Heavy physical activity may significantly reduce heart disease deaths, especially after age 45

The idea that heavy physical activity is a main trigger of heart attacks is widespread. Often endurance running and cardio-type activities are singled out. Some people refer to this as “death by running”. Others think that strength training has a higher lethal potential. We know based on the Oregon Sudden Unexpected Death Study that this is a myth ().

Here is some evidence that heavy physical activity in fact has a significant protective effect. The graph below shows the number of deaths from coronary heart disease, organized by age group, in longshoremen (dock workers). The shaded bars represent those whose level of activity at work was considered heavy. The unshaded bars represent those whose level of activity at work was considered moderate or light (essentially below the “heavy” level).

The data is based on an old and classic study of 6351 men, aged 35 to 74 years, who were followed either for 22 years, or to death, or to the age of 75. It shows a significant protective effect of heavy activity, especially after age 45 () . The numbers atop the unshaded bars reflect the relative risk of death from coronary heart disease in each age group. For example, in the age group 65-74, the risk among those not in the heavy activity group is 110 percent higher (2.1 times higher) than in the heavy activity group.

It should be noted that this is a cumulative effect, of years of heavy activity. Based on the description of the types of activities performed, and the calories spent, I estimate that the heavy activity group performed the equivalent of a few hours of strength training per week, plus a lot of walking and other light physical activities. The authors of the study concluded that “… repeated bursts of high energy output established a plateau of protection against coronary mortality.”

Heavy physical activity may not make you lose much weight, but has the potential to make you live longer.

The theory of supercompensation: Strength training frequency and muscle gain

Moderate strength training has a number of health benefits, and is viewed by many as an important component of a natural lifestyle that approximates that of our Stone Age ancestors. It increases bone density, muscle mass, and improves a number of health markers. Done properly, it may decrease body fat percentage.

Generally one would expect some muscle gain as a result of strength training. Men seem to be keen on upper-body gains, while women appear to prefer lower-body gains. Yet, many people do strength training for years, and experience little or no muscle gain.

Paradoxically, those people experience major strength gains, both men and women, especially in the first few months after they start a strength training program. However, those gains are due primarily to neural adaptations, and come without any significant gain in muscle mass. This can be frustrating, especially for men. Most men are after some noticeable muscle gain as a result of strength training. (Whether that is healthy is another story, especially as one gets to extremes.)

After the initial adaptation period, of “beginner” gains, typically no strength gains occur without muscle gains.

The culprits for the lack of anabolic response are often believed to be low levels of circulating testosterone and other hormones that seem to interact with testosterone to promote muscle growth, such as growth hormone. This leads many to resort to anabolic steroids, which are drugs that mimic the effects of androgenic hormones, such as testosterone. These drugs usually increase muscle mass, but have a number of negative short-term and long-term side effects.

There seems to be a better, less harmful, solution to the lack of anabolic response. Through my research on compensatory adaptation I often noticed that, under the right circumstances, people would overcompensate for obstacles posed to them. Strength training is a form of obstacle, which should generate overcompensation under the right circumstances. From a biological perspective, one would expect a similar phenomenon; a natural solution to the lack of anabolic response.

This solution is predicted by a theory that also explains a lack of anabolic response to strength training, and that unfortunately does not get enough attention outside the academic research literature. It is the theory of supercompensation, which is discussed in some detail in several high-quality college textbooks on strength training. (Unlike popular self-help books, these textbooks summarize peer-reviewed academic research, and also provide the references that are summarized.) One example is the excellent book by Zatsiorsky & Kraemer (2006) on the science and practice of strength training.

The figure below, from Zatsiorsky & Kraemer (2006), shows what happens during and after a strength training session. The level of preparedness could be seen as the load in the session, which is proportional to: the number of exercise sets, the weight lifted (or resistance overcame) in each set, and the number of repetitions in each set. The restitution period is essentially the recovery period, which must include plenty of rest and proper nutrition.

Note that toward the end there is a sideways S-like curve with a first stretch above the horizontal line and another below the line. The first stretch is the supercompensation stretch; a window in time (e.g., a 20-hour period). The horizontal line represents the baseline load, which can be seen as the baseline strength of the individual prior to the exercise session. This is where things get tricky. If one exercises again within the supercompensation stretch, strength and muscle gains will likely happen. (Usually noticeable upper-body muscle gain happens in men, because of higher levels of testosterone and of other hormones that seem to interact with testosterone.) Exercising outside the supercompensation time window may lead to no gain, or even to some loss, of both strength and muscle.

Timing strength training sessions correctly can over time lead to significant gains in strength and muscle (see middle graph in the figure below, also from Zatsiorsky & Kraemer, 2006). For that to happen, one has not only to regularly “hit” the supercompensation time window, but also progressively increase load. This must happen for each muscle group. Strength and muscle gains will occur up to a point, a point of saturation, after which no further gains are possible. Men who reach that point will invariably look muscular, in a more or less “natural” way depending on supplements and other factors. Some people seem to gain strength and muscle very easily; they are often called mesomorphs. Others are hard gainers, sometimes referred to as endomorphs (who tend to be fatter) and ectomorphs (who tend to be skinnier).

It is not easy to identify the ideal recovery and supercompensation periods. They vary from person to person. They also vary depending on types of exercise, numbers of sets, and numbers of repetitions. Nutrition also plays a role, and so do rest and stress. From an evolutionary perspective, it would seem to make sense to work all major muscle groups on the same day, and then do the same workout after a certain recovery period. (Our Stone Age ancestors did not do isolation exercises, such as bicep curls.) But this will probably make you look more like a strong hunter-gatherer than a modern bodybuilder.

To identify the supercompensation time window, one could employ a trial-and-error approach, by trying to repeat the same workout after different recovery times. Based on the literature, it would make sense to start at the 48-hour period (one full day of rest between sessions), and then move back and forth from there. A sign that one is hitting the supercompensation time window is becoming a little stronger at each workout, by performing more repetitions with the same weight (e.g., 10, from 8 in the previous session). If that happens, the weight should be incrementally increased in successive sessions. Most studies suggest that the best range for muscle gain is that of 6 to 12 repetitions in each set, but without enough time under tension gains will prove elusive.

