategy intended to prevent weight gain as well as to maintain weight loss over time.

Contribution of Physical Exercise to Total Energy Expenditure

Interindividual variation in total energy expenditure (TEE) is mainly a function of differences in body size and physical activity. The activity-induced energy expenditure (AIEE) as part of the TEE may contribute, under habitual conditions, to 5% in a very sedentary person [72] up to 75% in highly trained endurance athletes [73]. However, while total physical activity is positively correlated with TEE, the weight-reducing effect of intense physical activity often associated with structured exercise training/sport activities is less obvious [74]. Interventions comprising regular sessions of physical exercise of moderate or high intensity generally produced moderate weight loss, with considerable interindividual variability, that is less than what could be expected based on theoretical calculations of the energy cost of the exercise session per se [75]. The obvious explanation for this is that weight loss is generally accompanied by a coinciding upregulated motivation to eat, leading to compensatory increased energy intake. Another explanation is that total 24-hour energy expenditure is not increased to the theoretically expected level in order to defend body mass. The latter explanation could be due to compensatory decreases in other daily activities, blunting the effect of exercise on TEE. A number of studies have, however, shown that the addition of moderate amounts of nonstrenuous physical activities, at least without dietary restriction, does not lead to a decrease in other activities for the remainder of the day [76–80].

The increase in TEE associated with physical exercise has actually been shown to be higher than the energy cost of the training program per se. Based on the results of four exercise intervention studies in nonelderly subjects [76–79], researchers found that the intervention-induced increase in TEE was in average 1.9?MJ/d, and the calculated net energy cost of the training program was of 1.0?MJ/d. These findings, combined with indications of maintained postexercise behavior, suggest that the cost of the exercise intervention was twice that could be expected from measurements or calculations of energy expenditure during the imposed exercise. Although no effects on basal metabolism were found in these studies when assessed =36?h after the last exercise session, it is still possible that the excess postexercise energy expenditure within this time frame partly explained these discrepancies [32, 33, 81], however, probably not to the full extent, leaving a part of this increase in TEE unexplained. Whatever the mechanisms behind these findings may be, the studies of Westerterp [82] as well as Goran and Poehlman [83] suggest that the difference between expected and measured increases in TEE is affected by the individual physical “burden” of the intervention. In the first study [82], the running distance was doubled (from 25 to 50?km/week) without any additional increase in TEE. In the second study [83], elderly subjects performed 3 intense cycling sessions weekly for 8 weeks but TEE did not increase (-0.3?MJ/d) although the energy cost of the training intervention per se was expected to be in average 0.6?MJ/d.

We have previously reported that extremely fit endurance athletes of both sexes, expending in average 18.3?MJ/d (women) and 30.3?MJ/d (men) during periods of intense training, have approximately 15% higher basal metabolic rates than sedentary subjects matched for age, sex, and lean body mass, even =39?h postexercise [73, 84]. Likewise, resistance training for 26 weeks in a previously unfit elderly population studied by Hunter et al. [85] resulted in marked increase in TEE (965?kJ/d). This was, in addition to the energy expended during the training sessions (215?kJ/d), attributed to increased resting metabolic rate (365?kJ/d) as a result of increased lean body mass as well as to additional nontraining physical activities (288?kJ/d).

A negative energy balance as a result of exercise combined with an imposed energy restriction may potentially modify the effect of exercise on TEE. Studies looking at the influence of exercise in combination with energy restriction have found marginal further effects on body weight following the addition of exercise [86]. Body weight seems to be defended during caloric restriction, involving at least 3 mechanisms: (i) a decrease in resting metabolic rate, although partly counteracted by exercise [87]; (ii) a reduction in nonexercise physical activity [71, 88] and as recently suggested; (iii) an increase in work efficiency. For instance, Goldsmith et al. [89] showed that the work efficiency (energy output divided by energy expended above resting energy expenditure) was increased by 15% when bicycling at 50 Watts after a 10% weight reduction. This is in line with the explanation offered by Westerterp et al. [79] for the lack of further increased TEE paralleling the increased exercise volume described above.

Finally, it should be noted that dietary induced thermogenesis (DIT) increases in direct proportion to the increased TEE if energy balance is maintained. In the case of well-trained endurance athletes, this could result in DIT four times higher than an average sedentary subject. It may account for up to almost 1000?kcal/d in extreme cases and should be considered when assessing the different components of TEE.

Physical Exercise as a Buffer to the Deleterious Effects of Stress on Body Weight

The role of chronic stress in the etiology of obesity is increasingly recognized [90–96]. In turn, the stress response of obese people has been shown to be exaggerated, which may further increase the risk of weight gain. Stress management has then been suggested as a weight control strategy to stop this vicious cycle [90] and, expectedly, lifestyle interventions targeting stress reduction have shown weight-control benefits [97]. Interestingly, the state of low physical activation appears to intensify the acute response to psychological stressors [92], and consistently sedentary lifestyles potentiate the stress-related health complications such as obesity, particularly visceral obesity [91, 95]. Thus, physical activity is suggested to decrease the risk of stress-induced obesity by (i) directly reducing the stress response and (ii) indirectly buffering the harmful effect of stress, as presented in Figure 2.

Figure 2: Potential influence of exercise on the interaction between stress and obesity.

In the modern environment, where energy-dense foods are highly available and food cues very powerful, people tend to eat pleasurable foods to relieve stress [93]. However, the stress-induced feeding is not only the result of the behavior-facilitating environment. When under threat, human body activates the hypothalamic-pituitary-adrenocortical (HPA) axis and the sympathetic nervous system (SNS) which leads to the release of glucocorticoids (cortisol) and catecholamines (adrenaline and noradrenaline), respectively [91, 92]. An increase in circulating cortisol is generally accompanied by hyperinsulinemia which may become chronic in the context of continuous stress [91, 92, 94]. The combined action of cortisol and insulin is known to increase the intake of pleasurable foods [94], and insulin blunts fatty acid oxidation which could lead to body fat gain. In addition, some evidence shows that hypercortisolemia leads to a state of leptin resistance and is also associated to an elevated neuropeptide Y release [91]. Both hormones (cortisol and neuropeptide Y) are known to stimulate appetite [98]. In turn, eating hedonic foods appears to decrease the feeling of stress [94]. It affects the corticolimbic brain areas that regulate learning, memory, reward, mood, and emotions [93]. Therefore, the stress-induced feeding habit is reinforced every time something pleasurable is eaten to relieve psychological stress [94]. This vicious cycle must then be stopped by using another stress-reduction method [96] and, for its physiological and psychological effects, exercise practice seems a good option.

The neuroendocrine response to stress also influences fat deposition. In fact, the joint action of the HPA and SNS is to mobilize energy for the “fight or flight” response that has been for long time vital for humans [91]. In today’s society, where stress is predominantly of psychosocial nature, the mobilized substrates are not used and rather stored. Since visceral adipose tissue is particularly sensitive to the combined signal of insulin and glucocorticoids, stress results in fat accumulation in the viscera [90–92, 94]. Moreover, stress-induced visceral fat deposition is further accentuated by the antithermogenic effect of cortisol [90] and the concomitant dysregulation in the thyroid axes and in the secretion of sex steroid and growth hormone [91].

In general, cross-sectional studies show a negative association between physical activity and stress levels, though such association is not significant in all of them [95]. Only few longitudinal and quasiexperimental studies have been conducted on the topic, but their results tend to support the cross-sectional evidence. For example, regular joggers showed a reduced risk of perceiving a high level of stress compared to sedentary individuals (OR = 0.33) [99]. Moreover, experimental evidence in adolescents assigned to 10 weeks of high intensity aerobic training showed beneficial effects on perceived stress level in comparison to those who engaged in moderate aerobic or flexibility training or no exercise [100].

The main rationale for using exercise as a stress reduction strategy is mostly based on the cross-stressor adaptation hypothesis [101]. This theory suggests that a bout of exercise elicits a stress response which therefore leads to beneficial adaptations in the stress pathways that can transfer to psychosocial stressors. Although this hypothesis seemed promising in the 1990s, recent meta-analyses did not show a strong support [102–104]. Current research continues to investigate the possible mechanisms that may explain the stress-reducing effect of exercise.

The key question now is whether physical activity, which seems to modulate the level of stress, may interact in the relationship between stress and obesity. Research on such triadic relationship is at a very early stage. In fact, only one study verified specifically the three-way interaction. Yin et al. [96] showed that the interaction of stress and exercise predicted adiposity measures in adolescents. The authors of a recent review about the effect of exercise on stress and metabolic syndrome/obesity were also in favor of the beneficial effect of exercise on the relationship between stress and adiposity [92]. Different possible mechanisms suggesting that exercise training might protect against the stress-induced obesity have been proposed. Apart from its possible direct effect on the modulation of the stress response, exercise training improves insulin sensitivity, which might counteract the insulin resistance state produced by chronic hypercortisolemia [91]. The secretion of insulin could then be reduced which thereby may diminish its deleterious impact on energy intake. In addition, exercise training enhances oxidative capacity of skeletal muscle [91]. In the long run, this could prevent stress-induced fat deposition by routing the energy mobilized in response to stressor toward oxidation rather than storage. Regular exercise produces psychological improvements that may help buffering the harmful effects of stress. It has beneficial antidepressant and anxiolytic effect [91, 105] and, as shown in a recent meta-analysis, depression increases the risk to develop obesity [106]. Exercise training also improves sleep patterns [95, 105]. Considering that bad sleeping habits is itself a stressor [107] that has been associated with increased risk of obesity [108], physical activity can have a stress-buffer effect. There is also some evidence that exercise influences health-related behaviors, such as nutrition, and might help coping with life’s stress, particularly among high-risk individuals [95]. Then, when practiced on a regular basis, physical activity could help breaking the stress-feeding habits.

How Can We Deal with the Interindividual Variability in Exercise-Induced Weight Loss?

When promoting exercise training for weight loss purposes, we inevitably have to deal with the question of interindividual variability in the response to exercise training. Why some people lose weight by exercising whereas others do not? And how can we deal with these differences in a clinical setting?

Interindividual differences in the response to exercise training have been reported in the literature [75, 109, 110]. Although methodological issues can be responsible for the variability (e.g., differences in subjects’ baseline characteristics) in some cases, these apparent differences can also be attributed to either compensatory behavioral changes or physiological adaptations to training [75, 109]. The behavioral changes can be both volitional and nonvolitional and include compensatory eating, reduced daily life nonexercise activities, or simply lack of compliance to the prescribed exercise program. The physiological adaptations, as mentioned earlier, may include a lower resting metabolic rate, altered substrate utilization, and improved exercise efficiency due to a better physiological functioning of both circulatory and peripheral mechanisms and structures.

It is widely accepted that, when facing energy deficit, the body reacts by upregulating energy conserving compensatory responses. Independently or in combination, these different compensatory responses (both behavioral and physiological) act as a counterbalance mechanism when exercising for the purpose of weight loss. The timing and magnitude of these mechanisms can be different. However, it is evident that the behavioral changes (volitional or nonvolitional) contribute more to the compensatory component than the physiological adaptations [109, 111]. Furthermore, physiological adaptations to exercise training are not factors which can be eliminated and, thus, are not susceptible to any treatment or deliberate changes. Hence, this suggests that it is important to recognize that behavioral changes might be a strong limiting factor in terms of achieving a successful body weight regulation. It is therefore necessary to uncover the behavioral changes on an individual basis in order to address them. This not only advocates a more individual approach when planning exercise programs in a clinical weight loss setting, but more importantly the possibility to monitor the patients’ eating and activity patterns and to give them support and guidance targeting behavioral aspects. This personalized approach must also include other behavioral factors that have been shown to impede the weight loss response, such as sleep deprivation, stress, depressive symptoms, and weight cycling.

As stated previously in this paper, exercise training should not be isolated as a means to maintain weight stability or lose weight but should be considered as a way to promote health, quality of life and fight off diseases. The beneficial effect of engaging in regular exercise training is independent of weight loss, and for that reason alone, exercise training should be an integrated part of any weight loss program.

Conclusion

The vast majority of scientific evidence supports a beneficial role of exercise on achieving body weight stability and overall health. The goal is to find ways to motivate people to exercise and adopt healthy lifestyles. In order to achieve this objective, we must be innovative and creative in finding ways to fight against the modern way of living that drives excess energy intake relative to expenditure. Future research will be needed to give a better insight into the many issues impacting physical activity levels of people, including the barriers to healthy active living. Furthermore, we need to pay particular attention to the disparities in physical activity practice, because children with disabilities and those from low socio-economic status backgrounds are at a disadvantage. With experts around the world sounding the alarm about the consequences of escalading rates of obesity, type 2 diabetes, and cardiovascular disease, a concerted action including legislative measures to promote healthy active living is more than warranted [112]. Specifically, government intervention needs to take the form of appropriate legal and fiscal measures designed to make healthy choices more affordable, accessible, and acceptable. By doing so, we expect that the population as a whole will be healthier.

Acknowledgments

J.-P. Chaput, L. Klingenberg, M. Rosenkilde, and A. M. Sjöden are partly funded by the University of Copenhagen and the Nordea Foundation (OPUS Center). J.-A. Gilbert is supported by a studentship from the Danone Institute of Canada. A. Tremblay is partly funded by the Canada Research Chair in Environment and Energy Balance. The authors declared no conflict of interests.

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Copyright © 2011 Jean-Philippe Chaput et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

by EricT

by Patricia Prinz

PLOS Medicine 2004

There is a well-documented relationship between short sleep duration and high body mass index (BMI). In the largest study, a survey on sleep duration and frequency of insomnia in more than 1.1 million participants, increasing BMI occurred for habitual sleep amounts below 7–8 hours [1]. A recent prospective study found an association between sleep curtailment and future weight gain [2]. The mechanism linking short sleep with weight gain is unknown, but Mignot and colleagues' study in this month's PLoS Medicine [3] adds to the growing evidence implicating leptin and ghrelin, the two key opposing hormones involved in appetite regulation.

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More Sleep, Appetite, or Obesity Articles

Hormones That Regulate Appetite

Leptin, a peptide hormone secreted from white adipocytes, is implicated in the regulation of food intake and energy balance. The hormone acts on the central nervous system, in particular the hypothalamus, suppressing food intake and stimulating energy expenditure. Leptin production is primarily regulated by insulin-induced changes in adipocyte metabolism—its secretion levels correlate with adipocyte mass and lipid loads.

Leptin promotes inflammation. The hormone provides an interesting link between obesity and pathophysiological processes such as insulin resistance and atherosclerosis, and disorders such as autoimmune and cardiovascular diseases and the metabolic syndrome. Increased serum leptin levels in obesity and metabolic syndrome support the view that these disorders are in fact low-grade systemic inflammatory diseases, characterized by increased concentrations of proinflammatory cytokines like interleukin-6, tumor necrosis factor-a and leptin. Leptin's proinflammatory role suggests that it may link energy homeostasis to the immune system [4,5].Ghrelin is a peptide hormone that stimulates appetite, fat production, and body growth—leading to increased food intake and body weight. It is secreted into the circulation from the stomach, but is also synthesised in a number of other tissues, including the kidney, pituitary, and hypothalamus, suggesting that the hormone has both distant and local (endocrine and paracrine) effects. These effects include stimulating the secretion of growth hormone, prolactin, and adrenocorticotropic hormone, and a diabetogenic effect on carbohydrate metabolism [6].

The New Study

In this study of 1,024 participants in the population-based Wisconsin Sleep Cohort Study [7], Mignot and colleagues found that in persons sleeping less than 8 hours, increased BMI was proportional to decreased sleep [3]. The researchers also found that shorter sleep times were associated with increased circulating ghrelin and decreased leptin, a hormonal pattern that is consistent with decreased energy expenditure and increased appetite and obesity.

These findings confirm earlier clinical reports on the effects of sleep deprivation and extend them to include naturalistic sleep in a large, community-based population. The study provides an exciting addition to the growing literature showing relationships between sleep curtailment, metabolic hormones, and metabolic disorders (including obesity). The data have important implications for our understanding of obesity and related disorders in the general population, with one caveat: the study population was enriched with snorers, making the results less applicable to a general population.

Mignot and colleagues' data are in accord with human and animal studies that show that experimental curtailment of sleep leads to lower levels of leptin [8,9,10,11] and increased ghrelin [12]. The new study therefore lends some support to the interpretation that reduced sleep levels cause the hormonal changes.

But there is also evidence of opposite effects—that is, that administration of leptin [13] and ghrelin can alter sleep. Ghrelin administration has been found to increase non-REM sleep in humans and mice, possibly via its interactions with the sleep-inducing peptide growth hormone releasing hormone (GHRH). Ghrelin is an endogenous ligand of the growth hormone secretagogue receptor, making it a candidate for an endogenous sleep-promoting factor [14]. Mignot and colleagues' study is congruent with the idea that inadequate sleep enhances ghrelin secretion, which in turn acts as an endogenous sleep factor in humans. This is an important new area of research that could conceivably lead to more physiological sleep aids than are currently available, with profound implications for improved public health.

Overall, the available studies suggest the presence of reciprocal interactions between metabolic hormones and sleep, relationships that are poorly understood at present. Does sleep interact with metabolic hormones directly or via intervening factors such as sleep-related breathing disorders? Patients with obstructive sleep apnea have impaired sleep and higher ghrelin levels than BMI-matched controls, and treatment with continuous positive airway pressure reduces ghrelin to control levels [15]. Although sleep-disordered breathing (SDB) was measured in the present study, the SDB analyses were not shown, making it difficult to evaluate the influence of SDB on ghrelin and leptin in this population.

There is a clear need for well-controlled, population-based studies that allow us to examine multiple relevant factors simultaneously. The present study highlights the importance of shortened sleep in relation to obesity, leptin, and ghrelin, a good start toward this goal.

Sleep and Public Health

Many other important questions remain, such as the roles that other hormones, cytokines, and SDB play in obesity. Many of the unanswered questions have important implications for public health. For example, diabetes, visceral obesity, hypertension, and hyperinsulinemia commonly aggregate together in large populations, and are considered a “metabolic syndrome” that has been linked to SDB [16] and to inflammatory disorders [17]. To what extent does long-term sleep curtailment contribute to these and related public health issues?

The possible role of sleep restriction in autoimmune and inflammatory disorders is of particular interest in light of recent findings linking immune function with ghrelin and leptin. Ghrelin and its receptor are expressed in human T-lymphocytes, where they can inhibit cytokine activation, including interleukins, tumor necrosis factor-a and leptin [18]. Conversely, leptin stimulates cytokine activation and immune-cell proliferation, an effect that predisposes to inflammatory conditions [4]. Is it possible, then, that sleep-related changes in leptin and ghrelin influence the development of metabolic and immune disorders? Can biologically restorative sleep reverse disease progression? Can biologically restorative sleep be defined on the basis of metabolic hormone responses? Future research may answer some of these and other questions, further elucidating the role of sleep in public health.

About the Author

Patricia Prinz is research professor, Biobehavioral Nursing and Health Systems, and adjunct professor, Department of Psychiatry and Behavioral Sciences, at the University of Washington, Seattle, Washington, United States of America.

Competing interests

The author declares that she has no competing interests.

Abbreviations

BMI, body mass index; SDB, sleep-disordered breathing

References

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Copyright: © 2004 Patricia Prinz. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

by EricT

By Barbara Strasser and Wolfgang Schobersberger

Institute for Sports Medicine, Alpine Medicine and Health Tourism, University for Health Sciences, Medical Informatics and Technology, 6060 Hall in Tirol, Austria

Journal of Obesity 2011

Over the last decade, investigators have paid increasing attention to the effects of resistance training (RT) on several metabolic syndrome variables. Evidence suggests that skeletal muscle is responsible for up to 40% of individuals' total body weight and may be influential in modifying metabolic risk factors via muscle mass development. Due to the metabolic consequences of reduced muscle mass, it is understood that normal aging and/or decreased physical activity may lead to a higher prevalence of metabolic disorders. The purpose of this review is to (1) evaluate the potential clinical effectiveness and biological mechanisms of RT in the treatment of obesity and (2) provide up-to-date evidence relating to the impact of RT in reducing major cardiovascular disease risk factors (including dyslipidaemia and type 2 diabetes). A further aim of this paper is to provide clinicians with recommendations for facilitating the use of RT as therapy in obesity and obesity-related metabolic disorders.

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Introduction

The inclusion of resistance training (RT) as an integral part of an exercise therapy program has been endorsed by the American Heart Association [1], the American College of Sports Medicine [2], and the American Diabetes Association [3]. While these recommendations are primarily based on the effects of RT on muscle strength, cross-sectional studies have shown that muscle mass is inversely associated with all-cause mortality [4] and the prevalence of the metabolic syndrome [5], independent of cardiorespiratory fitness levels.

Aging is associated with a loss of both muscle mass and the metabolic quality of skeletal muscle. Sarcopenia, the loss of muscle mass associated with aging, is a main cause of muscle weakness in old age and leads consequently to an increased risk for development of obesity-associated insulin resistance and type 2 diabetes mellitus [6]. Research supports the use of RT to prevent an age-related decline in skeletal muscle mass (which is approximately 0.46?kg of muscle per annum from the fifth decade on). Strong evidence indicates that muscle maintains its plasticity and capacity to hypertrophy, even into the 10th decade of life [7–9]. However, there is some evidence to suggest that muscle strength and its effect on body composition and metabolic risk factors may be more important than muscle mass [10]. Accordingly, the term of “dynapenia” to qualify the loss of muscle strength with normal aging has been proposed [11]. A number of neural factors may be implicated in the age-associated loss of muscle strength, but also alterations in contractile properties are discussed.

Skeletal muscle is the primary metabolic target organ for glucose and triglyceride disposal and is an important determinant of resting metabolic rate. The potential consequences of age-related reduction in skeletal muscle mass are diverse, including reduced muscle strength and power, reduced resting metabolic rate, reduced capacity for lipid oxidation, and increased abdominal adiposity. With increasing adiposity, the insulin-mediated glucose uptake in skeletal muscle of elderly patients is reduced [12]. Evidence suggests that the maintenance of a large muscle mass may reduce metabolic risk factors—namely, obesity, dyslipidaemia, and type 2 diabetes mellitus—associated with cardiovascular disease [13–15]. Despite the fact that a high muscle mass is associated with a favourable metabolic profile, one study reported that a higher muscle mass can be associated with metabolic disturbances in obese women [16]. The possible mechanisms may include increased concentrations of free androgens due to diminished levels of SHBG, a protein-sparing effect due to increased lipid metabolism, and changes in muscle capillarization and fiber composition due to visceral adiposity.

For the most part, recommendations to treat or prevent overweight and obesity via physical activity have focused on aerobic endurance training (AET). Data suggest that RT may be an effective alternative for modifying metabolic risk factors. From this backdrop, the purpose of this review is to (1) evaluate the potential clinical effectiveness and biological mechanisms of RT in the treatment of obesity and (2) provide-up-to-date evidence on the impact of RT in reducing cardiovascular disease (CVD) risk factors, namely, dyslipidaemia and type 2 diabetes.

Metabolic Effects of Resistance Training

Weight Control

Both resting and activity-related energy expenditure declines with age [17]; decreased energy expenditure can have a major adverse effect on weight maintenance [18]. Studies on the usefulness of RT in the context of weight loss have demonstrated mixed results. Although it is clear that AET is associated with much greater energy expenditure during the exercise session than RT, some studies have shown that regular RT is effective in promoting weight loss in obese persons [19, 20]. A significant number of studies have shown that RT is associated with a decrease in fat mass (FM) and a concomitant increase in lean body mass (LBM) and thus has little or no effective change in total body weight [21–27]. RT increased muscle mass by a minimum of 1 to 2?kg in studies of sufficient duration.

The implementation of RT within a dietary intake restriction programme has been studied, along with a combined dietary restriction and AET programme [28–32]. In terms of relative effects, the addition of RT has been found to prevent the loss of LBM, secondary to dietary restriction [33, 34]. One study demonstrated that twice-weekly RT could prevent age-associated loss of LBM, as well as associated resting metabolic rate (RMR) which is closely correlated to losses in LBM [35]. RT contributes to elevations of RMR as a result of a greater muscle protein turnover [36]. Theoretically, a gain of 1?kg in muscle mass should result in an RMR increase of approximately 21?kcal/kg of new muscle. Thus, RT, when sustained over years or decades, translates into clinically important differences in daily energy expenditure and age-associated fat gains. For example, a difference of 5?kg in LBM translates to a difference in energy expenditure of 100?kcal per day (equivalent to 4.7?kg FM per year) [37]. However, a number of studies have shown that RT will increase RMR, at least if the training is intense enough to induce an increase in LBM [38–40].

