PURPOSE OF REVIEW: Update recent advancements regarding the effect of high-animal protein intakes on calcium utilization and bone health.

RECENT FINDINGS: Increased potential renal acid load resulting from a high protein (intake above the current Recommended Dietary Allowance of 0.8 g protein/kg body weight) intake has been closely associated with increased urinary calcium excretion. However, recent findings do not support the assumption that bone is lost to provide the extra calcium found in urine. Neither whole body calcium balance nor bone status indicators, negatively affected by the increased acid load. Contrary to the supposed detrimental effect of protein, the majority of epidemiological studies have shown that long-term high-protein intake increases bone mineral density and reduces bone fracture incidence. The beneficial effects of protein such as increasing intestinal calcium absorption and circulating IGF-I whereas lowering serum parathyroid hormone sufficiently offset any negative effects of the acid load of protein on bone health.

SUMMARY: On the basis of recent findings, consuming protein (including that from meat) higher than current Recommended Dietary Allowance for protein is beneficial to calcium utilization and bone health, especially in the elderly. A high-protein diet with adequate calcium and fruits and vegetables is important for bone health and osteoporosis prevention.

Background

For decades now, it’s often been thought, felt or claimed that a high dietary protein intake had a detrimental effect on calcium metabolism and bone health; certainly many groups promoting low-protein dietary approaches tend to echo/parrot this idea.

This idea came around in the mid-20th century but was based on some, shall we say, questionable research.  In it, totally purified proteins were given (that is, no other nutrients were present) and a loss of calcium in the body (in the urine) was documented.  It was simply assumed that this had a negative impact on bone health.

Despite later research showing that it was much more complicated than this (i.e. that proteins containing other nutrients had different effects and that other parts of the diet played a major role in the overall effect), this idea is simply repeated as if it were still unquestionably true.  I dealt with this issue to some degree in The Protein Book, in a chapter called Protein Controversies, which is reproduced here on the main site.

As well, there has long been a secondary data set (seemingly ignored by anti-protein folks) showing that higher protein diets actually IMPROVE bone healing following things such as breaks or fractures.  Clearly the idea that ‘protein is bad for bone’ is a bit more complicated than just a soundbite.  The review paper I want to look at today examines the topic in some detail.

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The Paper

The paper begins by pointing out that bone is over 50% protein to begin with and that there has long been concern that the modern Western diet is detrimental to bone health due to the production of acids within the body.  This is something I imagine readers have at least seen mentioned in recent years (I get the occasional question about it) with some going so far as to claim that the body’s pH is THE KEY to all health (some even claim that a drop in cellular pH is the cause of cancer).

While it’s not quite that cut and dry, clearly the modern Western diet tends to promote the production of metabolic acids and at least some degree of metabolic acidosis.  This is due to a number of factors including a high protein intake (proteins are acid promoting), insufficient fruit and vegetable intake (both of which are net base producing for the most part), along with other factors such as sodium and potassium balance (excessive sodium intake relative to potassium can increase the acid load of the body).  You can find long lists of foods online in terms of their net acid or base producing potential.

And certainly, as discussed briefly in Protein Controversies, acidosis can cause problems in the body.  It’s relevant to today’s paper in that the body appears to buffer this acid load by releasing calcium, presumably from bone.  In that current research is suggesting that the RDA for protein is actually too low for some populations (notably older individuals) and with the current interest in high-protein diets for weight/fat loss and maintenance, it’s important to know whether or not these dietary approaches are having negative impacts on bone health.

The paper looks in some detail at the issue of acid/base balance and calcium metabolism. As noted above, the generation of metabolic acids causes a number of effects in the body, all of which could potentially impact negatively on calcium metabolism and bone health.  As well, studies clearly show both that:

1. The generation of metabolic acids causes increased calcium loss in the urine
2. Counteracting acidosis with base-forming minerals (e.g. potassium bicarbonate) decreases calcium excretion

While the above is clear, the direct impact of dietary protein on bone health is a bit less clear with the results of more direct epidemiological data showing mixed results in terms of the actual impact on bone health.  As well, citing a review by Fenton, the paper points out that:

…neither calcium balance nor the bone resorption marker, N-telopeptides, was affected by diet-induced changes in net renal acid excretion despite a significant linear relationship between an increase in renal net acid excretion and urinary calcium.

That is, while it’s clear that increased dietary acid load causes increased urinary calcium excretion, it’s less clear if this has any real direct impact on the body’s net calcium balance or overall bone health.

Moving on to more direct effects, the paper looks at the very old data (using primarily purified proteins) showing that for every increase in dietary protein by 1 gram, there was a 1 mg increase in urinary calcium loss (raising the question of why not simply scale calcium intake to protein intake to offset this); this led to the assumption that bone health was being compromised.

However, in direct contrast to this, the majority of epidemiological studies find that a higher protein intake is associated with increased bone mineral density with only a few finding a negative impact.  As well, while weight loss per se tends to cause a decline in bone health, some research has found that high-protein weight loss diets reduce the loss of bone mineral content; that is, high-protein intakes on a diet are beneficial.

The primary acid formation from protein comes from the sulfur containing amino acids (cysteine and methionine) and these are found in higher amounts in animal vs. vegetable proteins; it’s often been assumed that a higher vegetable protein intake would therefore have less of an impact on bone health.

However, this also turns out to be incorrect; the paper points out that studies of high-meat protein intakes either show no overall effect on net calcium balance and a higher animal protein intake is actually associated with increased bone mineral density; as well studies show a negative association between vegetable protein and bone mineral density.

It’s worth noting that strength/power athletes, who have traditionally consumed a high-protein diet are typically found to have higher bone densities compared to sedentary individuals.  As the paper points out:

Changes in bone mass, muscle mass and strength track together; thus maintenance or an increase in muscle mass and function maintains or enhances bone strength and mineral density.

And while the increase in urinary calcium excretion with increasing protein cannot be simply ignored, current data suggest that this isn’t actually due to a loss of bone mass.  Rather, increased protein intake leads to increased calcium absorption from the gut; the loss in the urine is simply due to more calcium being absorbed.   The increased loss is simply due to more being absorbed from the diet; interestingly, this effect is more pronounced when calcium intake is low to begin with.

In terms of mechanism, higher protein intakes raise levels of the hormone IGF-1, which stimulates bone formation; this probably explains the benefits of a high-protein intake on bone healing.  As well, high protein intakes have been shown to decrease levels of parathyroid hormone (PTH), a hormone that is involved in the loss of bone mass.  Low protein intakes are associated with increased PTH and lowered bone mineral density.

Finally, as I mentioned in the introduction, you can’t simply look at protein intake outside of the rest of the diet and there are clear interactions with other nutrients.  I mentioned above that protein intake interacts with calcium intake, increased absorption.  As well, a high protein intake has been shown to increase bone health in older individuals when calcium and Vitamin D are supplemented.  Finally, ensuring a sufficient intake of fruits and vegetables (which neutralize the acid load of protein) should help to ensure the impact of dietary protein on bone health is positive rather than negative.

Summing up, the researchers conclude thus:

Although a high meat or protein intake increases renal acid load and urinary calcium excretion, recent findings do not support the claim that bone is the source of the extra calcium lost in the urine.  In addition, evidence is lacking that shows high-protein intakes, including that from animal sources, affect whole body calcium balance or contribute to osteoporosis development and fracture risk.

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Summing Up

I don’t have a whole lot to add to the above conclusion.  Clearly the negative impact of dietary protein on bone health would appear to be overstated to some degree. Under certain circumstances (low calcium/Vitamin D intake, insufficient intake of fruits and vegetables), it’s certainly possible that a high-protein intake could have negative impacts.  But again this comes down to an issue of context.   And in the context of sufficient net acid neutralizing foods (fruits, vegetables, sufficient potassium intake) along with sufficient calcium/Vitamin D intake, the impact of protein on bone health would appear to be positive overall.

Background

There has long been a question of why some people seem to be able to ‘eat anything they want’ and remain thin while others can do no such thing; in fact this is often used as an argument that The Energy Balance Equation is wrong.

More in fact, the paper I’m going to talk about today was once trotted out by several individuals as ‘proof’ that The Energy Balance Equation was incorrect.  Unfortunately all their discussion really ended up proving was that, as I suggest in The Energy Balance Equation, the issue was not the equation, but that they had no clue what they were talking about.  But I’m getting ahead of myself.

Certainly we all have seen, known (or in lucky situations been) that person who seems to ‘eat anything they want’ without gaining appreciable weight.  This is in contrast to those people who seem to be able to simply look at food and get fat. What’s going on?

At least part of what’s going on, and this is outside of the paper I’m going to discuss today, is that these folks in question often don’t eat as much as you think they are.  Certainly you may see them gorging on food acutely (at a single meal, perhaps out with friends) but what you often don’t see is what they are doing the rest of the day, or the day before, or the day after.

So while you may see the single enormous meal, what you don’t see is the smaller or non-existent meals that they are eating at other times of the day.  Or the compensations that occur a day or two later to drastically reduce their food intake and keep them in energy balance in the long-term.  So while you may assume that they eat like that all day every day, you don’t know that for sure.

But as it turns out, that’s not all that’s going on.  As I discussed in The Energy Balance Equation one mistake people often make is assuming that the output side of the equation is static; that the energy output of a given individual is invariant over time.  Thus if you plug in X calories and the person doesn’t gain exactly Y weight, the equation must be invalid.  This is wrong for a bunch of reasons discussed in that article not the least of which being that the out side of the equation changes in response to cahnges in food intake, activity and obesity.

For example, in response to both increases and decreases in food intake (as well as body weight), we know that basal or resting metabolic rate (BMR/RMR) can go up and down.  Similarly, the thermic effect of food (TEF) is related to the amount (and type) of food being eaten and will adjust upwards or downwards as well.

Activity of varying sort can be affected by energy intake as well as body weight (e.g. larger bodies burn more calories in movement).  Clearly the idea that the out side of The Energy Balance Equation is unchanging is wrong.  Yet people keep pretending that it is when they simply look at calories in or out and what they think should happen to body weight without accounting for those changes.

But as it turns out, changes in the above three factors don’t seem sufficient to explain some of what is seen when people are overfed with studies finding a huge individual variance in how much fat is gained with identical amounts of overfeeding and that brings me in a very roundabout way to today’s paper; while over 10 years old, this was a seminal study that goes a long way towards explaining the odd observation that some people are seemingly able to ‘eat’ whatever they want and not get fat.   The researchers wanted to try to determine mechanistically what might be causing that to occur.

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The Paper

It’s been known for quite some time that people show a rather large amount of variability in terms of actual fat/weight gain in response to overfeeding and the researchers wanted to try to figure out some of the mechanistic reasons why this might be the case.

Towards this goal, the study recruited 16 people (12 males and 4 female) who underwent body composition measurement (via DEXA) and total energy expenditure (measured by doubly labeled water) who were then overfed by 1000 calories per day for 8 straight weeks.

I’d note that both basal metabolic rate and TEF were measured via indirect calorimetry, mainly to see if changes there could explain anything about the measured results.  As a control, subjects were required to maintain their exercise type activity at very low levels; this was done to prevent folks from trying to compensate for the increased caloric intake by simply exercising more.  While slightly artificial in terms of how people often work in the real world, this was simply a way of controlling the study to see what else might be going on.

Over the course of the study, an average of 432 cal/day was stored and 531 was dissipated through increased energy expenditure: this accounted for 97% of the total (note: this means that the energy equation was essentially balanced in that all calories were accounted for, either being stored or burned; none magically went anywhere else).  However, looking at the averages obscure what was really happening.

Moving to individual results, fat gain varied from a low of 0.36 kg (0.79 lbs) to 4.23 kg (9.3 lbs) a 10 fold variance despite the same 1000 calorie/day increase in energy intake.  Changes in BMR and TEF were unable to explain this difference.  BMR went up only 5% accounting for 8% of the extra energy while TEF went up 14%, simply in response to the increased food intake; none of those changes showed any correlation with changes in fat mass.   As I noted above, exercise type activity was clamped at low levels so changes there can’t explain the difference either.

And that brings us to NEAT, an acronym referring to Non-Exercise Activity Thermogenesis.  As the researchers define it:

NEAT is the thermogenesis that accompanies physical activities other than volitional exercise, such as the activities of daily living, fidgeting, spontaneous muscle contraction, and maintaining posture when not recumbent.

Basically, think of NEAT as the calorie burn associated with all activities that aren’t formal exercise.  And that’s where the researchers saw the massive difference between subjects; while the average increase in NEAT across all subjects was 336 cal/day, the individual changes in NEAT varied from -98 (that is it actually went down in at least one person) to +692 cal/day.