The discussion above is not aimed at professional bodybuilders. There are a number of factors that can influence strength and muscle gain other than supercompensation. (Still, supercompensation seems to be a “biggie”.) Things get trickier over time with trained athletes, as returns on effort get progressively smaller. Even natural bodybuilders appear to benefit from different strategies at different levels of proficiency. For example, changing the workouts on a regular basis seems to be a good idea, and there is a science to doing that properly. See the “Interesting links” area of this web site for several more focused resources of strength training.

Reference:

Zatsiorsky, V., & Kraemer, W.J. (2006). Science and practice of strength training. Champaign, IL: Human Kinetics.

Dietary protein does not become body fat if you are on a low carbohydrate diet

By definition LC is about dietary carbohydrate restriction. If you are reducing carbohydrates, your proportional intake of protein or fat, or both, will go up. While I don’t think there is anything wrong with a high fat diet, it seems to me that the true advantage of LC may be in how protein is allocated, which appears to contribute to a better body composition.

LC with more animal protein and less fat makes particularly good sense to me. Eating a variety of unprocessed animal foods, as opposed to only muscle meat from grain-fed cattle, will get you that. In simple terms, LC with more protein, achieved in a natural way with unprocessed foods, means more of the following in one's diet: lean meats, seafood and vegetables. Possibly with lean meats and seafood making up more than half of one’s protein intake. Generally speaking, large predatory fish species (e.g., various shark species, including dogfish) are better avoided to reduce exposure to toxic metals.

Organ meats such as beef liver are also high in protein and low in fat, but should be consumed in moderation due to the risk of hypervitaminosis; particularly hypervitaminosis A. Our ancestors ate the animal whole, and organ mass makes up about 10-20 percent of total mass in ruminants. Eating organ meats once a week places you approximately within that range.

In LC liver glycogen is regularly depleted, so the amino acids resulting from the digestion of protein will be primarily used to replenish liver glycogen, to replenish the albumin pool, for oxidation, and various other processes (e.g., tissue repair, hormone production). If you do some moderate weight training, some of those amino acids will be used for muscle repair and potentially growth.

In this sense, the true “metabolic advantage” of LC, so to speak, comes from protein and not fat. “Calories in” still counts, but you get better allocation of nutrients. Moreover, in LC, the calorie value of protein goes down a bit, because your body is using it as a “jack of all trades”, and thus in a less efficient way. This renders protein the least calorie-dense macronutrient, yielding fewer calories per gram than carbohydrates; and significantly fewer calories per gram when compared with dietary fat and alcohol.

Dietary fat is easily stored as body fat after digestion. In LC, it is difficult for the body to store amino acids as body fat. The only path would be conversion to glucose and uptake by body fat cells, but in LC the liver will typically be starving and want all the extra glucose for itself, so that it can feed its ultimate master – the brain. The liver glycogen depletion induced by LC creates a hormonal mix that places the body in fat release mode, making it difficult for fat cells to take up glucose via the GLUT4 transporter protein.

Excess amino acids are oxidized for energy. This may be why many people feel a slight surge of energy after a high-protein meal. (A related effect is associated with alcohol consumption, which is often masked by the relaxing effect also associated with alcohol consumption.) Amino acid oxidation is not associated with cancer. Neither is fat oxidation. But glucose oxidation is; this is known as the Warburg effect.

A high-protein LC approach will not work very well for athletes who deplete major amounts of muscle glycogen as part of their daily training regimens. These folks will invariably need more carbohydrates to keep their performance levels up. Ultimately this is a numbers game. The protein-to-glucose conversion rate is about 2-to-1. If an athlete depletes 300 g of muscle glycogen per day, he or she will need about 600 g of protein to replenish that based only on protein. This is too high an intake of protein by any standard.

A recreational exerciser who depletes 60 g of glycogen 3 times per week can easily replenish that muscle glycogen with dietary protein. Someone who exercises with weights for 40 minutes 3 times per week will deplete about that much glycogen each time. Contrary to popular belief, muscle glycogen is only minimally replenished postprandially (i.e., after meals) based on dietary sources. Liver glycogen replenishment is prioritized postprandially. Muscle glycogen is replenished over several days, primarily based on liver glycogen. It is one fast-filling tank replenishing another slow-filling one.

Recreational exercisers who are normoglycemic and who do LC intermittently tend to increase the size of their liver glycogen tank over time, via compensatory adaptation, and also use more fat (and ketones, which are byproducts of fat metabolism) as sources of energy. Somewhat paradoxically, these folks benefit from regular high carbohydrate intake days (e.g., once a week, or on exercise days), since their liver glycogen tanks will typically store more glycogen. If they keep their liver and muscle glycogen tanks half empty all the time, compensatory adaptation suggests that both their liver and muscle glycogen tanks will over time become smaller, and that their muscles will store more fat.

One way or another, with the exception of those with major liver insulin resistance, dietary protein does not become body fat if you are on a LC diet.

How can carrying some extra body fat be healthy?

Most of the empirical investigations into the association between body mass index (BMI) and mortality suggest that the lowest-mortality BMI is approximately on the border between the normal and overweight ranges. Or, as Peter put it (): "Getting fat is good."

As much as one may be tempted to explain this based only on the relative contribution of lean body mass to total weight, the evidence suggests that both body fat and lean body mass contribute to this phenomenon. In fact, the evidence suggests that carrying some extra body fat may be healthy for many.

Yet, the scientific evidence strongly suggests that body fat accumulation beyond a certain point is unhealthy. There seems to be a sweet spot of body fat percentage, and that sweet spot may vary a lot across different individuals.

One interesting aspect of most empirical investigations of the association between BMI and mortality is that the participants live in urban or semi-urban societies. When you look at hunter-gatherer societies, the picture seems to be a bit different. The graph below shows the distribution of BMIs among males in Kitava and Sweden, from a study by Lindeberg and colleagues ().



In Sweden, a lowest mortality BMI of 26 would correspond to a point on the x axis that would rise up approximately to the middle of the distribution of data points from Sweden in the graph. It is reasonable to assume that this would also happen in Kitava, in which case the lowest mortality BMI would be around 20.

