Thursday, October 20, 2016

Virtual Paleo Summit video: What is your ideal weight?

You may want to check out my recent video at the (Virtual Paleo Summit) explaining the waist-to-weight ratio theory for estimation of one's ideal weight. The theory is also discussed below. It may look a little complex, but its application is very simple.


There is a significant amount of empirical evidence suggesting that, for a given individual and under normal circumstances, the optimal weight is the one that maximizes the ratio below, where: L = lean body mass, and T = total mass.

L / T

L is difficult and often costly to measure. T can be measured easily, as one’s total weight.

Through some simple algebraic manipulations, you can see below that the ratio above can be rewritten in terms of one’s body fat mass (F).

L / T = (T – F) / T = 1 – F / T

Therefore, in order to maximize L / T, one should maximize 1 – F / T. This essentially means that one should minimize the second term, or the ratio below, which is one’s body fat mass (F) divided by one’s weight (T).

F / T

So, you may say, all I have to do is to minimize my body fat percentage. The problem with this is that body fat percentage is very difficult to measure with precision, and, perhaps more importantly, body fat percentage is associated with lean body mass (and also weight) in a nonlinear way.

In English, it becomes increasingly difficult to retain lean body mass as one's body fat percentage goes down. Mathematically, body fat percentage (F / T) is a nonlinear function of T, where this function has the shape of a J curve.

This is what complicates matters, making the issue somewhat counterintuitive. Six-pack abs may look good, but many people would have to sacrifice too much lean body mass for their own good to get there. Genetics definitely plays a role here, as well as other factors such as age.

Keep in mind that this (i.e., F / T) is a ratio, not an absolute measure. Given this, and to facilitate measurement, we can replace F with a variable that is highly correlated with it, and that captures one or more important dimensions particularly well. This new variable would be a proxy for F. One the most widely used proxies in this type of context is waist circumference. We’ll refer to it as W.

W may well be a very good proxy, because it is a measure that is particularly sensitive to visceral body fat mass, an important dimension of body fat mass. W likely captures variations in visceral body fat mass at the levels where this type of body fat accumulation seems to cause health problems.

Therefore, the ratio that most of us would probably want to minimize is the following, where W is one’s waist circumference, and T is one’s weight.

W / T = waist / weight

Based on the experience of HCE () users, variations in this ratio are likely to be small and require 4-decimals or more to be captured. If you want to avoid having so many decimals, you can multiply the ratio by 1000. This will have no effect on the use of the ratio to find your optimal weight; it is analogous to multiplying a ratio by 100 to express it as a percentage.

Also based on the experience of HCE users, there are fluctuations that make the ratio look like it is changing direction when it is not actually doing that. Many of these fluctuations may be due to measurement error.

If you are obese, as you lose weight through dieting, the waist / weight ratio should go down, because you will be losing more body fat mass than lean body mass, in proportion to your total body mass.

It would arguably be wise to stop losing weight when the waist / weight ratio starts going up, because at that point you will be losing more lean body mass than body fat mass, in proportion to your total body mass.

One’s lowest waist / weight ratio at a given point in time should vary depending on a number of factors, including: diet, exercise, general lifestyle, and age. This lowest ratio will also be dependent on one’s height and genetic makeup.

Mathematically, this lowest ratio is the ratio at which d(W / T) / dT = 0 and d(d(W / T) / dT) / dT > 0. That is, the first derivative of W / T with respect to T equals zero, and the second derivative is greater than zero.

The lowest waist / weight ratio is unique to each individual, and can go up and down over time (e.g., resistance exercise will push it down). Here I am talking about one's lowest waist / weight ratio at a given point in time, not one's waist / weight ratio at a given point in time.

This optimal waist / weight ratio theory is one of the most compatible with evidence regarding the lowest mortality body mass index (, ). Nevertheless, it is another ratio that gets a lot of attention in the health-related literature. I am talking about the waist / hip ratio (). In this literature, waist circumference is often used alone, not as part of a ratio.

Friday, September 30, 2016

PLS Applications Symposium; 5 - 7 April 2017; Laredo, Texas

PLS Applications Symposium; 5 - 7 April 2017; Laredo, Texas
(Abstract submissions accepted until 1 March 2017)

*** Health researchers ***

The research techniques discussed in this Symposium are finding growing use among health researchers. This is in part due to steady growth in the use of the software WarpPLS (visit: among those researchers. For those interested in learning more, a full-day workshop will be conducted (see below).