In a randomized control trial [41], 35 overweight men were randomized to either a control group, a diet-only group, a diet group that performed AET, or a diet group that performed both AET and RT. After 12 weeks, the weight loss in the three intervention groups was similar and significant, of which 69%, 78%, and 97%, respectively, were accounted for by fat loss. This study highlights the potential for RT to provide a unique stimulus to spare catabolism of body protein, thus altering the relationship between the LBM and FM. Exercise provided no additional stimulus for greater weight loss compared with that obtained from dietary restriction alone. The diet-only group also demonstrated a significant reduction in LBM.

Another study randomly assigned 29 obese men to one of three 16-week treatments, which consisted of a hypocaloric diet alone or in combination with RT (at 80% of 1-RM) or AET [19]. Whereas reduction in weight (-12.4?kg) and total adipose tissue (-9.7?kg) were not significantly different between the three groups, LBM was only preserved after the exercise training (independent of the mode), compared with the diet-only group (-2.5?kg). The principal finding of this study was that dietary restriction combined with either AET or RT increased the influence of diet alone on insulin levels in obese men.

A further trial assessed whether increases in LBM and decreases in FM from 15 weeks of twice weekly supervised RT (at 80% of 1-RM) could be maintained over 6 months of unsupervised exercise [25]. Over the total 39 weeks of RT, the treatment group gained 0.89?kg more in LBM, lost 0.98?kg more in FM, and lost 1.63% more in percent body fat when compared to the control group. Findings demonstrated that twice weekly RT did not result in any significant weight loss, but potentially could prevent age-associated fat gains over a period of years. Cited as feasible, was the likelihood that the positive body composition changes associated with RT could be maintained in an unsupervised exercise program after completion of the supervised exercise regime.

In a more recent study [42], an 8-week regime of RT delivered 3 times weekly (at 60% of 1-RM) significantly changed participants’ body mass (+0.58%), percentage of body fat (-13.05%), LBM (+5.05%), and FM (-12.11%) when compared to the control group. This study supported a relationship between RT and body mass index (BMI), demonstrated by an increase in BMI. Therefore, the use of BMI in ascribing CVD risk should be used with caution in those individuals with an increased LBM (as would be expected following RT).

More recently, the effects of a 6-month RT program (at 50% to 80% of 1-RM) were analysed in relation to exercise-induced oxidative stress and homocysteine and cholesterol in normal-weight and overweight older adults [43]. Oxidative stress is suggested to be a potential contributor in the early and advanced stages of CVD [44]. In the study, 49 older adults were stratified by BMI and randomly assigned to either a control nonexercise group or an RT group. Findings demonstrated that lipid hydroperoxides (PEROXs) and homocysteine levels were lower in both the overweight and normal weight RT groups compared with control groups. Change in muscle strength was associated with homocysteine at 6 months, whereas the change in PEROXs was associated with the change in body fat. This study showed that RT reduces exercise-induced oxidative stress and homocysteine, regardless of adiposity. Such a result indicates that this protection can be afforded in an older, overweight/obese population as effectively as in healthy older adults, which might indicate protection against oxidative insults (i.e., ischemia). A potential mechanism for RT-induced reduction of oxidative stress could include contraction-induced antioxidant enzyme up-regulation [45].

Visceral Adipose Tissue

Adipose tissue is a major endocrine organ, secreting substances such as adiponectin, leptin, resistin, tumor necrosis factor a, interleukin 6, and plasminogen activator inhibitor-1 that may play a critical role in the pathogenesis of the metabolic syndrome [46]. Excessive central obesity and especially visceral adipose tissue have been linked with the development of dyslipidaemia, hypertension, insulin resistance, type 2 diabetes, and CVD [8, 12]. A relative increase in body fat is linked with a decline in insulin sensitivity in both obese and elderly individuals [47, 48].

Several studies have demonstrated decreases in visceral adipose tissue after RT programs [24, 26–28, 49, 50]. Treuth et al. observed significant decreases in visceral fat in older men and women after 16 weeks of RT [26, 27]. In two studies, Ross et al. measured regional fat losses after 16 weeks of exercise combined with dietary interventions in middle-aged obese men [28, 49]. In their first study [49], tests of both diet plus AET and diet plus RT (at 70% to 80% of 1-RM) elicited similar losses of visceral fat, which were greater than losses of whole-body subcutaneous fat. In a follow-up study [28], they isolated the effects of AET and RT (at 70% to 80% of 1-RM) by comparing the responses to diet alone. All 3 groups lost significant amounts of total body fat, and all 3 groups experienced a significantly greater visceral fat loss compared with whole-body subcutaneous fat loss. The changes amounted to a 40% reduction in visceral fat in the diet plus RT group, 39% in the diet plus AET group, and a 32% reduction in the diet-only group. One study raised the possibility of gender specificity in visceral fat reduction response to RT [24]. Hunter et al. studied older women and men after 25 weeks of RT (at 65% to 80% of 1-RM). Results demonstrated that both genders significantly increased muscle mass and decreased whole-body fat mass. However, women also lost a significant amount of subcutaneous and visceral adipose tissue (-6% and -11%, resp.), whereas the men did not.

Although more research is needed to clarify these possible gender-specific responses, the overall available body of literature supports the use of RT, with or without AET, and with or without diet modification, as an effective intervention in the reduction of abdominal obesity. It seems that RT has the potential to reduce visceral fat deposits through both immediate effects (e.g., during weight loss or weight maintenance) and delayed effects (during weight regain). The results of the two Ross et al. studies [28, 49] suggest a potential for low volume, high-intensity RT to achieve reductions in total and regional adipose tissue when used in conjunction with a calorie-restriction diet. However, this observation requires confirmation by additional studies.

Overall, strong evidence supports the notion that regular RT can effectively alter body composition in obese men and women, independently from dietary restriction. It has been shown that RT increases LBM, muscular strength, and resting metabolic rate, and mobilizes the visceral and subcutaneous adipose tissue in the abdominal region. Further, RT lowers exercise-induced oxidative stress and homocysteine levels in overweight and obese older adults, associated with CVD. Considering the benefits of RT on body composition in obese men and women, the question is are there any studies that have investigated the effects of RT in obese adolescents? The majority of RT research with children to date has focused on preadolescents and the safety and efficacy of this type of training rather than the potential metabolic health benefits. There is only a small amount of evidence that children and adolescents may derive metabolic health-related adaptations from supervised RT. However, methodological limitations within the body of this literature make it difficult to determine the optimal RT prescription for metabolic fitness in children and adolescents, and the extent and duration of such benefits. More robustly designed single modality randomized controlled trials utilizing standardized reporting and precise outcome assessments are required to determine the extent of health outcomes attributable solely to RT and to enable the development of evidence-based obesity prevention and treatment strategies in this cohort. Furthermore, further studies with postintervention follow-ups of at least six months are required in order to assess whether RT prescriptions can be maintained as part of a regular lifestyle, and whether improved body composition can be maintained over longer periods.

Metabolic Risk Reduction

Epidemiologic studies show a strong association for obesity with CVD [51] and type 2 diabetes (T2D) [52]. Obesity-induced risk factors such as plasma cholesterol, elevated plasma glucose, and elevated blood pressure increase the risk for CVD and have thus been called the “metabolic complications” of obesity [53]. Published evidence indicates that the risk for CVD associated with the metabolic syndrome is greater than the sum of its individual risk factors [54]. Apparent is that improved glycemic control, decreased fat mass, improved blood lipid profiles, and decreased blood pressure are important factors in reducing coronary heart disease (CHD) in people with metabolic risk.

Dyslipidaemia

At present, a small amount of conflicting data exists on the effects of RT on blood lipid levels in healthy elderly people, and in patients with dyslipidaemia. In a recent trial, 131 subjects were randomly assigned to an RT group, an AET group, a combined RT and AET group, or a nonexercising control group [55]. Findings demonstrated that exercise mode did not impact upon blood lipids. In contrast, total cholesterol (TC), low-density lipoprotein cholesterol (LDL), and plasma triglyceride (TG) were significantly lower in all groups. These data are comparable with another study that investigated the effects of RT and AET on metabolic parameters in 60 obese women [56]. After 20 weeks of training without diet, significant decreases in TG and TC levels were noted in each of the study groups. Fahlman et al. demonstrated that both AET and RT groups experienced increased high-density lipoprotein cholesterol (HDL-C) and decreased TG at the end of a 10-week training period in 45 healthy elderly women [57]. The RT group (at 80% of 1-RM) also had significantly lower LDL-C and TC compared with controls. These favourable changes occurred without concurrent changes in weight or diet.

None of the above studies included patients with abnormal lipid profiles. Unfortunately, no information is available on the effects of RT on subjects with dyslipidaemia. Several earlier studies examined the relationship between RT and plasma lipoprotein levels, with mixed results. In one study, premenopausal women were randomly assigned to an RT program or a control group for 5 months [58]. The RT group showed a significant decrease in TC and LDL-C, while no significant changes were noted in serum HDL-C or TG levels in either group. Changes in body composition showed no significant correlations with changes in TC or LDL-C. Another study determined the effects of 20 weeks of RT on lipid profiles in sixteen untrained males with abnormal lipoprotein-lipid levels and at least two other risk factors for CHD [59]. The training program resulted in no significant changes in plasma concentrations of TG, TC, and HDL-C. These results are in agreement with those that determined the effects of 12 weeks of RT (at 60% to 70% of 1-RM) on lipoprotein-lipid levels in sixteen sedentary obese women [60]. In contrast, another study examined the effects of 16 weeks of high-intensity RT on risk factors for CHD in eleven healthy, untrained males [13]. The RT program resulted in a 13% increase in HDL-C, a 5% reduction in LDL-C, and an 8% decrease in the TC/HDL-C ratio, despite not showing changes in body weight or percent body fat.

These findings indicate that RT has the potential to lower risk factors for CHD, independent of changes in body weight or body composition. The results of a prospective study that focused on lipid and lipoprotein levels in previously sedentary men and women undergoing 16 weeks of RT were similar [61]. Women participants demonstrated a 9.5% reduction of TC, a 17.9% decrease in LDL-C, and a 28.3% lowering of TG. Among the men, LDL-C was reduced by 16.2%, while the ratios of TC and LDL-C versus HDL-C were lowered by 21.6% and 28.9%, respectively. Thus, RT may result in favourable changes in lipid and lipoprotein levels in previously sedentary men and women. However, limitations exist; only one of the above-mentioned studies was conducted with subjects with dyslipidaemia, and no information is available about the effect of RT on patients with dyslipidaemia alone.

Type 2 Diabetes

Most available studies relate to AET in the treatment of insulin resistance (IR) and type 2 diabetes (T2D). Several systematic reviews focused on the relationship between exercise and/or physical activity and glycemic control in patients with T2D [62–64]. Results indicated that physical training significantly improves glycemic control and reduces visceral adipose tissue and plasma TG in people with T2D, even without weight loss.

RT has been shown to improve insulin-stimulated glucose uptake in patients with impaired glucose tolerance or manifest T2D [48]. RT, and subsequent increases in muscle mass, may improve glucose and insulin responses to a glucose load in healthy individuals [65, 66] and in diabetic men and women [67, 68] and improves insulin sensitivity in diabetic or insulin-resistant middle-aged and older men and women [68–71]. In addition, high-intensity RT has been found to decrease glycosylated haemoglobin (HbA1c) levels in diabetic men and women, regardless of age [21, 22, 72–76].

A recent meta-analysis of 27 randomized controlled trials examined the effects of different modes of exercise on glucose control, and risk factors for complications in patients with T2D [77]. Results demonstrated that differences among the effects of AET, RT, and combined training on HbA1c were minor. For training lasting =12 weeks, the overall effect was a small beneficial reduction (HbA1c 0.8%±0.3%). Aerobic and combined exercise had small or moderate effects on blood pressure (BP). All three modes of exercise produced trivial or unclear effects on blood lipids. The effects of RT on glycemic control and risk factors associated with CVD in T2D were small (HbA1c), unclear (BP), or trivial (blood lipids). Findings supported the notion that combined training was generally superior to RT alone.

The clinical significance of a 0.5% decrease in HbA1c can be gauged by examining large prospective intervention studies investigating morbidity and mortality outcomes in people with T2D [78]. Data suggests that a 1% rise in HbA1c represents a 21% increase in risk for any diabetes-related death, a 14% increased risk for myocardial infarction, and a 37% increased risk for microvascular complications. The impact of a decrease of 0.5% HbA1c equates to a 50% improvement towards a target value of 7% HbA1c, and a 25% improvement towards a normal value of 6% HbA1c, for a person diagnosed with 8% HbA1c.

It is unclear whether an improvement in glycemic control can be maintained in the longer term. For example, in the 6-month postintervention follow-up period reported by one author [79], participants continuing with supervised RT (at 70% to 80% of 1-RM) maintained the improvement in glycemic control, whilst in a 6-month home-based follow-up group, the improvements were lost [80]. The hypothesized reason for this difference is the difficulty of motivating people with T2D to maintain RT prescriptions as part of a regular lifestyle.

In another study [22], the combination of RT (at 70% to 80% of 1-RM) and moderate dietary restriction was associated with a threefold greater decrease in HbA1c levels after 6 months compared with moderate weight loss without RT. This result was not mediated by concomitant reductions in body weight, waist circumference, and FM. It is apparent that an increase in LBM after RT may be an important mediator in improved glycemic control. One study specifically discussed the effects of an increase in the number of GLUT4 transporters [48], because the transporter protein GLUT4 expression at the plasma membrane is related to fibre volume in human skeletal muscle fibres [81]. A further study found that the improvement in LBM after a 10-week moderate RT-program had a greater impact on HbA1c levels than the reduction in FM, suggesting that increases in muscle mass improved glycemic control [72]. Furthermore, RT-induced changes in HbA1c have been inversely correlated with changes in the quadriceps cross-sectional area [71]. It has been proposed that hyperglycemia has a direct adverse effect on muscle contractile function and force generation [82].

A recent meta-analysis sought to investigate the existence of a dose-response relationship between intensity, duration, and frequency of RT and the metabolic clustering in patients with T2D [83]. Findings demonstrated that RT significantly reduced glycated hemoglobin by 0.48% HbA1c (95% CI: 0.76 to -0.21, P=.0005), fat mass by 2.33?kg (95% CI: -4.71 to 0.04, P=.05), and systolic BP by 6.19?mmHg (95% CI: 1.00 to 11.38, P=.02). There was no statistically significant effect of RT on TC, HDL-C, LDL-C, TG, and diastolic BP. It appears that RT regimes of longer duration are most beneficial, whilst higher intensity more likely has a harmful effect on glycemic control. The meta-analysis confirmed the notion that RT does not increase BP (as was once thought), and that RT may even benefit resting BP. The BP-lowering effect of RT seems to be independent of weight loss and is believed to be mediated via reduced sympathetically induced vasoconstriction in the trained state [84, 85]. It should be noted that a decrease of approximately 6.2?mmHg for resting systolic BP is significant, since a reduction of as little as 3?mmHg in systolic BP has been estimated to reduce CHD by 5–9%, stroke by 8–14%, and all-cause mortality by 4% [86]. Progressively higher volumes of RT may reduce resting systolic BP, and more significantly, diastolic BP. Interpretive caution is warranted, due to the fact that the above analyses were based on a limited number of study groups.

Prescription of Resistance Training

It is well understood that when performed regularly and with sufficient intensity, RT stimulates skeletal muscle to synthesize new muscle proteins (hypertrophy). However, the effective amount of RT to promote muscle growth in relatively sedentary diseased or aged individuals is an area in need of further investigation. It is believed that 1 to 2 sets of 8 to 12 repetitions per set with an intensity greater that 60% of 1-repetition maximum (1RM—the maximum load that can be lifted once only throughout a complete range of motion), with 8 to 10 exercises per session and 2 to 3 sessions per week, are likely to be beneficial for maximising the health effects of increased skeletal muscle mass [87]. A recent study examining the effects of systematic RT in the elderly (76.2±3.2 years) demonstrated that RT consisting of two training sessions per week was at least as efficient as RT involving three trainings sessions per week, provided that the number of sets performed was equal [88]. These findings contradict results of a previous study reporting that RT three days per week elicits superior strength gains when compared with RT two days per week [89]. However, the latter study was low volume: higher frequency produced better results. A more recent review demonstrated that there was no difference in mean rates of increase in the whole muscle cross-sectional area between two and three RT sessions per week for longer periods of training [90]. But, caution is urged on the fact that the methods (machines, dynamometer) of measuring muscle strength and expressing it (absolute, relative to body weight or muscle mass) are not standardized. Thus, the true increases in muscle strength are difficult to determine in research protocols. Therefore, to compare results of different studies, muscle strength should be determined in kilo pound (kp) or Newton (N; SI unit).

Systematic reviews comparing RT frequencies in patients with metabolic or cardiovascular risk revealed no apparent association between RT frequencies and changes in risk factors for CVD [91, 92]. However, it should be noted that only a few studies were conducted with subjects with metabolic risk, and most of the included RT studies had a training frequency of three days per week. Regression-based analyses from recently performed meta-analysis by Strasser et al. suggest there is no apparent association between RT frequency and glycemic control, but indicate a trend to a negative correlation for some outcomes of lipid profile in patients with abnormal glucose regulation [83]. The effect of RT on resting systolic BP and diastolic BP seems to be dose-dependent, since decreases in resting BP were more pronounced when the RT program was of high volume. Apparent was that relatively modest increases in RT frequency had hypotensive effects, since resting BP was reduced to a greater extent when exercising three times per week compared to twice a week [83].

On the basis of a combination of literature findings and in-house laboratory results [21, 79, 88, 93–95], some basic recommendations for the design of programmes for elderly adults with metabolic risk based are provided. (i)During the first two weeks of exercise, the weights should be kept to a minimal level so that patients learn the exercise techniques. A minimal weight allows muscles to adapt to the training and prevents muscle soreness. (ii)From the third week, the objective of the training is hypertrophy. Participants should start with three sets per muscle group per week, on 3 nonconsecutive days of the week. One set should consist of 10–15 repetitions, without interruption, until severe fatigue occurs and completion of further repetitions is impossible. (iii)The training load should be systematically increased to keep the maximum possible repetitions between 10 to 15 per set. A repetition maximum of 10 to 15 repetitions corresponds with 60–70% 1-RM [15]. (iv)The number of sets for each muscle per week should be increased progressively every four weeks by one set to a maximum of 10?sets per week on (Table 1). (v)The RT program should consist of exercises for all major muscle groups. Exercises to strengthen the upper body could include bench press (pectoralis), chest cross (horizontal flexion of the shoulder joint), shoulder press (trapezius), pull downs (latissimus dorsi), bicep curls, tricep extensions, and exercises for abdominal muscles (sit-ups). Lower body exercises could include leg press (quadriceps femoris).

Table 1: Systematic adjustment of the weekly RT volume in sets per muscle group per week (S/MG/W) for improvement in maximum strength in rehabilitation, health, and leisure sports.

Conclusions

Based on this review of the literature, there is a strong support for the notion that RT is at least as effective as AET in reducing some major cardiovascular disease risk factors (Figure 1). Findings demonstrate that RT may be an effective alternative to improve body composition and maintain reduced FM in obese patients after exercise training or energy intake restriction. Furthermore, it has been shown that RT preferentially mobilizes the visceral and subcutaneous adipose tissue in the abdominal region. There is now substantial support for RT decreasing glycosylated hemoglobin levels in people with an abnormal glucose metabolism and improves tendency lipoprotein-lipid profiles. Decreased fat mass, improved glycemic control and blood lipid profiles are important for reducing microvascular and macrovascular complications in people with metabolic risk. On this basis, RT is considered a potential adjunct in the treatment of metabolic disorders by decreasing known major risk factors for metabolic syndromes. As such, RT is recommended in the management of obesity and metabolic disorders.

Figure 1: Percent change in metabolic parameters after 4 months RT (black) or AET (white) in patients with T2D. Whiskers represent standard deviation [18].

Conflict of Interests

The authors have no conflict of interests that are directly relevant to the content of this original research paper.

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Copyright © 2011 Barbara Strasser and Wolfgang Schobersberger. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

by EricT

By Christopher A. Magee¹, Xu-Feng Huang², Donald C. Iverson³, and Peter Caputi⁴

Journal of Obesity 2010

A growing number of studies have identified chronic sleep restriction as a potential risk factor for obesity. This could have important implications for how obesity is prevented and managed, but current understanding of the processes linking chronic sleep restriction to obesity is incomplete. In this paper, we examined some of the pathways that could underlie the relationship between chronic sleep restriction and obesity. This involved exploring some of the potential environmental, health, behavioral, and sociodemographic determinants of chronic sleep restriction, which require further investigation in this context. Three pathways that could potentially link chronic sleep restriction to obesity were then examined: (1) altered neuroendocrine and metabolic function, (2) impaired glucose regulation, and (3) waking behavior. The selected pathways linking chronic sleep restriction to obesity reviewed in this paper are presented in a schematic representation; this may be used to guide future research in this area. This area of research is important because it may lead to more effective interventions and strategies to combat the present obesity epidemic.

Introduction

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There are, however, other factors that may be contributing to the present obesity epidemic which need to be addressed. One factor that is receiving increased attention is chronic sleep restriction (<7 hours sleep a night). This is based on a number of recent epidemiological studies reporting that shorter sleep durations are associated with overweight and obesity in adults; see Cappuccio et al. [7] for a review. Some longitudinal data also indicate that short sleep predicts weight gain over a period of several years [8–12]. Furthermore, several laboratory-based studies have demonstrated that sleep restriction affects hormones involved in regulating energy balance, in a manner that is consistent with weight gain [13–18]. Interestingly, Chaput et al. [19] recently examined the relative contribution of nine risk factors for obesity (e.g., diet, physical activity, and sleep duration) over a six-year period. They found that sleep duration along with low calcium consumption and high disinhibition and restraint eating behavior significantly predicted weight gain; energy intake and physical activity were not significant predictors of weight gain.

These findings suggest that chronic sleep restriction could be a possible risk factor for obesity, and this may have implications for obesity management and prevention. However, our understanding of the relationship between chronic sleep restriction and obesity is incomplete as the processes through which chronic sleep restriction contributes to obesity, and the extent and magnitude of these effects, remain unclear. Furthermore, an important gap in the literature is that the causes, or determinants, of chronic sleep restriction have been largely overlooked. In this area of research, chronic sleep restriction has tended to be viewed as a result of behavioral sleep curtailment or an underlying sleep disorder [20]. This is a problem because the causes of chronic sleep restriction are likely to be complex and encompass many factors. These need to be identified and understood because they could influence the nature of the relationship between sleep and obesity and would likely have treatment implications.

Therefore, the purpose of the present paper is to integrate two important bodies of literature and examine (1) the determinants of chronic sleep restriction and (2) selected pathways linking chronic sleep restriction to obesity. This paper is not intended to provide a comprehensive review of these areas, as the relationship between chronic sleep restriction and obesity is likely to be complex and involve a multitude of processes. Instead, we focus on the factors and pathways that we feel are most important based on our assessment of the available literature. For example, we explore the following three potential pathways linking chronic sleep restriction to obesity: (1) neuroendocrine and metabolic pathways, (2) glucose regulation, and (3) waking behaviour.

It is also acknowledged that the association between chronic sleep restriction and obesity is likely to be bidirectional since obesity could also contribute to chronic sleep restriction (e.g., sleep apnea). However, this paper focuses primarily on chronic sleep restriction as a possible cause of obesity. We conclude the paper by presenting a schematic representation that integrates these pathways and provides an important framework that is currently lacking in the literature; this can be used to guide future research in this area.