One of the key differences between urbanites and hunter-gatherers is the greater energy expenditure among the latter; hunter-gatherers generally move more. This provides a clue as to why some extra body fat may be healthy among urbanites. Hunter-gatherers spend more energy, so they have to consume more “natural” food, and thus more nutrients, to maintain their lean body mass.

A person’s energy expenditure is strongly dependent on a few variables, including body weight and physical activity. Let us assume that a hunter-gatherer, due to a reasonably high level of physical activity, maintains a BMI of 20 while consuming 3,000 kilocalories (a.k.a. calories) per day. An urbanite with the same height, but a lower level of physical activity, may need a higher body weight, and thus a higher BMI, to consume 3,000 calories per day at maintenance.

And why would someone want to consume 3,000 calories per day? Why not 1,500? The reason is nutrient intake, particularly micronutrient intake – intake of vitamins and minerals that are used by the body in various processes. Unfortunately it seems that micronutrient supplementation (e.g., a multivitamin pill) is largely ineffective except in cases of pathological deficiency.

Urbanites may need to carry a bit of extra body fat to be able to have an appropriate intake of micronutrients to maintain their lean body structures in a healthy state. Obviously the type of food eaten matters a lot. A high nutrient-to-calorie ratio is generally desirable. However, we cannot forget that we also need to eat fat, in part because without it we cannot properly absorb the all-important fat-soluble vitamins. And dietary fat is the most calorie-dense nutrient of all.

Why not putting on extra muscle instead of carrying the extra fat? For one, that is not easy when you are a sedentary urbanite. Particularly after a certain age, if you try too hard you end up getting injured. But there is another interesting angle to consider. Humans, like many other animals, have genetic “protections” against high muscularity, such as the protein myostatin. Myostatin is produced mostly in muscle cells; it acts on muscle, by inhibiting its growth.

Say what? Why would evolution favor something like myostatin? Big, muscular humans could be at the top of the food chain by physical strength alone; they could kill a lion with their bare hands. Well, it is possible. (Many men like to think of themselves as warriors, probably because most of them are not.) But evolution favors what works best given the ecological niches available. In our case, it favored bigger and more plastic brains to occupy what Steve Pinker called a “cognitive niche”.

Even though fat mass is not inert, secreting a number of hormones into the bloodstream, the micronutrient “need” of fat mass is likely much lower than the micronutrient need of non-fat mass. That is, a kilogram of lean mass likely puts a higher demand on micronutrients than a kilogram of fat mass. This should be particularly the case for organs, such as the liver, but also applies to muscle tissue.

While gaining muscle mass through moderate exercise is extremely healthy, bulking up beyond one’s natural limitations may actually backfire. It could increase the demand for micronutrients above what a person can actually consume and absorb through a healthy nutritious diet. Some extra fat mass allows for a higher level of micronutrient intake at weight maintenance, with a lower demand for micronutrients than the same amount of extra lean mass.

Some people are naturally more muscular. Their frame and underlying organ-based capabilities probably support that. It is often visibly noticeable when they go beyond their organ-based capabilities. A common trait among many professional bodybuilders, who usually go beyond the genetic gifts that they naturally have, is an abnormal swelling of internal organs.

What complicates this discussion is that all of this seems to vary from individual to individual. People have to find their sweet spots, and doing that may not be the simplest of tasks. For example, even measuring body fat percentage with some precision is difficult and costly. Also, certain types of fat are less desirable than others – visceral versus subcutaneous body fat. It is not easy differentiating one from the other ().

How do you find your sweet spot in terms of body fat percentage? One of the most promising approaches is to find the point at which your waist-to-weight ratio is minimized ().

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.

Reference:

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.

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.

Slow versus slow-brisk walking: Effects on type 2 diabetics

I am not a big fan of reviewing new studies published in refereed journals, particularly those that make it to the news. I prefer studies that have been published for a while, so that I can look at citations to them – both positive and negative.

But I am making an exception here to a study by Kristian Karstoft and colleagues (the senior author is diabetes researcher Thomas Solomon: ), accepted for publication on 30 June 2014 in the fairly targeted and selective journal Diabetologia (full text freely available in a .zip file at the time of this writing: ).

This is a small study. Individuals diagnosed with type 2 diabetes, and who were not being treated for the condition, were allocated to three groups: a control group (CON), an “interval” walking group (IWT), and a slow walking group (CWT).

The groups had 8, 12, and 12 people in them, respectively. Those people in the IWT group alternated between walking briskly and slowly for 1 hour five times a week. Those in the CWT group only walked slowly. Those in the CON group supposedly did not do any targeted exercise.

One of the interesting findings of this study was that there was no difference in terms of health effects between the CWT and the CON groups. The only group that benefited was the IWT group. That is, those who alternated between walking briskly and slowly benefited in a way that was observable from the exercise, but those who walked slowly did not.

This study highlights two facts that I have mentioned here before, but that are often overlooked by those who suffer from type 2 diabetes or are on their way to developing the condition. They refer to visceral fat and are listed below. Visceral fat accumulates around the abdominal organs ().

- Type 2 diabetes is strongly associated with visceral fat accumulation, and is somewhat unrelated to subcutaneous fat accumulation (see the case of sumo wrestlers: ).

- Visceral fat is very easy to burn via glycolytic exercise, but does not seem to respond well to non-glycolytic exercise.

Glycolytic exercise burns sugar stored in muscle, in the form of glycogen, while it is being performed. This form of exercise raises growth hormone levels acutely. Weight training and sprints are types of glycolytic exercise, which also takes other names, such as glycogen-depleting and anaerobic exercise.

Often one sees prediabetics and type 2 diabetics avoiding this type of exercise because it pushes their blood glucose levels through the roof. That happens, however, only during the exercise. After, the benefits are tremendous and appear to clearly outweigh the possible problems associated with the temporary exercise-induced hyperglycemia.

Take a look at the last line of this cropped version of Table 1 from the study, shown below. The relevant line for the point made above is the one that refers to visceral fat volume. As you can see, those in the IWT group had the greatest reduction in visceral fat. This was also the only statistically significant reduction among the three groups; according to an analysis of variance (ANOVA) test, the probability that it was due to chance was lower than one tenth of one percent.