*** Only abstracts are needed for the submissions ***

The partial least squares (PLS) method has increasingly been used in a variety of fields of research and practice, particularly in the context of PLS-based structural equation modeling (SEM). The focus of this Symposium is on the application of PLS-based methods, from a multidisciplinary perspective. For types of submissions, deadlines, and other details, please visit the Symposium’s web site:

*** Workshop on PLS-SEM ***

On 5 April 2017 a full-day workshop on PLS-SEM will be conducted by Dr. Ned Kock, using the software WarpPLS. Dr. Kock is the original developer of this software, which is one of the leading PLS-SEM tools today; used by thousands of researchers from a wide variety of disciplines, and from many different countries. This workshop will be hands-on and interactive, and will have two parts: (a) basic PLS-SEM issues, conducted in the morning (9 am - 12 noon); and (b) intermediate and advanced PLS-SEM issues, conducted in the afternoon (2 pm - 5 pm). Participants may attend either one, or both of the two parts.

The following topics, among others, will be covered - Running a Full PLS-SEM Analysis - Conducting a Moderating Effects Analysis - Viewing Moderating Effects via 3D and 2D Graphs - Creating and Using Second Order Latent Variables - Viewing Indirect and Total Effects - Viewing Skewness and Kurtosis of Manifest and Latent Variables - Conducting a Multi-group Analysis with Range Restriction - Viewing Nonlinear Relationships - Conducting a Factor-Based PLS-SEM Analysis - Viewing and Changing Missing Data Imputation Settings - Isolating Mediating Effects - Identifying and Dealing with Outliers - Solving Indicator Problems - Solving Collinearity Problems.

Ned Kock
Symposium Chair

Sunday, September 25, 2016

Niacin turbocharges the growth hormone response to anaerobic exercise: A delayed effect

Niacin is also known as vitamin B3, or nicotinic acid. It is an essential vitamin whose deficiency leads to pellagra. In large doses of 1 to 3 g per day it has several effects on blood lipids, including an increase in HDL cholesterol and a marked decreased in fasting triglycerides. Niacin is also a powerful antioxidant.

Among niacin’s other effects, when taken in large doses of 1 to 3 g per day, is an acute elevation in growth hormone secretion. This is a delayed effect, frequently occurring 3 to 5 hours after taking niacin. This effect is independent of exercise.

It is important to note that large doses of 1 to 3 g of niacin are completely unnatural, and cannot be achieved by eating foods rich in niacin. For example, one would have to eat a toxic amount of beef liver (e.g., 15 lbs) to get even close to 1 g of niacin. Beef liver is one of the richest natural sources of niacin.

Unless we find out something completely unexpected about the diet of our Paleolithic ancestors in the future, we can safely assume that they never benefited from the niacin effects discussed in this post.

With that caveat, let us look at yet another study on niacin and its effect on growth hormone. Stokes and colleagues (2008) conducted a study suggesting that, in addition to the above mentioned beneficial effects of niacin, there is another exercise-induced effect: niacin “turbocharges” the growth hormone response to anaerobic exercise. The full reference to the study is at the end of this post. Figure 3, shown below, illustrates the effect and its magnitude. Click on it to enlarge.

The closed diamond symbols represent the treatment group. In it, participants ingested a total of 2 g of niacin in three doses: 1 g ingested at 0 min, 0.5 g at 120 min, and 0.5 g at 240 min. The control group ingested no niacin, and is represented by the open square symbols. (The researchers did not use a placebo in the control group; they justified this decision by noting that the niacin flush nullified the benefits of using a placebo.) The arrows indicate points at which all-out 30-second cycle ergometer sprints occurred.

Ignore the lines showing the serum growth hormone levels in between 120 and 300 min; they were not measured within that period.

As you can see, the peak growth hormone response to the first sprint was almost two times higher in the niacin group. In the second sprint, at 300 min, the rise in growth hormone is about 5 times higher in the niacin group.