Determinants of Chronic Sleep Restriction

Chronic sleep restriction is generally defined as habitual sleep durations that are less than 7 hours, but more than 4 hours, a night [21]. It is distinct from acute total sleep deprivation which refers to an absence of sleep over a minimum of 24 hours; this is neither a common nor chronic condition in humans. Chronic sleep restriction has become more common in recent decades, having more than doubled in the US since the 1960s [22, 23]. Approximately one-third of adults in the US currently report chronic sleep restriction and similar figures have been observed in other countries [24–26]. Chronic sleep restriction is increasingly recognized as a health concern because it is associated with motor vehicle and industrial accidents, health conditions such as hypertension, diabetes, obesity, and depression, and increased mortality [21, 27, 28].

Previous research investigating the link between chronic sleep restriction and obesity has not adequately addressed the underlying causes of chronic sleep restriction. Many studies have either overlooked the causes of chronic sleep restriction or viewed chronic sleep restriction as the result of voluntary sleep curtailment [20]. However, the determinants are likely to be complex and vary considerably between individuals. These need to be examined because the nature of the relationship between chronic sleep restriction and obesity could depend on, or vary according to, the precise cause of chronic sleep restriction. The potential to modify sleep duration to aid obesity prevention and management may also depend on the precise factor(s) that contribute to chronic sleep restriction. Therefore, the remainder of this section identifies some key determinants of chronic sleep restriction that require investigation in this context.

Sleep duration is influenced by a combination of genetic, health, sociodemographic, environmental, and behavioral factors; so there are likely a multitude of factors that could potentially contribute to chronic sleep restriction. Data from large-scale population studies have recently identified factors associated with chronic sleep restriction in adults at a population level. Most of these studies have been cross-sectional, but they do provide an insight into the factors that could be important predictors of chronic sleep restriction. In particular, sociodemographic factors such as increased age, lower education level, lower income, ethnicity, and nonmarried status have been identified as strong predictors of chronic sleep restriction [24, 25, 29–31]. Health behaviors such as smoking, excessive alcohol consumption, lower levels of physical activity, increased television viewing, shift work, long working hours, and increased time commuting to and from work have also been associated with shorter sleep durations [24, 25, 30–34]. Several studies also demonstrate that physical and mental health status strongly predict sleep duration. For example, chronic diseases such as diabetes or cardiovascular disease [24, 25, 29–31] and mental health conditions such as stress, depression, and sleep disorders (e.g., insomnia) have been associated with reduced sleep [35, 36]. Dramatic changes in our physical environments in recent decades may also be contributing to chronic sleep restriction. For example, it is argued that we increasingly live in a 24-hour society that is characterized by more artificial light; this has the potential to disrupt natural circadian rhythms and adversely affect sleep [37].

There are, therefore, many sociodemographic, behavioral/lifestyle, health, and environmental factors that could contribute to chronic sleep restriction. Importantly, the precise causes of chronic sleep restriction are likely to vary considerably between individuals. Some individuals may voluntarily limit the amount they sleep in order to meet work, social, or family demands, whilst others engage in behaviors such as cigarette smoking and excessive alcohol consumption that adversely impact on sleep duration. In other individuals, an underlying health condition, medication use, or factors associated with a low socioeconomic status could be the primary cause(s) of chronic sleep restriction.

If chronic sleep restriction is to be targeted as a modifiable risk factor for obesity, there is a need to better understand the underlying factors contributing to chronic sleep restriction in different populations. This is important for two main reasons. First, the nature and magnitude of the relationship between chronic sleep restriction and obesity may depend on the underlying causes of chronic sleep restriction; this could influence the mechanisms or pathways linking chronic sleep restriction to obesity and also how susceptible an individual is to the effects of sleep restriction. Second, the potential to modify sleep through interventions may differ based on the specific cause(s) of chronic sleep restriction. For example, factors such as mental health problems and sleep disorders may require interventions from specialist practitioners. In contrast, factors such as work hours could be targeted by behavioral interventions that do not necessarily aim to reduce work hours (as this could have adverse consequences, such as a loss of income), but rather attempt to minimize the impact of work hours on sleep patterns. This could perhaps be achieved by addressing factors such as television viewing and time spent commuting to and from work which may be limiting sleep duration in people who work long hours [33, 34]. Thus it is important that research investigating the relationship between chronic sleep restriction and obesity examines the determinants of chronic sleep restriction.

Hypothesised Pathways Linking Chronic Sleep Restriction to Obesity

There are many processes or pathways through which chronic sleep restriction could contribute to obesity and it is not feasible to address all of these in a single review. Instead we focus on the following three hypothesized pathways linking chronic sleep restriction to obesity: (1) neuroendocrine and metabolic pathways, (2) glucose regulation, and (3) waking behaviour. There is potential overlap between these pathways, but for the purposes of clarity, we discuss each of them separately.

The concept of energy balance is important in this context. A constant body weight depends on a balance between energy intake (diet) and energy expenditure (basal metabolic rate, physical activity, and thermogenesis). Under normal conditions energy balance is maintained by a complex regulatory system that involves multiple physiological pathways in the body which act on neural circuits to maintain body weight within a narrow range [38–40]. For example, the adipose tissue hormones leptin and adiponectin, the pancreatic hormone insulin, and the gastrointestinal hormones ghrelin, peptide YY3–36 (PYY), and glucagon-like peptide-1 (GLP-1) all act on hypothalamic circuits to influence energy balance. A chronic positive energy balance occurs when energy intake exceeds expenditure over a prolonged period of time; this has the potential to affect the processes involved in regulating body weight and can lead to obesity over time [39, 40].

Hypothesis 1. Sleep Restriction Alters Neuroendocrine and Metabolic Functioning

In a landmark series of experimental studies, Spiegel and colleagues [16–18, 41] demonstrated that short-term sleep restriction alters some neuroendocrine and metabolic hormones that are involved in the regulation of energy balance. In particular, six consecutive nights of sleep restriction (four hours sleep per night) were associated with increases in sympathetic nervous system (SNS) activity, evening cortisol levels and growth hormone levels (GH), and reductions in thyroid stimulating hormone (TSH), and leptin [16, 17, 41]. A follow-up study found that sleep restriction (four hours sleep per night) over two nights led to an 18% reduction in leptin and a 28% increase in ghrelin [18]. The increase in the leptin-to-ghrelin ratio corresponded with a 24% increase in hunger and a 23% increase in appetite that was mainly for energy dense foods. Other research groups have obtained similar results. For example, Guilleminault et al. [13] found that seven nights of sleep restriction (five hours sleep per night) led to a reduction in leptin rhythm amplitude. Schmid et al. [15] also found that a single night of 4.5-hour sleep led to an increase in ghrelin levels. Finally, Magee et al. [14] observed that two consecutive nights of five hours sleep led to a significant reduction in PYY levels and a corresponding decrease in satiety levels.

The profile of these hormonal changes is suggestive of increased energy intake, reduced energy expenditure and weight gain. For example, leptin, which is released in proportion to adipose tissue amount, acts on hypothalamic circuits to reduce energy intake and increase energy expenditure [42]. Insulin has many roles but also acts on hypothalamic circuits to reduce energy intake and increase energy expenditure [42, 43]. The reductions in leptin and insulin observed with sleep restriction are therefore suggestive of increased food intake and reduced energy expenditure. Ghrelin is released primarily from the stomach when nutrient levels are low and acts on hypothalamic pathways to stimulate food intake [44, 45]. The PYY3–36 molecule is released from the gastrointestinal tract in response to ingested nutrients and acts on the hypothalamus to reduce food intake [46, 47]. Therefore, the elevations in ghrelin and reductions in PYY observed with sleep restriction may be predictive of increased food intake.

Similarly, the increases in evening cortisol and GH levels may also be suggestive of weight gain. The elevation in GH levels observed by Spiegel et al. [41] was the result of an extended period of nocturnal GH secretion. This may have increased the amount of exposure of peripheral tissues to GH; if prolonged this could impact on glucose regulation in a way that leads to obesity (this is discussed in more detail below) [41]. However, GH has been shown to promote lean tissue and reduce the accumulation of adipose tissue [48]. The precise implications of the increases in GH observed with sleep restriction are therefore unclear and require further investigation.

The elevations in cortisol following sleep restriction suggest greater activity of the hypothalamic-pituitary-adrenal gland (HPA) axis. This could reflect increased stress levels as the HPA axis plays an important role in regulating the stress response [49]. Importantly, elevated cortisol levels have been shown to promote increased food intake and the accumulation of visceral fat in humans [50]. Similarly, since TSH normally functions to stimulate basal metabolic rate, the reductions in TSH with sleep restriction are suggestive of a reduction in energy expenditure.

The findings reviewed above indicate that short-term sleep restriction under controlled laboratory conditions alters neuroendocrine and metabolic hormones in a manner that is consistent with weight gain and obesity. However, we only have partial understanding of these mechanisms and there are a number of issues that remain to be addressed. First, it is not clear which brain mechanisms link sleep restriction with the observed alterations in metabolic and neuroendocrine functioning. The activation of the SNS with sleep restriction is one possibility since increased SNS activity inhibits the release of leptin from adipose tissue and may inhibit insulin release [51]. Increased SNS activity also inhibits vagal nerve activity, which could account for the rise in ghrelin level observed with sleep loss [52, 53]. However, increased SNS activity is typically associated with reductions in energy intake and increased energy expenditure over time [54]. Since this pattern is predictive of weight loss, increased activation of the SNS may not be the predominant mechanism through which sleep restriction alters energy balance and leads to weight gain. Instead, other mechanisms involving disruptions in the functioning of the suprachiasmatic nucleus (SCN) or activation of the HPA axis could be more important.

The SCN is located in the anterior hypothalamus and regulates the circadian rhythms of several physiological systems, including sleep, and the secretion of hormones involved in energy balance regulation [55]. Alterations in hormones such as leptin, cortisol, and TSH and GH observed with sleep restriction could therefore be the result of disrupted SCN output. As noted earlier, the increases in cortisol secretion observed with sleep restriction suggest increased activation of the HPA-axis, which is indicative of the stress response. The stress response serves an important adaptive purpose by supplying extra energy to body tissues in anticipation of a fight or flight response [49, 50, 56]. This leads to the release of cortisol which through a series of feedback loops signals the HPA axis to reduce cortisol secretion [57]. However, chronic or frequent stress can desensitise the HPA axis such that cortisol remains elevated; over time this can lead to an increase in visceral body fat since cortisol promotes fat accumulation and also inhibits the release of leptin [49, 50, 58]. Activation of the HPA axis may also explain the increased SNS activity observed with sleep restriction. Thus it is possible that the physiological changes observed with sleep restriction are the result of increased HPA axis activity and/or altered SCN output, which have a cascading effect on a number of physiological systems.

The second important consideration is that it is not clear whether the physiological effects of sleep restriction observed under laboratory conditions over a period of a few days are equivalent to prolonged (or chronic) sleep restriction as it occurs in free-living individuals. In particular, it is feasible to assume some degree of physiological adaptation to the effects of sleep restriction, but one can only speculate on the extent and nature of this adaptation. Moreover, the effects of sleep restriction as observed in laboratory-based settings may differ to the effects of chronic sleep restriction in free living adults. These are all important considerations because, as noted above, the causes of chronic sleep restriction could differ considerably between individuals and the impact of sleep restriction on energy balance may depend on the underlying cause of chronic sleep restriction. Finally, few studies have examined whether sleep restriction alters components of energy expenditure. Schmid et al. [59] demonstrated that sleep restriction led to a reduction in physical activity under free-living conditions, but there were no significant changes in food intake, hunger and appetite, and levels of leptin and ghrelin. This suggests that sleep restriction might alter energy expenditure but this requires further investigation in studies that also examine other components of energy expenditure such as basal metabolic rate or nonexercise activity thermogenesis. This is important because examining both sides of the energy equation (i.e., energy intake and expenditure) will be critical to understanding the processes through which sleep restriction promotes obesity.

Hypothesis 2. Sleep Restriction Alters Glucose Regulation

Another potential pathway linking chronic sleep restriction to obesity could involve disruptions in the regulation of glucose levels; this may also have implications for diabetes, which are discussed by Spiegel et al. [60]. Alterations in glucose regulation have been linked with weight gain and obesity. For example, Boulé et al. [61] found that lower blood glucose concentrations at the end of an oral glucose tolerance test (OGTT) predicted weight gain over a 6-year period. These results were explained according to the Glucostatic Theory of Appetite Control, which postulates that glucose plays an important role in the regulation of satiety and appetite [62]. In particular, reduced glucose utilization in important regions of the brain leads to perception of hunger and increased food intake, whereas higher glucose utilization in these same areas promotes a decrease in hunger and a cessation of eating [62].

Sleep restriction has been shown to affect glucose levels in humans. Spiegel et al. [17] found that six nights of sleep restriction led to a 30% reduction in glucose effectiveness (i.e., noninsulin dependent glucose utilization) and a 40% reduction in glucose utilization following intravenous glucose administration. These results have been supported by cross-sectional and prospective data. For example, Chaput et al. [63] found that habitually short sleepers had higher levels of fasting plasma glucose and insulin concentrations and lower blood glucose concentrations at the end of an OGTT. Chaput et al. [64] also found that individuals reporting short sleep had increased glucose area below fasting glucose concentrations; this is indicative of reactive hypoglycemia and predicted diabetes/impaired glucose tolerance at six-year followup.

Thus it is possible that chronic sleep restriction contributes to obesity by disrupting the regulation of glucose in a manner that promotes increased food intake. Chronic sleep restriction could potentially exert these effects via activation of the SNS or disruptions in hormones such as cortisol or GH [63]. As with the neuroendocrine pathways discussed above, there is a need for more longitudinal data examining whether prolonged sleep restriction does promote fat accumulation and lead to obesity by impairing glucose regulation.

Hypothesis 3. Sleep Restriction Affects Waking Behaviour

The third hypothesis discussed in this paper is that chronic sleep restriction contributes to obesity by affecting waking behavior, and in particular promoting patterns of behavior that cause weight gain. It is well documented, for example, that consumption of food with a high-energy content and sedentary behavior (e.g., television viewing, physical inactivity) are strong risk factors for obesity [6]. Chronic sleep restriction could lead to obesity by promoting these behaviors and this has received some empirical support.

Nedeltcheva et al. [65] for example, recently examined the effects of 14 consecutive days of sleep restriction on food intake, energy expenditure, and neuroendocrine hormones. In contrast to the studies conducted by Spiegel and colleagues [16–18], which involved a mild form of calorie restriction, Nedeltcheva et al. [65] provided food to participants ad libitum. Their results indicated that sleep restriction led to an increase in calorie consumption that was attributed to snacking particularly during the night when the individual would normally have been sleeping (this is unlikely to reflect the night eating syndrome). Food intake during meal time remained unchanged with sleep restriction, as did energy expenditure. The findings suggest that short sleepers could be more susceptible to weight gain because they have more time to eat. The increase in consumption because of greater exposure to food (rather than increased hunger) suggests that in addition to the homeostatic factors reviewed above, nonhomeostatic factors may also be involved in the relationship between chronic sleep restriction and obesity [66]. Thus, future studies will need to further investigate both homeostatic and nonhomeostatic pathways linking chronic sleep restriction to obesity.

Another plausible behavioral pathway linking chronic sleep restriction to obesity involves fatigue, since individuals who get insufficient sleep are more likely to experience fatigue and daytime sleepiness [67]. It is possible that individuals engage in behaviors such as consumption of high-energy drinks or food to counter the effects of fatigue. Fatigue may also render individuals less likely to engage in physical activity [68, 69] and more likely to engage in sedentary behaviors such as television viewing. This pattern of behavior could also promote a positive energy balance and may partially account for the association between chronic sleep restriction and obesity.

Integrating the Pathways Linking Chronic Sleep Restriction to Obesity

Chronic sleep restriction may be an important risk factor for obesity and could have implications for obesity prevention and management. However, current understanding of the processes underlying the relationship between chronic sleep restriction and obesity is limited. The purpose of the present paper was not to provide a definitive review of the literature, but rather to integrate two important aspects of this relationship which are summarized in Figure 1. First, we examined potential determinants of chronic sleep restriction, which have been largely overlooked in the literature. We hypothesize that chronic sleep restriction could be the result of a range of factors including mental health status, lifestyle/behavioral factors, chronic disease, and sociodemographic status. It is important that these are examined further because the precise causes of chronic sleep restriction, and the potential to modify these in therapeutic settings, are likely to differ considerably between individuals. Second we examined the following three potential pathways through which chronic sleep restriction could potentially promote weight gain and obesity: (1) metabolic and neuroendocrine functioning, (2) glucose regulation, and (3) waking behaviour. These pathways have the potential to promote a positive energy balance and may explain the mechanisms linking chronic sleep restriction to obesity. The first challenge for researchers will be to demonstrate that chronic sleep restriction does impact on these and other pathways not examined in this paper. This is important because the effects of short-term sleep restriction may not correspond with the effects of chronic sleep restriction, as there may be some form of physiological adaptation over time. Thus, more long-term prospective studies examining the associations between changes in sleeping patterns, body composition, and the pathways identified in Figure 1 are needed. There is also a need to investigate whether the magnitude of the association between chronic sleep restriction and obesity, and the underlying pathways, varies according to the causes of chronic sleep restriction.

Figure 1: Schematic representation of the pathways linking chronic sleep restriction to obesity.

It should be noted that the association between chronic sleep restriction and obesity is likely to be bidirectional and circular, and this is depicted in Figure 1. Therefore, although the primary purpose of this paper was to review evidence indicating that chronic sleep restriction contributes to obesity, it is also possible that obesity contributes to chronic sleep restriction. For example, symptoms of obesity such as pain and discomfort and comorbid conditions such as obstructive sleep apnea have been shown to impair and disrupt sleep. As a result, there is a need for more experimental or prospective research to delineate the magnitude of the effect of chronic sleep restriction on obesity.

A final consideration is that it is not clear whether chronic sleep restriction can be modified through interventions, and whether these changes are effective in preventing and managing obesity. Research addressing these issues will be important in determining not only the pathways linking chronic sleep restriction to obesity but also whether chronic sleep restriction is a risk factor that can be modified to treat and prevent obesity. Currently there is a 12-month randomized controlled trial being conducted in the US (clinical-trials.gov register number NCT00261898) that is examining whether increasing sleep duration in obese individuals who report short sleep affects body weight and other related variables (e.g., glucose regulation, neuroendocrine hormones). This study may provide clarification as to whether chronic sleep restriction can be targeted as a modifiable risk factor for obesity, but the results of this study have not yet been published. This area of research is significant given that the obesity epidemic continues to grow and poses a number of major health, social, and economic problems; targeting the amount we sleep could be an important step in combating this health problem.

Disclosure Statement

The authors report no financial conflicts of interest.

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Copyright © 2010 Christopher A. Magee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

by EricT

By Chrysi Koliaki, Alexander Kokkinos, Nicholas Tentolouris, and Nicholas Katsilambros

First Department of Propaedeutic Medicine, Laiko General Hospital, Athens University Medical School, Agiou Thoma 17, 115 27 Athens, Greece

International Journal of Peptides 2010

Ghrelin is a powerful orexigenic gut hormone with growth hormone releasing activity. It plays a pivotal role for long-term energy balance and short-term food intake. It is also recognized as a potent signal for meal initiation. Ghrelin levels rise sharply before feeding onset, and are strongly suppressed by food ingestion. Postprandial ghrelin response is totally macronutrient specific in normal weight subjects, but is rather independent of macronutrient composition in obese. In rodents and lean individuals, isoenergetic meals of different macronutrient content suppress ghrelin to a variable extent. Carbohydrate appears to be the most effective macronutrient for ghrelin suppression, because of its rapid absorption and insulin-secreting effect. Protein induces prolonged ghrelin suppression and is considered to be the most satiating macronutrient. Fat, on the other hand, exhibits rather weak and insufficient ghrelin-suppressing capacity. The principal mediators involved in meal-induced ghrelin regulation are glucose, insulin, gastrointestinal hormones released in the postabsorptive phase, vagal activity, gastric emptying rate, and postprandial alterations in intestinal osmolarity.

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Introduction

Ghrelin is a 28-amino-acid gastrointestinal peptide with appetite-stimulating, growth hormone-releasing and adipogenic properties [1–3]. It was originally characterized as the endogenous ligand for the hypothalamic-pituitary growth hormone secretagogue receptor type 1a (GHSR1a), stimulating the anterior gland of pituitary to produce GH [1–3]. In fact, ghrelin is the third physiological regulator of endogenous GH secretion, along with hypothalamic GH releasing hormone and somatostatin. Ghrelin is predominantly produced in the so-called X/A-like endocrine cells of gastric mucosa, and is subsequently released into bloodstream [4, 5]. Ghrelin-producing cells are mostly abundant in the oxyntic glands of gastric fundus [4, 5]. Given the widespread distribution of GHSR1a in the human body, ghrelin exerts pluripotent biological activities, affecting cardiovascular system, pancreatic endocrine function, gastrointestinal tract motility, gastric acid secretion, cell proliferation and metabolism [3].

More Ghrelin Articles

One of the most important actions of ghrelin is its regulatory role for long-term energy homeostasis and short-term food intake [6]. There is a competitive interaction between ghrelin and leptin in hypothalamus for feeding regulation. Ghrelin activates neuropeptide Y (NPY) and Agouti-related protein (AGRP) neurons in the hypothalamic arcuate nucleus, providing a central stimulus for increased food intake and reduced energy expenditure [7]. Intracerebroventricular administration of ghrelin in rodents and peripheral administration in humans has shown to promote weight gain, by reducing fat utilization and increasing food consumption [8, 9]. Ghrelin is actually the only known appetite-stimulating gastrointestinal hormone. It acts as a circulating orexigenic signal, and has been also implicated in preprandial hunger and meal initiation. Cummings et al. were the first to show that plasma ghrelin levels increase nearly twofold immediately before feeding onset, and are strongly suppressed by food ingestion, falling to trough (nadir) levels within an hour after meal initiation [10]. This pattern of secretion is interestingly reciprocal to that of insulin, which is preprandially low and increases gradually in the postabsorptive period [10]. Another interesting finding is that plasma ghrelin levels reflect human nutritional state [11]. Ghrelin secretion is typically up-regulated under conditions of chronic negative energy balance (anorexia nervosa, heart failure cachexia), and down-regulated in the setting of sustained positive energy balance (obesity). Furthermore, obese subjects fail to exhibit the normal postprandial decline of plasma ghrelin concentrations, observed in normal weight individuals [12].

The postmeal inhibition of gastric ghrelin production is proportional to energy load and is profoundly influenced by the meal’s macronutrient content [13, 14]. In rodents and normal weight humans, the postprandial drop in ghrelin levels is more pronounced after carbohydrate (CHO) meals than after protein- or fat-enriched diet manipulations [15, 16]. The type of ingested macronutrient seems to affect differentially the magnitude and pattern of postprandial ghrelin suppression. Whether it is the direct intraluminal contact of nutrients with gastric mucosa or the insulin-mediated metabolic response to nutrient ingestion more important for postprandial ghrelin suppression remains still controversial. There is currently growing evidence that ghrelin suppression does not require the presence of nutrients in either the stomach or the duodenum, but requires effective post-gastric and postabsorptive feedback mechanisms, possibly mediated by insulin and gastrointestinal hormones with anorexigenic potential [16]. Vagal activity, gastric emptying rate and postprandial increases of intestinal osmolarity are also active players in meal-induced ghrelin regulation [17, 18].

Despite the well-established stimulatory effect of ghrelin on appetite and eating behavior, little information is available regarding its relationship with fasting and postprandial energy expenditure in normal weight and obese humans. In rodents, ghrelin infusion promotes weight gain, both by increasing food intake and by decreasing energy expenditure and fat catabolism [8]. This effect is primarily due to an increase in caloric intake and respiratory quotient (RQ), suggestive of a switch from fatty acid oxidation to glycolysis leading ultimately to fat deposition. St-Pierre et al. examined the relationship between serum ghrelin and resting metabolic rate, thermic effect of food, fasting and postprandial RQ, physical activity level and peak aerobic capacity in 65 lean young women. Significant inverse correlations were reported between ghrelin, resting metabolic rate and thermic effect of food, persisting after adjustment for fat-free mass, fat mass and insulin levels [19]. These results suggest that higher levels of ghrelin are associated with low levels of resting and postprandial thermogenesis, indicating that the metabolic effects of ghrelin may extend far beyond the regulation of satiety and substrate oxidation, serving as a biomarker for decreased energy expenditure in humans. On the other hand, the relationship between ghrelin and energy expenditure in obesity constitutes a matter of debate. In a study by Marzullo et al., the obese subjects with low resting energy expenditure (impaired energy balance) exhibited lower active ghrelin levels, compared with obese subjects with high energy expenditure, indicating that ghrelin secretion and activity might be decreased in cases of obesity with impaired energy expenditure, as part of an obesity-related compensatory mechanism [20].