The ANOVA test is "parametric", in the sense that it assumes that the data is normally distributed. However, the authors did not report conducting a test of normality. Also, the sample is very small. Given these, "non-parametric" tests, such as multiple one-group-two-conditions tests run with WarpPLS (link to specific page of the .pdf file of a relevant academic paper: ) would not only be more advisable but also provide more much more information to readers.

If you compare the line showing visceral fat with the other two above it, within the body composition section of the table, you will notice another interesting pattern. In the IWT group the changes in average total body mass and total fat mass were also the greatest, but the largest change in percentage terms was the one in average visceral fat mass. Visceral fat mass is often correlated with total fat mass, with this correlation being a function of how sedentary individuals are, and it does not take a lot of it to cause serious problems.

Sumo wrestlers tend to have large ratios of total to visceral fat mass. Virtually all of their body fat is subcutaneous. They also carry a lot of muscle mass. They achieve these through intense glycolytic exercise alternated with periods of rest and consumption of large amounts of calorie-dense food. To these they add another ingredient - exercise in the fasted state, usually in the morning prior to a large breakfast. Exercise in the fasted state seems particularly conducive to visceral fat mobilization.

By the way, sumo wrestlers consume enormous amounts of carbohydrates, but as noted by Karam () have "low visceral fat, absent hyperglycemia and absent dyslipidemia despite massive subcutaneous obesity".

In my opinion the folks in the study by Karstoft and colleagues would have benefited even more, possibly a lot more, if they had alternated between sprinting and regular walking.

Why red meat consumption may appear unhealthy in scientific studies

There have been many academic articles in the past linking red meat intake with increased mortality, and there will be more in the future. I discussed one such article before here (, ). The findings in this article, which received an enormous amount of media attention, are the basis for my discussion in this post. I am interested in answering the question: Why red meat consumption may appear unhealthy in scientific studies?

This question leads to other questions, which are also addressed in this post. Can red meat intake be associated with increases and decreases in mortality, in the same study? Can red meat intake possibly cause increased mortality, at least for a percentage of the population?

All of the analyses discussed below have been conducted with the software WarpPLS (). This software supports multivariate analyses where relationships can be modeled as linear or nonlinear, with or without moderating effects included.

The ubiquitous J curve

The graph below shows how mortality varies with red meat intake. As you can see, the relationship is overall flat, meaning that red meat intake is overall unrelated with mortality. However, when we look at the two sets of points above and below the relationship line, for males and females, we see a different pattern. It appears that red meat intake and mortality are indeed significantly associated with one another, but in a J-curve pattern. That is, red meat intake is associated with increases and decreases in mortality, in the same study.

Each serving of red meat corresponds to approximately 84 g. Therefore, we could say, based on the graph above, that mortality would be minimized with consumption of a little less than 67 g/d of red meat (0.80*84) for males, and a little more than 115 g/d (1.37*84) for females. Not zero consumption, simply not a lot.

Now, one may say that this is very reasonable: a little bit of red meat is fine, but not too much. Generally females lose blood periodically, so they need a bit more than males. However, based on a number of other studies, it seems that the optimal intake amounts that we are seeing here are unusually low. If this is the case, what could be biasing the results?

Multivariate associations

Multivariate associations can distort results quite a lot. Such associations arise from correlations among multiple variables; correlations that should not per se be taken as strong indications of causality. Below are the correlations between “Red meat intake (servings/d)” and other relevant variables in the dataset taken from the study being considered here.

- Physical activity (MET-h/wk): -0.696. That is, increases in red meat intake are very strongly associated with decreases in physical activity in this study. One MET unit is equal to the energy produced per unit surface area of an average person seated at rest.

- Diabetes (%): 0.781. Increases in red meat intake are very strongly associated with increases in the percentages of individuals with diabetes.

- Food intake (cal/d): 0.604. Increases in red meat intake are strongly associated with increases in food intake in general.

- Current smoker (%): 0.519. Increases in red meat intake are strongly associated with increases in the percentages of smokers.

Let us take the physical activity variable, for example. It is inversely correlated with red meat intake, with a strong correlation coefficient, and it is unlikely that this correlation is due to direct causation - one way or the other. Below is the same graph as above, but now with labels indicating physical activity levels.

You can see that physical activity levels tend to be lower among females, which is in part due to them being on average smaller than males and thus burning fewer calories. Here you can see that physical activity is associated with mortality in a pattern that is pretty much the reverse of red meat intake. The reason for this is the strong inverse correlation between physical activity and red meat intake.

The highest mortality is associated with the lowest physical activity at the highest red meat intake. Interestingly, mortality goes up as one reaches the point at which physical activity is the highest at the lowest red meat intake.

Now take a look at the two graphs below. Both show the relationship between diabetes incidence and mortality. The first has biological sex indicated through legends. The second has physical activity levels indicated through labels.

One way to untangle the messy nature of the relationships above is to try to look for possible moderating effects, based on reasonable causal assumptions. One such assumption is that physical activity moderates the relationship between red meat intake and mortality.

The moderating effect of physical activity

The two graphs below show the relationships between red meat intake and mortality with (first graph) and without (second graph) the moderating effect of physical activity. Basically and with minimum statistical jargon, the numbers next to the arrows indicate the strengths of the associations (betas) and the probabilities that the associations are not real (Ps). By convention, a P value lower than 0.05 is normally seen as an indication that the association is strong enough to be considered real – i.e., not due to chance.

What the graphs above suggest is that increases in physical activity tend to make the relationship between red meat intake and mortality go from flat (or nonexistent) to negative. This is the meaning of the negative moderating coefficient next to the dashed arrow. In other words, as physical activity levels go up, more red meat intake is associated with less mortality.

The role of genetics

While being male or female means having different genetic profiles, with a full chromosome difference, the effect of biological sex on mortality appears to be confounded by the effect of physical activity. That is, physical activity, as measured in this study (using METs), is strongly correlated with biological sex, and also with mortality. As noted earlier, physical activity levels tend to be lower among females, which is in part due to them being on average smaller than males and thus burning fewer calories.

But another genetic factor that may influence the results and that is not included in this analysis is HFE hereditary haemochromatosis, a hereditary disease that leads to excessive intestinal absorption of dietary iron, resulting in iron overload. This genetic condition is relatively common in northern Europeans and their descendants, with a prevalence of 1 in 200 in this group. Factoid: it is quite common in Australia.