We know that growth hormone secretion may rise 300 percent with exercise, without niacin. According to this study, this effect may be “turbocharged” up to a 600 percent rise with niacin within 300 min (5 h) of taking it, and possibly 1,500 percent soon after 300 min passed since taking niacin.

That is, not only does niacin boost growth hormone secretion anytime after it is taken, but one still gets the major niacin increase in growth hormone at around 300 min of taking it (which is about the same, whether you exercise or not). Its secretion level at this point is, by the way, higher than its highest level typically reached during deep sleep.

Let me emphasize that the peak growth hormone level achieved in the second sprint is about the same you would get without exercise, namely a bit more than 20 micrograms per liter, as long as you took niacin (see Quabbe's articles at the end of this post).

Still, if you time your exercise session to about 300 min after taking niacin you may have some extra benefits, because getting that peak growth hormone secretion at the time you are exercising may help boost some of the benefits of exercise.

For example, the excess growth hormone secretion may reduce muscle catabolism and increase muscle anabolism, at the same time, leading to an increase in muscle gain. However, there is evidence that growth hormone-induced muscle gain occurs only when testosterone levels are elevated. This explains why growth hormone levels are usually higher in young women than young men, and yet young women do not put on much muscle in response to exercise.


Stokes, K.A., Tyler, C., & Gilbert, K.L. (2008). The growth hormone response to repeated bouts of sprint exercise with and without suppression of lipolysis in men. Journal of Applied Physiology, 104(3), 724-728.

Friday, August 26, 2016

Growth hormone may rise 300 percent with exercise: Acute increases also occur in cortisol, adrenaline, and noradrenaline

The figure below (click to enlarge) is from the outstanding book Physiology of sport and exercise, by Jack H. Wilmore, David L. Costill, and W. Larry Kenney. If you are serious about endurance or resistance exercise, or want to have a deeper understanding of exercise physiology beyond what one can get in popular exercise books, this book should be in your personal and/or institutional library. It is one of the most comprehensive textbooks on exercise physiology around. The full reference to the book is at the end of this post.

The hormonal and free fatty acid responses shown on the two graphs are to relatively intense exercise combining aerobic and anaerobic components. Something like competitive cross-country running in an area with hills would lead to that type of response. As you can see, cortisol spikes at the beginning, combining forces with adrenaline and noradrenaline (a.k.a. epinephrine and norepinephrine) to quickly increase circulating free fatty acid levels. Then free fatty acid levels are maintained elevated by adrenaline, noradrenaline, and growth hormone. As you can see from the graphs, free fatty acid levels are initially pulled up by cortisol, and then are very strongly correlated with adrenaline and noradrenaline.  Those free fatty acids feed muscle, and also lead to the production of ketones, which provide extra fuel for muscle tissue.

Growth hormone stays flat for about 40 minutes, after which it goes up steeply. At around the 90-minute mark, it reaches a level that is quite high; 300 percent higher than it was prior to the exercise session. Natural elevation of circulating growth hormone through intense exercise, intermittent fasting, and restful sleep, leads to a number of health benefits. It helps burn abdominal fat, often hours after the exercise session, and helps build muscle (in conjunction with other hormones, such as testosterone). It appears to increase insulin sensitivity in the long run.

Aerobic activities normally do not elevate growth hormone levels, even though they are healthy, unless they lead to a significant degree of glycogen depletion. Glycogen is stored in the liver and muscle, with muscle storing about 5 times more than the liver (about 500 g in adults). Once those reserves go down significantly during exercise, it seems that growth hormone is recruited to ramp up fat catabolism and facilitate other metabolic processes. Walking for an hour, even if briskly, is good for fat burning, but generates only a small growth hormone elevation. Including a few all-out sprints into that walk can help significantly increase growth hormone secretion.

Having said that, it is not really clear whether growth hormone elevation is a response to glycogen depletion, or whether both happen together in response to another stimulus or related metabolic process. There are other factors that come into play as well. For example, circulating growth hormone increase is moderated by sex hormone (e.g., testosterone, estrogen) secretion, thus larger growth hormone increases in response to exercise are observed in older men than in older women. (Testosterone declines more slowly with age in men than estrogen does in women.) Also, growth hormone increase seems to be correlated with an increase in circulating ketones.