The present review aims to shed light on the underlying mechanisms of postprandial ghrelin regulation. In addition, a significant body of clinical and experimental data will be discussed, elucidating the macronutrient-specific effect of several isocaloric test meals on postprandial ghrelin levels. We searched PubMed and other electronic databases for high quality articles written in English, using the following search terms: ghrelin, macronutrients, carbohydrate, protein, fat and postprandial response.

Underlying Pathophysiology of Postprandial Ghrelin Regulation

A recent study in humans demonstrated that ghrelin levels can be suppressed by sham feeding (when nutrients are only smelled, chewed or tasted without being swallowed), as well as by actual feeding, indicating the importance of cephalic response to nutrient intake and supporting the role of vagal activity for the control of postprandial ghrelin secretion [17]. The vagally mediated cephalic phase appears to have a major role in initiating the postprandial fall in ghrelin levels, which are thereafter maintained suppressed, by other—as yet not entirely elucidated—gastrointestinal or postabsorptive mechanisms, mediating the nutrient-related ghrelin response [17]. The gastric phase alone appears to play no role in the regulation of ghrelin secretion, because neither gastric distension alone, nor activation of chemical nutrient-sensing mechanisms of gastric mucosa (gastric chemosensitization) can modulate ghrelin levels [18]. In an interesting experiment in rats, intragastric infusion of glucose reduced ghrelin levels by approximately 50%, while water infusion had no effect [21]. However, when gastric emptying was prevented through the inflation of a pyloric cuff (gastric distension), glucose and water infusions were similarly ineffective to suppress ghrelin. These experimental findings indicate that gastric distension and chemosensation are both insufficient to induce a ghrelin response [21]. Prandial ghrelin regulation is probably mediated by intestinal signals generated downstream of Treitz ligament, meaning that postgastric feedback is definitely required for an adequate inhibitory ghrelin response [16]. In this intestinal phase of food ingestion, there seems to be a prominent macronutrient effect, determining the depth and duration of postprandial ghrelin suppression [17]. The macronutrient-related patterns of ghrelin response imply that either the direct exposure of gastrointestinal mucosa to ingested nutrients, or the increased circulating levels of nutrients or other related hormones, can influence postprandial ghrelin levels in a macronutrient-specific manner [17, 22]. Candidate mediators involved in the regulation of postprandial ghrelin secretion are glucose, insulin, cholecystokinin (CCK), glucagon-like peptide 1 (GLP-1), glucose-dependent insulinotropic polypeptide (GIP), peptide YY (PYY), pancreatic polypeptide (PP), oxyntomodulin and somatostatin (SS). Most of these molecules are gastrointestinal hormones, which delay gastric emptying and display insulinotropic and anorexigenic activities [18, 21].

Insulin and glucose are thought to be dynamic modulators of plasma ghrelin concentrations in rodents and humans [23–25]. Hyperglycemia and hyperinsulinemia tend to decrease, while hypoglycemia and insulin deficiency tend to increase circulating ghrelin levels. Both intravenous and oral administration of glucose leads to a significant decline in circulating ghrelin, indicating that ghrelin secretion may be suppressed, at least in part, by an increased plasma glucose level in healthy humans [23, 26]. For intravenous glucose administration in particular, Möhlig et al. showed that glucose elicits a significant decrease of plasma ghrelin concentrations, whereas intravenous free fatty acid or arginine load does not affect circulating ghrelin levels [27]. Insulin-mediated glucose uptake and metabolism may also control postprandial ghrelin levels, while insulin sensitivity is considered to be an important determinant of postprandial ghrelin suppression [28]. A great increase in plasma free fatty acids, as a result of constant intravenous lipid infusion, failed to suppress plasma ghrelin, while ghrelin decreased by almost 50% under hyperinsulinemic euglycemic clamp conditions [27].

The effect of acute hyperinsulinemia on plasma ghrelin concentrations is still a matter of debate. Clinical data regarding the existing interrelation between ghrelin and insulin are rather conflicting. According to the study of Flanagan et al., using a stepped hyperinsulinemic eu-, hypo- and hyperglycemic glucose clamp, insulin may suppress circulating ghrelin independently of glucose, although glucose might have an additional synergistic effect [29]. In the same direction, Murdolo et al. tested the hypothesis that insulin is the driving force for postprandial ghrelin suppression by comparing the effects of meal ingestion on plasma ghrelin levels between insulin-deficient patients with type 1 diabetes and healthy controls [30]. The investigators concluded that insulin is essential for meal-induced plasma ghrelin suppression, commenting that severe insulin deficiency in uncontrolled type 1 diabetic subjects may partly explain the episodes of hyperphagia observed in these patients, through compromising postprandial ghrelin response [30].

The exact mechanism of insulin affecting plasma ghrelin concentrations remains to be established. Insulin might inhibit ghrelin synthesis or secretion, either directly or indirectly. Studies in rats have shown that insulin-induced hypoglycemia increases, instead of decreasing, ghrelin mRNA levels in the gastric fundus, providing no evidence for a direct inhibitory action of insulin on ghrelin synthesis [11]. However, in healthy humans, plasma ghrelin concentrations are decreased during insulin-induced hypoglycemia, suggesting species-specific differences between rodents and humans [31]. Since ghrelin-producing cells are closely associated with the capillary network of the lamina propria of gastric mucosa, their function might be under endocrine control [5]. However, there is still no evidence for insulin receptors on the surface of ghrelin-producing cells. Indirect pathways for insulin suppressive effects on ghrelin synthesis and secretion include activation of hypothalamic insulin receptors and modulation of the cellular flux of glucose or free fatty acids [32]. There seems to be an interaction between nutrients and insulin in reducing circulating ghrelin levels. A greater insulin-induced glucose uptake by X/A-like cells might inhibit ghrelin synthesis and/or secretion [30].

Contrary to the findings mentioned above, Caixás et al. reported that, unlike food intake, the subcutaneous administration of a short-acting insulin analog was not able to suppress ghrelin levels [33]. In concordance with this study, Schaller et al. concluded that meal-related ghrelin suppression is not directly regulated by glucose or insulin, since a reduction in ghrelin was observed only at supraphysiological insulin concentrations, while hyperglycemia did not decrease ghrelin at all [34]. Contrary to Murdolo, Spranger et al. observed a substantial postprandial decrease of plasma ghrelin in an insulin-deficient patient with type 1 diabetes following a carbohydrate challenge, while a subsequent bolus of subcutaneous short-acting insulin induced no further changes in circulating ghrelin [35].

Taking everything into consideration, glucose and insulin are unlikely to explain the entire postprandial ghrelin response, since ghrelin remains suppressed long after the normalization of glucose and insulin levels, and furthermore, because lipids tend to suppress ghrelin in the absence of substantial increases in glucose or insulin [16]. Other hormonal mediators (such as CCK, GLP-1 and GIP) released in the postabsorptive phase in response to nutrient stimuli, appear to orchestrate the whole postmeal ghrelin response, enhancing the inhibitory effects of glucose and insulin, which are still remaining the principal contributors. The inverse temporal relationship between circulating concentrations of ghrelin and insulin, reported in a large number of clinical studies, substantiates the key regulatory role of insulin for ghrelin regulation [36–38].

It would be also interesting to examine the role of leptin in postprandial ghrelin regulation, since ghrelin and leptin are part of a dynamic peripheral feedback system that regulates body weight and energy homeostasis by modulating satiety. Data concerning the relationship between ghrelin and leptin at fasting and postprandial state are rather contradictory. In vitro studies have previously shown that leptin inhibits ghrelin production from the gastric mucosa, and leptin levels in humans appear to be inversely related to ghrelin concentrations [39]. On the other hand, the interesting observation by Cummings et al. that leptin and intermeal ghrelin levels display diurnal rhythms that are in phase with one another suggests that leptin and ghrelin might be coordinately regulated [10]. The same study reported a subtle postprandial drop in leptin levels that may reflect meal-related regulation of gastric leptin, according to the investigators [10]. The relationship between insulin and leptin is more clearly defined and may partially explain the ghrelin-leptin interrelationship. Previous studies indicate that leptin secretion is regulated by insulin-mediated glucose metabolism, suggesting that insulin is a positive modulator of leptin concentrations [40]. This is why consumption of high-fat meals and high-fructose beverages that produce smaller postprandial glucose and insulin responses compared with isocaloric high-carbohydrate meals, have shown to reduce 24-hour circulating leptin concentrations in humans [40]. Accepting that leptin and insulin are positively associated, it is conceivable that postprandial surges of insulin are related to increased leptin levels, and given the reciprocal relationship between insulin and ghrelin, a similar inverse relationship might be supported for leptin and ghrelin as well. Because insulin and leptin function as key signals conveying information on energy intake and body fat stores to the central nervous system for the long-term regulation of food intake and energy homeostasis, it is possible that reduced insulin and leptin production, as well as increased ghrelin levels, contribute to increased energy intake, weight gain, and obesity in humans [40].

Carbohydrate Ingestion and Postprandial Ghrelin Response

In an interesting study by Foster-Schubert et al., three different macronutrient preloads of equal caloric content, volume and energy density (protein-, fat-, and CHO-based beverages) were compared for their relative efficacy to suppress total and acylated (bioactive) ghrelin levels [41]. Total ghrelin levels decreased significantly more after CHO or protein ingestion than after lipids, while ghrelin’s nadir (lowest) levels were reached most rapidly after the CHO-enriched preload (within 99 minutes). For both acylated and total ghrelin concentrations, investigators observed marked and macronutrient-dependent differences during the first 3 hours versus the subsequent 3 hours of the postprandial study period. More specifically, they observed only after CHO ingestion a marked rebound of acylated ghrelin to 37% above baseline levels, during the second 3 hours of the 6 hour post-ingestive period. This study reveals a previously unidentified pattern in the response of acylated and total ghrelin after CHO ingestion. Ghrelin levels decreased in the initial 3 hours, followed by a marked overshoot to above the pre-ingestion baseline during the second 3 hours. No such overshoot was observed after protein or lipid ingestion, both of which suppressed acylated and total ghrelin levels until study completion. These observations suggest very different effects of high CHO meals in the early versus the later postabsorptive phase, indicating that ingested CHO might prompt an early hunger rebound. Such findings have important clinical implications for design of dietary regimens [41].

The faster gastric emptying after CHO ingestion, compared with lipids or proteins, can partly explain the strong and rapid postprandial ghrelin suppression in the first phase [41]. The rapid removal of CHO from stomach should cause a rapid, strong suppression of ghrelin levels, an effect that might be short lived, because these nutrients are quickly absorbed and metabolized. If insulin-mediated glucose disposal is more important for ghrelin suppression than mere insulin levels, the late post-CHO ghrelin overshoot may result from reduced intracellular glucose metabolism, when glucose levels decreased below baseline [41].

Not all types of dietary CHO are likely to have the same effect on postprandial ghrelin levels. In a study comparing high-glucose with high-fructose meals, the mean postprandial suppression of ghrelin was markedly attenuated and significantly less pronounced after consuming the high-fructose meals [40]. Fructose, unlike glucose, does not stimulate insulin secretion from pancreatic beta cells, presumably because of the low number of fructose transporters (GLUT5) on beta cell membrane [40]. Intravenous fructose infusion increases only marginally circulating insulin concentrations, while ingested fructose is ineffective in eliciting postprandial insulin secretion. What is more, fructose does not increase insulin-mediated glucose metabolism or circulating leptin levels [40]. Given the key role of insulin for postprandial ghrelin suppression, ingested fructose suppresses ghrelin poorly. The failure of fructose to effectively suppress ghrelin (impaired satiety), along with the reduced insulin and leptin concentrations, could lead to an increased caloric intake and ultimately contribute to obesity, during chronic consumption of diets high in fructose [40].

CHO-enriched test meals, containing both simple and complex carbohydrates, have been used in various clinical studies investigating the differential response of postprandial ghrelin to meals of different macronutrient composition. In all of them, and particularly in normal weight subjects, CHO ingestion provoked a significant postprandial ghrelin decline by approximately 30% from baseline values within 2 hours after meal onset [37, 38, 40–42]. A common finding in all these studies is the inverse correlation between postprandial ghrelin and insulin concentrations throughout the whole study period. While the suppressant effect of CHO on ghrelin levels is well established and taken for granted, the biphasic pattern of ghrelin suppression after CHO intake [41] and the clinically meaningful distinction between glucose and fructose [40], are novel thought-provoking findings that warrant further investigation.

Protein Ingestion and Postprandial Ghrelin Response

Dietary protein is considered to be the most satiating macronutrient [43]. The higher satiety associated with protein consumption may be at least partially mediated by a protein-induced prolonged postprandial ghrelin suppression [43]. Such a reduction in the orexigenic signal might delay the initiation of a subsequent feeding episode or lower hunger and energy intake. The prolonged suppression of ghrelin after protein intake might relate to the protracted emptying of proteins from stomach, causing a more sustained activation of post-gastric ghrelin-suppressing mechanisms [41]. Additional mechanisms that account for the significant satiating effect of dietary protein include the following: proteins have a larger thermic effect than CHO or fat, since they cannot be stored in the body, but need to be metabolized immediately [44]. Moreover, increased circulating concentrations of amino acids after protein intake stimulate hepatic gluconeogenesis preventing hypoglycemia, and thus promoting satiety [44]. In rats fed on protein-enriched diets, intestinal gluconeogenesis is also induced in the postabsorptive phase [45]. Last but not least, proteins stimulate the secretion of specific gastrointestinal peptides (CCK, GLP-1, GIP) that delay gastric emptying and increase satiety [44].

In a study of three isoenergetic meals (balanced, high-fat and high-protein) consumed by healthy young women, acylated ghrelin fell significantly after ingestion of both balanced and high-protein meals, while ghrelin persisted at significantly lower levels than baseline for a longer duration, following the high-protein meal [36]. Apart from prolonging postprandial ghrelin suppression, liquid protein preloads have also shown to prolong the elevation of anorexigenic gastrointestinal hormones, such as CCK and GLP-1 [43]. These responses are observed irrespective of the type of protein consumed (soy, whey, or gluten) [43]. In support of this, Lang et al. has demonstrated no effect of protein type (egg albumin, casein, gelatin, soy protein, pea protein and wheat gluten) on satiety, 24 hour energy intake and postprandial glucose and insulin concentrations [46]. In a further randomized crossover study in healthy adult males, the high-protein meal maintained significantly lower ghrelin levels at 180 minutes compared with the high-CHO and high-fat meals, indicating that dietary protein exhibits longer-term postprandial ghrelin suppression and enhanced satiety [37]. According to Blom et al., the high-protein breakfast decreased postprandial ghrelin secretion more than did the high-CHO breakfast [44]. It also increased glucagon and CCK, tended to increase GIP and GLP-1, and decreased gastric emptying rate, without affecting however ad libitum energy intake.

Despite the accumulating evidence supporting the satiating and ghrelin-suppressing capacity of dietary protein, there have been a few studies suggesting that protein ingestion stimulates, instead of suppressing, ghrelin levels [38, 47], while an additional study indicated that the satiating effect of protein is practically unrelated to postprandial ghrelin secretion [48].

Fat Ingestion and Postprandial Ghrelin Response

Ingested lipids appear to suppress the orexigenic hormone ghrelin less effectively than do CHO or protein [41]. The relatively weak ability of this macronutrient to suppress ghrelin can be attributed to the poor stimulation of insulin secretion by lipids as well as to the lower osmolarity of lipid meals and beverages. Postprandial increases of intestinal osmolarity are believed to promote ghrelin suppression. However, lipids contribute fewer osmolar units compared with an isocaloric consumption of CHO or proteins [41].

In a study by Pavlatos et al., total ghrelin levels did not decline significantly after a fat-rich meal in normal weight women, as opposed to an isoenergetic protein-rich meal [49]. In a similarly designed study by Tentolouris et al., fat consumption has also displayed a diminished capacity to induce satiety [50]. In this study, the effect of two isocaloric test meals (one rich in CHO and one rich in fat) on postprandial active ghrelin concentrations was comparatively evaluated in lean and obese women. After the fat-rich meal, active ghrelin levels were not significantly suppressed, even in the lean participants. The investigators conclude that increased fat intake might promote obesity not only through its high caloric content and adverse metabolic effects, but also through its failure to suppress postprandial hunger. Erdmann et al. reported a different (more delayed) time pattern of ghrelin suppression after fat ingestion, compared with CHO [47]. More specifically, the fat-rich meal decreased plasma ghrelin levels, but the nadir was reached towards the end of the study period, namely at 180 minutes.

The potential impact of varying fatty acid composition (saturated, monounsaturated and polyunsaturated fat) on postprandial ghrelin response has been only scarcely investigated. In a relevant double-blind crossover study, researchers assessed two high-fat test meals, one with a high saturated to unsaturated fat ratio (70/30) and the other with a low ratio (55/45), and concluded that increasing saturated fat consumption had no deleterious effects on fasting and postprandial plasma ghrelin concentrations [51].

Effect of BMI on Nutrient-Related Ghrelin Regulation

BMI, body fat and indices of central fat distribution are inversely associated with fasting plasma ghrelin concentrations. A large number of clinical studies have shown that obese subjects tend to display lower total and acylated ghrelin levels in the fasting state compared with normal weight individuals [50]. This finding appears to be an appropriate compensatory response, so that obese individuals will not get any fatter and lean individuals will not get any thinner (adaptive mechanism for prevention of obesity and cachexia resp.). It has been proposed that the sustained positive energy balance observed in obesity suppresses maximally circulating ghrelin levels, and thus limits flexibility for further short-term feeding regulation. The impaired cholinergic (vagal) regulation of postprandial drop in ghrelin concentrations might be also responsible for the dysregulated ghrelin control in obese subjects [52]. Furthermore, obese subjects are often insulin-resistant and thus hyperinsulinemic, and insulin is a well established inhibitory signal for ghrelin secretion. To the best of our knowledge, the differential rate and magnitude of preprandial rise in ghrelin levels has not been comparatively evaluated in lean and obese individuals. As already mentioned, it is widely accepted that obese subjects exhibit significantly lower fasting ghrelin concentrations than lean, but whether the rate of preprandial ghrelin increase is actually differentiated between lean and obese subjects has been scarcely addressed. In fact, most of the studies that used frequent blood sampling protocols in order to assess the diurnal plasma ghrelin profile in subjects of varying BMI (preprandial and postprandial hormonal alterations), reported no specific BMI-related differences between lean and obese participants in terms of preprandial rate of ghrelin increase [10].

Another important aspect of ghrelin regulation in obese subjects is the blunted postprandial ghrelin response. This means that obese subjects have low ghrelin levels preprandially, but postprandial ghrelin secretion is not sufficiently suppressed, suggesting a severe defect in ghrelin-induced satiety mechanisms, which makes them feel still hungry, even though they have just completed their meal. Pavlatos et al. have shown that neither a protein- nor a fat-rich meal was able to elicit a significant acute ghrelin response in obese women [49]. In the same direction, Tentolouris et al. reported that a high-CHO meal (with a well established ghrelin-suppressing potential in lean individuals) was also insufficient to suppress postprandial active ghrelin levels in obese women, indicating a considerable secretory and possibly satiety impairment in these subjects [50]. Another interesting conclusion of the same study was that, the leaner a person is, the higher his fasting ghrelin is, and the steepest its postprandial decline. This means that a lean subject feels quite hungry before meals, but afterwards feels easily satiated. Apart from ghrelin, additional hormonal factors that contribute to this auto-regulation of body weight homeostasis in normal weight subjects include GLP-1, GIP, and PYY, which delay gastric emptying, induce satiety and prevent hyperphagia, and are significantly more functional in lean subjects compared with the obese [53]. However, as a person gains weight, this autoregulatory effect appears to become severely compromised. An obese subject cannot experience postprandial fullness, independently of the macronutrient composition of his meal [50].

The fact that lean subjects display higher fasting ghrelin levels than obese does not necessarily mean that they also consume greater amounts of food. On the contrary, food intake is most likely to be increased in obese subjects, because of the blunted postprandial ghrelin response, as described above. Besides, the effect of ghrelin on hunger and satiety sensations is not necessarily translated into alterations in ad libitum energy intake, as shown by the study of Erdmann et al. [38]. An additional study by Druce et al. showed that low-dose infusion of ghrelin increased ad libitum energy intake at a buffet meal only in the obese group, and not in the lean, indicating that obese people are highly sensitive to the appetite-stimulating effects of ghrelin, even when the circulating ghrelin is low [54]. As a result, the absolute difference of fasting ghrelin levels between lean and obese subjects is not a major determinant of subsequent food intake, since other factors such as endogenous sensitivity to circulating ghrelin, ghrelin activity and postprandial ghrelin changes are thought to play an important role, as well.

Additional factors that can in part explain the suppressed basal ghrelin levels in obese subjects include hyperleptinaemia and increased circulating levels of IL-1b (interleukin 1b), since both leptin and IL-1b are thought to inhibit ghrelin secretion [7]. Hyperleptinaemia is observed frequently in obesity due to leptin resistance, and high levels of IL-1b and other inflammatory mediators are also a common finding in patients with obesity and metabolic syndrome. Concerning the blunted postprandial ghrelin response in obese subjects, the impaired post-meal elevation of gastrointestinal hormones with anorexigenic and insulinomimetic properties, such as GLP-1, GIP and PYY, has been implicated as an additional significant contributor [55]. As far as insulin resistance is concerned, its role for ghrelin regulation is different in fasting and postprandial state. In fasting, insulin resistance and thus hyperinsulinemia lead to decreased fasting ghrelin levels. Fasting plasma ghrelin concentrations are lower in insulin-resistant obese adults, compared with equally obese individuals with relatively higher insulin sensitivity [28]. On the other hand, postabsorptive insulin resistance and impaired intracellular insulin signaling lead to inadequately suppressed and thus increased levels of ghrelin, since insulin sensitivity is regarded as prerequisite for sufficient postprandial ghrelin suppression [28].

The macronutrient-specific effect of meals on postprandial ghrelin levels has interesting implications only in normal weight individuals. In the obese population, the macronutrient effect appears to become blunted and is therefore of minor importance. This hypothesis is further corroborated by a recent study by Heinonen et al., where obese individuals with metabolic syndrome elicited no differences in plasma ghrelin or feelings of hunger and satiety, after consuming two high-CHO meals producing different insulin responses (whole-grain rye bread and wheat bread) [56]. Despite the different insulin response, ghrelin levels did not change in obese patients in response to either type of bread meals. In addition, ghrelin levels did not correlate with insulin or glucose, indicating that regulation of ghrelin might be altered in obese patients with metabolic syndrome independently of insulin [56]. An additional study by Moran et al. revealed a dysregulation of ghrelin homeostasis in overweight women with polycystic ovary syndrome (PCOS), suggesting that women with PCOS exhibit similar ghrelin abnormalities with obese women (down-regulated fasting ghrelin, blunted postprandial ghrelin suppression), and this disorder was not differentially affected by diet macronutrient composition [53].

Diet-induced weight loss, contrary to gastric bypass surgery where ghrelin levels remain dramatically decreased, has shown to elevate fasting ghrelin levels and normalize postprandial ghrelin response [57]. This means that when a person loses a significant amount of weight by diet he might feel a greater preprandial desire to eat, but his postprandial satiety is significantly improved. The greater sensitivity to vagal stimulation after weight loss may result in a more pronounced drop in postprandial ghrelin levels, in addition to the improvement in insulin sensitivity, which is a major determinant of postprandial ghrelin suppression [57]. Romon et al. reported that diet-induced weight reduction preferentially improves ghrelin response to a high-CHO meal, compared with a high-fat meal, indicating that weight loss might selectively improve the response of ghrelin to carbohydrate [57].