This level of prevalence matters when you are looking at mortality levels that vary along only approximately 8 in 1,000, as in this study. That translates to 0.4 in 200; much less than the prevalence of HFE hereditary haemochromatosis in northern Europeans and their descendants. That is, HFE hereditary haemochromatosis may be a major confounder in our analyses above, one that has not been controlled for. The study included 37,698 men from the Health Professionals Follow-up Study (1986-2008) and 83,644 women from the Nurses' Health Study (1980-2008). There must have been many individuals with HFE hereditary haemochromatosis in the sample.

In summary …

Based on all of the above, I think it is quite possible that for those who suffer from HFE hereditary haemochromatosis, both biological sex and physical activity affect the relationship between red meat intake and mortality.

Past menopause, women who suffer from HFE hereditary haemochromatosis should consider reducing their red meat intake, as well as intake of iron from other sources (particularly from pills). The same goes for men with the condition. Male and post-menopausal female sufferers should consider regularly donating blood.

Both men and women who suffer from HFE hereditary haemochromatosis should consider significantly increasing their level of physical activity to reduce the likelihood of iron overload. (This would be good for anyone.)

Why physical activity? Because iron is used to transport oxygen and in biological redox reactions, both of which are significantly increased during and after physical activity. In those who tend to accumulate iron in tissues, physical activity creates an increase in demand for iron that can balance the increased supply from iron-rich sources.

Our bodies evolved in the context of physical activity, often intense physical activity, and are thus maladapted for sedentary behavior.

Should you do resistance exercise to failure?

Doing resistance exercise to failure is normally recommended for those who want to maximize strength and muscle mass gains from the exercise. Yet, going to failure tends to significantly increase the chances of injury, after which the ability to do resistance exercise is impaired – also impairing gains, in the long term.

From an evolutionary perspective, getting injured is clearly maladaptive. Prey animals that show signs of injury, for example, tend to be targeted by predators. There is also functional loss, which would be reflected in impaired hunting and gathering ability.

So, assuming that going to failure is at least somewhat unnatural, because of a higher likelihood of subsequent injuries, how can it be advisable in the context of resistance exercise?

The graph below is from a study by Izquierdo and colleagues (). They randomly assigned several athletes to two exercise conditions, namely resistance training to failure (RF) and not to failure (NRF). A control group of athletes did not do any resistance exercise. The athletes were tested at four points in time: before the initiation of training (T0), after 6 wk of training (T1), after 11 wk of training (T2), and after 16 wk of training (T3).

The graph above shows the gains in terms of weight lifted in two exercises, the bench press and squat. It is similar to other graphs from the study in that it clearly shows: (a) improvements in the amount of weight lifted over time for both the RF and NRF groups, which reflect gains in strength; and (b) no significant differences in the improvements for the RF and NRF groups.

When you look at the results of the study as a whole, it seems that RF and NRF are associated with slightly greater or lesser gains, depending on the type of exercise and the measure of gains employed. The differences are small, and one can reasonably conclude that no significant difference in overall gains exists between RF and NRF.

It is clear that going to failure leads to increased metabolic stress, and that increased metabolic stress is associated with greater secretion of anabolic hormones (). How can this be reconciled with the lack of a significant difference in gains in the RF and NRF groups?

The graph below provides a hint as to the answer to this question. It shows resting serum cortisol concentrations in the participants. As you can see, after 16 wk of training (T3) cortisol levels are higher in the RF group, which is particularly interesting because the NRF group had higher cortisol levels at baseline (T0). Cortisol is a catabolic hormone, which may in this case counter the effects of the anabolic hormones, even though going to failure is expected to lead to greater anabolic hormone secretion.

It seems that cortisol levels tend to go up over time for those who go to failure, and go down for those who do not. I am not sure if this is a strictly metabolic effect. There may be a psychological component to it, as strength and power gains over time tend to be increasingly more difficult to achieve (see schematic graph below); perhaps leading to some added mental stress as well, as one tries to continue increasing resistance (or weight) while regularly going to failure.

And, of course, it is also possible that the results of the study reviewed here are a statistical “mirage”. The authors explain how they controlled for various possible confounders by adjusting the actual measures. This approach is generally less advisable than controlling for the effects of confounders by including the confounders in a multivariate analysis model ().

Nevertheless, in light of the above I am not so sure that regularly doing resistant exercise to failure is such a good idea.

Doing crossfit and looking like a bodybuilder?

Top crossfit athletes like Annie Thorisdottir and Rich Froning Jr. (pictured below; photos from Crossfitthestables.com and List09.com) look like bodybuilders even though their training practices are markedly different from those of most top natural bodybuilders. It is instructive, from a human physiology perspective, to try to understand why.

First of all we should make it clear that what makes Annie Thorisdottir and Rich Froning Jr. look the way they do is not only crossfit training. Genetics plays a key role here. Some people don’t accept this argument at all. Can you imagine someone arguing that top basketball players are generally tall because the stretching and reaching moves inherent in playing basketball make them tall? Top basketball players are not tall because they play basketball; the causality is stronger in the opposite direction: they play basketball because they are tall. The situation is not all that different with top crossfit competitors.

Often people will point at before and after photos as evidence that anyone can achieve the level of muscularity of a champion natural bodybuilder, if they do the right things. The problem with these before and after photos is that one can “go down” in terms of muscularity and definition quite a lot, but there is a clear ceiling in terms of “going up”. For example, if one goes from competitive marathon running to competitive bodybuilding, after a few years the difference will be dramatic if the person has the genetics necessary to gain a lot of muscle.

In other words, those who have the genetics to become very muscular can lose muscle and/or gain body fat to the point that they would look like they don’t have much genetic potential for muscle gain. Someone who doesn’t have the required genetics, on the other hand, will also be very effective at losing muscle and/or gaining body fat, but will be much more limited at the upper end of the scale.

The table below is from a widely cited and classic study by Fryburg and colleagues on the effects of growth hormone, insulin, and amino acid infusion on muscle accretion of protein. The article is available online as a PDF file (). The measurements shown on the table were taken basally (BAS) and at 3 h and 6 h after the start of the infusions, one of which was of a balanced amino acid mixture that raised arterial phenylalanine concentration to about twice what it was before the infusion. Phenylalanine is one of the essential amino acids present in muscle ().

