Monday, January 30, 2012

Paleo Diet Article in Sound Consumer

I recently wrote an article for my local natural foods grocery store, PCC, about the "Paleolithic" diet.  You can read it online here.  I explain the basic rationale for Paleo diets, some of the scientific support behind it, and how it can be helpful for people with certain health problems.  I focused in particular on the research of Dr. Staffan Lindeberg at the University of Lund, who has studied non-industrial populations using modern medical techniques and also conducted clinical diet trials using the Paleo diet.
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Kleiber's law and its possible implications for obesity

Kleiber's law () is one of those “laws” of nature that is both derived from, and seems to fit quite well with, empirical data. It applies to most animals, including humans. The law is roughly summarized through the equation below, where E = energy expenditure at rest per day, and M = body weight in kilograms.

Because of various assumptions made in the original formulation of the law, the values of E do not translate very well to calories as measured today. What is important is the exponent, and what it means in terms of relative increases in weight. Since the exponent in the equation is 3/4, which is lower than 1, the law essentially states that as body weight increases animals become more efficient from an energy expenditure perspective. For example, the energy expenditure at rest of an elephant, per unit of body weight, is significantly lower than that of a mouse.

The difference in weight does not have to be as large as that between an elephant and a mouse for a clear difference in energy expenditure to be noticed. Moreover, the increase in energy efficiency predicted by the law is independent of what makes up the weight; whether it is more or less lean body mass, for example. And the law is very generic, also applying to different animals of the same species, and even the same animal at different developmental stages.

Extrapolating the law to humans is quite interesting. Let us consider a person weighing 68 kg (about 150 lbs). According to Kleiber's law, and using a constant multiplied to M to make it consistent with current calorie measurement assumptions (see Notes at the end of this post), this person’s energy expenditure at rest per day would be about 1,847 calories.

A person weighing 95 kg (about 210 lbs) would spend 2,374 calories at rest per day according to Kleiber's law. However, if we were to assume a linear increase based on the daily calorie expenditure at a weight of 68 kg, this person weighing 95 kg would spend 2,508 calories per day at rest. The difference of approximately 206 calories per day is a reflection of Kleiber's law.

This difference of 206 calories per day would translate into about 23 g of extra body fat being stored per day. Per month this would be about 688 g, a little more than 1.5 lbs. Not a negligible amount. So, as you become obese, your body becomes even more efficient on a weight-adjusted basis, from an energy expenditure perspective.

One more roadblock to go from obese to lean.

Now, here is the interesting part. It is unreasonable to assume that the extra mass itself has a significantly lower metabolic rate, with this fully accounting for the relative increase in efficiency. It makes more sense to think that the extra mass leads to systemic adaptations, which in turn lead to whole-body economies of scale (). In existing bodies, these adaptations should happen over time, as long-term compensatory adaptations ().

The implications are fascinating. One implication is that, if the compensatory adaptations that lead to a lower metabolic rate are long term, they should also take some time to undo. This is what some call having a “broken metabolism”; which may turn out not to be “broken”, but having some inertia to overcome before it comes back to a former state. Thus, lower metabolic rates should generally be observed in the formerly obese, with reductions compatible with Kleiber's law. Those reductions themselves should be positively correlated with the ratio of time spent in the obese and lean states.

Someone who was obese at 95 kg should have a metabolic rate approximately 5.6 percent lower than a never obese person, soon after reaching a weight of 68 kg (5.6 percent = [2,508 – 2,374] / 2,374). If the compensatory adaptation can be reversed, as I believe it can, we should see slightly lower percentage reductions in studies including formerly obese participants who had been lean for a while. This expectation is consistent with empirical evidence. For example, a study by Astrup and colleagues () concluded that: “Formerly obese subjects had a 3–5% lower mean relative RMR than control subjects”.

Another implication, which is related to the one above, is that someone who becomes obese and goes right back to lean should not see that kind of inertia. That is, that person should go right back to his or her lean resting metabolic rate. Perhaps Drew Manning’s Fit-2-Fat-2-Fit experiment () will shed some light on this possible implication.

A person becoming obese and going right back to lean is not a very common occurrence. Sometimes this is done on purpose, for professional reasons, such as before and after photos for diet products. Believed it or not, there is a market for this!