Acute Effect of Ethanol and Smoking on Plasma Ghrelin Levels

Zimmermann et al. addressed the interesting question, whether acute ethanol ingestion affects ghrelin secretion [58]. Ghrelin declined significantly within 15 minutes after alcohol drinking, fell to a minimum of 66% of baseline at 75 minutes and remained suppressed until the last sample at 2 hours. Given that alcohol seems to acutely attenuate circulating ghrelin levels and is also known for its satiating power, one might expect from alcohol to promote weight loss. However, its considerable caloric density and its detrimental overall health effects should not be overlooked.

As far as smoking is concerned, in an interesting study by Kokkinos et al., acute cigarette smoking induced no significant suppression of post-smoking ghrelin in habitual smokers, possibly desensitized to any possible effect of smoking on ghrelin, through prolonged nicotine exposure [59]. On the other hand, there was a progressive decline of ghrelin in non-smokers, reaching its nadir 60 minutes after smoking. Fasting total ghrelin levels were not significantly different between smokers and non-smokers, indicating that smoking is unlikely to exert a long-term anorectic effect in smoking populations. The significant decrease in circulating ghrelin after smoking cessation, reported by Lee et al., provides further evidence for lack of correlation between smoking status and suppressed plasma ghrelin concentrations [60].

Summary, Conclusions, and Perspectives

Many clinical studies have used isoenergetic test meals (protein-, fat- and CHO-rich) in order to examine the relative efficacy of each macronutrient to suppress postprandial ghrelin. Even though the overall concept in these studies is common, the experimental design (meal composition, measured parameters, blood sampling intervals, duration of post-ingestive period) is slightly or moderately different. This discrepancy may be in part responsible for heterogeneity in findings. Trying to delineate the central message behind all these divergent data, carbohydrate appears to be the most effective macronutrient in terms of postprandial ghrelin suppression, possibly because of its glucose-elevating and insulin-secreting effect. However, recent data indicate that CHO ingestion may provoke a delayed ghrelin rebound in the later postabsorptive period, questioning the role of CHO-rich meals in weight loss dietary approaches. Besides, all types of dietary CHO are not equally effective. Fructose-enriched meals display a poor ghrelin-suppressing capacity, promoting increased caloric intake, weight gain and obesity under conditions of chronic consumption. The most satiating macronutrient appears to be dietary protein. Protein induces prolonged ghrelin suppression and elevation of gut-derived anorexigenic hormones that delay gastric emptying regardless of the type of protein consumed. However, the influence of solid forms of protein (turkey, pork) on postprandial ghrelin levels may require assessment over a longer period of time than 3-4 hours, since slow gastric emptying delays postprandial ghrelin nadir. As far as fat is concerned, it appears to be the least potent ghrelin-suppressant, even in normal weight subjects. Some studies have shown that fat decreases ghrelin concentrations, but later or more weakly than other macronutrients. At the same time, other studies report that fatty meals have absolutely no effect on postprandial ghrelin levels. In obese subjects, postprandial ghrelin response is blunted, and the macronutrient effect on ghrelin levels appears to be rather neutral. However, weight loss restores ghrelin response and leads to a significant improvement of ghrelin-mediated appetite regulation.

From now on, it would be interesting to evaluate the long-term effect of macronutrient-enriched diet manipulations on fasting and postprandial ghrelin levels. Additional parameters that could possibly influence ghrelin response and should be further investigated are food form and viscosity (liquid, solid, semi-solid products), portion size and meal duration.

Abbreviations

GH: Growth hormone
GHSR1a: Growth hormone secretagogue receptor type 1a
NPY: Neuropeptide Y
AGRP: Agouti-related protein
CCK: Cholecystokinin
GLP-1: Glucagon-like peptide 1
GIP: Glucose-dependent insulinotropic polypeptide
PYY: Peptide YY
PP: Pancreatic polypeptide
SS: Somatostatin.

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Copyright © 2010 Chrysi Koliaki et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Copyright © 2010 Hindawi Publishing Corporation. All rights reserved.

by EricT

By Amy M. Egras¹, William R. Hamilton², Thomas L. Lenz³, and Michael S. Monaghan⁴

Journal of Obesity 2011

Objective. To review the literature on fat modifying dietary supplements commonly used for weight loss. Methods. Recently published randomized, placebo-controlled trials were identified in PubMed, MEDLINE, International Pharmaceutical Abstracts, Cochrane Database, and Google Scholar using the search terms dietary supplement, herbal, weight loss, obesity, and individual supplement names. Discussion. Data for conjugated linoleic acid (CLA), Garcinia cambogia, chitosan, pyruvate, Irvingia gabonensis, and chia seed for weight loss were identified. CLA, chitosan, pyruvate, and Irvingia gabonensis appeared to be effective in weight loss via fat modifying mechanisms. However, the data on the use of these products is limited. Conclusion. Many obese people use dietary supplements for weight loss. To date, there is little clinical evidence to support their use. More data is necessary to determine the efficacy and safety of these supplements. Healthcare providers should assist patients in weighing the risks and benefits of dietary supplement use for weight loss.

Introduction

The prevalence of obesity has continued to increase over the last several years in the United States. Per the National Health and Nutrition Examination Survey (NHANES) for the 2007-2008 year, the prevalence of obesity, defined as a body mass index (BMI) = 30?kg/m2, among adults was greater than 30% and those who were overweight or obese (BMI = 25?kg/m2) was almost 70% for both men and women. The trend over the past 20 years has shown an increase in the prevalence of obesity of six to seven percent every 10 years [1]. In addition, health care costs are approximately 42% higher for obese patients when compared to normal-weight patients [2].

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Dietary supplements for weight loss are marketed to offer patients improved success that is faster and easier than calorie reduction and increased exercise. Despite concerns with efficacy and safety, these products continue to be an appealing alternative or adjunct to weight management [3, 4]. A national survey published in 2008 found that 33.9% of adults who have made a weight loss attempt had used a dietary supplement to do so. It was also found that the use was more common among women, younger adults, minorities, and those with less education and lower incomes [5]. Reasons why patients may opt for dietary supplements include the perception that they are “natural” and perhaps safer than prescription medications. In addition, patients often do not perceive a need to seek the assistance of a healthcare professional with these alternative therapies, and they also may be an alternative to previously failed attempts with conventional approaches [6].

Despite widespread use, there is still limited data on the safety and efficacy of the products currently on the market. Because dietary supplements are viewed as food and not drugs, they are not regulated by the Food and Drug Administration (FDA). Instead, under the Dietary Supplement Health and Education Act (DSHEA), dietary supplements can be marketed without evidence to support efficacy and safety. If a dietary supplement appears to be unsafe after being marketed, the FDA can then decide whether or not to have the product removed from the market. This was the case for the weight loss supplement ephedra which was removed from the market in 2004 after reports of serious health risks [5]. The literature published in the arena of weight loss continues to be plagued by concerns such as: small studies, inconsistency with participant body weight (BMI), variation in length of studies, use of exercise, and a variety of products at differing dosages.

Several mechanisms are proposed to differentiate how these products work. These include products that claim to be: fat blockers, lipotropics or fat busters, thermogenic or energy modifiers, and products that can change carbohydrate metabolism, water elimination, or the feeling of satiety or fullness. The purpose of this paper is to review the literature on dietary supplements currently being marketed and promoted for weight loss via the mechanisms of altered fat absorption, fat metabolism and/or the storage of fat.

Methods

Published articles and abstracts were identified using PubMed, MEDLINE, International Pharmaceutical Abstracts, Cochrane Database, and Google Scholar with the search terms dietary supplement, herbal, weight loss, obesity, and by using individual supplement names. The primary emphasis was on pertinent articles based on human trials involving overweight subjects that were performed in a randomized and placebo-controlled process. Additional articles were identified from the references of the retrieved literature. Only studies that tested single dietary supplement products were included in this paper. Studies which included multiple products were not included due to inconclusive evidence of the effectiveness of individual products.

Fat Modifying Supplemental Weight Loss Products

Conjugated Linoleic Acid

Conjugated linoleic acid (CLA) is a naturally occurring fatty acid that is found in beef and dairy products [7]. Studies conducted in animals have shown that CLA is effective in reducing body fat mass, increasing insulin sensitivity, decreasing plasma glucose levels, is anticarcinogenic, and may have positive effects on atherosclerosis [8]. Conjugated linoleic acid has been indicated for the use in cancer, diabetes, hypertension, and hypercholesterolemia, as well as weight loss and body fat reduction. In regards to weight loss, CLA is believed to work by promoting apoptosis in adipose tissue. Many animal studies have shown CLA to be effective in weight loss and body fat reduction. This information has led to an increased interest as to whether or not CLA would have the same effects in humans [7].

Blankson et al. [9] performed a randomized, double-blind, placebo-controlled trial in which 52 patients (35 women and 17 men) with a BMI between 25 to 35?kg/m2 were randomized to receive CLA 1.7, 3.4, 5.1, or 6.8 grams per day or placebo (9 grams of olive oil) for 12 weeks. At the end of 12 weeks, the results demonstrated a decrease in body fat mass (BFM) of 1.73?kg in the group receiving CLA 3.4 grams (P=.05) and a decrease of 1.3?kg in the group receiving 6.8 grams (P=.05). There were no statistical differences seen in body mass, BMI, or lean body mass (LBM). The most common adverse effects reported were gastrointestinal effects [9].

In a double-blind, placebo-controlled study performed by Risérus et al. [10], 24 men with an average baseline BMI of 32?kg/m2 were randomized to receive either CLA 4.2 grams per day or placebo for 4 weeks. Along with other weight related endpoints, this study also evaluated the effects of CLA on sagittal abdominal diameter (SAD) as abdominal obesity has been linked with the metabolic syndrome. At the end of the study period, it was found that there was a significant decrease in SAD (-0.57?cm, P=.04). However, there was no difference seen in body weight or BMI. There were no adverse events reported [10].

A randomized, double-blind, placebo-controlled study by Smedman and Vessby [11] evaluated the effects of CLA 4.2 grams per day versus placebo for 12 weeks on percent body fat, body weight, BMI, and SAD in 50 patients (25 men and 25 women) with an average BMI of 25?kg/m2. The results showed a 3.8% decrease in body fat in those receiving CLA (P=.05). There was no change seen in body weight, BMI, and SAD. CLA was well tolerated by all participants [11].

CLA is marketed as either a triacylglycerol or free fatty acid (FFA). Gaullier et al. [8] decided to not only evaluate the effectiveness of CLA on weight loss, but to see if either CLA-triacylglycerol or CLA-FFA is more efficacious than the other. This was a double-blind, placebo-controlled trial in which 180 patients (31 men and 149 women) with an average BMI of 28?kg/m2 were randomized to receive 4.5 grams of olive oil (placebo), 4.5 grams 80% CLA-FFA, or 4.5 grams 76% CLA-triacylglycerol for 12 months. The results demonstrated a significant decrease in BFM in both the CLA-FFA and CLA-triacylglycerol groups compared to placebo (-1.7 and -2.4, resp.; P<.05). In the CLA-triacylglycerol group, there was also a significant decrease in body weight (-1.8?kg versus 0.2?kg; P<.05) and BMI (-0.6?kg/m2 versus 1.8?kg/m2; P<.05) when compared with placebo. The CLA-FFA group demonstrated an increase in LBM (2.0?kg versus 0; P<.05). All adverse events reported were rated as “mild” or “moderate” with the most common being gastrointestinal side effects [8].

Of the 180 participants of this study, 134 continued on in an open-label study for another 12 months. All participants remained in their original treatment arm, but all were treated with 4.5 grams daily of CLA-triacylglycerol for the remaining 12 months. While there was no additional decrease in body weight or BFM, this study did demonstrate that participants were able to maintain their weight loss. While two patients were observed to have an increase in aspartate amino transferase (ASAT), these levels returned to normal once CLA was discontinued (P=.002, CLA-FFA; P=.009, CLA-triacylglycerol). Overall, this study demonstrated that CLA use is safe over 24 months and may be beneficial in initial weight loss and may help with maintaining weight loss and reductions in BFM. The most commons adverse events reported were gastrointestinal [12].

Studies reviewed indicate that CLA appears to be safe with the most common adverse effects being gastrointestinal (GI). Overall, it appears as though that CLA helps to reduce BFM and SAD in patients, but minimal effect on BMI or body weight. In addition, CLA may be beneficial in helping to maintain changes in body composition such as reductions in BFM.

Garcinia cambogia (Hydroxycitric Acid)

Hydroxycitric acid (HCA) is the active ingredient found in the fruit of the Garcinia cambogia plant [25]. It is believed that hydroxycitric acid aids in weight loss by inhibiting lipogenesis by inhibiting the adenosine triphosphate (ATP)-citrate-lyase enzyme which is responsible for converting citrate to acetyl-coenzyme A and ultimately fatty acid synthesis. It is also theorized that HCA may improve exercise endurance by increasing lipid oxidation and decreasing carbohydrate metabolism and stimulate appetite suppression [13, 25].

Heymsfield et al. [13] performed a randomized, double-blind, placebo-controlled trial to measure the effects of HCA on body weight change and fat mass. The 135 participants (19 men and 116 women) with an average BMI of 32?kg/m2 were randomized to receive 3000?mg of Garcinia cambogia (1500?mg of HCA) per day or placebo along with a high fiber, low-calorie diet for 12 weeks. The placebo group lost 4.1?kg, and the HCA group lost 3.2?kg. While the results within each separate treatment arm was significant when compared to baseline, there were no differences between the groups (P=.14). In addition, the placebo group demonstrated a decrease in percent body fat mass of 2.16%, and the HCA group demonstrated a 1.44% decrease. Again, there were no differences between the groups (P=.08). The most commonly reported adverse events were headache, upper respiratory tract symptoms, and gastrointestinal symptoms. However, there were no differences between the HCA group and placebo [13].

A double-blind, placebo-controlled parallel group study performed by Mattes and Bormann [14] enrolled 89 mildly overweight females to evaluate HCA on weight loss and appetite suppression. The participants were randomized to receive 2.4 grams of Garcinia cambogia (1.2 grams of HCA) or placebo per day, in addition to a low calorie diet. At the end of 12 weeks, it was noted that the HCA group lost significantly more weight than the placebo group (3.7±3.1?kg versus 2.4±2.9?kg). There were no changes on appetitive variables [14, 26].

While HCA appears to be well tolerated, there is limited data with regards to its efficacy. The data that is available, however, does not demonstrate significant weight loss. Therefore, Garcinia cambogia or HCA is not recommended at this time.

Chitosan

Chitosan is a form of chitin that comes from the shells of crustaceans such as shrimp, lobster, and crab. Several in vitro studies have shown that chitosan binds dietary fats and bile acids. Because of this proposed mechanism of action, it is theorized that chitosan may be useful for weight control, as well as for a treatment of hypercholesterolemia [15–18, 27, 28].

A randomized, double-blind placebo-controlled trial performed by Pittler et al. [15] examined the effects of chitosan on weight loss. Thirty-four patients (6 men and 28 women) with a BMI of approximately 26?kg/m2 were randomized to receive one gram chitosan or placebo twice daily for 28 days. At the end of the study period, there was no difference in body weight or BMI between the two groups. Adverse effects reported with chitosan were minor. The most common complaint was constipation [15].

Schiller et al. [16] evaluated the use of rapidly soluble chitosan in weight loss and reducing body fat in 59 participants with an average BMI of 32?kg/m2 consuming a high fat diet. In this double-blind, placebo-controlled trial, the participants were randomized to receive 1500?mg of chitosan or placebo twice a day with the largest meals of the day for eight weeks. At the end of the study period, patients in the chitosan group lost 1?kg (P<.005), and the BMI was significantly decreased by 0.3?kg/m2 (P<.01). Patients in the placebo group gained 1.5?kg (P<.001), and the BMI was significantly higher by 0.6?kg/m2 (P<.01). When treatment was compared to placebo, weight and BMI were significantly higher in the placebo group (P<.0001 and P<.05, resp.). The most common adverse effects reported were gastrointestinal, flatulence, increased stool bulkiness, bloating, nausea, and heartburn. This study demonstrated that chitosan may be an effective weight loss supplement [16].

Ni Mhurchu et al. [17] evaluated the effects of chitosan on 250 patients (44 men and 206 women) with a BMI 35 to 36?kg/m2. This double-blind, placebo-controlled trial randomized patients to receive three grams of chitosan per day or placebo in addition to receiving standardized dietary and lifestyle advice. The trial was conducted over 24 weeks. At the end of the study period, the chitosan group lost more weight than placebo (-0.39 versus +0.17?kg, P=.03). There were ten serious adverse events, four of which occurred in the chitosan group. These included three hospitalizations and one cancer incidence. Thirty-six participants in the chitosan group reported some minor adverse events which were primarily gastrointestinal related [17].

In a randomized, double-blind, placebo-controlled trial by Kaats et al. [18], 150 overweight adults were randomized to three study groups: three grams of chitosan per day and a behavior modification program, placebo and a behavior modification program, or a minimum intervention control group. The trial was conducted over 60 days. At the end of the study period, participants in the chitosan group demonstrated a significant reduction in weight compared to control (-2.8 versus +0.8 pounds, P<.001) and a decrease in fat mass compared to control (P=.006). When compared to placebo, the chitosan group demonstrated a decrease in weight (-2.8 versus -0.6 pounds, P=.03), a decrease in percent fat (-.08% versus +0.4%, P=.003), a decrease in fat mass (-2.6 versus +0.6 pounds, P=.001), and an increase in body composition improvement (BCI) (+2.4 versus -1.9 pounds, P=.002) [18].

Chitosan is well tolerated with the most common adverse effects being gastrointestinal. Based on the above studies, it appears as though chitosan may be effective to help aid weight loss. Because of limited data thus far, chitosan cannot be recommended at this time. Chitosan, however should be avoided in individuals with a shellfish allergy [27].

Pyruvate

Pyruvate is a three-carbon compound that is a byproduct of glucose metabolism. It is unclear how pyruvate works to promote weight loss, but in rats a lower respiratory exchange ratio has been demonstrated indicating that there was increased utilization of fat and an elevation in resting metabolic rate [21, 29].

Stanko et al. and Kalman et al. both performed several studies evaluating the use of pyruvate in weight loss [19–21]. The study performed by Stanko et al. [19] was a double-blind, placebo-controlled trial which evaluated body composition with a low energy diet and supplementation with pyruvate. Fourteen women with a BMI 27.8 to 52.7?kg/m2 were placed on a low energy diet and then randomly assigned to receive either 30 grams of pyruvate plus 16 grams of calcium pyruvate per day or placebo for 21 days. The pyruvate group lost 0.22?kg compared to 0.17?kg in the placebo group (P<.05), and there was also a decrease in BMI of 2.2?kg/m2 compared to 1.5?kg/m2 in the placebo group (P<.05). In addition, the pyruvate group lost 7.3% fat versus 5.4% in the placebo group (P<.05) [19].

In the first study by Kalman et al. [20] a randomized, double-masked, placebo-controlled trial was performed in which participants received six grams/day of pyruvate, placebo, or nothing (control group) along with diet and exercise counseling. The 51 participants (25 men, 26 women) enrolled had a BMI greater than 25?kg/m2. At the end of six weeks, fat mass decreased significantly (-12.2%; P<.001), percent body fat decreased significantly (-12.4%; P<.001), and lean body mass increased (+2.4%; P=.001) in the pyruvate group when compared to baseline. The placebo and control groups did not demonstrate any significant changes in fat mass, percent body mass, and lean body mass [20].

Kalman et al. [21] performed another six week, double-blind, placebo-controlled trial. Twenty-six subjects (10 men, 16 women) with a BMI greater than 25?kg/m2 were randomly assigned to receive six grams of pyruvate per day or placebo. At the end of the trial, there was a 1.6% decrease in body weight (P<.001), a 14% decrease in body fat (P<.001), and an 11.7% decrease in percent body fat (P<.001) in the pyruvate group. There were no significant changes in the placebo group [21].

In studies published thus far, pyruvate has demonstrated that it may be beneficial for weight loss. In addition, it tends to be well tolerated with minimal adverse effects. The most common adverse effect is gastrointestinal upset [29]. However, the trials conducted so far have had small sample sizes and have only been performed for short periods of time. Although it appears to be safe, there is no data on the long term use of pyruvate.
2.1.5. Irvingia gabonensis

Irvingia gabonensis is a mango-like fruit that comes from the deciduous forest tree found in West Africa [22, 30]. It is theorized that Irvingia gabonensis works by inhibiting adipogenesis by down-regulating peroxisome proliferator-activated receptor gamma (PPAR-gamma) which is responsible for the differentiation of adipocytes. In addition, it also observed that adiponectin levels increase and leptin levels decrease in patients given Irvingia gabonensis. Irvingia gabonensis has also been used to treat hypercholesterolemia [30].

The first trial was performed by Ngondi et al. in 2005. This was a randomized, double-blind, placebo-controlled trial that enrolled 40 obese subjects in Cameroon. Subjects were randomly assigned to receive 350?mg of Irvingia gabonensis seed extract or placebo for 4 weeks. At the end of the 4 weeks, the Irvingia gabonensis group was observed to have a decrease in body weight of 5.6±2.7% (P<.0001), a decrease in waist circumference of 5.07±3.18% (P<.0001), and a decrease in hip circumference of 3.42±2.12% (P<.0001). The placebo group observed a decrease in body weight of 1.32±0.41%. There was no change in percent body fat for both the treatment and placebo groups [22].

A second article by Ngondi et al. [23] also examined the effects of Irvingia gabonensis on weight loss. This was a double-blind, placebo-controlled trial in which 102 natives of Cameroon with a BMI of 26 to 40?kg/m2 were randomized to receive 150?mg of Irvingia gabonensis or placebo daily for ten weeks. At the end of the study period, there was a significant difference between the treatment group and placebo for body weight (-12.8?kg versus -0.7?kg; P<.01), waist circumference (-16.19?cm versus -5.3?cm; P<.01), and percent body fat (-6.3% versus -1.99%; P<.05) [23].

Although the current data looks encouraging, to date there is limited data on the use if Irvingia gabonensis in weight loss. It appears to be safe and well tolerated as the most common adverse effects are headache, flatulence, and difficulty sleeping [30]. Due to the limited data, Irvingia gabonensis cannot be recommended at this time.

Chia Seed (Salvia hispanica)

Chia seed, or Salvia hispanica, is a sprout that has high concentrations of omega-3-fatty acids, alpha-linoleic acid, and fiber [24, 31]. It has been hypothesized that these components of the seeds would not only help with diseases such as hypercholesterolemia or diabetes, but that it may also be beneficial in weight loss. Nieman et al. [24] performed a single-blinded, trial in which 76 overweight/obese participants were randomized to receive 50 grams of chia seed daily or placebo. At the end of 12 weeks, it was noted that there were differences pre- and poststudy on body mass or body composition [24]. While considered safe in the short term, there is limited data to suggest the use of chia seeds for weight loss.

Conclusion

Because the prevalence of obesity in the United States is significant, many people turn to the use of supplemental products as an assist with weight loss efforts. While there are several dietary supplements being marketed for the use in weight loss via several different mechanisms of action, there is very little clinical evidence to support their use. Conjugated linoleic acid, pyruvate, and Irvingia gabonensis have shown some potential benefit for weight loss. However, more data is necessary to draw any definitive conclusions on the use of dietary supplements for weight loss. Continued research is needed in this area to aid health care providers as well as the public in general. Health care providers should be aware of the weight loss products available to their patients and assist patients in determining the risks and benefits of supplement use for weight loss.

References

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2. E. A. Finkelstein, J. G. Trogdon, J. W. Cohen, and W. Dietz, “Annual medical spending attributable to obesity: payer-and service-specific estimates,” Health Affairs, vol. 28, no. 5, pp. w822–w831, 2009.

3. National Institutes of Health, U.S. Department of Health and Human Services, Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults, NIH publication, Bethesda, Md, USA, 1998.

4. H. M. Blanck, M. K. Serdula, and M. K. Serdula, “Use of nonprescription dietary supplements for weight loss is common among Americans,” Journal of the American Dietetic Association, vol. 107, no. 3, pp. 441–447, 2007.

5. J. L. Pillitteri, S. Shiffman, J. M. Rohay, A. M. Harkins, S. L. Burton, and T. A. Wadden, “Use of dietary supplements for weight loss in the united states: results of a national survey,” Obesity, vol. 16, no. 4, pp. 790–796, 2008.