There were four experimental conditions, two with only amino acid infusion, one with insulin and amino acid infusions, and one with insulin-like growth factor 1 (IGF-1) and amino acid infusions. Protein synthesis and breakdown numbers are based on phenylalanine kinetics inferences. The balance number is based on the synthesis and breakdown numbers; the former minus the latter. Note that at BAS the balance is always negative; this implies a net amino acid loss from muscle. At BAS the measurements were taken after a 12 h fast.

All infusions – of insulin, IGF-1, and amino acids – were continuously applied during the 6 h period. There was no exercise involved in this infusion study, and the amino acid mixture was balanced; as opposed to focused on certain amino acids, such as BCAAs.

The numbers in the table suggest that insulin infusion brings the balance to positive territory at the 3-h mark, with the effect wearing down at 6 h. IGF-1 infusion brings the balance to positive territory at 3 h, with the effect increasing and almost doubling at 6 h. Amino acid infusion alone brings the balance to positive territory a bit at 3 h and 6 h, and much less than when it is combined with insulin or IGF-1 infusions.

The effects of these infusions were due to both reductions in breakdown (amino acid loss) and increases in synthesis. We see that insulin exerts its effect on the balance primarily by suppressing breakdown. IGF-1 exerts its effect on the balance primarily by increasing synthesis. The effect of IGF-1 on the balance is significantly stronger than those of insulin and amino acid infusions, even when these latter two are taken together.

While this is an infusion study, one can derive conclusions about what would happen in response to different types of exercise and nutrients. Under real life conditions, insulin will increase in response to ingestion of carbohydrates and/or protein. IGF-1 will increase in response to growth hormone (GH) elevation, of which a major trigger is intense exercise.

The type of exercise that leads to the highest elevation of GH levels is intense exercise that raises heart rate significantly and rapidly. Examples are sprints, large-muscle resistance exercise, and resistance exercise involving multiple muscles at the same time. At the very high end of GH secretion are exercises that use large upper and lower body muscles at the same time, such as the deadlift. At the low end of GH secretion are localized small-muscle exercises, such as calf raises and isolated curls.

Anecdotally it seems that, at least for beginners, those exercises that lead to the highest GH secretion are the least “comfortable” for them. That is, those are the exercises that cause the most “huffing and puffing”. So next time you do an exercise like that, use this as a motivator: these are the exercises with the biggest return on investment; whether you are looking for health improvement, muscle gain, or both.

Competitive crossfit practitioners tend to favor variations of high-intensity interval training (HIIT), with an emphasis on a blend of endurance and strength exercises. Endurance and strength are both needed in crossfit competition. Competitive bodybuilders tend to focus more on strength, often exercising with more resistance or weight than competitive crossfit practitioners.

Extrapolating from the infusion study, one could argue that high GH secretion exercises are critical for amino acid accretion in muscle. Both groups mentioned above – competitive crossfit practitioners and competitive bodybuilders – exercise in ways that lead to high GH secretion. Surprising as this may sound (to some), if you do chin-ups, you’ll probably have better results in terms of biceps hypertrophy than if you do isolated bicep curls. This will happen even though the overall load on the bicep muscles will be lower with the chin-ups. The reason is that the GH secretion will be significantly higher with the chin-ups, because more muscles are involved at the same time, including large ones (e.g. the lats).

It is interesting to see competitive crossfit practitioners talking about needing to lose some weight but not being able to (). The reason is that they do not have much body fat to lose, and the types of exercise that they do create such a powerful stimulus toward positive nitrogen balance () that they end up gaining weight even as they restrict calorie intake.

Carbohydrate ingestion prior to exercise may raise insulin levels, but will blunt GH secretion; protein without carbohydrate, on the other hand, will raise insulin levels without blunting GH secretion (). Whether ingesting protein immediately before exercising is necessarily good in the long run is an open question, however, because GH secretion is likely to be greater for someone who is exercising in the fasted state, as GH secretion is in part a response to glycogen depletion (, ). And, as we have seen from the infusion study, GH secretion is disproportionately important as a positive nitrogen balance factor.

Compensatory adaptation applied to human biology () suggests that the body responds to challenges over time, in a compensatory way. Which scenario poses the bigger challenge: (a) high GH exercise with more amino acid loss during the exercise, or (b) high GH exercise with less amino acid loss during the exercise? I think it is (a), because the message being sent to the body is that “we need more muscle to do all of this and still compensate for the loss during exercise”.

Maybe this is why top crossfit practitioners end up looking like bodybuilders, and cannot lose muscle even when a slightly lighter frame would make them more competitive in crossfit games. Their bodies are just responding to the stimuli they are getting.

HCE user experience: The anabolic range may be better measured in seconds than repetitions

It is not uncommon for those who do weight training to see no gains over long periods of time for certain weight training exercises (e.g., overhead press), even while they experience gains in other types of exercise (e.g., regular squats).

HealthCorrelator for Excel (HCE) and its main outputs, coefficients of association and graphs (), have been helping some creative users identify the reasons why they see no gains, and break out of the stagnation periods.

It may be a good idea to measure the number of seconds of effort per set; in addition to other variables such as numbers of sets and repetitions, and the amount of weight lifted. In some cases, an inverted J curve, full or partial (just the left side of it), shows up suggesting that the number of seconds of effort in a particular type of weight training exercise is a better predictor of muscle gain than the number of repetitions used.

The inverted J curve is similar to the one discussed in a previous post on HCE used for weight training improvement, where the supercompensation phenomenon is also discussed ().

Repetitions in the 6-12 range are generally believed to lead to peak anabolic response, and this is generally true for weight training exercises conducted in good form and to failure. It is also generally believed that muscular effort should be maintained for 20 to 120 seconds for peak anabolic response.

The problem is that in certain cases not even 12 repetitions lead to at least 20 seconds of effort. This is usually the case when the repetitions are performed very quickly. There are a couple of good reasons why this may happen: the person has above-average muscular power, or the range of motion used is limited.

What is muscular power, and why would someone want to limit the range of motion used in a weight training exercise?