That is, in at least one subject, approximately 700 calories of the 1000 extra was burned off via NEAT.  That’s in addition to the increase in BMR and TEF which would have burned off even more of the total calories.  The researchers calculated that the increase in NEAT in the greatest responder would be the equivalent of strolling for 15 minutes per hour during waking hours.

In this vein, in the review of the Bodybugg/GoWearFit I mentioned that even small increases in activity over the course of the day can end up having a massive impact on overall energy balance because of how it can really add up.  The subjects with the increase in NEAT effectively had that happen without trying.

Of more importance, changes in NEAT directly predicted fat gain (or the lack thereof): people who showed the greatest increase in NEAT showed the smallest fat gain and vice versa.   I’d note in finishing out the paper that the four worst responders in terms of NEAT were the 4 female subjects; this really isn’t news inasmuch as we’ve also known for decades that women get the short end of the stick in terms of both weight gain and loss.

I’d also mention that this paper did nothing to determine the mechanisms behind NEAT (later studies have tried, and done poorly, at determining what is the actual cause of the increase in NEAT) only mentioning that NEAT seems to be a familial trait (suggesting a genetic basis).  Other later studies have shown that NEAT is essentially subconscious, people either do it or don’t.

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My comments:

There’s really not a huge amount to say about this paper; it’s a point of interest without a lot of practical application.  I only bring it up to make the point that many people’s assumptions about what does or does not disprove The Energy Balance Equation tend to stem with their misunderstanding of things; especially their failure to realize that the out side of the equation is not static.  And this goes especially for the NEAT component of energy expenditure, with individual increases in NEAT varying massively from one person to another.

Of more relevance, not only is the out side of the equation not static, there appears to be quite a bit of variability involved.  While some people get effectively no (or a negative) increase in NEAT with overfeeding, which makes their gaining of fat quite easy, others have essentially won the genetic lottery: in response to overfeeding, they subconsciously ramp up small calorie burning activities that add up over the course of the day to burn off the excess.

To beat that dead horse, the equation isn’t wrong, the out side of the equation in terms of NEAT simply differs massively between people especially in terms of the NEAT response.  The people who can apparently ‘eat like gluttons’ and not gain weight appear to have a physiological mechanism by which they burn off the excess, essentially protecting them from fat gain.

In that vein, I’d mention at least one other study that compared the response to overfeeding and dieting in terms of metabolic rate adjustment.  It found that those individuals who showed the greatest increase in metabolic rate to overfeeding showed the least drop in response to dieting; by contrast those people who showed the least increase to overfeeding showed the biggest drop with dieting.

That study posited the existence of spendthrift (big increase with overfeeding/small decrease with dieting) and thrifty (small increase with overfeeding/big decrease with dieting) physiologies.  Clearly the first has as huge benefit in terms of both avoiding weight gain as well as losing it if necessary; the second group will have a much larger problem.

As I mentioned above, follow up work to this seminal paper has done little to determine the mechanisms behind it (which might lead to some way of increasing NEAT in those not disposed to it).  It appear to be genetic and more or less subconscious.  Of course, that doesn’t stop people from consciously trying to do things to increase their activity levels and energy expenditure outside of formal exercise.  All of the old behavioral strategies such as taking the stairs instead of the elevator, parking further away, etc. all end up adding up over time.

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Chaput JP, Tremblay A. Obesity and Physical Inactivity: The Relevance of Reconsidering the Notion of Sedentariness. Obes Facts. (2009)2(4):249-254.

The population statistics of most countries of the world are indicating that industrialization and computerization have been associated with an increase in sedentariness and more recently with a significant shift from healthy weight to overweight. In general, this change in the overweight/obesity prevalence is attributed by health professionals to suboptimal diet and physical activity practices. However, recent data raised the possibility that excess weight gain might also be the outcome of changes imposed by our ‘24-hour’, hectic lifestyle. Parallel to an increase in body weight, one has observed a reduction in sleep time and an increase in knowledge-based work (KBW) that appear as a growing necessity in a context of economic competitiveness and globalization. Sleep and cognitive work both exert a trivial effect on energy expenditure and may thus be considered as sedentary activities. However, their respective effect on energy intake is opposite. Indeed, an increase in the practice of the most sedentary activity, i.e. sleep, is associated with a hormonal profile facilitating appetite control whereas KBW appears as a stimulus favoring a significant enhancing effect on food intake. Television viewing is another example of sedentary activity that has been shown to increase the intake of high-density foods. These observations demonstrate that the modern way of living has favored a change in human activities whose impact goes well beyond what has traditionally been attributed to a lack of physical exercise. Therefore, we will need to reconsider the notion of ’sedentariness’ which includes several activities having opposing effects on energy balance.

My comments: Traditionally, the treatment of obesity has focused on two primary components which are dietary intake and energy output; there are both good and bad reasons for this that I’ll address in a future article or research review but the fact remains that those two factors tend to represent the things we have the most control over (e.g. we can’t do anything about genetics, or about what mom did while she was pregnant).  I tend to think I’ve spent enough time on the site talking about diet that I needn’t get into it here so I’m going to focus on the activity end of things.

Now, as discussed in Metabolic Rate Overview as well as The Energy Balance Equation, there are 4 primary components on the energy out side of the energy balance equation: Basal metabolic rate (BMR), Thermic effect of Food (TEF), Thermic Effect of Activity (TEA) and Spontaneous Physical Activity/Non-Exercise Activity Thermogenesis (SPA/NEAT).  The two I’m going to focus on relative to today’s research review are the last two, a recent separation whereby formal exercise and all other daily activities have been separated out.

Now, traditionally obesity treatment has also focused on the exercise end of the equation but there have always been a few problems with this.  Perhaps the largest relative to what I want to talk about today (and I’ll be doing a very thorough article at a later date on this so please be patient in the comments) is that the amount of exercise that is or can usually be done is actually fairly trivial compared to the rest of the day.

That is, the hour someone might spend engaged in exercise is still pretty small compared to what’s happening the other 23 hours of the day.  And, as many have found out by using tools such as the Bodybugg/GoWear Fit, small changes during the majority of the day (e.g. getting up every so often during the day to walk around at work for 8 hours) end up having a far larger impact on daily energy expenditure compared to the hour of exercise they might do.  As many have also found, being very inactive for those same 8 hours (e.g. jockeying a computer desk) doesn’t burn many more calories than laying in bed.

Which is all a very long introduction to today’s paper which looks in some detail at two of the major changes in modern life that contribute to our overall ‘inactivity’ during the day: sleep and what the researchers decided to call knowledge based work (KBW).  Sleep is fairly explanatory but, by KBW, they are referring to things such as school, jobs involving thought and concentration and even potentially video games.  Basically anything where you’re sitting on your ass for most of it but having to involve your brain rather intently.

And while both activities fall under the heading of ‘inactivity’ (in that you burn very few calories during either of them), they actually end up having not only different but diametrically opposed effects on the potential for weight gain and obesity.  Basically, just saying that ‘inactivity causes weight gain’ is simplistic and, as it turns out, incorrect.  The type of inactivity is relevant here.

In the case of sleep, and there has been a tremendous amount of literature in this regards in recent years, it’s turning out that sleep deprivation does rather horrible things not only for overall health but for weight gain and obesity risk.  Sleep deprivation tends to decrease leptin level (removing the tonic ‘block’ that leptin exerts on appetite/hunger) and raise level of ghrelin (the only hormone shown to directly stimulate hunger in humans).   I discuss both hormones in detail in the series on Hormones of Bodyweight Regulation but the end result of such a shift will be an increase in hunger/appetite along with a negative effect on calorie partitioning.  Hormones such as cortisol, thyrotropin hormone (involved in thyroid function) and others are also impacted positively by sufficient sleep and negatively by too little sleep.  As the authors state:

Hence the beneficial effect of sleep go well beyond its role in the restoration and maintenance of tissue structure and function…Despite the low energy cost of sleep, population studies have repeatedly shown that a short average duration of sleep is associated with excess body weight…recent research evidence showed that an average nightly sleep of 7-8 h in adults is associated with a lower risk of obesity, type 2 diabetes, coronary heart disease and all-cause mortality.

Hard to get much clearer than that.  Basically, despite the unbelievably low caloric cost of sleeping (usually around 1 cal/min), the indirect impact is massive in terms of the benefits for getting enough sleep and harm for not.

Moving on to the other topic of the paper we get to KBW, again referring to activities such where you’re sedentary but engaged in large amount of mental activity.  The paper mentions work, school, even video games and computer ‘chatting’ (you Facebook people know who you are) and other related activities as potential examples of KBW.

And, as you might expect, while similarly sedentary like sleeping, the impact of KBW on appetite and body weight regulation tend to be rather negative.  The brain, unlike skeletal muscle, can’t use fat for fuel and studies have shown that intense thinking can screw blood glucose levels; this is relevant as some work shows that falling or lowered blood glucose can stimulate hunger.  And usually for junkier food (which is invariably found in large amounts in the work space).

Studies have found that even short bouts of intense KBW can increase total energy and fat intake as well.    In one, for example, females were assigned to a 45-min mental work session and then provided an ad-lib buffet.  Despite only burning 3 extra calories during the task, the KBW group ate 229 more calories compared to a group that only rested.  In the long-term, this adds up big time.

And while it hasn’t been studied directly, the researchers question whether such things as video games and Internet chatting might be similarly stimulatory of appetite (and let’s be honest, is anybody eating non-junk food when they play WoW).  The amount of time spent watching Tv is a known risk factor for obesity in children, increasing the intake of high-energy density tasty foods; whether this is related to the same mechanism as KBW such as work or studying is currently unknown.

Finally, it’s worth mentioning that Tv and computer involvement is often done late at night and this can interrupt sleeping patterns (the constant influx of photos into the eyes makes it harder to get to sleep).  So there’s a potential double whammy.

Summing up: So that’s that, a quick look at two different types of ‘inactivity’ that end up having diametrically opposed effects on the risk for weight gain, obesity and other health risks. Those two are sleep, perhaps the most sedentary activity of all (unless you get lucky) and knowledge based work (KBW).

Getting sufficient sleep, something that is becoming harder for many to do (by choice or life requirement) is a key aspect of not only overall health but limiting obesity risk.  With good sleep hygiene and habits (e.g. get off the computer earlier, go to bed a bit earlier every night), this is at least within the realm of some people’s control.

The issue of KBW is tougher as folks have to make a living and many jobs involve long hours of KBW (often in an environment where nothing but crappy food is available).  Clearly quitting your job and sleeping all day, while attractive, isn’t an option for most.  At least being aware that intense bouts of KBW can screw with blood glucose and appetite may help with finding strategies around it.

Keeping better snack foods handy to stave off hunger following such work efforts would be one strategy, I have to wonder if a small amount of carbohydrate during the activity would help to stabilize blood glucose.  Perhaps Gatorade can come up with a ‘Conference Call Gatorade XXXtreme’ version of their drink.  Yes, XXXtreme with three X’s.

There is substantial evidence that static stretching may inhibit performance in strength and power activities. However, most of this research has involved stretching routines dissimilar to those practiced by athletes. The purpose of this study was to evaluate whether the decline in performance normally associated with static stretching pervades when the static stretching is conducted prior to a sport specific warm-up. Thirteen netball players completed two experimental warm-up conditions. Day 1 warm-up involved a submaximal run followed by 15 min of static stretching and a netball specific skill warm-up. Day 2 followed the same design; however, the static stretching was replaced with a 15 min dynamic warm-up routine to allow for a direct comparison between the static stretching and dynamic warm-up effects. Participants performed a countermovement vertical jump and 20m sprint after the first warm-up intervention (static or dynamic) and also after the netball specific skill warm-up. The static stretching condition resulted in significantly worse performance than the dynamic warm-up in vertical jump height (-4.2%, 0.40 ES) and 20m sprint time (1.4%, 0.34 ES) (p<0.05). However, no significant differences in either performance variable were evident when the skill-based warm-up was preceded by static stretching or a dynamic warm-up routine. This suggests that the practice of a subsequent high-intensity skill based warm-up restored the differences between the two warm-up interventions. Hence, if static stretching is to be included in the warm-up period, it is recommended that a period of high-intensity sport-specific skills based activity is included prior to the on-court/field performance.

My Comments: As I discussed recently in The Importance of Context, people these days seem to love them some absolutes and there tends to be no shortage of them to go around, especially when it comes to training.  Always do this, never do that, you get the idea.   The situational context is irrelevant, there are simply black and white absolutes that apply across the board.