6. D. Heber, “Herbal preparations for obesity: are they useful?” Primary Care: Clinics in Office Practice, vol. 30, no. 2, pp. 441–463, 2003.

7. “Conjugated linoleic acid,” in Natural Medicines Comprehensive Database, Therapeutic Research Faculty, Stockton, Calif, USA, Updated (periodically).

8. J.-M. Gaullier, J. Halse, K. Høye, K. Kristiansen, H. Fagertun, H. Vik, and O. Gudmundsen, “Conjugated linoleic acid supplementation for 1 y reduces body fat mass in healthy overweight humans,” American Journal of Clinical Nutrition, vol. 79, no. 6, pp. 1118–1125, 2004.

9. H. Blankson, J. A. Stakkestad, H. Fagertun, E. Thom, J. Wadstein, and O. Gudmundsen, “Conjugated linoleic acid reduces body fat mass in overweight and obese humans,” Journal of Nutrition, vol. 130, no. 12, pp. 2943–2948, 2000.

10. U. Risérus, L. Berglund, and B. Vessby, “Conjugated linoleic acid (CLA) reduced abdominal adipose tissue in obese middle-aged men with signs of the metabolic syndrome: a randomised controlled trial,” International Journal of Obesity, vol. 25, no. 8, pp. 1129–1135, 2001.

11. A. Smedman and B. Vessby, “Conjugated linoleic acid supplementation in humans—metabolic effects,” Lipids, vol. 36, no. 8, pp. 773–781, 2001.

12. J.-M. Gaullier, J. Halse, K. Høye, K. Kristiansen, H. Fagertun, H. Vik, and O. Gudmundsen, “Supplementation with conjugated linoleic acid for 24 months is well tolerated by and reduces body fat mass in healthy, overweight humans,” Journal of Nutrition, vol. 135, no. 4, pp. 778–784, 2005.

13. S. B. Heymsfield, D. B. Allison, J. R. Vasselli, A. Pietrobelli, D. Greenfield, and C. Nunez, “Garcinia cambogia (hydroxycitric acid) as a potential antiobesity agent,” Journal of the American Medical Association, vol. 280, no. 18, pp. 1596–1600, 1998.

14. R. D. Mattes and L. Bormann, “Effects of (-)-hydroxycitric acid on appetitive variables,” Physiology and Behavior, vol. 71, no. 1-2, pp. 87–94, 2000.

15. M. H. Pittler, N. C. Abbot, E. F. Harkness, and E. Ernst, “Randomized, double-blind trial of chitosan for body weight reduction,” European Journal of Clinical Nutrition, vol. 53, no. 5, pp. 379–381, 1999.

16. R. N. Schiller, E. Barrager, A. G. Schauss, and E. J. Nichols, “A randomized, double-blind, placebo-controlled study examining the effects of a rapidly soluble chitosan dietary supplement on weight loss and body composition in overweight and mildly obese individuals,” Journal of the American Nutraceutical Association, vol. 4, pp. 42–49, 2001.

17. C. Ni Mhurchu, S. D. Poppitt, and S. D. Poppitt, “The effect of the dietary supplement, chitosan, on body weight: a randomised controlled trial in 250 overweight and obese adults,” International Journal of Obesity, vol. 28, no. 9, pp. 1149–1156, 2004.

18. G. R. Kaats, J. E. Michalek, and H. G. Preuss, “Evaluating efficacy of a chitosan product using a double-blinded, placebo-controlled protocol,” Journal of the American College of Nutrition, vol. 25, no. 5, pp. 389–394, 2006.

19. R. T. Stanko, D. L. Tietze, and J. E. Arch, “Body composition, energy utilization, and nitrogen metabolism with a 4.25- MJ/d low-energy diet supplemented with pyruvate,” American Journal of Clinical Nutrition, vol. 56, no. 4, pp. 630–635, 1992.

20. D. Kalman, C. M. Colker, R. Stark, A. Minsch, I. Wilets, and J. Antonio, “Effect of pyruvate supplementation on body composition and mood,” Current Therapeutic Research, vol. 59, no. 11, pp. 793–802, 1998.

21. D. Kalman, C. M. Colker, I. Wilets, J. B. Roufs, and J. Antonio, “The effects of pyruvate supplementation on body composition in overweight individuals,” Nutrition, vol. 15, no. 5, pp. 337–340, 1999.

22. J. L. Ngondi, J. E. Oben, and S. R. Minka, “The effect of Irvingia gabonensis seeds on body weight and blood lipids of obese subjects in Cameroon,” Lipids in Health and Disease, vol. 4, article 12, 2005.

23. J. L. Ngondi, B. C. Etoundi, C. B. Nyangono, C. M. F. Mbofung, and J. E. Oben, “IGOB131, a novel seed extract of the West African plant Irvingia gabonensis, significantly reduces body weight and improves metabolic parameters in overweight humans in a randomized double-blind placebo controlled investigation,” Lipids in Health and Disease, vol. 8, article 7, 2009.

24. D. C. Nieman, E. J. Cayea, M. D. Austin, D. A. Henson, S. R. McAnulty, and F. Jin, “Chia seed does not promote weight loss or alter disease risk factors in overweight adults,” Nutrition Research, vol. 29, no. 6, pp. 414–418, 2009.

25. “Garcinia cambogia,” in Natural Medicines Comprehensive Database, Therapeutic Research Faculty, Stockton, Calif, USA, Updated (periodically).

26. L. Dara, J. Hewett, and J. K. Lim, “Hydroxycut hepatotoxicity: a case series and review of liver toxicity from herbal weight loss supplements,” World Journal of Gastroenterology, vol. 14, no. 45, pp. 6999–7004, 2008.

27. “Chitosan,” in Natural Medicines Comprehensive Database, Therapeutic Research Faculty, Stockton, Calif, USA, Updated (periodically).

28. A. B. Jull, C. Ni Mhurchu, D. A. Bennett, C. A. E. Dunshea-Mooij, and A. Rodgers, “Chitosan for overweight or obesity,” Cochrane Database of Systematic Reviews, no. 3, Article ID CD003892, 2008.

29. “Pyruvate,” in Natural Medicines Comprehensive Database, Therapeutic Research Faculty, Stockton, Calif, USA, Updated (periodically).

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31. “Salvia hispanica,” in Natural Medicines Comprehensive Database, Therapeutic Research Faculty, Stockton, Calif, USA, Updated (periodically).

Copyright © 2011 Amy M. Egras et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

by EricT

Departments of Pharmacology and Anesthesiology, New York Medical College, Valhalla, NY 10595, USA

Journal of Obesity 2011

Sympathomimetic agents have a poor history of long-term success in the treatment of obesity. From earlier experiences with amphetamine and its analogs, to more recent drugs with direct effects on adrenergic receptors or indirect effects from release of catecholamines or inhibition of reuptake, cardiovascular toxicity (strokes and cardiac arrhythmias) has been the major concern. These concerns also extended to food supplements containing ephedra alkaloids and may require consideration for current supplements containing the sympathomimetic drug, synephrine.

Sibutramine is the most recent drug with sympathomimetic activity that has been recognized by regulatory agencies as having cardiovascular adverse effects that may outweigh its potential value as a weightloss drug. Sibutramine is marketed in Europe under several trade names, including Reductil, Reduxade, Ectiva, Sibutral, Zelium, and others. Meridia is the only brand name in the United States. The European Medicines Agency took definitive action on January 21, 2010 in advising against the continued prescribing of the drug, and Abbott Laboratories suspended sales of the drug in Europe [1]. This action was prompted by a preliminary evaluation of results from the Sibutramine Cardiovascular OUTcome (SCOUT) trial reported by the United States FDA [2]. The FDA has not imposed a ban on the drug but has obtained a change in the boxed warning label to contraindicate its use in patients with a history of cardiovascular disease and in all individuals over 65 years of age (updated warning, April 15, 2010). These regulatory actions were prompted by an early review of the SCOUT trial [3] that revealed an incidence of cardiovascular events of 11.4% in patients receiving sibutramine compared to 10% in those receiving placebo. This was an unexpected finding; the hypothesis in the design of the study was that an anticipated weight loss from the use of sibutramine would be associated with a reduction in the incidence of cardiovascular events when compared to that observed in patients receiving the placebo treatment [3].

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Sibutramine was not developed as a sympathomimetic drug. In early studies, it was first evaluated for its potential as an antidepressant medication, and its behavioral effects were apparently explained by its inhibition of both 5-hydroxytryptamine (5-HT) and norepinephrine reuptake in the central nervous system (CNS) [4, 5]. The inhibition of monoamine uptake is produced almost completely by two metabolites of sibutramine [5]. The hypophagic effect of sibutramine was recognized later, and this became the focus of its development as an antiobesity drug [6–8]. Its sympathomimetic effects (an increase in heart rate and blood pressure), which are of concern in relation to the observed increase in adverse cardiovascular events, would appear to be primarily related to a peripheral inhibition of norepinephrine reuptake and interrelationships with sympathetic outflow from the CNS [9, 10]. The increases in heart rate and blood pressure would be expected to follow from inhibition of norepinephrine reuptake by sibutramine (mostly its metabolites) at sympathetic nerve endings in the heart and blood vessels. This effect would be magnified during physical activity or other conditions of stress that would increase sympathetic nerve activity and norepinephrine overflow in the periphery. These increases in heart rate and blood pressure are seen, despite the fact that sibutramine is known to blunt the intensity of central sympathetic outflow by virtue of a2-adrenergic suppression of sympathetic activity in the CNS. The locus of this action is considered to include a 2-adrenergic receptors in the lower portion of the brain stem (medulla oblongata) [11]. This is also the presumed site of action of the antihypertensive drug, clonidine. The extent to which sympathetic outflow is decreased by this central inhibitory effect of overflow norepinephrine may be limited, because sibutramine has been observed to rapidly (within days) desensitize central a2-adrenergic receptors in animal studies [12].

Amphetamines, phentermine, and phenyl-propanolamine (PPA) are sympathomimetic amines that were once widely used for the treatment of obesity because of their anorexic effects [13, 14]. They are all contraindicated for this use at this time because of their adverse cardiovascular and/or behavioral profiles. They are primarily indirect acting amines, that is, they release norepinephrine from sympathetic nerve endings. These amines all have an a-methyl substitution, which results in inhibition of monoamine oxidase and an intensification and prolongation of their pharmacological effects. In addition, the dextro isomer of amphetamine, dexamphetamine, is a potent inhibitor of norepinephrine reuptake, which may be the basis for its greater potency over the levoisomer [13]. The unregulated sale of supplements containing ephedra alkaloids is probably the most recent example of the discontinued use of a group of sympathomimetic entities for weight loss. These supplements were banned by the FDA in 2004 because of their unreasonable risks of cardiovascular toxicity. The complete ban on ephedra sales was challenged in 2005; that challenge was overturned on appeal by the FDA, with confirmation of the ban in 2006. The United States Supreme Court declined to review a further industry appeal in 2007. (I have commented previously on the cardiovascular risks of ephedra alkaloids [15].)

As one of several consultants to the FDA before the imposition of the ephedra ban [16], I conducted an evaluation of the pharmacology and inherent safety concerns of ephedra alkaloids. The principal focus in that analysis concerned ephedrine. Although the natural herb, Ephedra sinica (Ma Huang), contains three sympathomimetic agents in addition to ephedrine (pseudoephedrine, methylephedrine, and phenylpropanolamine), ephedrine is the most potent and is typically present in the highest concentration among the four alkaloids [17]. The sympathomimetic impact of ephedrine results from a combination of multiple actions. Ephedrine is classified as both a direct and indirect acting amine. Its indirect effects result primarily from the fact that it causes a displacement release of norepinephrine from sympathetic nerve endings. In addition to releasing norepinephrine, ephedrine blocks the reuptake of norepinephrine into sympathetic nerve terminals. Ephedrine is also an inhibitor of the degradation of norepinephrine by monoamine oxidase. Thus, by three mechanisms, ephedrine magnifies the intensity and duration of action of norepinephrine that is released [13, 14, 18]. Obviously, this entire process is further amplified during periods of physical exercise, where sympathetic nerve activity is increased. In addition to indirect effects of ephedrine, it also has direct vasoconstrictor (a1-adrenergic), cardiac acceleration and increased contractility (ß1-adrenergic), and bronchodilator (ß2-adrenergic) effects. This triad of effects is similar to that of epinephrine. The direct effects of ephedrine summate with its indirect effects to produce its total cardiovascular impact.

A major negative impact of ephedrine upon the heart is its ability to induce cardiac arrhythmias. The primary mechanism involved in this risk is the fact that ephedrine and all sympathomimetic drugs that activate ß1-adrenergic receptors (either directly or through release, inhibition of reuptake, or inhibition of metabolism of norepinephrine) shorten the refractory period of the conducting system of the heart and cardiac muscle [11]. This is particularly problematic in an individual who is exercising, because the increased sympathetic outflow with exercise augments the release of norepinephrine, which itself has ß1-adrenergic activity. This adds to the shortening of refractoriness of cardiac cells. A shortening of the refractory period of myocardial cells is an essential element in the induction of a re-entrant arrhythmia, that is, an impulse may now encounter receptive cells, cells that would ordinarily be refractory and would not allow for a premature activation. That premature activation can permit an abnormal route of electrical excitation, that is, an arrhythmia [19]. In addition, myocardial ischemia caused by exercise and/or exacerbated by the presence of coronary artery obstruction also results in a shortening of the refractory period of myocardial cells [19, 20]. Since the presence of coronary artery disease may be unrecognized in many individuals during routine activities, the summation of drug and stress effects on the electrophysiology of the heart requires consideration. Recent data presented by the American Heart Association shows that the incidence of cardiovascular disease is 39.6 percent in the age group 40 to 59 years (identical for men and women) and 73.6 percent and 73.1 percent in the age group 60 to 79 years for men and women, respectively [21].

In addition to the risks of stroke associated with blood pressure elevations produced by sympathomimetic drugs, increases in blood pressure may also contribute an arrhythmogenic potential. With increases in blood pressure, compensatory baroreceptor-mediated predominance of the parasympathetic nervous system does result in a slowing of heart rate in comparison to the effect that would take place as a result of ß 1-adrenergic activation alone. Although this may reduce the extent of blood pressure elevation, it does not reduce the arrhythmogenic potential of the electrophysiological processes discussed above. In fact, the participation of the parasympathetic nervous system to slow heart rate does so by release of its mediator, acetylcholine. Acetylcholine also shortens the refractory period of atrial tissues, which may be causally related to the induction of atrial fibrillation, atrial flutter, or paroxysmal supraventricular tachycardia [11]. Thus, direct sympathomimetic stimulation of ß1-adrenergic receptors, indirect effects through released norepinephrine, ischemia, and compensatory acetylcholine release, may each be primary or may summate to induce cardiac arrhythmias.

As a result of the loss of availability of ephedra-containing products, the sympathomimetic amine, synephrine, appears to have become an important focus of the food supplement industry. Synephrine is chemically related to the well-known vasopressor drug, phenylephrine; phenylephrine is the meta-hydroxy-phenyl isomer of synephrine. As with phenylephrine, synephrine increases blood pressure through its a1-adrenergic-mediated vasoconstrictor effect [13]. There are a number of supplements currently on the market that identify synephrine as a major ingredient of the product. Information about these products is readily available through searches for synephrine. Various claims are made as to the value of this constituent for weight loss and development of physical fitness. Several of these commercial products contain a fairly well-characterized extract of Citrus aurantium (bitter orange), which is called “ADVANTRA Z”. ADVANTRA Z is supplied by Nutratech, Inc. [22]. The descriptions of patents for ADVANTRA Z indicate that, in addition to synephrine, it contains three other orally active sympathomimetic agents, N-methyltyramine, hordenine, and octopamine [13]. The Nutratech website suggests a daily intake of 100 to 120mg of synephrine, in two to three divided servings.

The potential cardiovascular effects of the doses of synephrine present in ADVANTRA Z can be placed in perspective because synephrine is the active ingredient in the orally active vasopressor drug, oxedrine tartrate. A common trade name for this preparation in Europe is Sympatol [23]. Oxedrine tartrate is not marketed in the United States. Sympatol carries the clinical indication for the treatment of hypotension, that is, to raise blood pressure, with oral doses of 100 to 150mg, up to three times a day. To compare doses of synephrine in ADVANTRA Z with clinical doses of Sympatol, it is necessary to correct for the fact that Sympatol is the racemic mixture of synephrine (the l-isomer has the predominant sympathomimetic activity) and that it is the tartrate salt. (The l-isomer of synephrine is the form in citrus extracts [24, 25].) Therefore, the molecular weight of synephrine tartrate is 484.5. Of this, the l-synephrine content is 167.2 or 34.5% of the dose.

Thus, the single oral dose range of 100 to 150mg of Sympatol supplies 34.5 to 51.8mg of l-synephrine. If 3 doses are given per day, the total daily dose would be 103.5 to 155.4mg of synephrine. This overlaps the suggested intake for ADVANTRA Z of 100 to 120mg of synephrine per day. As discussed above, in addition to the danger of stroke associated with drug-induced increases in blood pressure, compensatory responses to these increases, mediated through the parasympathetic nervous system, may be arrhythmogenic. Also, these concerns would be expected to be enhanced in conjunction with physical effort. The National Center for Complementary and Alternative Medicine, National Institutes of Health, has noted the potential risks for cardiovascular toxicity from consumption of extracts of Citrus aurantium [26].

In summary, sympathomimetic agents do not have a successful record for the treatment of weight loss. They apparently possess an unreasonable cardiovascular toxic potential that emerges when the drugs are applied to a broad spectrum of the population, many of whom may have unrecognized risk factors.

Disclosure

The author was compensated as a consultant to the US Food Administration for evaluation of sympathomimetic agents in food supplements.

References

1. European Medicines Agency, “European Medicines Agency recommends suspension of marketing authorisations for sibutramine,” January 2010, http://www.ema.europa.eu/pdfs/human/referral/sibutramine/3940810en.pdf.

2. “Follow-up to the November 2009 early communication about an ongoing safety review of sibutramine,” Department of Health and Human Services, U.S. Food and Drug Administration, Meridia, January 2010, http://www.fda.gov/Drugs/DrugSafety/PostmarketDrugSafetyInformationforPatientsandProviders/DrugSafetyInformationforHeathcareProfessionals/ucm198206.htm.

3. “Early communication about an ongoing safety review of Meridia (sibutramine hydrochloride),” Department of Health and Human Services, U.S. Food and Drug Administration, November 2009, http://www.fda.gov/Drugs/DrugSafety/PostmarketDrugSafetyInformationforPatientsandProviders/DrugSafetyInformationforHeathcareProfessionals/ucm191650.htm.

4. W. R. Buckett, P. C. Thomas, and G. P. Luscombe, “The pharmacology of sibutramine hydrochloride (BTS 54 524), a new antidepressant which induces rapid noradrenergic down-regulation,” Progress in Neuro-Psychopharmacology and Biological Psychiatry, vol. 12, no. 5, pp. 575–584, 1988.

5. G. P. Luscombe, R. H. Hopcroft, P. C. Thomas, and W. R. Buckett, “The contribution of metabolites to the rapid and potent down-regulation of rat cortical ß-adrenoceptors by the putative antidepressant sibutramine hydrochloride,” Neuropharmacology, vol. 28, no. 2, pp. 129–134, 1989.

6. M. Fantino and A.-M. Souquet, “Effects of metabolites 1 and 2 of sibutramine on the short-term control of food intake in the rat,” International Journal of Obesity, vol. 19, article 145, 1995.

7. J. C. G. Halford, D. J. Heal, and J. E. Blundell, “Effects in the rat of sibutramine on food intake and the behavioral satiety sequence,” British Journal of Pharmacology, vol. 114, p. 387P, 1995.

8. H. C. Jackson, M. C. Bearham, L. J. Hutchins, S. E. Mazurkiewicz, A. M. Needham, and D. J. Heal, “Investigation of the mechanisms underlying the hypophagic effects of the 5-HT and noradrenaline reuptake inhibitor, sibutramine, in the rat,” British Journal of Pharmacology, vol. 121, no. 8, pp. 1613–1618, 1997.

9. A. L. Birkenfeld, C. Schroeder, and C. Schroeder, “Paradoxical effect of sibutramine on autonomic cardiovascular regulation,” Circulation, vol. 106, no. 19, pp. 2459–2465, 2002.

10. K. Heusser, S. Engeli, and S. Engeli, “Sympathetic vasomotor tone determines blood pressure response to long-term sibutramine treatment,” Journal of Clinical Endocrinology and Metabolism, vol. 92, no. 4, pp. 1560–1563, 2007.

11. T. C. Westfall and D. P. Westfall, “Adrenergic agonists and antagonists,” in Goodman & Gilman’s the Pharmacological Basis of Therapeutics, L. L. Brunton, J. S. Lazo, and K. L. Parker, Eds., pp. 237–295, McGraw-Hill, New York, NY, USA, 11th edition, 2006.

12. D. J. Heal, M. R. Prow, J. Gosden, G. P. Luscombe, and W. R. Buckett, “A comparison of various antidepressant drugs demonstrates rapid desensitization of a2-adrenoceptors exclusively by sibutramine hydrochloride,” Psychopharmacology, vol. 107, no. 4, pp. 497–502, 1992.

13. “Peripheral adrenergic mechanisms,” in Textbook of Pharmacology, W. C. Bowman and M. J. Rand, Eds., pp. 11.1–11.49, Blackwell Scientific, Oxford, UK, 2nd edition, 1980.

14. “The diet and diet-induced diseases. Appetite control. Pharmacologically active constituents of food,” in Textbook of Pharmacology, W. C. Bowman and M. J. Rand, Eds., pp. 43.1–43.51, Blackwell Scientific, Oxford, UK, 2nd edition, 1980.

15. M. A. Inchiosa Jr., “Concerning ephedra alkaloids for weight loss,” International Journal of Obesity, vol. 31, no. 9, p. 1481, 2007.

16. M. A. Inchiosa Jr., “Reports from Outside Consultants,” Department of Health and Human Services, U.S. Food and Drug Administration. Federal Register Notice—65 FR 17510, April 2000—Dietary Supplements Containing Ephedrine Alkaloids, http://www.fda.gov/Food/DietarySupplements/GuidanceComplianceRegulatoryInformation/RegulationsLaws/ucm079601.htm Document ID: FDA-2000-N-0284-0019.8, http://www.regulations.gov/search/Regs/home.html#documentDetail?R=0900006480934c08.

17. L. M. White, S. F. Gardner, B. J. Gurley, M. A. Marx, P.-L. Wang, and M. Estes, “Pharmacokinetics and cardiovascular effects of Ma-Huang (Ephedra sinica) in normotensive adults,” Journal of Clinical Pharmacology, vol. 37, no. 2, pp. 116–122, 1997.
18. “Noradrenergic transmission,” in Pharmacology, H. P. Rang, M. M. Dale, J. M. Ritter, and P. Gardner, Eds., pp. 139–163, Churchill Livingstone, New York, NY, USA, 4th edition, 2001.

19. D. M. Roden, “Antiarrhythmic drugs,” in Goodman & Gilman’s the Pharmacological Basis of Therapeutics, L. L. Brunton, J. S. Lazo, and K. L. Parker, Eds., pp. 899–932, McGraw-Hill, New York, NY, USA, 11th edition, 2006.

20. “The heart and drugs affecting cardiac function,” in Textbook of Pharmacology, W. C. Bowman and M. J. Rand, Eds., pp. 22.1–22.85, Blackwell Scientific, Oxford, UK, 2nd edition, 1980.

21. American Heart Association, “Heart disease and stroke statistics-2010 update. (Slide 10),” http://www.americanheart.org/downloadable/heart/12626426574432010%20Stat%20charts%20FINAL.ppt#343,10,Slide 10.

22. Nutratech Inc., “Advantra Z®. The next generation weight loss & fitness ingredient. Applications/dosage guidelines,” http://www.nutratechinc.com/advz/advz.php?p=4.

23. S. C. Sweetman, Ed., Martindale the Complete Drug Reference, Pharmaceutical Press, London, UK, 36th edition, 2009.

24. I. Stewart, W. F. Newhall, and G. J. Edwards, “The isolation and identification of l-synephrine in the leaves and fruit of citrus,” Journal of Biological Chemistry, vol. 239, pp. 930–932, 1964.