Muscular power is different from muscular strength, and is normally distributed (bell curve) across the population, like most human traints (). Muscular power is related to the speed with which an individual can move a certain amount of weight. Muscular strength is related to the amount of weight moved. Frequently people who perform amazing feats of strength, like Dennis Rogers (), have above-average muscular power.

As for limiting the range of motion used in a weight training exercise, one of the advantages of doing so is that it reduces the risk of injury, as a wise commenter pointed out here some time ago (). It also has the advantage of increasing the number of variations of an exercise that can be used at different points in time; which is desirable, as variation is critical for sustained supercompensation ().

The picture below is from a YouTube video clip showing champion natural bodybuilder Doug Miller performing 27 repetitions of the deadlift with 405 lbs (). Doug is one of the co-authors of the book Biology for Bodybuilders, which has been reviewed here ().

The point of showing the video clip above is that the range of repetitions used would be perceived as quite high by many bodybuilders, but is nevertheless the one leading to a peak anabolic response for Doug. If you pay careful attention to the video, you will notice that Doug completes the 27 repetitions in 45 seconds, well within the anabolic range. If he had completed only 12 repetitions, at about the same pace, he would have done that a few seconds before hitting the 20-second mark.

Doug completes those 27 repetitions relatively quickly, because he has above-average muscular power, in addition to having above-average muscular strength.

Protein powders before fasted weight training? Here is a more natural and cheaper alternative

The idea that protein powders should be consumed prior to weight training has been around for a while, and is very popular among bodybuilders. Something like 10 grams or so of branched-chain amino acids (BCAAs) is frequently recommended. More recently, with the increase in popularity of intermittent fasting, it has been strongly recommended prior to “fasted weight training”. The quotation marks here are because, obviously, if you are consuming anything that contains calories prior to weight training, the weight training is NOT being done in a fasted state.

(Source: Ecopaper.com)

Most of the evidence available suggests that intermittent fasting is generally healthy. In fact, being able to fast for 16 hours or more, particularly without craving sweet foods, is actually a sign of a healthy glucose metabolism; which may complicate a cause-and-effect analysis between intermittent fasting and general health. The opposite, craving sweet foods every few hours, is generally a bad sign.

One key aspect of intermittent fasting that needs to be highlighted is that it is also arguably a form of liberation ().

Now, doing weight training in the fasted state may or may not lead to muscle loss. It probably doesn’t, even after a 24-hour fast, for those who fast and replenish their glycogen stores on a regular basis ().

However, weight training in a fasted state frequently induces an exaggerated epinephrine-norepinephrine (i.e., adrenaline-noradrenaline) response, likely due to depletion of liver glycogen beyond a certain threshold (the threshold varies for different people). The same is true for prolonged or particularly intense weight training sessions, even if they are not done in the fasted state. The body wants to crank up consumption of fat and ketones, so that liver glycogen is spared to ensure that it can provide the brain with its glucose needs.

Exaggerated epinephrine-norepinephrine responses tend to cause a few sensations that are not very pleasant. One of the first noticeable ones is orthostatic hypotension; i.e., feeling dizzy when going from a sitting to a standing position. Other related feelings are light-headedness, and a “pins and needles” sensation in the limbs (typically the arms and hands). Many believe that they are having a heart attack whey they have this “pins and needles” sensation, which can progress to a stage that makes it impossible to continue exercising.

Breaking the fast prior to weight training with dietary fat or carbohydrates is problematic, because those nutrients tend to blunt the dramatic rise in growth hormone that is typically experienced in response to weight training (). This is not good because the growth hormone response is probably one of the main reasons why weight training can be so healthy ().

Dietary protein, however, does not seem to significantly blunt the growth hormone response to weight training; even though it doesn't seem to increase it either (). Dietary protein seems to also suppress the exaggerated epinephrine-norepinephrine response to fasted weight training. And, on top of all that, it appears to suppress muscle loss, which may well be due to a moderate increase in circulating insulin ().

So everything points at the possibility that the ingestion of some protein, without carbohydrates or fat, is a good idea prior to fasted weight training. Not too much protein though, because insulin beyond a certain threshold is also likely to suppress the growth hormone response.

Does the protein have to be in the form of a protein powder? No.

Supplements are made from food, and this is true of protein powders as well. If you hard-boil a couple of large eggs, and eat only the whites prior to weight training, you will be getting about 8-10 grams of one of the highest quality protein "supplements" you can possibly get. Included are BCAAs. You will get a few extra nutrients with that too, but virtually no fat or carbohydrates.

Finding your sweet spot for muscle gain with HCE

In order to achieve muscle gain, one has to repeatedly hit the “supercompensation” window, which is a fleeting period of time occurring at some point in the muscle recovery phase after an intense anaerobic exercise session. The figure below, from Vladimir Zatsiorsky’s and William Kraemer’s outstanding book Science and Practice of Strength Training () provides an illustration of the supercompensation idea. Supercompensation is covered in more detail in a previous post ().

Trying to hit the supercompensation window is a common denominator among HealthCorrelator for Excel (HCE) users who employ the software () to maximize muscle gain. (That is, among those who know and subscribe to the theory of supercompensation.) This post outlines what I believe is a good way of doing that while avoiding some pitfalls. The data used in the example that follows has been created by me, and is based on a real case. I disguised the data, simplified it, added error etc. to make the underlying method relatively easy to understand, and so that the data cannot be traced back to its “real case” user (for privacy).

Let us assume that John Doe is an intermediate weight training practitioner. That is, he has already gone through the beginning stage where most gains come from neural adaptation. For him, new gains in strength are a reflection of gains in muscle mass. The table below summarizes the data John obtained when he decided to vary the following variables in order to see what effects they have on his ability to increase the weight with which he conducted the deadlift () in successive exercise sessions:
    - Number of rest days in between exercise sessions (“Days of rest”).
    - The amount of weight he used in each deadlift session (“Deadlift weight”).
    - The amount of weight he was able to add to the bar each session (“Delta weight”).
    - The number of deadlift sets and reps (“Deadlift sets” and “Deadlift reps”, respectively).
    - The total exercise volume in each session (“Deadlift volume”). This was calculated as follows: “Deadlift weight” x “Deadlift sets” x “Deadlift reps”.