- Calorie expenditure estimation varies a lot depending on the equation used. The multiplier used here was 78,  based on Cunningham’s equation, and assuming 10 percent body fat. The calorie expenditure for the same 68 kg person using Katch-McArdle’s equation, also assuming 10 percent body fat, would be about 1,692 calories. That would lead to a different multiplier.

- The really important thing to keep in mind, for the purposes of the discussion presented here, is the relative decrease in energy expenditure at rest, per unit of weight, as weight goes up. So we stuck with the 78 multiplier for illustration purposes.

- There is a lot of variation across individuals in energy expenditure at rest due to other factors such as nonexercise activity thermogenesis ().

Friday, January 27, 2012

Insulin and Obesity: Another Nail in the Coffin

There are several versions of the insulin hypothesis of obesity, but the versions that are most visible to the public generally state that elevated circulating insulin (whether acute or chronic) increases body fatness.  Some versions invoke insulin's effects on fat tissue, others its effects in the brain.  This idea has been used to explain why low-carbohydrate and low-glycemic-index diets can lead to weight loss (although frankly, glycemic index per se doesn't seem to have much if any impact on body weight in controlled trials). 

I have explained in various posts why this idea does not appear to be correct (1, 2, 3), and why, after extensive research, the insulin hypothesis of obesity lost steam by the late 1980s.  However, I recently came across two experiments that tested the hypothesis as directly as it can be tested-- by chronically increasing circulating insulin in animals and measuring food intake and body weight and/or body fatness.  If the hypothesis is correct, these animals should gain fat, and perhaps eat more as well. 

Read more »

Monday, January 23, 2012

What Causes Insulin Resistance? Part VII

In previous posts, I outlined the factors I'm aware of that can contribute to insulin resistance.  In this post, first I'll list the factors, then I'll provide my opinion of effective strategies for preventing and potentially reversing insulin resistance.

The factors

These are the factors I'm aware of that can contribute to insulin resistance, listed in approximate order of importance.  I could be quite wrong about the order-- this is just my best guess. Many of these factors are intertwined with one another. 
Read more »

All diets succeed at first, and eventually fail

It is not very hard to find studies supporting one diet or another. Gardner and colleagues, for example, conducted a study in which the Atkins diet came out on top when compared with the Zone, Ornish, and LEARN diets (). In Dansinger and colleagues’ study (), on the other hand, following the Atkins diet led to relatively poor results compared with the Ornish, Weight Watchers, and Zone diets.

Often the diets compared have different macronutrient ratios, which end up becoming the focus of the comparison. Many consider Sacks and colleagues’ conclusion, based on yet another diet comparison study (), to be the most consistent with the body of evidence as a whole: “Reduced-calorie diets result in clinically meaningful weight loss regardless of which macronutrients they emphasize”.

I think there is a different conclusion that is even more consistent with the body of evidence out there. This conclusion is highlighted by the findings of almost all diet studies where participants were followed for more than 1 year. But the relevant findings are typically buried in the papers that summarize the studies, and are almost never mentioned in the abstracts. Take for example the study by Toubro and Astrup (); Figure 3 below is used by the authors to highlight the study’s main reported finding: “Ad lib, low fat, high carbohydrate diet was superior to fixed energy intake for maintaining weight after a major weight loss”.

But what does the figure above really tell us? It tells us, quite simply, that both diets succeeded at first, and then eventually failed. One failed slightly less miserably than the other, in this study. The percentage of subjects that maintained a weight loss above 25 kg (about 55 lbs) approached zero after 12 months, in both diets. This leads us to the conclusion below, which is always missing in diet studies even when the evidence is staring back at us. This is arguably the conclusion that is the most consistent with the body of evidence out there.

All diets succeed at first, and eventually fail.

In using the terms “succeed” and “fail” I am referring to the diets’ effects on the majority of the participants. This is in fact better demonstrated by the figure below, from the same study by Toubro and Astrup; it is labeled as Figure 2 there. Most of the participants at a certain weight, lose a lot of weight within a period of 1 year or so, and after 2 years (see the two points at the far right) are at the same original weight again. What is the average time to regain back the weight? From what I’ve seen in the literature, all the weight and some tends to be regained after 2-3 years.