25. F. Kusu, K. Matsumoto, K. Arai, and K. Takamura, “Determination of synephrine enantiomers in food and conjugated synephrine in urine by high-performance liquid chromatography with electrochemical detection,” Analytical Biochemistry, vol. 235, no. 2, pp. 191–194, 1996.

26. National Institutes of Health, National Center for Complementary and Alternative Medicine. Bitter Orange,http://nccam.nih.gov/health/bitterorange/#cautions.

Copyright © 2011 Mario A. Inchiosa Jr. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

by EricT

www.BurnTheFat.com

WEIGHT LOSS POP QUIZ: What are 3 things that ALL 8 of these advertisements have in common?

• “Burn 30 lbs in 3 weeks - no diet!”
• “Lose 9 Pounds Every 11 Days!”
• “Lose a pound a day without diet or exercise!”
• “Lose 2 pounds a day without dieting!”
• “Lose 30 pounds In 30 Days!”
• “Lose 20 lbs in 3 weeks!”
• “Burn 30 lbs in 25 days!”
• “Lose 10 Pounds This Weekend!”

ANSWER:

(1) They are all FALSE, (2) they are all DECEPTIVE…

I just did an Internet search for “how fast should you lose weight” and these are just a small sample of ACTUAL ADS that are running this very moment. They sure are enticing, aren’t they? They play on your emotions and on your desire for instant gratification.

But did you know that…

(3) these claims are all actually ILLEGAL, says the Federal Trade Commission (FTC)

“We have known for some time now that there is a serious problem with weight-loss product advertising,” said FTC Chairman Timothy J. Muris. “Reputable marketers continue to take care to avoid false and misleading claims, but it appears that too many unscrupulous marketers are making false claims promising dramatic and effortless weight loss to sell their products. It is not fair to consumers; it is not fair to legitimate businesses, it is illegal, and it will not be tolerated.”

You might be asking, “Ummm, if it will not be tolerated, then why do we keep seeing these ads?” Ah yes, well, God bless the Internet, On Google, you can put up an ad and have it showing in 15 minutes. You can then have it taken down just as fast. Same goes for websites. The FTC couldn’t keep up with OFFLINE false advertising, how are they possibly going to keep up with it ONLINE??? And it’s only going to get worse.

There’s only so much the FTC and other consumer watchdog organizations can do. It’s up to YOU to educate YOURSELF and know the red flags and warning signs of bogus weight loss claims.

Here’s what else the FTC says about why these types of advertising claims are so damaging:

* “The deceptive promotion of quick and easy weight loss solutions potentially fuels unrealistic expectations on the part of consumers. consumers who believe that it really is possible to lose a pound (of fat) a day may quickly lose interest in losing a pound a week.”

* “The proliferation of “fast and easy” fixes undermines the reality of what it takes to lose weight. People who need to lose weight are buying empty promises.”

I believe that the weight loss education industry has been knocked a few steps backward in the last few years due to (1) the internet and (2) the horrendous reality TV shows that actually encourage people to attempt “extreme” body makeovers or see who can lose weight the fastest. The winners (or shall we say, the “losers”, as if that’s a flattering title to earn), are rewarded generously with fortune, fame and congratulations.

These shows are damaging and despicable. I’m shocked that so many millions tune in and I’m even more surprised how many people think this garbage is “inspiring.”

Let’s face it. Everyone wants to get the fat off as quickly as possible - and having that desire is not wrong – it’s simply human nature. Patience is the one thing you never seem to have when you’ve got a body fat problem. You want the fat gone and you want it gone now!

Like the FTC said, with what we see on TV these days and with web page after web page of fast weight loss claims, you actually start to believe it’s doable and you’re no longer interested in a healthy 1-2 lbs weight loss per week. In fact, you even see people with your own eyes losing weight incredibly fast. How do you deny it’s possible when you see THAT?

Well, the answer comes to you when you expand your time perspective and see where those people are 6, 12, 18 months from now. Deep in your heart, you KNOW the answer…

The faster you lose weight, the more muscle you will lose right along with the fat, and that can really mess up your metabolism.

An even bigger problem with fast weight loss is that it just won’t last. The faster you lose, the more likely you are to gain it back. It’s the the “yo-yo diet effect” - weight goes down, but always comes back up.

What Really Matters Is Not How Much WEIGHT You Lose, But How Much FAT You Lose

Where did your weight loss come from? Did you lose body fat or lean body mass? “Weight” is not the same as “fat.” Weight includes muscle, bone, internal organs as well as lots and lots of water…

Don’t Be Fooled By Water Weight Losses

One thing you should also know is that it’s very common to lose 3 - 5 pounds in the first week on nearly any diet and exercise program and often even more on low carb diets (because low carb diets deplete glycogen and every gram of glycogen holds 3 grams of water). Just remember, its NOT all fat - WATER LOSS IS NOT FAT LOSS - AND WATER LOSS IS TEMPORARY!

The only way to know if you’ve actually lost FAT is with body composition testing. For home body fat self-testing, I recommend the Accu-Measure skinfold caliper as first choice. Even better, get a multi site skinfold caliper test from an experienced tester at a health club, or even an underwater (hydrostatic) or air (bod pod) displacement test.

From literally hundreds of client case studies, I can confirm that it’s rare to lose more than 2 to 3 lbs of weight per week without losing some muscle along with it. If you lose muscle, you are damaging your metabolism and this will lead to a plateau and ultimately to weight relapse.

The Biggest Weight Loss Mistake That Is FATAL To Your Long Term Success

Lack of patience is one of the biggest mistakes people make when it comes to losing body fat. If you want to lose FAT, not muscle, and if you want to keep the fat off for good, then you have to take off the pounds slowly (of course, if you want to crash diet the weight off fast, lose muscle with the fat and gain all the fat back later, be my guest!).

This is one of the toughest lessons that overweight men and women have to learn - and they can be very hard learners. They fight kicking and screaming, insisting that they CAN and they MUST lose it faster.

Then you have these TV shows that encourage the masses that rapid, crash weight loss is okay. To the producers of these shows, I say SHAME ON YOU! To the personal trainers, registered dieticians and medical doctors who are associated with these programs, I say DOUBLE SHAME ON YOU, because you of all people should know better. These shows are not “motivating” or “inspiring” - they are DAMAGING! They are a DISGRACE!

The rapid weight loss being promoted by the media for the sake of ratings and by the weight loss companies for the sake of profits makes it even harder for legitimate fitness and nutrition professionals because our clients say, “But look at so and so on TV - he lost 26 pounds in a week!”

Sure, but 26 pounds of WHAT - and do you have any idea what the long term consequences are?

Short term thinking… foolish.

Do it the right way. The healthy way. Take off pounds slowly, and steadily with a sensible lifestyle program like my Burn The Fat, Feed The Muscle system that includes the important elements of cardio training, strength training and proper nutrition.

Measure your body fat, not just your body weight, and make this a new lifestyle, not a race, and you will never have to take the pounds off again, because they will be gone forever the first time.

Tom Venuto, author of
Burn The Fat Feed The Muscle

Founder & CEO of
Burn The Fat Inner Circle

About the Author:

Tom Venuto is the author of the #1 best seller, Burn the Fat, Feed the Muscle: Fat Burning Secrets of the World’s Best Bodybuilders and Fitness Models. Tom is a lifetime natural bodybuilder and fat loss expert who achieved an astonishing 3.7% body fat level without drugs or supplements. Discover how to increase your metabolism and burn stubborn body fat, find out which foods burn fat and which foods turn to fat, plus get a free fat loss report and mini course by visiting Tom's site at: www.BurnTheFat.com

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The links on this page are affiliate links.

by EricT

The Open Sports Medicine Journal, 2010

Excess weight and obesity and the resulting risk factors are major contributing factors to skyrocketing health-care costs. Between 3.9 and 10.33 billion euros are spent in Germany each year on treating obesity, which can lead to serious health problems such as diabetes, hypertension, and coronary heart disease. In a study incorporating data from medical examinations of men born in 1974 routinely conducted to assess fitness for military service, the German Federal Ministry of Health found that 154 out of 1000 men were slightly to moderately overweight and 15 were severely obese [1]. The prevalence of obesity has also increased dramatically in the U.S. during the last decade. An estimated 97 million American adults are overweight or obese, making obesity the nation’s second most common cause of preventable death [2].

Abstract: The objective of the following meta-analysis was to determine what kind of treatment, or combination of treatments, has the greatest effect on weight loss in overweight and obese adults.

A systematic search was conducted of the available literature published between 1993 and 2006 that covered randomized controlled trials on overweight and obese subjects who underwent treatment consisting of physical exercise and/ or changes in diet. The scope of the search thus incorporated seven relevant databases. Using 6,545 key word combinations, the electronic search yielded a total of 36,869 abstracts. 13 relevant studies with a total of 826 subjects (BMI > 25; 17 - 68 years of age) met the meta-analysis criteria. The courses of treatment included “diet (d)”, “physical exercise (pe)”, “diet and physical exercise (dpe)”, and “no intervention (ni)”. The results confirmed the hypothesis that the combined intervention “dpe” had the greatest effect with regard to weight loss. The single treatments “pe” and “d” also led to weight loss, with “d” having a significantly greater effect than “pe”. The main reason for the small sample size of thirteen studies out of 36,819 was that the experimental design and/or procedures of most studies were inadequate. A common error was a failure to assign subjects randomly to the different treatment groups. The results of our meta-analysis indicate that a combination of diet and physical exercise is the best form of treatment to induce weight loss in overweight individuals in the first weeks, followed by physical exercise to maintain weight loss.

Download full PDF Article: Effects of Exercise, Diet, and a Combination of Exercise and Diet in Overweight and Obese Adults – A Meta-Analysis of the Data

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© Schaar et al.; Licensee Bentham Open.
This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

by EricT

BY Ock K. Chun¹, Chin E. Chung², Ying Wang³, Andrea Padgitt⁴ and Won O. Song⁵

Nutrients 2010

This study was designed to document changes in total sugar intake and intake of added sugars, in the context of total energy intake and intake of nutrient categories, between the 1970s and the 1990s, and to identify major food sources contributing to those changes in intake. Data from the NHANES I and III were analyzed to obtain nationally representative information on food consumption for the civilian, non-institutionalized population of the U.S. from 1971 to 1994. In the past three decades, in addition to the increase in mean intakes of total energy, total sugar, added sugars, significant increases in the total intake of carbohydrates and the proportion of carbohydrates to the total energy intake were observed. The contribution of sugars to total carbohydrate intake decreased in both 1–18 y and 19+ y age subgroups, and the contribution of added sugars to the total energy intake did not change. Soft drinks/fluid milk/sugars and cakes, pastries, and pies remained the major food sources for intake of total sugar, total carbohydrates, and total energy during the past three decades. Carbonated soft drinks were the most significant sugar source across the entire three decades. Changes in sugar consumption over the past three decades may be a useful specific area of investigation in examining the effect of dietary patterns on chronic diseases.

Introduction

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Sugars are a ubiquitous component of our food supply. They are consumed as a naturally occurring component of our diet and as additions to foods during processing, preparation, or at the table. A healthy diet contains at least some amount of naturally occurring sugars, because monosaccharides, such as glucose and fructose, and disaccharides, such as sucrose and lactose, are integral components of fruit, vegetables, dairy products, and many grains [1]. Sugars also add desirable sensory effects and promote enjoyment of foods. Over the years, however, sugar intake has been claimed to be associated with several diet-related chronic diseases: diabetes, CVD, obesity, dental caries, and hyperactivity in children [2,3]. One of overwhelming concerns regarding sugars is the potential for excess energy intake from sugars resulting in weight gain and displacement of more nutrient-dense foods [2]. However, little attention has been given to the contribution of sugar and carbohydrates to total energy intake.

In explaining the relationship of certain nutrients or dietary patterns to chronic disease, it is important to examine not only the total intake of macronutrients and their components, but also their contribution to total energy intake. We have reported on the association between diabetes biomarkers and increased percent of energy intake from carbohydrates [4], and reported changes in U.S. children’s beverage consumption patterns in the past decades [5]. However, previous studies on the trends of nutrient intakes in the U.S. [6-9] provided only limited evidence to help explain health impacts associated with consumption of simple and complex carbohydrates, with their ratios to total carbohydrate intake, and with changes in food supply and processing practices. Furthermore, these earlier studies utilized vastly different methodologies, complicating any effort to draw inferences about the relationship of sugar intake to health impacts [10].

Trends of sugar consumption in the U.S. have typically been calculated based on the per capita sugar consumption estimates reported annually by the Economic Research Service using the market disappearance data [11]. These per capita estimates, however, do not take account of differences between the amount purchased and the amount actually consumed. Therefore, it is important to use data on actual consumption, gathered utilizing relatively consistent survey and sampling methods to achieve representative results for the national population. Previous studies analyzing a series of NHANES data documented a steep increase in the prevalence of obesity from mid 1970s through 2000 [12], and a number of studies have shown a significant association between sugar and obesity, especially in children and adolescents [13-15]. Therefore, the objectives of this study were to determine trends in the U.S. population and age subgroups, from the 1970s to the 1990s, in total sugar intake and intake of added sugars, in their contribution to total energy intake, and in the food groups contributing principally to sugar and energy intakes. The data for the study were drawn from the National Health and Nutrition Examination Survey, NHANES, I and III (National Center for Health Statistics 1985; 1998).

Methods

Characteristics of Datasets

The NHANES I and III were conducted by the National Center for Health Statistics and the Centers for Disease Control and Prevention (NCHS/CDC) through interviews, questionnaires and examinations. The purpose of the surveys was to obtain nationally representative information on the health and nutritional status of the civilian, non-institutionalized population of the U.S. (NHANES I, 1971–1975; NHANES III, 1988–1994) [16-18]. Details of survey procedures, handling of samples and analytical procedures are described elsewhere [18]. The characteristics of the NHANES I and NHANES III data sets are shown in Table 1. Data for NHANES I were gathered from 20,195 participants, ages 1 to 74 y, and for NHANES III were gathered from 28,663 participants, ages 1 to 90 y. Subjects with unreliable and incomplete dietary recall records as coded by NCHS were excluded in this study.

A listing of the 1,823 unique foods recorded during collection of the 24-hour dietary recall data in the NHANES I was matched to a corresponding food item listed in the NHANES III nutrient database by name and nutrient composition. Both criteria were used to determine the most suitable match for all 1,823 foods. An appropriate match could not be identified for 12 foods (Table 1). 2.2. Study Design An individual’s total sugar (g·d-1) intake was calculated as the sum of glucose, fructose, galactose, sucrose, maltose, and lactose intakes. The definition of “added sugars” was taken from the National Cancer Institute [19]: white sugar, brown sugar, raw sugar, corn syrup, corn syrup solids, high fructose corn syrup, malt syrup, maple syrup, pancake syrup, fructose sweetener, liquid fructose, honey, molasses, anhydrous dextrose, crystal dextrose, saccharin, and aspartame that are eaten separately or used as ingredients in processed or prepared foods. Data of total and added sugars were not available in the NHANES I database. Thus intakes of these nutrients for NHANES I were estimated by matching food codes to those listed in the NHANES III food composition tables. U.S.D.A.’s 53 food categories were used to estimate the food sources of dietary sugar and other nutrients [20].

Statistical Methods

All data analyses were carried out using SAS, release 8.1 (SAS Institute Inc, Cary, NC, USA) and Survey Data Analysis for multi-stage sample designs professional software package (SUDAAN, release 8.01, Research Triangle Institute, Research Triangle Park, NC, USA) [21]. SUDAAN was used to increase the validity of the results by computing variance estimates and test statistics for a stratified, multistage probability survey design. Sample weights were applied to all analyses to account for the unequal probability of selection, non-coverage, and non-response bias resulting from over-sampling of low-income persons, adolescents, the elderly, African-Americans, and Hispanics. Means and standard errors for all nutrients examined were calculated using PROC DESCRIPT in SUDAAN.

Table1. Characteristics of NHANES I and NHANES III datasets.

^a 12 food items in the individual food consumption data file of NHANES I could not be matched, because these items were not described in the food description file of NHANES I.

Results

Food Code Matches between NHANES I and NHANES III

Estimates of the NHANES I subjects’ nutrient intake levels generated by our food code matching technique (adopted from NHANES III) were comparable to those resulting from analysis of the original food codes of NHANES I. The values resulting from the food code matching technique and the analysis of the NHANES I data were, respectively: for total intake of food and beverages, 2,070 vs. 2,070 g·d-1; for total energy intake, 1,988 vs. 2,000 kcal·d-1; for total carbohydrate intake, 224 vs. 236 g·d-1; and for percent of energy intake from fat, 36% vs. 36% (Table 2).

Since the original NHANES I database did not contain sugar intake data, some means of estimating those intakes had to be devised. Since nearly identical values were obtained for the four test nutrient variables from food code matching estimates and from analysis of the original NHANES I data, we felt confident in using the food code matching technique to estimate sugar intake levels for NHANES I participants.

Changes in Sugar and Added Sugar Intake Levels from NHANES I to NHANES III Compared with NHANES I, the mean dietary intake levels in NHANES III were greater for total energy intake (+144 kcal d-1; +7%), total sugar intake (+10 g d-1; +8%), intake of added sugars (+9 g d-1; +12%), and total carbohydrate intake (+40 g d-1; +18%). The results differed considerably by age subgroup. The change in mean total energy intake for participants ages 1 to 18 was lower by 3%, whereas it was higher by 11% for participants ages 19+. Mean total sugar intake and intake of added sugars increased for participants ages 1–18 by +0% and +5%, respectively, whereas the means for participants ages 19+ increased by +14% and +18%, respectively.

Table 2. Comparison of the mean nutrient intakes of the subjects in the NHANES I estimated based on the original and matched data.^a,b

^a Sample includes those with reliable and complete dietary interview data.
^b Means are sample-weighted.
^c Nutrient intakes were calculated from original data of NHANES I (1971–1975).
^d Nutrient intakes were estimated by NHANES III food composition table through matching food codes of NHANES I to NHANES III.
^e Percent differences of matched means compared with original means.

Sources of Energy and Sugars in the U.S. Diets

Changes in major contributing food items, from NHANES I to NHANES III,
for participants ages 1–18 y: Major contributing food items for total energy intake changed (in descending order of importance) from fluid milk/breads/meats to mixtures of mainly grain/fluid milk/breads. Major contributing food items for total carbohydrate intake changed from breads/fluid milk/carbonated soft drinks to carbonated soft drinks/mixtures of grain/breads. Major contributing food items for total sugar intake changed from fluid milk/carbonated soft drinks/cakes, pastries, pies to carbonated soft drinks/ fluid milk/fruitades and drinks. Major contributing food items for intake of added sugars changed from carbonated soft drink/candies, sweets/cakes, pastries, pies to carbonated soft drinks/fruitades and drinks/candies, sweets.

Changes in major contributing food items for adult participants (age 19+ y)
for the same period: Major contributing food items for total energy intake changed from meats/breads/fluid milk to mixtures of mainly grain/breads/mixed meat dishes. Major contributing food items for total carbohydrate intake changed from breads/carbonated soft drinks/cakes, pastries, pies to breads/carbonated soft drinks/mixtures of grain. Major contributing food items for total sugar intake changed from carbonated soft drinks/fluid milk/sugars to carbonated soft drinks/cakes, pastries, pies/fluid milk. Major contributing food items for intake of added sugars changed from carbonated soft drinks/sugars/cakes, pastries, pies to carbonated soft drinks/cakes, pastries, pies/sugars.

The most salient feature of the changes in food items contributing to total energy intake is the rise of “mixtures of mainly grain” from relatively insignificant to the most significant contributor in both age subgroups. This food item includes mixtures having a grain product as a main ingredient, such as burritos, tacos, pizza, egg rolls, quiche, spaghetti with sauce, rice and pasta mixtures; frozen meals in which the main course is a grain mixture; noodle and rice soups; and baby-food macaroni and spaghetti mixtures [20].

The major food groups contributing to total sugar intake and intake of added sugars have remained carbonated soft drinks/fluid milk/sugars, cakes, pastries, and pies. Soft drinks were identified as the most significant source of added sugars, contributing 27 g of sugar intakes daily in NHANES III. The percentage of total sugar intake from soft drinks significantly increased by 49% and 39% for ages 1–18 and 19+, respectively, from NHANES I to NHANES III. In contrast, total sugar intake from milk and milk products dropped by 44% in 1–18 y subjects and 46% in 19+ y subjects, respectively, during the same time period. Sugar intake levels from cookies and breakfast grains remained relatively the same during this time period.

Contribution of Individual Sugars to Total Sugar Intakes

There were differences in the two time periods in the relative contribution of major food groups to average intakes of individual sugars, as a consequence both of changes in food processing and changes in food preferences [22,23]. Carbonated soft drinks, however, remained the greatest contributor to glucose and fructose intakes in all age groups, and fluid milk remained the principal source for lactose intake (Appendices A and B). Cakes, pastries and pies remained the principal source for sucrose intake in the 1–18 y age subgroup. Contribution of glucose and fructose to total sugar intake increased from 17% to 22% (23.4 to 30.7 g·d-1) and 16% to 21% (22 to 27 g·d-1), respectively, for 1–18 y old subjects and 18% to 22% (20.3 to 27.9 g·d-1) and 18% to 21% (21.7 to 29.7 g·d-1) for over 19 y old subjects, respectively. Lactose intake has deceased for three decades owing to the decrease in milk consumption and the contribution of lactose to total sugar intake decreased from 22% to 16% (30.9 to 21.6 g·d-1) for 1–18 y old subjects and from 16% to 11% (17.3 to 14.2 g·d-1) for over 19 y old subjects, respectively (Figure 1).

Figure 1. Comparison of the contribution (%) of individual sugars*
to the total sugar intakes between the NHANES I and III by age subgroups.

*Sum of fructose does not include metabolized fructose from sucrose.

Discussion

Research findings on the assessment of added sugar intake in the U.S. population have been based on two main sources of data: the U.S. Food Supply Data (FSD) series [24] and the Continuing Survey of Food Intakes by Individuals (CSFII) [25,26], both products of the USDA. The Food Supply Series tracks the quantities of foods that flow through the food marketing system.

The FSD estimates are made at the commodity level. As a result, the data can be used to track changes in the total volumes (and population averages) of specific wholesale products (cane sugar, beet sugar, the various corn sweeteners) that contribute to sugar intake, and categories of their uses (as in beverages and baked goods, for example) [27]. However, since there are losses to domestic use by individual consumers through both waste at various stages of processing, and export, use of these data for population averages requires adjustment of the estimates to account for these losses. The resulting data is therefore less exact than could be hoped for [24]. The CSFII, which has been considered an ideal metric for the concept of added sugars in both Dietary Guidelines and the Food Guide Pyramid [27], provides data on food and nutrient intakes during only 1988–1991, 1994–1996 and 1998. Since 2002, this nationwide dietary intake database has been integrated with NHANES and the data collected as part of NHANES on a yearly basis. The NHANES databases provide a superior longitudinal data source, since they contain earlier data than the CSFII and have a longer period of continuity.

The Institute of Medicine [28] reported that people whose diets are high in added sugars have lower intakes of essential nutrients (Ca, Mg, Mg, Fe, Zn, vitamin A and E). It further suggests that added sugars should comprise no more than 25 percent of total calories consumed. In the present study, contribution of each macronutrient to the increased total energy intake was taken into consideration. We observed a significant increase in the total intake of carbohydrates (224 to 264 g·d-1) and the ratio of carbohydrates to the total energy intake (45 to 50%), while the contribution of sugars to total carbohydrate intake has decreased in both 1–18 y (57 to 54%) and 19+ y (52 to 47%) (Figure 2); while the contribution of added sugars to the total energy intake has not changed. These findings point to the need for more research into the particular nutritional components related to specific health concerns.