Heavy resistance exercise seems to lead to a higher growth hormone elevation per unit of time than endurance exercise. That is, an intense resistance training session lasting only 30 minutes can lead to an acute circulating growth hormone response, similar to that shown on the figure. The key seems to be reaching the point during the exercise where muscle glycogen stores are significantly depleted. Many people who weight-train achieve this regularly by combining a reasonable number of sets (e.g., 6-12), with repetitions in the muscle hypertrophy range (again, 6-12); and progressive overload, whereby resistance is increased incrementally every session.

Progressive overload is needed because glycogen reserves are themselves increased in response to training, so one has to increase resistance every session to keep up with those increases. This goes on only up to a point, a point of saturation, usually reached by elite athletes. Glycogen is the primary fuel for anaerobic exercise; fat is used as fuel in the recovery period between sets, and after the exercise is over. Glycogen is expended proportionally to the number of calories used in the anaerobic effort. Calories are expended proportionally to the total amount of weight moved around, and are also a function of the movements performed (moving a certain weight 1 feet spends less energy than moving it 3 feet). By the way, not much glycogen is depleted in a 30-minute session. The total caloric expenditure will probably be around 250 calories above the basal metabolic rate, which will require about 63 g of glycogen.

Many sensations are associated with reaching the glycogen depletion level required for an acute growth hormone response during heavy anaerobic exercise. Often light to severe nausea is experienced. Many people report a “funny” feeling, which is unmistakable to them, but very difficult to describe. In some people the “funny” feeling is followed, after even more exertion, by a progressively strong sensation of “pins and needles”, which, unlike that associated with a heart attack, comes slowly and also goes away slowly with rest. Some people feel lightheaded as well.

It seems that the optimal point is reached immediately before the above sensations become bothersome; perhaps at the onset of the “funny” feeling. My personal impression is that the level at which one experiences the “pins and needles” sensation should be avoided, because that is a point where your body is about to “force” you to stop exercising. (Note: I am not a bodybuilder; see “Interesting links” for more extensive resources on the subject.) Besides, go to that point or beyond and significant muscle catabolism may occur, because the body prioritizes glycogen reserves over muscle protein. It will break that protein down to produce glucose via gluconeogenesis to feed muscle glycogenesis.

That the body prioritizes muscle glycogen reserves over muscle protein is surprising to many, but makes evolutionary sense. In our evolutionary past, there were no selection pressures on humans to win bodybuilding tournaments. For our hominid ancestors, it was more important to have the glycogen tank at least half-full than to have some extra muscle protein. Without glycogen, the violent muscle contractions needed for a “fight or flight” response to an animal attack simply cannot happen. And large predators (e.g., a bear) would not feel intimated by big human muscles alone; it would be the human’s response using those muscles that would result in survival or death.

Overall, selection pressures probably favored functional strength combined with endurance, leading to body types similar to those of the hunter-gatherers shown on this post.

Even though the growth hormone response to exercise can be steep, the highest natural growth hormone spike seems to be the one that occurs at night, during deep sleep.

Exercising hard pays off, but only if one sleeps well.

Thursday, July 28, 2016

There are more geniuses among men than among women, and more idiots too

Deary and colleagues (2007) conducted an interesting study on differences in intelligence scores among men and women. In the context of this blog, this study highlights yet one more counterintuitive and intriguing aspect of Darwinian evolution, adding to points previously made in other posts (see here, and here). Evolution may look simple at first glance, but that is a bit of a mirage. In my opinion, to really understand it one has to understand the mathematics underlying it, a lot of which comes from the field of population genetics.

What makes the study by Deary and colleagues (2007) particularly interesting is that its participants were opposite-sex siblings. This helped control for the influence of environmental factors. The downside is that the effect sizes might have been decreased, because of the high gene correlation among siblings, so we could expect larger differences between unrelated groups of men women. The differences, as you will see, are not in overall scores, but in score dispersion.

Let us get straight to the point made by the study. On average, men and women seem to score equally well on intelligence tests. The main difference is that there is more variation in the scores achieved by men than by women, which leads to an interesting effect: there are more geniuses and more idiots among men than among women.

This does NOT mean that a man’s genius is of a higher order; just that there is a tendency for more men to be geniuses (and idiots) than women in any random population sample. The women who are geniuses can be super geniuses, like two-time Nobel Prize winner Marie Curie, the first PERSON to receive such an honor. Albert Einstein is said that have greatly admired her intelligence.