The regained weight is not at all lean body mass. It is primarily, if not entirely, body fat. In fact, many studies suggest that those who diet tend to have a higher percentage of body fat when they regain their original weight; proportionally to how fast they regain the weight lost. Since the extra body fat tends to cause additional problems, which are compounded by the dieting process’ toll on the body, those dieters would have been slightly better off not having dieted in the first place.

Guyenet and Schwartz have recently authored an article that summarizes quite nicely what tends to happen with both obese and lean dieters (). Take a look at Figure 2 of the article below. The obese need to lose body fat to improve health markers, and avoid a number of downstream complications, such as type 2 diabetes and cancer (). Yet, with very few exceptions, the obese (and even the overweight) remain obese (or overweight) after dieting; regardless of the diet.

So what about those exceptions, what do they do to lose significant amounts of body fat and keep it off? Well, I rarely use myself as an example for anything in this blog, but this is something with which I unfortunately/fortunately have personal experience. I was obese, lost about 60 lbs of weight, and kept it off for quite a while already (). Like most of the formerly obese, I can very easily gain body fat back.

But I don’t seem to be gaining back the formerly lost body fat, and the reason is consistent with some of the studies based on data from the National Weight Control Registry, which stores information about adults who lost 30 lbs or more of weight and kept it off for at least 1 year (). I systematically measure my weight, body fat percentage, and a number of other variables; probably even more than the average National Weight Control Registry member. Based on those measurements, I try to understand how my body responds in the short and long term to stimuli such as different exercise, types of food, calorie restriction, sleep patterns etc.

And I act accordingly to keep any body fat gain from happening; by, for example, varying calorie intake, increasing exercise intensity, varying the types of food I eat etc. With a few exceptions (e.g., avoiding industrial seed oils), there is no generic formula. Customization based on individual responses and cyclical patterns seems to be a must.

Looking back, it was relatively easy for me to lose all that fat. This is consistent with the studies summarized in this post; all diets that rely on caloric reduction work marvelously at first for most people. The really difficult part is to keep the body fat off. I believe that this is especially true as the initial years go by, and becomes easier after that. This has something to do with initial inertia, which I will discuss soon in a post on metabolic rates and their relationship with overall body mass.

For people living in the wild, I can see one thing working in their favor. And that is not regular starvation; sapiens is too smart for that. It is laziness. Hunger has to reach a certain threshold for people to want to do some work to get their food; this acts as a natural body composition regulator, something that I intend to discuss in one of my next posts. It seems that people almost never become obese in the wild, without access to industrial foods.

As for living in the wild, in spite of the romantic portrayals of it, the experience is not as appealing after you really try it. The book Yanomamo: The Fierce People () is a solid, if not somewhat shocking, reminder of that. I had the opportunity to meet and talk at length with its author, the great anthropologist Nap Chagnon, at one of the Human Behavior and Evolution Society conferences. The man is a real-life Indiana Jones ().

In the formerly obese, the body seems to resort to “guerrilla warfare”, employing all kinds of physiological and psychological mechanisms, some more subtle than others, to make sure that the lost fat is recovered. Why? I have some ideas, which I have discussed indirectly in posts throughout this blog, but I still need to understand the whole process a bit better. My ideas build on the notion of compensatory adaptation ().

You might have heard some very smart people say that you do not need to measure anything to lose body fat and keep it off. Many of those people have never been obese. Those who have been obese often had not cleared the 2-3 year “danger zone” by the time they made those statements.

There are many obese or overweight public figures (TV show hosts, actors, even health bloggers) who embark on a diet and lose a dramatic amount of body fat. They talk and/or write for a year or so about their success, and then either “disappear” or start complaining about health issues. Those health issues are often part of the “guerrilla warfare” I mentioned above.

A few persistent public figures will gain the fat back, in part or fully, and do the process all over again. It makes for interesting drama, and at least keeps those folks in the limelight.

Sunday, January 22, 2012

Three Announcements

Chris Highcock of the blog Conditioning Research just published a book called Hillfit, which is a conditioning book targeted at hikers/backpackers.  He uses his knowledge and experience in hiking and conditioning to argue that strength training is an important part of conditioning for hiking.  I'm also a hiker/backpacker myself here in the rugged and beautiful Pacific Northwest, and I also find that strength training helps with climbing big hills, and walking farther and more easily with a lower risk of injury.