Several recent studies have suggested total sugar intake and intake of added sugars in the U.S. is related to the development of chronic diseases [26]. Some of these studies in particular identify carbonated soft drinks as a major contributor to energy intake and body weight gain [13-15]. Harnack et al. [29] reported that children's soft drink consumption had increased during the past three decades by providing 188 kcal·d-1 extra energy to soft drink consumers beyond that to non-consumers [29]. St-Onge [15] further suggested that these changes in food intakes among children may partly explain the rise in childhood obesity in the past few years. Adolescents consuming high sugar diets are also reported to be at increased risk for poor health [30] and consumption of sugar-added beverages may contribute to weight gain among adolescents probably due to their contribution to total energy intake [13].

The present study shows that energy intake in the 1–18 year subgroup actually decreased during the past three decades, unlike the increase of energy intake among 19+ y age subgroup. In addition, the percentages of energy intake from total carbohydrates increased by 4% and 5% in the 1–18 y and 19+ y age subgroups, respectively, while those from added sugar intake increased by only 1% in both age subgroups. Therefore, even though current trends in health promotion emphasize the importance of increasing carbohydrate intake and reducing fat intake (particularly saturated fat intake), concern has focused on sugar consumption from soft drinks as a main contributor to total energy intake.

Consumption of added sugars in the U.S. has increased steadily as documented by both FSD and nationwide food consumption survey data. According to U.S. FSD, per capita consumption of added sugars by Americans went from 111 g·d-1 in 1970 to 131 g d-1 in 1996, an increase of 23% [24]. These data are adjusted for spoilage, other losses accumulated throughout the marketing system and home waste losses. Food consumption survey data also demonstrate an increase in intake of added sugars. According to the USDA CSFII of Americans over 2 year old, consumption of added sugars rose from 64 g·d-1 in 1989–1991 to 84 g·d-1 in 1994–1996, an increase of 31% in less than ten years. In 1989– 1991, added sugars accounted for 13.2% of total daily energy intake, whereas in 1994–1996 they accounted for 15.8% [27].

Although the data from each source indicate an increase in the consumption of added sugars, these increases have not previously been considered in the context of overall changes in macronutrient contribution to total energy intake. Data in the present study confirm the increase in intake of added sugars found in earlier studies, but while the increase in the intake of added sugars during the past three decades was 12% (77 g·d-1 to 86 g·d-1), its contribution to the energy intake rose less than 4%. This may be too little to account for the increased prevalence in obesity during the same period. Consistently, Sun and Empie [31] failed to find any association between obesity risk and usual sugar-sweetened beverage consumption in adults via analyzing databases of CSFII-1989–1991, CSFII-1994–1998, NHANES III, and combined NHANES 1999–2002 [31]. Animal studies show that carbohydrate-induced obesity is not unique to sweet-tasting sugars, but can also be produced by bland-tasting polysaccharides [32]. These studies as well as the present findings suggest that other carbohydrate categories which contribute more to total energy intake may be more important in examining the growing prevalence of obesity.

A more serious nutritional change related to the increase in intake of added sugars may be the apparent substitution of carbonated soft drink consumption for consumption of fluid milk [5]. Fluid milk was the principal nutritional contributor of energy intake for the 1–18 y age group in the 1970s. Its decreased contribution in the 1990s, and the increased contribution of carbonated soft drinks, may account for much of the decrease in total energy intake and percent energy intake from fat in that age
group, as well as the decrease in intakes of calcium and lactose [6]. Overall, the effect of increased intake of added sugars, as it has replaced intake of intrinsic sugars such as lactose and fructose, has been to compromise the intake of more nutritious foods and impeded compliance with current dietary guidelines [6].

The amount and type of carbohydrate intake have also received significant attention with increasing prevalence of type 2 diabetes [33], which is highly associated with overweight. The switch from sucrose to high-fructose corn syrup (HFCS) as the sweetener, particularly in the US beverage industry since 1980s, has been suggested to explain the exponential growth of obesity in the U.S. [10] Gross et al. [33] reported that increased consumption of HFCS contributed to the increase of energy intakes and consequently to the prevalence of chronic diseases such as type 2 diabetes. Since fructose has higher sweet intensity than sucrose, theoretically the amount of HFCS to yield the same hedonic values would be less than that of sucrose. Clinical and epidemiological studies [10,34,35] have studied the effects of sucrose and fructose on incidence of obesity and other chronic diseases based on the estimates of consumption. Teff et al. [10], for example, estimated per capita consumption of added fructose being 81 g·d-1. The authors based their estimation of added fructose consumption on the average per capita FSD of 1997 [36], and then combined fructose from HFCS and fructose in the sucrose molecule [10]. In the present study of the NHANES III, we documented that American’s fructose consumption is 30 g·d-1 and 27 g·d-1 for 1–18 y and 19+ y sub-groups, respectively. Both groups consumed an average of 54 g·d-1 of sucrose.

Differences between the two studies are noteworthy (28 g·d-1 vs. 81 g·d-1). We find it important to understand why, in order to assist future investigations in this important area of research. First, per capita disappearance data differ vastly from actual consumption [36]. According to the USDA report [24], loss of refined and beet sugars at retail, food service and consumer levels is estimated to be 31%. Secondly, dietary intake data of an individual or population are reported as consumed in the form of food, beverage and supplements, not in metabolized forms. The USDA [20] and DHHS [17] provide dietary intake data of individual forms of simple sugars, i.e., glucose, fructose, galactose, lactose, sucrose, maltose, etc. If one was to estimate the total fructose intake by including fructose metabolized from sucrose, others may argue that glucose metabolized from maltose or starch should be considered in the glucose consumption estimates. Another consideration coming from the study of Duffey and Popkin [37] is that the concept of “total fructose” (including metabolized fructose from sucrose) might hide the truth that fructose consumption has been increasing, because their study showed that total fructose has changed relatively little compared with the change in free fructose and HFCS over the past two decades. American’s per capita consumption of HFCS has increased along with glucose consumption in the U. S. However, the estimated fructose intake cited in the research papers has been overestimated, and might potentially mislead the nutritional science community.

Figure2. Comparison of the contribution (%) of individual
carbohydrates to the total carbohydrate intakes between the NHANES I
and III by age subgroups.

Our study has limitations. Firstly, since NHANES I included people aged 1–74 years, while NHANES III included people aged 1–90 years, the data for 19+ y subpopulation in the two datasets were not identical. NHANES I (1971–1975) and NHANES III (1988–1994) had different food codes to reflect changes in prevalent dietary behaviors, food commodities and lifestyles in the different time periods. The NHANES I database did not contain estimates of sugar intake levels. Using NHANES I and NHANES III to examine trends in sugar intakes, therefore, required us to develop a food code matching technique. Considering the long time span between the two surveys, the food composition under the same food name might have changed. For example, high-fructose corn syrup (HFCS) has been used as added sweetener, however, the percentage of HFCS of total sweetener has dramatically increased from 0.5% to 37.5%, although total fructose (sum of free fructose and fructose contained in sucrose) availability changed only slightly over the same time period [37]. Although data for HFCS consumption are not available in 1970’s, the results in Table 2 showed that our matching technique was effective and efficient in analyzing unknown sugar information in NHANES I.

Conclusions

The choice of database is critical in estimating food and nutrient intake. The technique we developed to match food codes in the NHANES datasets allows for their use as a source of reliable data on nutrient and energy intakes in general, and sugar intakes in particular, in the U.S. increased intakes of total and added sugars and carbohydrates have primarily accounted for the increase in energy intakes over the last two decades. The present study indicates that the overall increase in carbohydrate intake has by far exceeded the increase in intake of added sugars, and, thus, more specifically identifies the principal nutritional contribution associated with the rapid rise in obesity in the U.S. over the past three decades. In particular, although soft drink consumption is a major contributor to increased energy intake, the contribution to energy intake from “mixtures of mainly grain” has increased dramatically and is now the principal contributor to energy intake. Increased carbohydrate intake overall is mainly due to the increased availability and consumption of prepared, frozen and takeout meal combinations. Overall, this study points to the need for ongoing research on the specific nutritional contributors to total energy intake, and their potential contribution to increasing prevalence of obesity.

Abbreviations: National Health and Nutrition Examination Survey (NHANES); cardiovascular diseases (CVD); National Center for Health Statistics and the Centers for Disease Control and Prevention (NCHS/CDC); U.S. Food Supply Data (FSD); Continuing
Survey of Food Intakes by Individuals (CSFII); high-fructose corn syrup (HFCS)

Comments

References

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© 2010 by the authors; licensee MDPI, Basel, Switzerland. This article is an Open Access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).

by EricT

Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, 1300 South 2nd St Suite 300, Minneapolis, MN 55454, USA

International Journal of Behavioral Nutrition and Physical Activity 2008

Eating away from home has increased in prevalence among US adults and now comprises about 50% of food expenditures. Calorie labeling on chain restaurant menus is one specific policy that has been proposed to help consumers make better food choices at restaurants. The present review evaluates the available empirical literature on the effects of calorie information on food choices in restaurant and cafeteria settings.

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Computer-assisted searches were conducted using the PUBMED database and the Google Scholar world wide web search engine to identify studies published in peer-review journals that evaluated calorie labeling of cafeteria or restaurant menu items. Studies that evaluated labeling only some menu items (e.g. low calorie foods only) were excluded from the review since the influence of selective labeling may be different from that which may be expected from comprehensive labeling.

Six studies were identified that met the selection criteria for this review. Results from five of these studies provide some evidence consistent with the hypothesis that calorie information may influence food choices in a cafeteria or restaurant setting. However, results from most of these studies suggest the effect may be weak or inconsistent. One study found no evidence of an effect of calorie labeling on food choices. Each of the studies had at least one major methodological shortcoming, pointing toward the need for better designed studies to more rigorously evaluate the influence of point-of-purchase calorie labeling on food choices.

Conclusion: More research is needed that meets minimum standards of methodological quality. Studies need to include behavioral outcomes such as food purchase and eating behaviors. Also, studies need to be implemented in realistic settings such as restaurants and cafeterias.

Introduction

Eating out has become increasingly common in the US [1], with Americans now spending almost half of their food dollars on foods away from home [2]. Food eaten away from home at fast food and other restaurants has garnered particular scientific interest recently because it is associated with higher energy, fat and saturated fat intake; lower intake of fiber and calcium; greater consumption of hamburgers, French fries and soft drinks, and lower fruit and vegetable intake [1,3-9]. Several prospective studies have shown that frequent eating away from home at restaurants, especially fast food restaurants, is associated with excess weight gain over time, compared to infrequent restaurant use [3,4,10-13]. In the largest and longest study conducted to date, frequent fast food restaurant eating was associated with significantly greater weight gain among 3031 adults over a 15 year follow-up period [10,11].

Calorie labeling on chain restaurant menu boards is one public health policy that has been proposed to help consumers make better food choices at restaurants [14]. To elaborate, it has been argued that consumers need to be informed about the calories in the menu items at restaurants because without this information they may have little awareness or the number of calories in the foods they are purchasing. It is thought that such information is most effective at the point of choice, and therefore should be displayed on menus or menu boards next to the food items. An example of this type of calorie labeling format is provided in Figure 1.

What is known about the effect of calorie labeling on restaurant meal choices? The intent of this paper is to review published research on the effect of point-of-purchase calorie labeling on cafeteria and restaurant menu food choices. Following the literature review the findings are synthesized with reference to the broader conceptual model of the restaurant food choice process. Recommendations for the design of new research are made in light of the conceptual model and the available studies. It is hoped that the review of the scientific findings may inform policy development and implementation in the area of food labeling in restaurant settings.

Methods

In accord with guidelines for conducting and reporting systematic reviews [15] a structured approach was utilized to identify, review, and draw conclusions from studies that have examined the effect of point-of-purchase calorie labeling on food choices. With regard to identifying relevant studies, computer-assisted searches were conducted using the PUBMED database and the Google Scholar World Wide Web search engine. These searches were conducted in February and March of 2008 using various combinations of the following key words: nutrition labeling, calorie labeling, nutrition education, point-of-purchase, restaurant, cafeteria, and fast food. Articles were also identified from references from published research and reviews.

Studies that evaluated labeling only some menu items (e.g., labeling low calorie foods only) were excluded from the review since the influence of selective labeling may be different from that which may be expected from comprehensive labeling. In addition, studies evaluating calorie labeling in settings other than restaurants or cafeterias were excluded from the review because consumers may consider a different set of factors when purchasing food from grocery stores versus purchasing a meal from a restaurant or cafeteria. The review was also restricted to studies written in English and reported in peer reviewed publications.

A complete copy of each article meeting study inclusion criteria was obtained and reviewed by each author (LJH, SAF). Key information about each study (e.g. study aims, study design, sample size, etc.) were recorded on a spreadsheet to facilitate comparing and summarizing study results.

Results

Six studies were identified that met the selection criteria for this review [16-21]. To summarize, results from five of these studies provided some evidence consistent with the hypothesis that calorie information may influence food choices in a cafeteria or restaurant setting [16-20]. However, results from most of these studies suggest a weak or inconsistent effect. One study found no evidence of an effect of calorie labeling on food choices [21]. Each of the studies had at least one major methodological shortcoming, pointing toward the need for better designed studies to more rigorously evaluate the influence of point-of-purchase calorie labeling on food choices.

An example of a portion of a restaurant menu with calorie information provided for menu items.

Studies that Reported Significant Effects

Cinciripini et al.evaluated the influence of calorie labeling in a University cafeteria frequented by undergraduate students using an ABA experimental design [18]. During an eight week pre-labeling period trained observers recorded the food selections of cafeteria patrons. Calorie labeling was then implemented for eight weeks during which time food selections were recorded by trained observers. The labeling involved listing the calorie information for menu items on two large signs placed on tripods at each entrance of the cafeteria. Leaflets were distributed to cafeteria patrons during the first ten days of the labeling period to draw attention to the signs and explain their use. After labeling was discontinued (postlabeling period), the food selections of cafeteria patrons were observed for an eight week period. Selections of foods in seven categories were analyzed stratified by sex and bodyweight status.

Results indicated that selection of carbohydrate rich foods (e.g. breads and starchy vegetables) was statistically significantly lower during the labeling period compared to baseline for all sex and bodyweight groups. No other food group was significantly different during the labeling period across all sex and bodyweight groups. However, red meat selection was significantly lower during calorie labeling in all groups except overweight females. Selection of regular-fat dairy products was significantly lower during the labeling period among normal weight males and normal weight females only. Selection of high fat foods/desserts/sauces and vegetable/soup/fruit/low-fat dairy groups was significantly lower only among overweight females. Selection of salads was higher during the calorie labeling versus baseline period among lean men. No significant differences in selection were found for the chicken / fish / turkey food category. The effect of labeling on the energy content of meals selected was not examined.

Balfour et al. evaluated the effect of computerized nutrition information on food choices in cafeterias in two worksite settings, one located in a hospital and the other within the corporate office of an oil company [17]. Customers entering each cafeteria were invited to choose their meal using a computer system that displayed the calories, saturated fat, fiber, and added sugar content of meals selected. After the nutrition feedback about the initial meal choice was received a second meal choice could be made if desired. In this study only approximately 45% of those entering the cafeterias agreed to use the system. Among those using the system 17% at the oil company and 15% at the hospital changed their meal choice in response to the nutrition information. Among this subset the average energy composition of the second meal selection was significantly lower than the first meal choice. For example, among those who made a second meal choice at the cafeteria located with a corporate office building the average energy content of the meal selected initially was 711 kcal compared to 606 kcal for the second meal choice.

Using an AB experimental design Milich et al. evaluated the effect of calorie labeling on food choices among 450 women eating at a hospital cafeteria [19]. Following a two week baseline period during which food choices of women eating at the cafeteria were recorded by trained observers food prices in the cafeteria were unexpectedly increased. To cope with this unexpected change food choices were observed for one week following this price change, and then calorie labeling was implemented with food choices observed for an additional one week period. The calorie labels were placed as close as possible to food items sold in the cafeteria. The labels consisted of a 5 cm × 5 cm card on which calories were printed in red ink. The average calorie composition of meals selected during the calorie labeling period (459 kcal/meal) was significantly lower than that of the baseline (507 kcal/meal) and price increase (525 kcal/meal) periods (p < 0.02). Results were found to be similar across bodyweight category.

In contrast to the other studies just described which focused on adults, Yamamoto et al. evaluated the effect of calorie labeling on restaurant food choices in a sample of adolescents recruited from a middle school and high school [16]. Each participant (n = 106) was asked to order a dinner meal of their choice from three different restaurant menus (McDonald's, Panda Express, and Denny's). These mock menu orders were recorded by study staff and then the participants were shown a version of each menu that included calorie and fat content information for all menu items. After viewing the calorie and fat labeled menus participants were asked if they would like to change their meal order. If they responded affirmatively their new order was recorded. A modest fraction of participants (31of 106) changed one or more of their meal orders when shown the menus with calorie and fat information. In total, 54 meal orders were modified, with 43 modified in a way that resulted in a lower calorie meal and 11 modified in a manner that resulted in a higher calorie meal relative to the initial meal choice.

The final study that reported results in support of calorie labeling was a mail survey conducted to examine whether the provision of nutrition information would influence consumers' attitudes and purchase intentions for restaurant menu items [20]. In this study 482 adults in a south-central state were mailed a packet that included 1 of 6 randomly assigned menus and a survey that included a series of questions related to the menus. The six menus sent varied with respect to the type of nutrition information included (calories, fat, saturated fat, trans fat, and sodium; calories only; no nutrition information) and whether daily value information was included (included or excluded). All of the menus contained the following four menu items: hamburger platter, chef's salad, chicken breast dinner, and turkey sandwich. It was hypothesized that providing calorie information that is inconsistent with people's expectation would decrease their purchase intentions for the high calorie foods. For example, purchase intentions should be lower for the burger and for the Chef salad because the calories were higher than people expected.

Completed surveys were returned by 241 adults resulting in a response rate of 50%. Results indicated that for two of the four menu items, reported purchase intentions were significantly different between those who received the calories only menu relative to those who received a menu without nutrition information. Hamburger platter purchase intentions were lower and turkey sandwich purchase intentions were higher among those who received the calorie-only menu compared to those who received a menu without nutrition information. Purchase intentions for the chicken meal and chef salad were comparable between these two groups. These results are thus mixed with regard to the expectation. It was initially hypothesized that orders for chef salad would decline when participants were given information that showed the high calorie content of the salad. Limitations of the study were that only four food items were included and there was no initial a priori measure of people's "expected" calories for each of the items. Consequently, it is not known whether any inconsistency was actually created among participants by presenting the calorie information.

Study that Reported No Effect of Calorie Labeling on Food Choices

Mayer et al. evaluated the influence of calorie labeling on food choices in the cafeteria of a Fortune 500 company office building using an ABA experimental design [21]. Food items selected by cafeteria patrons were recorded by trained observers during a four week baseline period. Following this period the calorie content of all food items were listed on index cards placed near foods. Along with this labeling a nutrition awareness game was implemented and raffles were held one day per week to encourage selection of three lower calorie menu items. The labeling and promotional activities occurred over a four week period during which trained observers recorded the meal choices of patrons. The labeling and promotions activities were then discontinued, and meal choices of patrons were recorded by the observers for four weeks post-intervention. The mean number of calories per tray during each experimental phase was similar (462, 454, and 464 calories during each period respectively; p > 0.38). Sample sizes and participation rates were not included in the published report.

Discussion

Results from five of the six studies included in this review provide some support for the supposition that calorie information may have a positive influence (i.e., fewer calories purchased or selected) on food choices in a cafeteria or restaurant setting. It is important to note though that the magnitude of the effects seen tended to be small. Also, results were inconsistent in some studies. For example, Burton et al. found purchase intentions to be affected by calorie labeling for just two of the four foods included on the study menu (21) and Yamomato et al. found that only about 20% of intended food orders were modified following provision of calorie information for restaurant menu items (19).

Conceptual models of food choice behaviors often consider a broader time frame of food decision-making and include broader contextual effects such as family relationships, age and life course [22-24]. Sobal and colleagues found that people often explain current food choices in terms of both past experiences and current situations [22]. For example, a person with a lifelong history of eating vegetables may make different food choices at a restaurant than a person who only recently began eating vegetables. Personal influences, such as physiological, psychological, and emotional factors; resources such as money, time, transportation and skills; and social factors such as relationships, families, and roles; and contexts such as households and neighborhoods, are some of the levels at which food choices may be influenced. These influences operate through individual level personal food systems, which include the personal values people place on factors such as taste, convenience, cost, health and managing personal relationships. When viewed in the context of broader conceptual models of the food choice decision-making process, the apparent limited effect of calorie labeling on food choices may reflect the variety of factors beyond nutrition information that influence food purchase decisions.

Results from recent studies suggest that factors such as taste, price, convenience and social relationships tend to be rated as more important considerations than nutrition when making restaurant meal choices [25,26]. For example, among a convenience sample of adults who eat at fast food restaurants regularly 57.9% rated nutrition as very important or somewhat important when selecting foods from a fast food restaurant. In contrast, 96.1%, 89.6% and 87.2% rated taste, convenience, and price as important or very important, respectively [25].

The effect of point-of-purchase calorie labeling on food choices could possibly be strengthened if the weight given to this information and its expected outcome is increased. For example, the value of considering calories when making food choices at restaurants could be strengthened through promotional messages combined with the calorie labels. Several studies provide support for this supposition [27-29]. For example, in a study evaluating the effect of calorie labeling on vending machines sales Bergen et al. found labeling to have an effect on sales only when accompanied by a promotional poster [28]. Likewise French et al. found low-fat labeling in vending machines to influence sales only when the labeling was provided in tandem with an educational poster [27]. These results suggest that modest promotional efforts may prompt consumers to give nutrition information greater consideration in the food selection process.

It is important to note that the studies included in this review have a number of significant methodological shortcomings. First and foremost, four of the six studies evaluated calorie labeling in worksite [17,19,21] or university [18] cafeteria settings. Nutrition information provided in a restaurant setting may be utilized differently than information provided in a cafeteria setting because individuals may consider a different set of factors when they select foods from a restaurant versus an employee cafeteria. For example, eating at a restaurant may be viewed as an occasion to treat oneself or splurge (e.g., You Deserve a Break Today™) whereas moderation may be a greater consideration when eating in a cafeteria. Two of the studies evaluated calorie labeling on restaurant menus [16,20]. However, both measured intended rather than actual food choices. Consequently, social desirably bias in reporting is a significant concern in these studies. "Simulation" studies of intended or hypothetical food choices also fail to incorporate the social nature of food choices and economic factors that might influence food choices. Also, food choices might occur at the restaurant level, not at the food item level within the restaurant. For example, a person whose food choice is barbequed ribs would probably not choose to go to McDonalds for a meal, so the food choice itself may be made prior to arriving at the restaurant and forms the basis for the choice of restaurant. Other major weaknesses of the studies reviewed include use of quasi-experimental designs [17-19,21] where factors other than the experimental conditions being tested may have differed across test periods due to lack of randomization.

Conclusion

Better designed studies to more rigorously evaluate the influence of point-of-purchase calorie labeling on restaurant food choices are needed. Ideally experimental studies measuring actual food choices in restaurant settings would be conducted, thus maximizing both internal and external validity of results. However, it may be difficult to find restaurants willing to participate in these types of studies due to concern that the type of menu manipulation to be evaluated may have an adverse effect on revenue. Thus, consideration should be given to designing quasi-experimental studies in municipalities or regions where mandatory calorie labeling regulations have been implemented. For example, the implementation of a mandatory restaurant calorie labeling rule in New York City in 2008 [30] presents opportunities for evaluating
calorie labeling in a naturalistic setting.

Despite the methodological limitations of the studies included in this review, results across studies uniformly indicate that calorie labeling may have a beneficial effect on food choices made away from home. However, the effect is likely limited in magnitude. This limited effect may reflect the low level of importance many consumers place on nutrition when eating out. It may also reflect the multi-level nature of food choices, with influences occurring at the individual level prior to the restaurant, and other strong environmental influences at the restaurant, such as food choices, prices and other promotional activities at the point-of-purchase, and the influence of other people at the point of choice. Multiple levels of influence may need to be targeted in tandem, including consumer attitudes about calories when eating out, in order for calorie labeling to have a more substantial influence on restaurant
food choices.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

LH conducted the literature search. LH and SF reviewed each of the articles. Both authors also drafted the manuscript and approved the final version.

Acknowledgements

This paper was supported by the Healthy Eating Research program of the Robert Wood Johnson Foundation.

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