As an illustration of this score dispersion effect, Deary and colleagues (2007) note that: “… for example, in terms of indices of scientific achievement, men were awarded 545 out of the 557 Nobel prizes awarded for science.” On the “idiot” end of the scale: there are a lot more men than women in prison, and one common denominator of prison inmates is that they tend to score very low on intelligence tests. (This is not to say that all criminals have low intelligence; perhaps mostly the ones that get caught do.)

Having said that, it is important to acknowledge that there are multiple types of intelligence, and even multi-indicator intelligence coefficients are usually poor approximations of an overall measure of intelligence (if there is one). This does not invalidate the main point of this post, which is related to score variability.

The table below (from: Deary and colleagues, 2007; click on it to enlarge; full reference at the end of this post) shows scores obtained by men and women (1,292 pairs of opposite-sex siblings) in various subtests of the Armed Services Vocational Aptitude Battery (ASVAB) test.

Note that nearly all of the differences between means (i.e., averages) are significant, but the direction of the differences (captured by the signs of the Cohen’s d coefficients, which are measures of effect size) varies a lot. That is, on several subtests (e.g., “Arithmetic”) men score higher, but in others (e.g., “Numerical operations”) women score higher. It all comes down to men and women scoring equally well overall.

Now look at the columns showing the standard deviations (“SD”) for men and women. In all subtests but two (“Coding speed” and “Numerical operations”) the standard deviation is higher for men; in many cases significantly higher (e.g., 44 percent higher for “Mechanical comprehension”). The standard deviations are about the same for “Coding speed” and “Numerical operations”. What this means is that variability in scores is nearly always higher, often significantly higher, among men than among women. I prepared the schematic figure below to illustrate the effect that this has on the numbers of individuals at the extremes.

The figure above shows two (badly drawn) quasi-normal distributions of scores. (This post shows a better illustration of a normal distribution.) The red curve refers to a distribution with a lower standard deviation than the blue curve; the latter is flatter. Each point on a curve reflects the number of individuals obtaining a particular score, which would be indicated on the horizontal axis. The number of individuals with that score is on the vertical axis. As you can see, the numbers of individuals scoring very high and low (geniuses and idiots, if the scores reflected intelligence) are greater for the blue curve, which is the curve with the higher standard deviation (higher dispersion of scores). The farther one goes to the left or right (the extremes), the bigger this difference becomes.

What does this have to do with evolution?

Well, there are a few possibilities, two of which appear to be particularly compelling. Maybe this effect is due to a combination of these two.

One is that ancestral women, like women today, selected mating partners based on a wide range of traits. Ancestral men on the other hand, like modern men, focused on a much smaller set of traits (Buss, 1995). The end result is more variation in traits, generally speaking, among men than among women. This refers to traits in general, not only intelligence. For example, there seems to be more variation in height among men than among women.

The other possible explanation is that, in our ancestral past, staying out of the extremes of intelligence was associated with higher survival success in both sexes. It seems that the incidence of certain types of mental disease (e.g., schizophrenia) is quite high among geniuses. This leads to more deaths due to related issues – suicide, depression leading to the metabolic syndrome, etc. And this is today, where geniuses can find many opportunities to “shine” in our complex urban societies. In our ancestral past the cognitive demands would have been much lower, and so would the practical value of being a genius.

If staying out of the extremes has indeed enhanced survival success in our evolutionary past, then it is reasonable to expect more women to fit that pattern than men. As with almost any “thing” that enhances survival success, women (especially pre-menopausal) naturally have more of that “thing” than men (e.g., HDL cholesterol).

The reason is that women are more important for the survival of any population than men; today and 1 million years ago. A population of 99 women and 1 man can potentially generate 99 children every few years. Here inbreeding in subsequent generations will be a problem, but that is better than extinction. A population with 99 women and 99 men (or even 1,000 men) will not generate significantly more children.


Buss, D.M. (2003). The evolution of desire: Strategies of human mating. New York, NY: Basic Books.

Deary, I.J., Irwing, P., Der, G., & Bates, T.C. (2007). Brother–sister differences in the g factor in intelligence: Analysis of full, opposite-sex siblings from the NLSY1979. Intelligence, 35(5), 451-456.