And a recent never is that you should never ever static stretch before high-intensity training of any sort with endless coaches and gurus repeating that idea.  And certainly this seems to be based on quite a body of research.  A number of studies have shown that extensive static stretching done immediately prior to various types of exercise performance such as vertical jumping, sprinting and weightlifting impair strength and/or power output.

Now, as I mentioned in Warming Up for the Weight Room Part 1, even if static stretching does decrease strength and power outputs, there may still be times to do it before training.  Usually this is in the case of a severe muscular tightness that impairs either technique or safety.  In that context, proper technique and not hurting the person is far outweighed by any decrease in performance.

However, I made another point in that article which was this: many of the studies don’t really reflect how athletes typically go about their training.  That is, anyone who has trained as an athlete or actually coached athletes in the real world knows that it’s fairly rare (especially among strength/power type athletes, endurance guys are often years behind the curve) to go straight from static stretching immediately into high-performance work.  At the very least some type of drills are generally done between the two, usually more than that (e.g. multiple progressive intensity sports specific warm-ups) is done.

There is also an issue of the extent of stretching: many of the negative performance studies have used levels of static stretching that far exceed what most athletes would ever do in practice (again, something anyone who’s actually worked with athletes would know).  That is, it would be rare to hold a stretch for 2-4 minutes in the real world, static stretching of perhaps 30 seconds per muscle group would be far more realistic.  Yet it is generally that type of extremely prolonged static stretching that has been tested and found to impair performance (some studies have shown shorter stretching periods to have a similar negative impact).

Which brings us to today’s study which set to test the above in a more real-world type of situation.

The study examined 13 netball players from the Australian Institute of Sport.  Both groups first performed a sub-maximal run as a general warm-up.  Then one group performed static stretching (9 stretches held for 30 second each) and the other performed a dynamic warm-up consisting of 16 rather common dynamic movements.  Both the static and dynamic warm-ups lasted 15 minutes. After a short-rest, both groups were tested on 20m sprint and vertical jump.  Then both groups performed a netball specific skill warm-up consisting of various short sprints, shuffling, accelerations, direction changes and jumping.  Then the performance tests were performed a second time to see if anything had changed.

And the results?  Well, in keeping with previous work, the static stretching routine did in fact hurt performance on the 20 m sprint and vertical jumping compared to the dynamic warm-up.  However, after performing the specific skills warm-ups described above, results were no different on the second set of performance tests.  That is, any loss of performance due to static stretching was eliminated simply by performing a variety of sport specific skills prior to the maximal effort testing.

Basically, by testing the athletes in a situation that more accurately reflects how athletes actually train, they found that much of the concern over static stretching is unfounded.  As they state in the discussion:

The results suggest that if an inhibitory effect was present after static stretching, that the SKILL component of the warm-up routine was able to dissipate the negative effect.  This supports the suggestion by Young and Behm that practice attempts of the required tests may offset potential negative effects of static stretching.

The also note that their results are in contrast to another study examining both a dynamic performance warm-up and a static-stretching warm-up but in that study, the static stretching was done after the performance warm-up and immediately prior to the testing.  Basically, order of warm-up matters which I also discussed in Warming Up for the Weight Room Part 1.  And so long as it’s followed by some sort of dynamic, skill specific, progressive warm-up (e.g. progressively heavier warm-up sets in the weight room, increasingly faster pickups in sprinting, etc), static stretching appears to not be quite the absolute no-no that many have made it out to be.

Quoting from the researchers conclusions:

The most important findings from this study were that a dynamic warm-up routine is superior to static stretching when preparing for powerful performance; however, these differences can be eliminated if followed by a moderate to high intensity sport specific skill warm-up.

Summing Up: Basically, static stretching is only a problem if it’s done too extensively (e.g. stretches held for very extended periods) and is not followed by appropriate sport-specific warm-ups between the end of static stretching and maximal performance (testing or training).  Which isn’t how real athletes generally train anyhow.  Which is something any performance coach who has actually worked with athletes should know anyhow.

Mercader J. Mozambican grass seed consumption during the middle stone age. Science. (2009) 326(5960):1680-3.

The role of starchy plants in early hominin diets and when the culinary processing of starches began have been difficult to track archaeologically. Seed collecting is conventionally perceived to have been an irrelevant activity among the Pleistocene foragers of southern Africa, on the grounds of both technological difficulty in the processing of grains and the belief that roots, fruits, and nuts, not cereals, were the basis for subsistence for the past 100,000 years and further back in time. A large assemblage of starch granules has been retrieved from the surfaces of Middle Stone Age stone tools from Mozambique, showing that early Homo sapiens relied on grass seeds starting at least 105,000 years ago, including those of sorghum grasses.

My Comments: In recent years, there has been quite an explosion in interest in the supposed diet of our paleolithic ancestors, essentially in an attempt to explain part of why humans are having so much trouble with the modern diet.  So far as I can tell the first paper was written in the Mid-80’s or so but even more recently it’s become quite the fad/cult/area of interest for a lot of people.

Now, while an entire article could be written about this, it’s important to note that nobody knows for sure what we ate during our evolution.  Even researchers in the field (Cordain and Eaton are two of the major ones) have arrived at rather drastically different conclusions about what our diets contained based on their assumptions because it’s all basically a lot of guesswork.  We end up with estimations based on a bunch of assumptions and not much more.

Much of it comes from an analysis of a book called the Ethnographic Atlas, a work done years ago by non-scientists who wrote down (sort-of) what extant non-modernized people were eating.  From that, various researchers, making various assumptions about the relative proportions of animal and vegetable foods in the diet have thrown out some ideas about what our evolutionary diet contained.  Those researchers have often reached utterly differing ideas based on which built-in assumptions they started with.  Other suggestions about our ancestral diet have been made by examining the current intake of extant hunter-gatherer tribes with the implicit assumption that their food intake is representative of our intake during our evolution.

I’d note that it’s unlikely that there was any singular evolutionary diet in the first place.  Humans have shown the ability to adjust to all but the most extreme environments and show an amazing ability to adapt to drastically differing diets as well.  Human ancestors evolving in say Alaska would have had far different foods available than someone living in the arid plains in Africa.  Even examining the extant hunter-gatherer tribes demonstrates this in spades: the diet of an Alaskan Inuit is radically different from say an African Bushman simply due to the difference in environment and what is available to them.  So there is no single ancestral diet in terms of the quantities, proportions or types of food that would have been eaten in the first place.

At best we can probably say with some degree of certainty that our ancestors didn’t have many of the foods available to us today.  That is, Cap’n Crunch, Ben and Jerry’s Ice Cream and Bud Light weren’t part of our evolutionary diet because they didn’t exist (much to the loss of our ancestors).  Beyond that, we can’t say with much certainty what they did eat; it’s mostly guessing because folks weren’t alive to say for sure.  And while it may be safe to assume that extant hunter-gatherer tribes are representative, it’s still an assumption.

Now, while there are many different interpretations to the ‘paleo-diet’ craze, at least one thing that most seem to agree on was that refined grains were absolutely not part of the evolutionary diet.   Bloggers, apparently unclear on the concept of irony, go on constantly about how ‘Paleo man didn’t have grains, so you shouldn’t eat them.’  Apparently that same logic doesn’t apply to the computers they use to blog with, the Internet that they blog on, their Blackberries that they use to Twitter about their blog updates, modern cars that they use to get to work or the houses they live in.  Paleo man didn’t have those either but I don’t see these folks giving those up.  Guess they only want to give up the easy stuff when it’s convenient.  But I digress.

That is, it’s generally assumed that refined grains (being currently blamed for much of modern health problems) weren’t a major part of our diet until the agricultural revolution, about 10,000 years ago.  It’s also assumed that that span of time is insufficient for man to have evolved to deal with them.  I’ll only address this second assumption by pointing readers to a new book called The 10,000 Year Explosion: How Civilization Accelerated Human Evolution wherein the authors make a rather good argument that, contrary to common belief, not only did human evolution continue once humans became civilized, that it accelerated.

Rather, in looking at today’s second paper, I want to address that first assumption: that our evolutionary diet was devoid of any type of refined cereal grain.  I imagine that, if you’ve read this far, you can guess what I’m going to say about it and what the second study concluded.

The researchers were examining cave artifacts in a cave site in Mozambique which have been dated to somewhere between 42000 and 105,000 years ago.   They mention that excavation in 2007 retrieved 555 artifacts.  Of those, 70 stone tools were analyzed and were chosen to represent the broadest range of potential plant uses.  This includes scrapers, grinders, points, flakes and miscellaneous tools.  These were analyzed and while 20% contained no starch residue, the other 80% were found to contain starch granules with the number on each tool ranging from 1 to 650.  It’s worth noting that the quantity of granules found on the scrapers was massively larger than what is found naturally in the cave, that is, they were brought into the cave.

The majority of starch granules (89%) were identified as sorghum, a grass showing a large complex of cultivated, wild and weedy types.  The researchers note that the starch granules found on the tools analyzed are structurally identical to modern sorghum plants.  As the researchers state:

The Mozambican data show that Middle Stone Age groups routinely brought starchy plants to their cave sites and that starch granules go attached to and preserved on stone tools.  I cannot prove that starch from all stone tools represents direct tool function…These early grinders are simply modified cobbles and core tools, with a suspected use that conforms to the technological action of “diffuse resting percussion” and “pounding”, which allows the grinding of plant materials.

Put differently, while more research will certainly be needed to verify or refute this claim, data that is a bit more direct than “Assumptions based on a book some guys wrote years and years ago” suggest that as far back as 100,000 years ago, humans had found a way to refine and consume at least some grains for their diet.  Or as the researchers state more directly in the abstract above:

A large assembly of starch granules has been retrieved from the surfaces of Middle Stone Age tools from Mozambique, showing that early Homo Sapiens relied on grass seeds starting at least 105,000 years ago, including those of sorghum grasses.

And even if you don’t buy the argument of the book I referenced above, that 10,000 years is more than sufficient to allow adaptation to changes in diet, it would be hard to argue that 105,000 years isn’t time enough to adapt to some degree.

With the sheer volume of research appearing on a weekly basis, this will at least help me to look at data in a more timely fashion.  I’d mention that, for anyone who wants an even better look at a lot of studies, you’d be well served to consider Alan Aragon’s monthly Research Review which I reviewed in the confusingly titled Alan Aragon Research Review – Product Review.

In any case, today I want to look at two recent studies which are:

1. Deglaire et al. Hydrolyzed dietary casein as compared with the intact protein reduces postprandial peripheral, but not whole-body, uptake of nitrogen in humans. Am J Clin Nutr. (2009) 90(4):1011-22.
2. West et. al. Elevations in ostensibly anabolic hormones with resistance exercise enhance neither training-induced muscle hypertrophy nor strength of the elbow flexors. J Appl Physiol. 2009 Nov 12.

For each study I’ll give a brief background to the topic, look at what was done and then jump straight to the conclusions with some final summing up.  As noted above, some of the detail will be left out but I figure that anyone who is that interested in the details of methodology and such will simply get ahold of the full paper and read it themselves.

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Deglaire et al.  Hydrolyzed dietary casein as compared with the intact protein reduces postprandial peripheral, but not whole-body, uptake of nitrogen in humans. Am J Clin Nutr. (2009) 90(4):1011-22.

BACKGROUND: Compared with slow proteins, fast proteins are more completely extracted in the splanchnic bed but contribute less to peripheral protein accretion; however, the independent influence of absorption kinetics and the amino acid (AA) pattern of dietary protein on AA anabolism in individual tissues remains unknown. OBJECTIVE: We aimed to compare the postprandial regional utilization of proteins with similar AA profiles but different absorption kinetics by coupling clinical experiments with compartmental modeling. DESIGN: Experimental data pertaining to the intestine, blood, and urine for dietary nitrogen kinetics after a 15N-labeled intact (IC) or hydrolyzed (HC) casein meal were obtained in parallel groups of healthy adults (n = 21) and were analyzed by using a 13-compartment model to predict the cascade of dietary nitrogen absorption and regional metabolism. RESULTS: IC and HC elicited a similar whole-body postprandial retention of dietary nitrogen, but HC was associated with a faster rate of absorption than was IC, resulting in earlier and stronger hyperaminoacidemia and hyperinsulinemia. An enhancement of both catabolic (26%) and anabolic (37%) utilization of dietary nitrogen occurred in the splanchnic bed at the expense of its further peripheral availability, which reached 18% and 11% of ingested nitrogen 8 h after the IC and HC meals, respectively. CONCLUSIONS: The form of delivery of dietary AAs constituted an independent factor of modulation of their postprandial regional metabolism, with a fast supply favoring the splanchnic dietary nitrogen uptake over its peripheral anabolic use. These results question a possible effect of ingestion of protein hydrolysates on tissue nitrogen metabolism and accretion.