John’s ability to increase the weight with which he conducted the deadlift in each session is measured as “Delta weight”. That was his main variable of interest. This may not look like an ideal choice at first glance, as arguably “Deadlift volume” is a better measure of total effort and thus actual muscle gain. The reality is that this does not matter much in his case, because: John had long rest periods within sets, of around 5 minutes; and he made sure to increase the weight in each successive session as soon as he felt he could, and by as much as he could, thus never doing more than 24 reps. If you think that the number of reps employed by John is too high, take a look at a post in which I talk about Doug Miller and his ideas on weight training ().

Below are three figures, with outputs from HCE: a table showing the coefficients of association between “Delta weight” and the other variables, and two graphs showing the variation of “Delta weight” against “Deadlift volume” and “Days of rest”. As you can see, nothing seems to be influencing “Delta weight” strongly enough to reach the 0.6 level that I recommend as the threshold for a “real effect” to be used in HCE analyses. There are two possibilities here: it is what it looks it is, that is, none of the variables influence “Delta weight”; or there are effects, but they do not show up in the associations table (as associations equal to or greater than 0.6) because of nonlinearity.

The graph of “Delta weight” against “Deadlift volume” is all over the place, suggesting a lack of association. This is true for the other variables as well, except “Days of rest”; the last graph above. That graph, of “Delta weight” against “Days of rest”, suggests the existence of a nonlinear association with the shape of an inverted J curve. This type of association is fairly common. In this case, it seems that “Delta weight” is maximized in the 6-7 range of “Days of rest”. Still, even varying things almost randomly, John achieved a solid gain over the time period. That was a 33 percent gain from the baseline “Deadlift weight”, a gain calculated as: (285-215)/215.

HCE, unlike WarpPLS (), does not take nonlinear relationships into consideration in the estimation of coefficients of association. In order to discover nonlinear associations, users have to inspect the graphs generated by HCE, as John did. Based on his inspection, John decided to changes things a bit, now working out on the right side of the J curve, with 6 or more “Days of rest”. That was difficult for John at first, as he was addicted to exercising at a much higher frequency; but after a while he became a “minimalist”, even trying very long rest periods.

Below are four figures. The first is a table summarizing the data John obtained for his second trial. The other three are outputs from HCE, analogous to those obtained in the first trial: a table showing the coefficients of association between “Delta weight” and the other variables, two graphs (side-by-side) showing “Delta weight” against “Deadlift sets” and “Deadlift reps”, and one graph of “Delta weight” against “Days of rest”. As you can see, “Days of rest” now influences “Delta weight” very strongly. The corresponding association is a very high -0.981! The negative sign means that “Delta weight” decreases as “Days of rest” increase. This does NOT mean that rest is not important; remember, John is now operating on the right side of the J curve, with 6 or more “Days of rest”.

The last graph above suggests that taking 12 or more “Days of rest” shifted things toward the end of the supercompensation window, in fact placing John almost outside of that window at 13 “Days of rest”. Even so, there was no loss of strength, and thus probably no muscle loss. Loss of strength would be suggested by a negative “Delta weight”, which did not occur (the “Delta weight” went down to zero, at 13 “Days of rest”). The two graphs shown side-by-side suggest that 2 “Deadlift sets” seem to work just as well for John as 3 or 4, and that “Deadlift reps” in the 18-24 range also work well for John.

In this second trial, John achieved a better gain over a similar time period than in the first trial. That was a 36 percent gain from the baseline “Deadlift weight”, a gain calculated as: (355-260)/260. John started with a lower baseline than in the end of the first trial period, probably due to detraining, but achieved a final “Deadlift weight” that was likely very close to his maximum potential (at the reps used). Because of this, the 36 percent gain in the period is a lot more impressive than it looks, as it happened toward the end of a saturation curve (e.g., the far right end of a logarithmic curve).

One important thing to keep in mind is that if an HCE user identifies a nonlinear relationship of the J-curve type by inspecting the graphs like John did, in further analyses the focus should be on the right or left side of the curve by either: splitting the dataset into two, and running a separate analysis for each new dataset; or running a new trial, now sticking with a range of variation on the right or left side of the curve, as John did. The reason is that nonlinear relationships tend to distort the linear coefficients calculated by HCE, hiding a real relationship between two variables.

This is a very simplified example. Most serious bodybuilders will measure variations in a number of variables at the same time, for a number of different exercise types and formats, and for longer periods. That is, their “HealthData” sheet in HCE will be a lot more complex. They will also have multiple instances of HCE running on their computer. HCE is a collection of sheets and code that can be copied, and saved with different names. The default is “HCE_1_0.xls” or “HCE_1_0.xlsm”, depending on which version you are using. Each new instance of HCE may contain a different dataset for analysis, stored in the “HealthData” sheet.

It is strongly recommended that you keep your data in a separate set of sheets, as a backup. That is, do not store all your data in the “HealthData” sheets in different HCE instances. Also, when you copy your data into the “HealthData” sheet in HCE, copy only the values and formats, and NOT the formulas. If you copy the formulas, you may end up having some problems, as some of the cells in the “HealthData” sheet will not be storing values. I also recommend storing values for other types variables, particularly perception-based variables.

Examples of perception-based variables are: “Perceived stress”, “Perceived delayed onset muscle soreness (DOMS)”, and “Perceived non-DOMS pain”. These can be answered on Likert-type scales, such as scales going from 1 (very strongly disagree) to 7 (very strongly agree) in response to self-prepared question-statements like “I feel stressed out” (for “Perceived stress”). If you find that a variable like “Perceived non-DOMS pain” is associated with working out at a particular volume range, that may help you avoid serious injury in the future, as non-DOMS pain is not a very good sign (). You also may find that working out in the volume range that is associated with non-DOMS pain adds nothing in terms of muscle gain.

Generally speaking, I think that many people will find out that their sweet spot for muscle gain involves less frequent exercise at lower volumes than they think. Still, each individual is unique; there is no one quite like John. The relationship between “Delta weight” and “Days of rest” varies from person to person based on age; older folks generally require more rest. It also varies based on whether the person is dieting or not; less food intake leads to longer recovery periods. Women will probably see visible lower-body muscle gain, but very little visible upper-body muscle gain (in the absence of steroid use), even as they experience upper-body strength gains. Other variables of interest for both men and women may be body weight, body fat percentage, and perceived muscle tone.