Sunday, June 26, 2016

The amounts of water, carbohydrates, fat, and protein lost during a 30-day fast

When it comes to losing fat and maintaining muscle, at the same time, there are no shortcuts. The process generally has to be slow to be healthy. When one loses a lot of weight in a few days, most of what is being lost is water, followed by carbohydrates. (Carbohydrates are stored as liver and muscle glycogen.) Smaller amounts of fat and protein are also lost. The figure below (see reference at the end of post) shows the weights in grams of stored water, carbohydrates (glycogen), fat, and protein lost during a 30-day water fast.

On the first few days of the fast a massive amount of water is lost, even though drinking water is allowed in this type of fast. A significant amount of glycogen is lost as well. This is no surprise. About 2.6 g of water are lost for each 1 g of glycogen lost. That is, water is stored by the body proportionally to the amount of glycogen stored. People who do strength training on a regular basis tend to store more glycogen, particular in muscle tissue; this is a compensatory adaptation. Those folks also tend to store more water.

Not many people will try a 30-day fast. Still, the figure above has implications for almost everybody.

One implication is that if you use a bioimpedance scale to measure your body fat, you can bet that it will give you fairly misleading results if your glycogen stores are depleted. Your body fat percentage will be overestimated, because water and glycogen are lean body mass. This will happen with low carbohydrate dieters who regularly engage in intense physical exercise, aerobic or anaerobic. The physical exercise will deplete glycogen stores, which will typically not be fully replenished due to the low intake of carbohydrates.

Light endurance exercise (e.g., walking) is normally easier to maintain with a depleted “glycogen tank” than strength training, because light endurance exercise relies heavily on fat oxidation. It uses glycogen, but more slowly. Strength training, on the other hand, relies much more heavily on glycogen while it is being conducted (significant fat oxidation occurs after the exercise session), and is difficult to do effectively with a depleted “glycogen tank”.

Strength training practitioners often will feel fatigued, and will probably be unable to generate supercompensation, if their “glycogen tank” is constantly depleted. Still, compensatory adaptation can work its “magic” if one persists, and lead to long term adaptations that make athletes rely much more heavily on fat than the average person as a fuel for strength training and other types of anaerobic exercise. Some people seem to be naturally more likely to achieve this type of compensatory adaptation; others may never do so, no matter how hard they try.

Another implication is that you should not worry about short-term weight variations if your focus is on losing body fat. Losing stored water and glycogen may give you an illusion of body fat loss, but it will be only that – an illusion. You may recall this post, where body fat loss coupled with muscle gain led to some weight gain and yet to a much improved body composition. That is, the participants ended up leaner, even though they also weighed more.

The figure above also gives us some hints as to what happens with very low carbohydrate dieting (i.e., daily consumption of less than 20 grams of carbohydrates); at least at the beginning, before long term compensatory adaptation. This type of dieting mimics fasting as far as glycogen depletion is concerned, especially if protein intake is low, and has many positive short term health benefits. The depletion is not as quick as in a fast because a high fat and/or protein diet promotes higher rates of fat/protein oxidation and ketosis than fasting, which spare glycogen. (Yes, dietary fat spares glycogen. It also spares muscle tissue.) Still, the related loss of stored water is analogous to that of fasting, over a slightly longer period. The result is a marked weight loss at the beginning of the diet. This is an illusion as far as body fat loss is concerned.

Dietary protein cannot be used directly for glycogenesis; i.e., for replenishing glycogen stores. Dietary protein must first be used to generate glucose, through a process called gluconeogenesis. The glucose is then used for liver and muscle glycogenesis, among other things. This process is less efficient than glycogenesis based on carbohydrate sources (particularly carbohydrate sources that combine fructose and glucose), which is why for quite a few people (but not all) it is difficult to replenish glycogen stores and stimulate muscle growth on very low carbohydrate diets.

Glycogen depletion appears to be very healthy, but most of the empirical evidence seems to suggest that it is the depletion that creates a hormonal mix that is particularly health-promoting, not being permanently in the depleted state. In this sense, the extent of the glycogen depletion that is happening should be positively associated with the health benefits. And significant glycogen depletion can only happen if glycogen stores are at least half full to start with.