My Comments: Ever since the pioneering work in the 90’s on fast and slow proteins, there has been continued interest in the digestion speed of proteins and how that impacts on metabolism, performance and, of course, muscle growth. In recent years, there have been many claims made for the superiority of faster proteins to slower in terms of ’speeding amino acids to muscle’ in terms of promoting growth.

As well, as many may note, a recent commercial product (T-nations Anaconda), who’s anabolic claims were analyzed in perhaps the most commented article on the site in Alan’s Aragon’s guest article Supplement Marketing on Steroids, has recently been released to the market.

For background, hydrolysates are simply whole proteins that have been pre-digested (through the addition of enzymes during production) to some degree.  The theory being that, due to this pre-digestion, the hydrolysate will be digested in the stomach faster, getting aminos into the bloodstream faster and, presumably, having a better effect on skeletal muscle than slower proteins.

But is it true?  Guess.

The above study examined this issue by feeding 21 subjects 2 test meals containing ~26.5 grams of either intact casein or it’s hydrolysate; the protein had been marked with radioactive nitrogen so that it’s fate after ingestion could be tracked over the next 8 hours.  The test meals also contained 96 grams of carbohydrate and 23 grams of fat; this is worth noting as adding other nutrients to fast proteins often makes them behave more like slow proteins.  I’ll spare you the methodology, sufficed to say that tracking protein after it enters the body is brutally complicated and involves a lot of modelling and various measurements of blood amino acid levels and such.

Here’s what the study found.  Over the time course studied (8 hours after ingestion), the hydrolyzed casein product showed greater losses from digestion (that is, less was absorbed).  As well, a greater amount of the hydrolysate was oxidized for energy through deamination (a process by which the amino group is stripped off the carbon backbone).  Finally, a larger amount of the casein hydrolysate was used by the splanchnic bed (gut and intestines) with significantly less of the total protein reaching the bloodstream or peripheral tissues (muscles).

To quote the researchers:

Despite similar overall net postprandial protein utilization, our results indicate important differences in metabolic partitioning and kinetics between protein sources characterized by a preferential utilization of dietary nitrogen by for splanchnic protein syntheses after HC [hydrolyzed casein] ingestion at the expense of the incorporation into peripheral tissues.

Translating that into English: hydrolyzed casein is digested more poorly, gets burned for energy to a greater degree and gets used more by the gut than intact casein; the end result of this is that hydrolyzed casein provides LESS amino acids to skeletal muscle after ingestion than intact casein protein.

So not only is the claim that hydrolysates are better at providing aminos faster to skeletal muscle wrong, the reality is actually exactly reversed: intact casein is better for providing aminos to the muscle.  I’d note that other studies have found this as well: in one, intact protein provided MORE branched-chain amino acids into the bloodstream than a hydrolyzed form.

I’d add to this that, as I discussed in The Protein Book, other data supports the idea that slower proteins may actually be superior to faster proteins for muscle growth; in one set of studies, for example, milk protein (a mix of slow and fast proteins) resulted in greater hypertophy than soy (a fast protein) over 8 weeks of training and supplementation.  As well hydrolyzed proteins tend to taste like bleach; it’s no coincidence that Anaconda has to come with a separate flavoring intensifier: hydrolysates are gag-inducing.  They can’t be consumed straight.

Summing up: Hydrolysates are not only not superior to intact protein in terms of providing amino acids to skeletal muscle, they are distinctly inferior.  Their fast digestion speed leads to greater digestive losses, more oxidation via deamination and provides less amino acids to skeletal muscle.  That’s on top of tasting like vomit.  Or at least making you want to.

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West et. al. Elevations in ostensibly anabolic hormones with resistance exercise enhance neither training-induced muscle hypertrophy nor strength of the elbow flexors. J Appl Physiol. 2009 Nov 12.

The aim of our study was to determine whether resistance exercise-induced elevations in endogenous hormones enhance muscle strength and hypertrophy with training. Twelve healthy young men (21.8 +/- 1.2 y, BMI = 23.1 +/- 0.6 kg(.)m(-2)) independently trained their elbow flexors for 15 weeks on separate days and under different hormonal milieu. In one training condition, participants performed isolated arm curl exercise designed to maintain basal hormone concentrations (low hormone, LH); in the other training condition, participants performed identical arm exercise to the LH condition followed immediately by a high volume of leg resistance exercise to elicit a large increase in endogenous hormones (High Hormone, HH). There was no elevation in serum growth hormone (GH), insulin-like growth factor (IGF-1) or testosterone after the LH protocol, but significant (P < 0.001) elevations in these hormones immediately and 15 and 30 min after the HH protocol. The hormone responses elicited by each respective exercise protocol late in the training period were similar to the response elicited early in the training period indicating that a divergent post-exercise hormone response was maintained over the training period. Muscle cross-sectional area increased by 12% in LH and 10% in HH (P < 0.001) with no difference between conditions (condition x training interaction, P = 0.25). Similarly, type I (P < 0.01) and type II (P < 0.001) muscle fiber CSA increased with training with no effect of hormone elevation in the HH condition. Strength increased in both arms but the increase was not different between the LH and HH conditions. We conclude that exposure of loaded muscle to acute exercise-induced elevations in endogenous anabolic hormones enhances neither muscle hypertrophy nor strength with resistance training in young men. Key words: testosterone, growth hormone, IGF-1, anabolism.

My Comments: For several decades now, there has been intense focus on the acute hormonal response to training.  This started back in the 80’s where researchers, interested in growth did a rather cursory examination of elite powerlifters and bodybuilders, made some assumptions about muscle size, made some even bigger assumptions about how they trained, and then proceeded to reach some staggeringly poor conclusions.

Basically, what they observed was that bodybuilders were bigger than powerlifters, which is debatable in the first place.  They also observed that powerlifters typically used low reps and long rest periods and bodybuilders (remember: this was the Arnold era) trained with high reps and short rest periods.  Thus they concluded that high reps and short rest stimulated muscle growth and went looking for reasons why this was the case.  I’d note that this is not really how you’re supposed to do science: you don’t reach your conclusion and go find reasons why it’s right.  You test hypotheses and draw your conclusions from that.  But I digress.

And the main focus for a while was potential differences in hormonal response to training, primarily focusing on testosterone and growth hormone (GH).  The basic study design that was followed was to compare the acute hormonal response to either 3 sets of 5 repetitions with a long rest interval (3 minutes) to sets of 10 with a 1 minute rest interval.  Repeatedly, studies showed that the first type of training boosted testosterone and the second GH.  Entire training schemes have grown out of this but there was a problem: nobody ever bothered to see if these acute (usually less than 10-15 minute) bumps in hormones actually did anything.

Nevermind that this makes little sense anyhow for a variety of reasons.  Not the least of which is that women have higher GH levels than men and get a bigger GH response to training, yet they don’t grow better.  If anything, with the known impact of testosterone on muscle growth, if there was to be any benefit to this, you’d expect the lower rep/heavy work to be superior.  Yet the researchers were arguing that it wasn’t.   There was a logic missing in the argument (not the least of which being the assumption that powerlifters had smaller muscles than bodybuilders) that seemed to get skipped over.

In addition to the science, there is a long held belief, echoed in various places (including the comments section of another contentious article I wrote titled Squats vs. Leg Press for Big Legs) that certain movements, notably squats and deadlifts, will have full-body growth stimulating properties, generally mediated through the hormonal response.

It’s not uncommon to see people recommending things like “If you want big arms, squat/train legs.” for example.  Essentially, heavy leg work is touted as being the key to overall growth.  Nevermind that the same people who make this argument will often complain about “All those guys in the gym with huge upper bodies and no legs” without realizing that the two ideas contradict one another (that is, if leg training is required for growth, how can guys get huge upper bodies without training legs).  But I digress again.

In any case, this study examined the issue directly with a somewhat confusing study design: twelve healthy young men trained their biceps on different days of the week under different training conditions.  In what they called the low-hormone condition, the biceps were trained all by themselves; no other exercise was done.  In the other called the high-hormone condition, the biceps were trained and then a large-volume of leg training was done to elevate the supposedly anabolic hormones.

Does that make sense, all subjects trained both arms, but on different days and under different conditions.  And the training was far enough apart that the hormonal response from the leg training wouldn’t have impacted the low-hormone training session.  This training was followed for 15 weeks and subjects consumed protein both before and after the training (so there was nutritional support).

Hormone levels were measured and while there was no significant change in hormones in the low-hormone situation, in the high-hormone situation, there were increases in lactate, growth hormone, free and total testosterone and IGF-1 with the peak occurring approximately 15 minutes after the leg work.

And, if the hormonal response to heavy leg training actually has any impact, what you’d expect to see is that one arm, the one trained along with the leg training, would grow better.

Did it happen? Guess.

Both maximal strength and muscle cross sectional area increased identically in both arms to the tune of a 20% vs. 19% increase in strength for low- vs. high-hormones and an increase in skeletal muscle cross sectional area of 12% vs. 10% in low- vs. high-hormones.  These differences were not statistically significant. Quoting the researchers:

Despite vast differences in hormone availability in the immediate post- exercise period, we found no differences in the increases in strength or hypertrophy in muscle exercised under low or high hormone conditions after 15 weeks of resistance training. These findings are in agreement with our hypothesis and previous work showing that exercise-induced hormone elevations do not stimulate myofibrillar protein synthesis (36) and are not necessary for hypertrophy (37). Thus, our data ((36) and present observations), when viewed collectively, lead us to conclude that local mechanisms are of far greater relevance in regulating muscle protein accretion occurring with resistance training, and that acute changes in hormones, such as GH, IGF-1, and testosterone, do not predict or in any way reflect a capacity for hypertrophy.

I don’t think it gets any clearer than that and I’d note that another recent study titled “Resistance exercise-induced increases in putative anabolic hormones do not enhance muscle protein synthesis or intracellular signalling in young men.” by the same group found the exact same thing.

Summing Up: Leg training has no magic impact on overall growth, most of which is determined locally (through mechanisms of tension and fatigue mediated by changes in local muscular metabolism).  If you want big arms, train arms.  If you want big legs, train legs.

And if folks are wondering why empirically ‘folks who train legs hard’ seem to get big compared to those who don’t, I’d offer the following explanation: folks willing to toil on heavy leg work work hard.  Folks too lazy to train legs hard often don’t.  And it’s the overall intensity of the training that is causing the difference, not the presence or absence of squats per se. Which is why guys who only hammer pecs and guns get big pecs and guns even if they couldn’t find the squat rack in the gym: the small acute hormonal responses to training are simply irrelevant to overall growth.

McClave SA, Snider HL. Dissecting the energy needs of the body. Curr Opin Clin Nutr Metab Care. (2001) 4(2):143-7.

The majority of the resting energy expenditure can be explained by the energy needs of a few highly metabolic organs, making up a small percentage of the body by weight. The relationship of the specific size, individual metabolism, and proportional contribution to the actual body weight and total energy expenditure for each of these organs is a dynamic process throughout growth and development, the onset of disease, and changes in nutritional status. Defining the energy needs of the individual tissues and organ systems immeasurably enhances our understanding of the body’s response to these clinical processes, which otherwise could not easily be evaluated by focusing solely on total energy expenditure, fat-free mass, nitrogen imbalance, or actual body weight. Recently reported studies have served mainly to reinforce concepts described previously, and clarify some areas of controversy.

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Background

Last month, I answered a Q&A on Reducing Body Fat Percentage by Gaining Muscle and in that article I mentioned that the actual caloric burn of skeletal muscle is actually quite low compared to what is often claimed.  In the comments section someone mentioned a recent seminar where the value of 50 cal/lb for muscle was thrown out and asked for clarification on my claim.

Unlike previous research reviews, today’s paper isn’t an actual study but rather a review paper so my discussion will be a little bit different in terms of what I want to look at.  The paper itself is actually fairly technical and I don’t want to focus so much on the technical aspects as on the concepts and implications that the paper deals with as they pertain to issues of body composition.

More specifically I want to look at some of the common claims that are often thrown around in the world of body composition such as “Adding muscle mass significantly raises metabolic rate.” and “Fat cells burn no calories, they are metabolically inert.”  While this paper was examining the issue from a different perspective, it actually provides good data on both questions.

Specifically today’s paper examines in some detail how different tissues of the body (e.g. muscle vs. fat vs. organs) contribute to the body’s resting energy expenditure.  As well, factor such as disease, growth/development and under-nutrition are examined in terms of how they impact on different tissues in the body and their energy expenditure.

As I discuss in detail in Metabolic Rate Overview, there are four primary components to total daily energy expenditure: Resting Energy Expenditure (REE), Thermic Effect of Activity (TEA), Thermic Effect of Food (TEF) and Non-Exercise Activity Thermogenesis/Spontaneous Physical Activity (NEAT/SPA).

Of those four, resting energy expenditure plays the major role in total daily energy expenditure, generally comprising 65-70% of the total.  So looking at the differential impact of each tissue on REE tends to give  pretty decent picture of what’s going on.

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The Paper

The paper begins with an introduction to the overall concepts, pointing out estimating REE in individuals of different body sizes has been classically difficult. While body weight per se is a decent indicator, REE actually tends to scale better with body surface area.  However, this gives no indication of which tissues (and in what proportion) are contributing to overall REE.

Readers may have seen the statement that ‘The largest predictor of REE is lean body mass” and there is certainly some truth to that.  However, lean body mass (aka fat free mass) only predicts 53-88% of the variability in energy expenditure.  There are a number of reasons for this not the least of which being that lean body mass/fat free mass is not a single homogeneous tissue.

Rather, as discussed in What Does Body Composition Mean, lean body mass represents organs, skeletal muscle, bone, skin and basically everything in the body that isn’t fat mass.  And as you’ll see shortly, each of those tissues burns very different numbers of calories on a day to day basis.  Which means that variability in the amounts and proportions of those tissues will impact on overall resting energy expenditure.

Next the paper discusses the different methodologies used to estimate the resting energy expenditure of different tissues.  I don’t want to get into huge detail on this. Suffice to say that newer technology has allowed for more and more accurate methods of estimating the caloric expenditure of different tissues in the body.

While they are still not error-free (nothing in science ever is), some of the newer methods of measurement may explain why some of the oft-held beliefs about caloric expenditure and values that are often thrown out are turning out to be wrong.   Of course that also means that future developments may render current values incorrect.

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The Normal Human

The next topic addressed in the paper is an examination of the different tissues and how they contribute to resting energy expenditure in a fairly ‘average’ human being.  I’ve reproduced Table 1 from the paper below, honestly this was the main reason I wanted to examine this paper, to get this chart up on the site.

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Other refers to bone, skin, intestines and glands.
Note: the lungs have not been measured for methodological reasons but have been estimated at 200 kcal/kg similar to the liver.

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As you can see above, and quite contrary to what is commonly stated, skeletal muscle actually has a fairly low resting energy expenditure, roughly 6 calories per pound.  This is contrast to very old values of 100 calories/pound or even more recent claims that a pound of muscle will raise metabolic rate by 40-50 calories per pound.

Additionally, an in contrast to what is commonly claimed, fat cells do burn calories.  Admittedly the value is not massive (roughly 2 calories per pound) but the idea that fat cells are completely inert is also incorrect.  We now know that fat cells produce a variety of hormones, etc. (e.g. leptin, adiponectin) and that expends calories.  Again, not much per unit mass of fat, but for someone carrying a lot of fat mass, this does add up.

Perhaps of more relevance, and getting back to the paper per se, the primary contributor to resting energy expenditure comes from the organs with the liver, heart, kidneys and brains contributing roughly 70-80% of total resting energy expenditure.  This is despite the fact that they only make up approximately 7% of total body weight.  That is, despite their relatively small weight, they are simply massively metabolically active on a day to day basis.

In contrast, while skeletal muscle may contribute roughly 40% of total weight (a little bit less in women), it only contributes 28% of total resting energy expenditure.  Essentially, the relatively small caloric burn of a single pound of muscle mass is made up for by the sheer quantity of it.   Which doesn’t change the fact that adding muscle mass still won’t have a massive impact on resting energy expenditure.

To put that into mathematical perspective, gaining 20 pounds of muscle would be expected to increase resting energy expenditure by approximately 120 calories per day.  Certainly that does have an impact overall (equivalent to perhaps 10 minutes per day of moderate intensity cardio) but also keep in mind the time frames involved to gain that much muscle mass.  Expecting that adding a bit of muscle to have massive impacts on metabolic rate in the short-term is simply unrealistic; a few pounds gained simply won’t have any major impact.

Rather, I would expect that any real impact of building muscle mass on The Energy Balance Equation is going to come through the training done to stimulate/maintain muscle mass increases along with the caloric cost of building the muscle in the first place.   But once it’s there, the caloric expenditure at rest of skeletal muscle is simply very low.

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Factors Affecting Energy Expenditure

Having examined the average contribution of different tissues to the body, the researchers then look at a host of other topics, only a few of which I’m going to really look at in any detail.

Growth and development is covered first, examining how the ratios of energy expenditure to body weight changes over the lifespan.  Since most reading this are full grown adults, the changes that occur from childhood to maturity don’t seem that relevant.

One issue of some importance is covered next and that’s the effect of differences in body size between individuals.  In general, if you look at two people of different body sizes, larger folks tend to have lower resting energy expenditures relative to their body mass.  This is most likely related to differences in the proportion of organ weight (recall from above that the organs contribute the most to overall resting energy expenditure) to total body weight.

Meaning this: on average, organ weight won’t vary much between individuals.  So if one person is larger than another, that difference in size is likely to occur through changes in either muscle mass or fat tissue, neither of which makes massive contributions to resting energy expenditure (and differences in body composition won’t have nearly the impact that most think given the relatively small difference in caloric expenditure between muscle mass and fat mass).

Practically, this means that equations that estimate resting energy expenditure based solely on body weight will tend to overestimate larger individuals to some degree.  Of course, as I recently discussed in Adjusting the Diet, since all estimates of energy expenditure and/or caloric intake have to be adjusted based on real-world changes anyhow, I’m not sure how important this is practically.

I should probably address a question that I imagine will come up in the comments, given the enormous variability in energy expenditure per pound of tissue, where does the quick estimate of 10-11 calories/pound (22-24 cal/kg) come from?  And the answer is that it’s basically a weighted average of the above values.  That is, if you took the values for caloric expenditure/unit weight times their contribution to overall weight and worked it out, you’d get a value that was pretty close to the quick estimate value.  Again, this will tend to vary based on actual body size due to differences in the relative contribution of each tissue to the body’s total weight.

Next the researchers looked at the impact of both undernutrition and refeeding on energy expenditure at rest.  During underfeeding, they point out that skeletal muscle and fat are generally the major tissue lost while organs are spared.  This tends to have the impact of raising the relative proportion of energy expenditure to body weight (because the low energy expenditure tissues are being lost).  Of course, with extended dieting, there is also an adaptive component of metabolic rate reduction as all tissues in the body tend to slow their overall energy expenditure.

In contrast, during refeeding, there is often a hypermetabolic state that occurs, possibly due to increases in protein synthesis, core temperature and the thermic effect of food.  As well, there are a number of hormonal effects that occur when calories are raised, a topic I discuss in more detail in The Full Diet Break, all of which may have potentially beneficial impacts on overall energy expenditure and metabolic rate.

Finally the researchers examine the impact of disease and injury on energy expenditure but I don’t find that terribly relevant to this article.

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Summing Up

The main point that I wanted to make with today’s research review was to clear up some of the oft-held (and unfortunately incorrect) ideas regarding the impact of things like skeletal muscle mass and fat mass on resting energy expenditure.  Based on current data, the idea that skeletal muscle burns massive numbers of calories would appear to be 100% incorrect.

Rather, skeletal muscle actually burns fairly few calories on a per pound basis; it primarily has a major impact on resting energy expenditure because there is a good bit of it.  But adding even moderate amounts of muscle are unlikely to massively impact on energy expenditure.  As noted above, I expect the major effect to be from the effort of stimulating muscle mass gains along with the energy needed to synthesize that muscle tissue.  But once it’s there it doesn’t burn many calories.

Rather, the majority of resting energy expenditure is generated by the organs which, despite their small size, burn a massive number of calories per unit weight.  Someone on the support forum jokingly asked “So how do I hypertrophy my liver?”

Finally, fat cells, while not having much of a calorie burn do burn calories.  In fact, they are only about 1/3rds of the burn of skeletal muscle (2 cal/lb vs. 6 cal/lb respectively).  While low, someone carrying a lot of fat will have this add up and it will contribute to overall resting energy expenditure.

Stallknecht B et. al. Are blood flow and lipolysis in subcutaneous adipose tissue influenced by contractions in adjacent muscles in humans? Am J Physiol Endocrinol Metab. 2007 Feb;292(2):E394-9.

Aerobic exercise increases whole-body adipose tissue lipolysis, but is lipolysis higher in subcutaneous adipose tissue (SCAT) adjacent to contracting muscles than in SCAT adjacent to resting muscles? Ten healthy, overnight-fasted males performed one-legged knee extension exercise at 25% of maximal workload (Wmax) for 30 minutes followed by exercise at 55% Wmax for 120 minutes with the other leg and finally exercised at 85% Wmax for 30 minutes with the first leg. Subjects rested for 30 minutes between exercise periods. Femoral SCAT blood flow was estimated from washout of (133)Xe and lipolysis was calculated from femoral SCAT interstitial and arterial glycerol concentrations and blood flow. In general, blood flow as well as lipolysis was higher in femoral SCAT adjacent to contracting than adjacent to resting muscle (time 15-30 min: blood flow: 25% Wmax: 6.6 +/- 1.0 vs. 3.9 +/- 0.8 ml 100 g(-1) min(-1), P < 0.05; 55% Wmax: 7.3 +/- 0.6 vs. 5.0 +/- 0.6, P < 0.05; 85% Wmax: 6.6 +/- 1.3 vs. 5.9 +/- 0.7, P > 0.05; lipolysis: 25% Wmax: 102 +/- 19 vs. 55 +/- 14 nmol 100 g(-1) min(-1), P = 0.06; 55% Wmax: 86 +/- 11 vs. 50 +/- 20, P > 0.05; 85% Wmax: 88 +/- 31 vs. -9 +/- 25, P < 0.05). In conclusion, blood flow and lipolysis are generally higher in SCAT adjacent to contracting than adjacent to resting muscle irrespective of exercise intensity. Thus, specific exercises can induce “spot lipolysis” in adipose tissue. Key words: exercise, spot lipolysis, microdialysis.

Background

The idea of spot reduction is one that has floated around the fitness body recomposition world for decades.   Men want the ever desirable six-pack and can be seen doing abs until the cows come home, women try to slim hips and thighs with endless reps on the inner/outer thigh machine.

Hour long ‘abs’ or ‘buns/thighs’ classes filled with nearly an hour of high rep movements for the specific area can be found in most commercial gyms.  Even in the bodybuilding world, where people really should know better, some still argue that spot reduction can occur and that working a given muscle group will help reduce fat in that specific area.   I addressed this topic somewhat in The Stubborn Fat Solution since some of what I discuss in that book could readily be confused with spot reduction (it’s not).

For the most part, the idea of spot reduction has been resoundly denied by folks in the field (with the occasional heretic or book seller suggesting it is still possible). Various lines of research are usually cited including those showing no difference in skinfolds in the arms of tennis players (who typically use one arm more than the other).

An example I’ve often used is that “If spot reduction worked, people who ate a lot should have skinny faces.” A bit silly but I think it gets the point across.  If working a specific muscle group reduced fat only in that area, that’s how it should work. But it doesn’t.  Or certainly doesn’t seem to.  But, for the most part, the idea hasn’t been directly tested to my knowledge.

In that context, I should note for the sake of background that there are three primary steps involved in fat loss that might potentially be influenced although today’s study only focuses on two.  Those steps are

1. Lipolysis (the actual fat breakdown)
2. Blood flow (critical for transport of the broken down fat to other tissues for ‘burning’)
3. Oxidation (the actual ‘burning’ of fat in tissues such as the liver or skeletal muscle)

Is it possible that performing local activity can impact on some aspect of the above in a way that might make spot reduction or performing endless reps of local exercises worthwhile in terms of fat loss?  That’s what today’s study set out to examine: do contractions in a specific muscle impact on either lipolysis or blood flow (oxidation was not measured) in the adjacent fat cells.

And although it was published several years ago, it still seems to be making the rounds (being cited as ‘evidence’ for spot reduction); as well, the idea of spot reduction is one that refuses to die.  So it’s worth seeing what the real or potentially real effects actually are.

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The Study

Using a couple of different methods (that I’m not going to detail) to measure actual blood flow and lipolysis , the study had subjects perform lower body exercise (they called it one leg leg extension but this probably means one legged cycling) at various intensities while resting the other leg.  That way, blood flow/lipolysis could be measured for the exercise versus the unexercised leg.