Wilmore, J.H., Costill, D.L., & Kenney, W.L. (2007). Physiology of sport and exercise. Champaign, IL: Human Kinetics. [Note: the figure may be found in a different edition.]

Thursday, May 26, 2016

Insulin responses to foods rich in carbohydrates and protein

Insulin is often presented as a hormone that is at the core of the diseases of civilization, particularly because of the insulin response elicited by foods rich in refined carbohydrates and sugars. What is often not mentioned is that protein also elicits an insulin response and so do foods where carbohydrates are mixed with fat. Sometimes the insulin responses are way more than one would expect based on the macronutrient compositions of the foods.

Holt et al. (1997; full reference at the end of this post) conducted a classic study of insulin responses. This study has been widely cited, and paints an interesting picture of differences in insulin responses to various foods. But you have to be careful where you look. There has been some confusion about the results because of the way they are often reported in places like Wikipedia and on various Internet sites that refer to the study.

The key thing to bear in mind when reviewing this study is that the amounts of food used were designed to have the same calorie content: 1000 kJ or 240 kcal (i.e., 240 calories). This led to wild variations in the size of the portions that are compared and their weight in grams. Also, some of the food portions are probably not what people usually eat in one sitting.

In Holt et al.’s (1997) study the participants were 41 lean and healthy university students. They were fed 1000 kJ (240 kcal) portions of the test foods on separate mornings after a 10-hour fast overnight. Blood insulin levels were measured at different times within a 120-minute period after each meal. An insulin score was then calculated from the area under the insulin response curve for each food; white bread was used as the reference food.

Part of Table 2 on page 1267 is shown below (the full text version of the paper is linked at the end of this post), just to illustrate the types and amounts of food served, and the macronutrient breakdown for each food. I hope you can see what I meant when I said that some of the food portions are probably not what people usually eat in one sitting. I don’t think it would be hard to find someone who would eat 158 g of beef steak in one sitting, but 333 g of fish is a little more difficult. Fish has a higher proportion of protein than beef steak, and thus is more satiating. The same goes for 625 g of orange, about 6 oranges. Foods that have more fat have more calories per gram; hence the smaller portions served for high-fat foods.

Table 4 of the article is a bit long, so I am providing it in two parts below. AUC stands for “area under the curve”. As you can see, for isocaloric portions of different foods (i.e., with the same amount of calories), there is a huge variation in insulin response. The insulin AUCs are shown on the second numeric column from the left. Also note that the insulin responses (AUC) for white bread varied in different meals. This complicates things a bit, but at least provides a more realistic view of the responses since each participant served as his or her own control.

Look at the third column from the right, which shows the insulin responses per gram of each food, compared with the response to white bread, always shown at the top for each group of related foods (e.g., protein-rich foods). The gram-adjusted response for whole-meal bread is rather high, and so is the glucose response. The gram-adjusted insulin response to potatoes is less than one-third of the response to white bread, even though the non-gram-adjusted glucose response is higher. The insulin response to beef is also less than one-third of the response to white bread, gram-for-gram. Even cheese leads to a gram-adjusted response that is about half the one for white bread, and I don’t think many people will eat the same amount of cheese in one sitting as they would do with white bread.

In summary, insulin responses to protein-rich foods are often 50 to 70 percent lower than responses to equivalent amounts of refined carbohydrate-rich foods. Also, insulin responses to unrefined carbohydrate-rich foods (e.g., potato, fruits) are often 70 to 90 percent lower than responses to equivalent amounts of refined carbohydrate-rich foods.

Why do insulin levels go up in response to dietary protein?

One of the reasons is that insulin is needed for tissue protein synthesis. That is, increased circulating protein (as amino acids) and insulin have a net anabolic effect, promoting muscle growth and inhibiting muscle breakdown. (Muscle protein synthesis and breakdown happen all the time; the net effect defines whether muscle grows or shrinks.) In this respect, insulin acts in conjunction with other hormones, such as growth hormone and insulin-like growth factor 1.


Holt, S.H., Miller, J.C., & Petocz, P. (1997). An insulin index of foods: The insulin demand generated by 1000-kJ portions of common foods. American Journal of Clinical Nutrition, 66, 1264-1276.