This allowed them to compare lipolysis and blood flow in response to local exercise to the non-exercised control leg.  This is actually critically important as any type of whole body exercise would tend to have systemic effects; that is impacting on fuel metabolism all over the body.  By limiting exercise to a single leg, the researchers were able to measure the response only in the fat cells close to the muscles being worked and compare that to the unworked msuscle to see what differences occurred.

Exercise was performed at 25%, 55% and 85% of maximum power output with a 30 minute break and the subjects switched legs from one intensity to the next.  This also acted as a control so that the previous bout of exercise wasn’t impacting on the next bout, since the previously exercised leg got the longer break.  As mentioned above, blood flow and lipolysis was compared between the exercised leg and the rested leg to see what difference the exercise had.

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Results

And, as indicated in the abstract above, both lipolysis and blood flow were increased for the exercised vs. non-exercised leg although this only occurred at the two lower intensities of exercise.    At the highest intensity of exercise, no change was seen.

Before getting to specific numbers, a question worth addressing is why this would have happened.  The researchers proposed two possible reasons for their observation.

First, local changes in hormones (or a synergy between changes in hormones and blood flow) are most likely responsible but there is a larger question of why this would occur in the first place, a point that the researchers specifically made.  By why I mean why the system would work that way in terms of improving physiological functioning.

The reason for asking this question is this: fat mobilized from a specific area of body fat (say the thigh) can’t actually be used for fuel by the muscle underneath it (e.g. the quadriceps).  The blood flow of skeletal muscle and fat cells are separate and any fat mobilized from an adjacent area will go into local circulation; again, it can’t be used directly by that muscle.

So there’s no really logical physiological reason that working a given muscle would would cause fatty acids to be mobilized; that muscle can’t use them.  Of course, physiology doesn’t have to be logical to work a certain way and worrying about the reasons why instead of the observation of what happened can make you lose the forest for the trees.

Related to this, the researchers point out clearly that there is no indication that these results will actually result in spot reduction as fat stores in the affected areas could simply be replenished after the exercise bout.  They didn’t measure fat storage after the exercise bout stopped and process that occurs quite often is fatty acid re-esterification, basically mobilized fat that isn’t burned off elsewhere in the body simply gets stored back in the fat cell.  In some exceedingly strange cases, fat mobilized in one area of the body can be restored in fat cells somewhere else.

The researchers also suggest that localized increase in temperature, which can also impact on blood flow may have also been involved in the measured response.  I discuss this aspect of fat cell mobilization in The Stubborn Fat Solution as local temperature is known to impact on blood flow in the area.  Cold tends to cause vasoconstriction and heat vasodilation so there might actually be some logic to those rubber belts and such that warm the area before exercise.

In any case, for whatever reasons, through whatever mechanism, working a given muscle for 30 minutes at low to moderate intensities did increase fat cell lipolysis in blood flow.

Aha!  Spot reduction is possible, right?  Hang on.

Although clearly local exercise did impact on fat cell lipolysis and blood flow, you might note something I left out of the above discussion: the acutal quantitative impact of this.  That is, how much extra fat was actually mobilized for fuel, potentially to be burned off.

Addressing that very thing, based on the measured changes in blood flow and lipolysis, the researchers estimate that, in 30 minutes of local exercise, an additional .6-2.1 milligrams (one milligram is one thousandth of a gram) per 100 grams of adipose tissue adjacent to contracting muscle was mobilized.

Let me put that in context.   First let’s assume that you’re carying a whopping 5 kg (11.1 pounds) of fat in a specific area.

If local exercise can mobilize 0.6-2.1 milligrams of fat per 100 grams of fat mass, that works out to:

0.6-2.1 mg/100 grams * 1000 grams/kg * 5 kg = 30-105 milligrams of fat.

Or 0.03-0.1 gram of extra fat mobilized in 30 minutes of activity.

Now, a single pound of fat (0.454 kg) contains about 400 grams of fat so our hypothetical 11.1 pounds of fat contains 4,440 grams of fat.  And 30 minutes of local activity mobilized at most 0.1 gram of fat.  Whoo hoo.  You’ll be ripped in about 1000 years.

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Summing Up

And, so far as I’m concerned, that should be the death knell for the idea of spot reduction.  Yes, there appears to be an effect whereby working a given muscle impacts on local fat cell metabolism but the effect is completely and utterly irrelevant in quantatitive terms.  The amount of fat mobilized due to increased hormones or blood flow is simply insignificant to anything in the real world.

There is also the fact that, compared to something like full body cardio types of activities, local single muscle group activities burn tiny amounts of calories.  Doing cardio for 30 minutes at even a reasonable caloric burn of 5 cal/minute (very easy) burns 150 calories.  If you get say 90% fat utilization for fuel, you’ve burned 15 grams of fat.  Compared to the 0.1 gram you might mobilize doing crunches or leg lifts.

As well, the whole body activity will impact on fuel utilization and hormones in ways that much more massively impact on lipolysis and blood flow.  Simply, spending an hour doing localized exercise pales in comparison to the fat loss effects of even moderate cardio.  Wasting time with ab or buns/thighs classes is simply a waste of time in terms of any sort of local fat reduction.

Kouri EM, et. al.Fat-free mass index in users and nonusers of anabolic-androgenic steroids.  Clin J Sport Med. (1995) 5(4):223-8.

We calculated fat-free mass index (FFMI) in a sample of 157 male athletes, comprising 83 users of anabolic-androgenic steroids and 74 nonusers. FFMI is defined by the formula (fat-free body mass in kg) x (height in meters)-2. We then added a slight correction of 6.3 x (1.80 m – height) to normalize these values to the height of a 1.8-m man. The normalized FFMI values of athletes who had not used steroids extended up to a well-defined limit of 25.0. Similarly, a sample of 20 Mr. America winners from the presteroid era (1939-1959), for whom we estimated the normalized FFMI, had a mean FFMI of 25.4. By contrast, the FFMI of many of the steroid users in our sample easily exceeded 25.0, and that of some even exceeded 30. Thus, although these findings must be regarded as preliminary, it appears that FFMI may represent a useful initial measure to screen for possible steroid abuse, especially in athletic, medical, or forensic situations in which individuals may attempt to deny such behavior.

Background

Last Thursday, I published a guest article by Alan Aragon entitled Supplement Marketing on Steroids, which was a scientific and technical analysis of recent claims regarding rates of muscle mass gain and potential maximum size by the website Testosterone.nation.  In a different context, this topic was previously covered on this site in the article What Is my Genetic Muscular Potential?

As expected, this caused quite an uproar as can be seen in the comments section of that article.

Of course, similar discussion has gone on on the T-nation site itself although, I should note that the term ‘discussion’ is debatable at best.  The moderators at T-nation are very censorship heavy, controlling information flow with an iron fist.  They ensure that only certain responses will be seen to keep their readers from the truth of their nonsensical claims.  My friend Matt Perryman made two posts to his site AmpedTraining which are worth reading in this regards:

Monday Morning Censorship Protest Real T-Men Speak Out

Real T-Men Speak Out Part 2

But I’m getting a bit off topic.

In the article What Is my Genetic Muscular Potential, one of the models presented is that of Casey Butt, who did an analysis of top bodybuilders over many years to develop an equation that predicts maximum potential for muscle growth.  There are several assumptions inherent in his model including that the bodybuilders are natural, along with the idea that bodybuilders represent the pinnacle of muscle building potential.

I’d note that Casey’s model lined up quite well with the models presented by myself, Alan Aragon and Martin Berkhan.  We all approached it from a slightly different direction but, based on our combined experience over the years, all ended up at basically the same place.

But among other gems of argument on T-nation was criticism that Casey’s model was inaccurate because it only examined bodybuilders from ages past (another was that athletes with better muscular potential would go into sports that weren’t bodybuilding).

That is, that current improvements in nutrition and training will have improved the muscular potential for natural bodybuilders beyond what Casey’s analysis shows.  Never mind that the three other models presented by myself, Alan and Martin and that includes top natural bodybuilding competitors completely line up with it perfectly as well.

Which brings us to today’s paper.

As another piece of background, I assume that readers are familiar with the concept of the Body Mass Index (BMI).  BMI gives a relationship of weight to height and is often used to determine things like under-, normal and over-weight.  With certain caveats, discussed in Body Composition Methods Part 1 BMI can be a reasonably accurate measure but it suffers from a major problem for athletes: it doesn’t distinguish between fat mass and lean body mass.

Towards this goal, researchers have tried to develop what they call a Fat-Free Mass Index (FFMI) conceptually similar to the BMI but a measure of fat free mass (another term for lean body mass) relative to height.  Which is what this study looked at…in both users and non-users of steroids.  They wanted to see if there were any fundamental differences in the FFMI that either occurred or were achievable between users of steroids and nonusers.  Clearly this ties in to the current debate over genetic maximums along with rates of muscle gain.

Just for completeness FFMI is defined as fat free mass / height squared.

Where fat free mass is in kg and height is in meters.

If you want to calculate your current FFMI (for example, to compare it to the values I’m going to discuss below), there is an easy to use online

FFMI Calculator.

The Study

The researchers recruited 156 men, athletes, from gyms in the Boston and Los Angeles area, of those 156 men 134 data points were used.  A full physical examination was given including measurements of height, weight and body fat, the latter computed from the sum of 6 skinfold measurements and the Jackson and Pollock equation.  A history of previous steroid use was obtained by personal interview along with urine testing. Now we might quibble with this as athletes are known to lie about drug use.

But as the researchers state:

Briefly, no evidence suggested that any athlete had deliberately misrepresented his steroid use, nor did any urine test contradict an athlete’s verbal report.

Of course, that still doesn’t prove anything, athletes have been beating drug tests for years.  But within the context of this paper, that’s as good as it’s going to get.

To the above data set of 156 men, an additional 23 men were recruited from a separate study examining the impact of testosterone cypionate, the same measurements were given to them.  So of the total 157 individuals (134 from the first group, 23 from the second group), 74 (47%) had never used steroids and 83 (53%) had used steroids.  Fifty-two of the subjects had used steroids within the past year.

Adding to the validity of the data set, in the context of Alan’s article and this debate, the researchers state (bold added for emphasis) that:

The nonusers included many dedicated bodybuilders. Several had competed successfully in “natural” bodybuilding contests, two held world records in strength events, and many others were recognized by their associates as highly successful weightlifters.  Thus the nonuser group probably included individuals who closely approached the maximum limits of muscularity that could be attained without drugs.

I’m quoting that bit in full so that people can’t try to dismiss this in the comments section or on forums by trying to argue that these were recreational lifters or that they didn’t train hard: these guys were near the top of the heap in terms of natural competitors.

Results

In any case, with this data set in tow, the researchers calculated the FFMI for both the steroid user and nonuser groups.   I’ve reproduced the results in full in the table below; please note that I added the column for lean body mass which I simply calculated by taking weight in kg by body fat percentage (without the error bars).

Table 1: Characteristics of Steroid Users and Nonusers

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Now, as the results above show, even with steroid use, the users were still considerably lighter on average than even the typical IFBB pro with only 175 pounds of lean body mass.  Contest weights in the 200’s are common nowadays; of course modern bodybuilders use far more than just anabolic steroids.  Growth hormone, IGF-1 and all kinds of ancillaries are in use now.

And, again, look at the nonusers.  An average lean body mass of 158 pounds.  Of course, that’s not taking into account the error bars. Some of the subjects in each group were larger than this and some smaller.

Just for the hell of it, let’s see what the absolute best values we can get out of the above are.

I’ll assume the heaviest body weight for both the steroid and non-steroid users and the lowest body fat percentage so it’s an equal comparison; this calculation will show the absolute biggest guys in both groups in terms of how much lean body mass that they can carry.

All I’ve done is taken the average weight plus the error bar for weight and average body fat percentage minus the error bar for body fat percentage.  I’ve shown the numbers in the table below.

Table 2: Maximum Lean Body Mass in the Sample Group

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Those are the two biggest guys in the sample size, again using the highest body weight and lowest body fat percentage.  Notice anything, for example how the values line up if you go back and look at Supplement Marketing on Steroids or What Is my Genetic Muscular Potential?

In this MODERN sample of top level bodybuilders using drugs or not, the steroid users are about what Arnold was at his peak (average competition weight of 235).   And not a single one of the nonusers exceeds the maximums set by my, Alan, Martin or Casey’s model in What Is my Genetic Muscular Potential.

This is despite being world record holders and top level competitors in natural bodybuilding.   Presumably they are using every modern nutritional and training trick available.  And they still can’t break the model’s predictions.  Not a single one of them.

The researchers concluded, based on this that the upper limits of FFMI in non-steroid users is roughly 25 with an abrupt stopping point; steroid users can surpass this with FFMI values as high as 32 occurring in this study for the largest individual.  But for nonusers, 25 is it.

In support of this, the researchers obtained other data, similar to Casey’s original analysis.  Using data on the Mr. American winners from the years of 1939-1959 (a time when presumably training and nutrition was improving) an estimated FFMI was done.  With one or two outliers, none exceeded a FFMI of 25.

My Comments

Now the researchers were using this whole approach to basically try to find a way, more or less, to determine whether or not a given individual was on steroids.  Basically, they found through their sample that, without drugs, there is simply a cap on how much fat free mass an individual can carry.

And a FFMI of roughly 25 represents the natural limit.  And this value hasn’t changed since 1930.  Because human genetics haven’t changed.  And no amount of training or nutrition will EVER change that.

Of course that’s not really why I choose to analyze this study.  This paper represents a meticulously analyzed modern data set showing that, assertion by T-nation moderators to the contrary, the potential for muscular gain in natural athletes has not gone up or changed due to improvements in training, nutrition or anything else.  Claims that ‘We have naturals exceeding 230 pounds of lean body mass on our forums’ are either delusion, lies, or both.  Ok, not exactly.

In that context, it is worth noting a specific comment by the researchers which I will again quote in full:

Fourth, our formula may not be satisfactory for fat individuals.  Because a gain in the fat component of the body is consistently accompanied by some gain in the lean component, it is possible that fat individuals might be able to exceed substantially an FFMI of 25 without steroids.

It is amusing to note that, invariably the pictures of T-nation members held up as ‘proof’ that LBM can go higher than what’s in my, Alan, Martin or Casey’s models are invariably carrying a tremendous amount of body fat.  But the reality is that, dieted down, the loss of connective tissue, etc. that accompanied the development of their frank obesity would bring them right back down to the numbers predicted by the various models.  They wouldn’t end up with more than 200 pounds LBM after the two plus years of dieting it would take to get the fat off.

And, while I’m sure this paper will do nothing to quell the claims of the T-nation moderators or the folks who want to believe that they are the lone exception, the evidence and research based facts speak for themselves.  Natural limits exist and no amount of magic pills, powders or potions will let you exceed them unless those magic pills are anabolic steroids.  That’s reality folks, it may not be pretty or sexy but it is true.

Please not that, again, I’ve turned off moderation for this article to encourage discussion and meaningful debate (in direct contrast to the T-nation approach).  Please keep it civil and I will be keeping an eye for outright trolling or what have you.

Tabata I. et. al.  Effects of moderate-intensity endurance and high-intensity intermittent training on anaerobic capacity and VO2max.  Med Sci Sports Exerc. (1996) 28(10):1327-30.

This study consists of two training experiments using a mechanically braked cycle ergometer. First, the effect of 6 wk of moderate-intensity endurance training (intensity: 70% of maximal oxygen uptake (VO2max), 60 min.d-1, 5 d.wk-1) on the anaerobic capacity (the maximal accumulated oxygen deficit) and VO2max was evaluated. After the training, the anaerobic capacity did not increase significantly (P > 0.10), while VO2max increased from 53 +/- 5 ml.kg-1 min-1 to 58 +/- 3 ml.kg-1.min-1 (P < 0.01) (mean +/- SD). Second, to quantify the effect of high-intensity intermittent training on energy release, seven subjects performed an intermittent training exercise 5 d.wk-1 for 6 wk. The exhaustive intermittent training consisted of seven to eight sets of 20-s exercise at an intensity of about 170% of VO2max with a 10-s rest between each bout. After the training period, VO2max increased by 7 ml.kg-1.min-1, while the anaerobic capacity increased by 28%. In conclusion, this study showed that moderate-intensity aerobic training that improves the maximal aerobic power does not change anaerobic capacity and that adequate high-intensity intermittent training may improve both anaerobic and aerobic energy supplying systems significantly, probably through imposing intensive stimuli on both systems.

Background

In recent years, training and the Internets have become interval crazy.  Everybody wants to do nothing but interval training all the damn time (with some even proclaiming that any non-interval training is not only useless but downright detrimental).

Now, I’ve written extensively about this in what must be about a 12 part series on Steady State vs. Interval Training here on the site.  I’m not going to rehash the entirety of that series, mind you; go read it.  But simply, both intervals and steady state have their place in training.  Arguments that one is inherently or always superior to the other has more to do with marketing than reality.

But among other aspects of this particular meme, the idea of the Tabata protocol (often abbreviated Tabatas) gets bandied about all the time.  And the problem is that people are using the term to describe something that they don’t really understand.  What has happened is that a bunch of people who don’t really know what they are talking about have written so much about the protocol that what it actually is or accomplishes has been completely diluted.

So I figured I’d undilute it by actually examining the study that the whole set of claims and supposed ‘protocols’ are based on.  Because, as is so often the case, what people think they are doing as ‘Tabatas’ are nothing like what the actual study did.  And most people who think they are doing the Tabata protocol are doing absolutely nothing of the sort.

As a bit of history, the protocol was actually originally developed by a Japanese speed skating coach and later studied by researchers; I bring this up because speed skating is actually a very peculiar sport in a lot of ways (something that I have insight into as I’ve spent the last 5 years training full time as a skater).  But I’m not going to get that into detail here; I simply mention it for completeness.

The Study

The study set out to compare both the anaerobic and aerobic adaptations (in terms of one parameter only, VO2 max) to two different protocols of training.  The study recruited 14 active male students who were, at best moderately trained (VO2 max was roughly 50 ml/kg/min which is average at best; elite endurance athletes have values in the 70-80 range).

All work including the pre- and post tests were done on a mechanically braked bicycle ergometer; this is an important point that is often ignored and I’ll come back to in the discussion.  Every test or high-intensity workout was proceeded by a 10 minute warm-up at 50% of VO2 max (This is maybe 60-65% maximum heart rate).

The two primary tests were VO2 max and the maximal accumulated oxygen deficit (this is a test of anaerobic capacity, basically people with higher anaerobic capacity can generate a larger oxygen deficit) and then subjected to one of two training programs.

The first program was a fairly standard aerobic training program, subjects exercised 5 days/week at 70% of VO2 max for 60 minutes at a cadence of 70 RPMs for 6 straight weeks.  The intensity of exercise was raised as VO2 max increased with training to maintain the proper percentage.  VO2 max was tested weekly in this group and the maximal accumulated oxygen deficit was measured before, at 4 weeks and after training.

The second group performed the Tabata protocol.  For four days per week they performed 7-8 sets of 20 seconds at 170% of VO2 max with 10 seconds rest between bouts, again this was done after a 10 minute warm-up.  When more than 9 sets could be completed, the wattage was increased by 11 watts.  If the subjects could not maintain a cadence of 85RPM, the workout was ended.

On the fifth day of training, they performed 30 minutes of exercise at 70% of VO2 max followed by 4 sets of the intermittent protocol and this session was designed to NOT be exhaustive.  The anaerobic capacity test was performed at the beginning, week 2, week 4 and the end of the 6 week period; VO2 max was tested at the beginning and at week 3, 5 and the end of training.

Results

For group 1, the standard aerobic training group, while there was no increase in anaerobic capacity, VO2 max increased significantly from roughly 52 to 57 ml/kg/min (I say roughly because the paper failed to provide vaules, I’m going by what’s in the graphic below).  Frankly, given the lack of anaerobic contribution to steady state training, the lack of improvement in this parameter is absolutely no surprise.

For group 2, both the anaerobic capacity and VO2 max showed improvements.  VO2 max improved in the interval group from 48 ml/kg/min to roughly 55 ml/kg/min (see graphic below).  It is worth noting that the interval group was starting with a lower value and may have had more room for improvement.  Also note that they still ended up with a lower Vo2 max than the steady state group.

I’ve put Figure 2 from the paper (showing improvements in VO2 max) below

Click to Enlarge

As I noted, pay attention to the fact that the Tabata group (black line, filled circles) started lower than the steady state group, they also still ended up lower than the steady state group.  As well, note that pattern of improvement, the Tabata group got most of their improvement in the first 3 weeks and far less in the second three weeks.  The steady state group showed more gradual improvement across the entire 6 week period but it was more consistent.  As the researchers state regarding the Tabata group

After 3 wk of training, the VO2 max had increased significantly by 5+-3ml.kg/min.  It tended to increase in the last part of the training period but no significant changes [emphasis mine] were observed.

Basically, the Tabata group improved for 3 weeks and then plateaued despite a continuingly increasing workload.  I’d note that anaerobic capacity did improve over the length of the study although most of the benefit came in the first 4 weeks of the study (with far less over the last 2 weeks).

My Comments

First and foremost, there’s no doubt that while the steady state group only improved VO2 max, it did not improve anaerobic capacity; this is no shock based on the training effect to be expected.  And while the Tabata protocol certainly improved both, not only did the Tabata group still end up with a lower VO2 at the end of the study, they only made progress for 3 weeks before plateauing on VO2 max and 4 weeks for anaerobic capacity.

Interestingly, the running coach Arthur Lydiard made this observation half a century ago; after months of base training, he found that only 3 weeks of interval work were necessary to sharpen his athletes.  More than that was neither necessary nor desirable.  Other studies using cycling have found similar results: intervals improve certain parameters of athletic performance for about 3 weeks or 6 sessions and then they stop having any further benefit.

I’ve asked this question before but for all of the ‘All interval all the time’ folks, if intervals stop working after 3-4 weeks, what are people supposed to do for the other 48-49 weeks of the year.  Should they keep busting their nuts with supra-maximal interval training for no meaningful results?

On that note, it’s worth mentioning that the Tabata group actually did a single steady state workout per week.  Is it at all possible that this contributed to the overall training effect (given that 70% VO2 max training improved VO2 max in the steady state only group)?  Does anybody else find it weird that the Tabata promoters ignore the fact that the Tabata group was doing steady state work too?

It’s also relevant to note that the study used a bike for training.  This is important and here’s why: on a stationary bike, when you start to get exhausted and fall apart from fatigue, the worst that happens is that you stop pedalling.  You don’t fall off, you don’t get hurt, nothing bad happens.  The folks suggesting high skill movements for a ‘Tabata’ workout might want to consider that.  Because when form goes bad on cleans near the end of the ‘Tabata’ workout, some really bad things can happen.  Things that don’t happen on a stationary bike.

As well, I want to make a related comment: as you can see above the protocol used was VERY specific. The interval group used 170% of VO2 max for the high intensity bits and the wattage was increased by a specific amount when the workout was completed.  Let me put this into real world perspective.

My VO2 max occurs somewhere between 300-330watts on my power bike, I can usually handle that for repeat sets of 3 minutes and maybe 1 all out-set of 5-8 minutes if I’m willing to really suffer.  That’s how hard it is, it’s a maximal effort across that time span.

For a proper Tabata workout, 170% of that wattage would be 510 watts (for perspective, Tour De France cyclists may maintain 400 watts for an hour).  This is an absolutely grueling workload.  I suspect that most reading this, unless they are a trained cyclist, couldn’t turn the pedals at that wattage, that’s how much resistance there is.

If you don’t believe me, find someone with a bike with a powermeter and see how much effort it takes to generate that kind of power output.  Now do it for 20 seconds.  Now repeat that 8 times with a 10 second break.  You might learn something about what a Tabata workout actually is.

My point is that to get the benefits of the Tabata protocol, the workload has to be that supra-maximal for it to be effective.  Doing thrusters or KB swings or front squats with 65 lbs fo 20 seconds doesn’t generate nearly the workload that was used during the actual study.  Nor will it generate the benefits (which I’d note again stop accruing after a mere 3 weeks).  You can call them Tabatas all you want but they assuredly aren’t.

Finally, I’d note that, as I discussed in Predictors of Endurance Performance VO2 max is only one of many components of overall performance, and it’s not even the most important one.  Of more relevance here, VO2 max and aerobic endurance are not at all synonymous, many people confuse the two because they don’t understand the difference between aerobic power (VO2 max) and aerobic capacity (determined primarily by enzyme activity and mitochondrial density within the muscle).  Other studies have shown clearly that interval work and steady state work generate different results in this regards, intervals improve VO2 max but can actually decrease aerobic enzyme activity (citrate synthase) within skeletal muscle.

The basic point being that even if the Tabata group improved VO2 max and anaerobic capacity to a greater degree than the steady state group, those are not the only parameters of relevance for overall performance.

Summing Up

First, here’s what I’m not saying.  I’m not anti-interval training, I’m not anti-high intensity training.  I am anti-this stupid-assed idea that the only type of training anyone should ever do is interval training, based on people’s mis-understanding and mis-extrapolation of papers like this.

High-intensity interval training and the Tabata protocol specifically are one tool in the toolbox but anybody proclaiming that intervals can do everything that anyone ever needs to do is cracked. That’s on top of the fact that 99% of people who claim to be doing ‘Tabatas’ aren’t doing anything of the sort.

Because 8 sets of 20″ hard/10″ easy is NOT the Tabata protocol and body-weight stuff or the other stuff that is often suggested simply cannot achieve the workload of 170% VO2 max that this study used.  It may be challenging and such but the Tabata protocol it ain’t.

Gardner CD et. al. Comparison of the Atkins, Zone, Ornish, and LEARN diets for change in weight and related risk factors among overweight premenopausal women: the A TO Z Weight Loss Study: a randomized trial. JAMA. (2007) 297(9):969-77.

CONTEXT: Popular diets, particularly those low in carbohydrates, have challenged current recommendations advising a low-fat, high-carbohydrate diet for weight loss. Potential benefits and risks have not been tested adequately. OBJECTIVE: To compare 4 weight-loss diets representing a spectrum of low to high carbohydrate intake for effects on weight loss and related metabolic variables. DESIGN, SETTING, AND PARTICIPANTS: Twelve-month randomized trial conducted in the United States from February 2003 to October 2005 among 311 free-living, overweight/obese (body mass index, 27-40) nondiabetic, premenopausal women. INTERVENTION: Participants were randomly assigned to follow the Atkins (n = 77), Zone (n = 79), LEARN (n = 79), or Ornish (n = 76) diets and received weekly instruction for 2 months, then an additional 10-month follow-up. MAIN OUTCOME MEASURES: Weight loss at 12 months was the primary outcome. Secondary outcomes included lipid profile (low-density lipoprotein, high-density lipoprotein, and non-high-density lipoprotein cholesterol, and triglyceride levels), percentage of body fat, waist-hip ratio, fasting insulin and glucose levels, and blood pressure. Outcomes were assessed at months 0, 2, 6, and 12. The Tukey studentized range test was used to adjust for multiple testing. RESULTS: Weight loss was greater for women in the Atkins diet group compared with the other diet groups at 12 months, and mean 12-month weight loss was significantly different between the Atkins and Zone diets (P<.05).

Mean 12-month weight loss was as follows: Atkins, -4.7 kg (95% confidence interval [CI], -6.3 to -3.1 kg), Zone, -1.6 kg (95% CI, -2.8 to -0.4 kg), LEARN, -2.6 kg (-3.8 to -1.3 kg), and Ornish, -2.2 kg (-3.6 to -0.8 kg). Weight loss was not statistically different among the Zone, LEARN, and Ornish groups. At 12 months, secondary outcomes for the Atkins group were comparable with or more favorable than the other diet groups.  CONCLUSIONS: In this study, premenopausal overweight and obese women assigned to follow the Atkins diet, which had the lowest carbohydrate intake, lost more weight and experienced more favorable overall metabolic effects at 12 months than women assigned to follow the Zone, Ornish, or LEARN diets. While questions remain about long-term effects and mechanisms, a low-carbohydrate, high-protein, high-fat diet may be considered a feasible alternative recommendation for weight loss.

Background

When this paper came out a couple of years ago, there was a ton of press about this study with further claims of low-carb metabolic advantages (several fitness related blogs have already stated that the Atkins diet generated significantly greater weight losses) based on it; of course as you’ll see the claims that were made based on the results aren’t quite as astounding as made them out to be.

I want to point out up front that I am hardly against low-carb diets even though my comments about them often leads people to think that especially when people start talking about ‘metabolic advantages’ of such diets.  For goodness sake, my first book The Ketogenic Diet was dedicated to nothing but low-carbohydrate diets and many of my diet books incorporate some type of carbohydrate restriction.

However, while I like low-carb diets and think that they are appropriate under some (but assuredly not all) situations, I don’t believe that the research supports much of a metabolic advantage (in terms of being able to lose more weight/fat at the same or higher caloric intakes). If such diets have an advantage in terms of dieting, it tends to have more to do with adherence and food intake due to appetite suppression. Which is still a benefit, mind you.  But is different than what is often being claimed.

I would entreat people to reread the above paragraph two or three more times before they start entering comments about how I’m anti-lowcarb diets.

The Study

Which brings us to this week’s study. The researchers set out to compare 4 diets of drastically different carbohydrate intake. The first was Atkins which is a very low-carbohydrate diet. The second was the Zone which is a moderate carb diet (40% carbs, 30% protein, 30% fat). The third is something called the LEARN diet which is your basic food Pyramidy type of diet with 55-60% carbs and saturated fat below 10% of total calories. Finally was Dean Ornish’s extremely high-carbohydrate, very low-fat (10% or less) diet. These diets were chosen to represent the spectrum of diets from very low carbohydrate to very high carbohydrate.

The subjects were premenopausal women between the ages of 25-50 with a body mass index between 27 and 40 who had been weight stable for the previous 2 months. Folks were excluded for various reasons. 311 subjects entered the study and were randomly assigned to one of the four groups with about 70 subjects per group.

All subjects were given a copy of the respective book and a dietitian explained the details of the diet to each. For the first 8 weeks of the study, all subjects attended a 1 hour class per week. For the remaining 10 months of the study, they were on their own.

Subjects did receive email and phone contact from the staff between the 2 and 6 month mark and the 6 and 12 month mark and small financial incentives were given for completing the data collection at the 2, 6 and 12 month time point.

Diet was assessed by a 3 day food recall (I’ll come back to this below) and energy expenditure was estimated by a 7 day activity recall.  Subjects were measured for height, weight, body fat was done by DEXA. A number of blood measures including total cholesterol and blood triglycerides were measured. So was fasting insulin and blood glucose along with blood pressure.

The Results

Ok, before getting into the details, I want to look at the overall results since that’s most of what people focused on. After 12 months on the diet, the respective weight losses were

• Atkins: 4.7 kg (10.3 lbs)
• Zone: 1.6 kg (3.5 lbs)
• LEARN: 2.6 kg (5.7 lbs)
• Ornish: 2.2 kg (4.8 lbs)

So yes, the Atkins group did get better results, 2.5 kg or more weight loss than the other diets over the span of a year. And, according to the self-reported food intakes (an issue that I’ll discuss momentarily), they did it eating the same number of calories as the other groups with both groups reporting a reduction in food intake over the length of the study. AHA, more weight loss on the same calories, there’s your metabolic advantage….

Ok, first and foremost, let’s be realistic: regardless of the fact that Atkins got double the weight loss, those results suck.  Ten pounds weight loss in one year amounts to a 3/4 lb weight loss per month in the Atkins group and half or less than that in the other groups.  By contrast, low-calorie diets that are highly controlled can generate a 7kg/15 lb weight loss over 4 weeks.  My own Rapid Fat Loss Handbook can do that in 2 weeks in some people.

Yes, fine, the study points out that even small weight losses can improve health but what dieter would be happy with that? Not many.  More like 10 lbs weight loss in 2 months. I didn’t pick that value out of a hat.

The paper shows changes in body weight at each of the 2, 6 and 12 month time spans.  And Table 3 in the paper, which I show below shows how each diet affected weight.  It tells the entire tale so far as I’m concerned.

Click to see a larger version

There are a few key observations to make from this.  The first is this: at the 2 month mark, the Atkins group was already 4kg down in body weight while the other groups had lost about 2.5 kg or so.  Recall from above that the total difference in weight loss between Atkins and the other groups was only about 2kg.  So most of the difference between the diets occurred in the first 2 months.

Now, it’s well-established that ketogenic diets can cause significant water loss in the first couple of weeks, water loss can range from 1-15 lbs over that time frame.  I’m a little guy but I can drop 7 lbs in 3 days of carbohydrate restriction or about 2.5 kg.  So the 2 kg ‘advantage’ of the Atkins diet not only could be due to water loss but in all likelihood is due to water loss.  Other studies, lasting from 4 days to 2 weeks show the same 2kg difference in weight loss.  All of which occurs early on and likely represents water drops due to carbohydrate restriction.

Another interesting point is that over the next 10 months, the Zone, LEARN and Ornish group didn’t lose an additional pound and even showed a slight trend towards regain. Read that and let it sink in for a few minutes.  Over a 12 month diet, after a small weight loss in the first 2 months, there was no additional weight loss for the next 10 months.

Rather, the entirety of their weight loss occurred during the first 8 weeks when they went to weekly meetings and they didn’t lose an ounce for the remaining 10 months.  In contrast, the Atkins group had lost about 2 kg more at the 6 month mark and regained over a kilogram at the 12 month mark, leading to the final results reported above.

As mentioned, the groups all self-reported eating roughly the same number of calories and reducing their caloric intake over the length of the study.  But let’s think about that rationally for a second: are we to honestly believe that the three groups which didn’t lose an additional pound over 10 months truly ate less over that time period? Was every single person in this study one of the metabolic miracles that exist in droves on the Internet, that can eat less let magically maintain weight?

Or is it more likely, as with tons of studies done previously, that they were under-reporting their food intake and actually eating more (possibly quite a bit more as studies of carb based diets show a systematic under-reporting of anywhere from 30-50%) than they thought or said they were?

Now, call me a cynic but I think you know where my opinion on this lays.  As I discuss in the research review Ketogenic Low-Carbohydrate Diets have no Metabolic Advantage over Nonketogenic Low-Carbohydrate Diets, when calories are strictly controlled (and protein intake is identical), there is simply no metabolic advantage or greater fat loss to be had.    Another study, which I will eventually review for the site was unable to find any measurable difference in metabolic rate for ketogenic vs. carb-based diets.  As well, caloric misreporting on carb-based diets is known to be prevalent and the only logical answer to the claims of this study (e.g. the Atkins dieters lost more weight despite ‘eating the same amount’) is that the self-reported food intakes are invalid (as they usually are).

So even was slightly greater for the Atkins diet, it’s not because of any inherent metabolic advantage. It’s because, under uncontrolled conditions, people on ketogenic diets typically eat less. In this study, they only ate a little bit less because they only lost about 2 lbs over the 10 months of the uncontrolled study period.

It’s worth noting that the researchers point out that the weight loss trajectories, meaning the trends over time, indicate that the Atkins dieters were regaining weight.  So over a longer time period for the study, the differences in weight loss would have been even smaller between the Atkins group and the others.

I should mention that based on the self-reported food intakes, a lot of the criticism of this study has to do with overall compliance to the actual diets. For example, Ornish has complained that the dieters were eating 30% fat when he prescribes only 10% fat; hence it wasn’t his diet. And the Atkins dieters were eating almost 35% carbs at the end of the study. So it wasn’t really an Atkins diet.

There is some truth to this and does raise some questions about the inherent validity of the study.  However, it also raises the point that, in free living subjects, people usually suck at adhering or properly following diets.  So even with those flaws, this study probably represents how people actually diet in the real world.

I should mention that some of the health measures (blood lipids, etc) did show a slight advantage to one or the other diets but the differences were small and hardly significant. You could generate more changes with just about any reasonable diet that actually too a decent amount of weight/fat off of someone than with the pitiful results this study found.

Summing Up

First, what I’m not saying.  As usual, folks will find a way to read this as “Lyle dislikes low-carbohydrate diets” which is incorrect.  Like all dietary approaches they have their pros and cons and are appropriate in some conditions and not in others.

For many people, they make controlling calories easier, for people with insulin resistance they often improve health parameters to a greater degree than carb-based diets.  Those are advantages to be sure but they aren’t the ones that most are fixated on (e.g. the idea that you’ll somehow lose tons of weight and fat while ignoring caloric intake).

Clearly, despite some of the current claims, simply reducing carbs doesn’t magically ‘cure’ obesity if calories don’t come down.  And studies like this demonstrate that.  Even if the Atkins diet was slightly superior to the other diets, the simply fact is that the overall weight loss was minor in all groups (this is a common finding among many studies where caloric restriction isn’t put into place).

Ten pounds total weight loss (or even true fat loss) in a year of dieting is crap results, plain and simple; whether or not it was slightly better than the other approahces wouldn’t seem to be that massively significant.  Especially when you consider that 8 of those 10 pounds occurred in the first 2 months, meaning that there was only 2 lbs more net weight loss over the next ten months of the study (the equivalent of 0.2 lbs weight loss per month).