Richard Nikoley of the blog Free the Animal has also published a book called Free the Animal: Beyond the Blog, where he shares his strategies for losing fat and improving health and fitness.  I haven't had a chance to read it yet, but Richard has a reasonable perspective on diet/health and a sharp wit. 

Also, my friend Pedro Bastos has asked me to announce a one-day seminar at the University of Lisbon (Portugal) by Dr. Frits Muskiet titled "Vitamins and Minerals: A Scientific, Modern, Evolutionary and Global View".  It will be on Sunday, Feb 5-- you can find more details about the seminar here.  Dr. Muskiet is a researcher at the Groningen University Medical Center in the Netherlands.  He studies the impact of nutrients, particularly fatty acids, on health, from an evolutionary perspective.  Wish I could attend. 

Wednesday, January 18, 2012

What Causes Insulin Resistance? Part VI

In this post, I'll explore a few miscellaneous factors that can contribute to insulin resistance: smoking, glucocorticoids/stress, cooking temperature, age, genetics and low birth weight.


Smoking tobacco acutely and chronically reduces insulin sensitivity (1, 2, 3), possibly via:
  1. Increased inflammation
  2. Increased circulating free fatty acids (4)
Paradoxically, since smoking also protects against fat gain, in the very long term it may not produce as much insulin resistance as one would otherwise expect.  Diabetes risk is greatly elevated in the three years following smoking cessation (5), and this is likely due to the fat gain that occurs.  This is not a good excuse to keep smoking, because smoking tobacco is one of the most unhealthy things you can possibly do.  But it is a good reason to tighten up your diet and lifestyle after quitting.

Read more »

Monday, January 16, 2012

The China Study II: Wheat’s total effect on mortality is significant, complex, and highlights the negative effects of low animal fat diets

The graph below shows the results of a multivariate nonlinear WarpPLS () analysis including the variables listed below. Each row in the dataset refers to a county in China, from the publicly available China Study II dataset (). As always, I thank Dr. Campbell and his collaborators for making the data publicly available. Other analyses based on the same dataset are also available ().
    - Wheat: wheat flour consumption in g/d.
    - Aprot: animal protein consumption in g/d.
    - PProt: plant protein consumption in g/d.
    - %FatCal: percentage of calories coming from fat.
    - Mor35_69: number of deaths per 1,000 people in the 35-69 age range.
    - Mor70_79: number of deaths per 1,000 people in the 70-79 age range.

Below are the total effects of wheat flour consumption, along with the number of paths used to calculate them, and the respective P values (i.e., probabilities that the effects are due to chance). Total effects are calculated by considering all of the paths connecting two variables. Identifying each path is a bit like solving a maze puzzle; you have to follow the arrows connecting the two variables. Version 3.0 of WarpPLS (soon to be released) does that automatically, and also calculates the corresponding P values.

To the best of my knowledge, this is the first time that total effects are calculated for this dataset. As you can see, the total effects of wheat flour consumption on mortality in the 35-69 and 70-79 age ranges are both significant, and fairly complex in this model, each relying on 7 paths. The P value for mortality in the 35-69 age range is 0.038; in other words, the probability that the effect is “real”, and thus not due to chance, is 96.2 percent (100-3.8=96.2). The P value for mortality in the 70-79 age range is 0.024; a 97.6 percent probability that the effect is “real”.

Note that in the model the effects of wheat flour consumption on mortality in both age ranges are hypothesized to be mediated by animal protein consumption, plant protein consumption, and fat consumption. These mediating effects have been suggested by previous analyses discussed on this blog (). The strongest individual paths are between wheat flour consumption and plant protein consumption, plant protein consumption and animal protein consumption, as well as animal protein consumption and fat consumption.

So wheat flour consumption contributes to plant protein consumption, probably by being a main source of plant protein (through gluten). Plant protein consumption in turn decreases animal protein consumption, which significantly decreases fat consumption. From this latter connection we can tell that most of the fat consumed likely came from animal sources.

How much fat and protein are we talking about? The graphs below tell us how much, and these graphs are quite interesting. They suggest that, in this dataset, daily protein consumption tended to be on average 60 g, whatever the source. If more protein came from plant foods, the proportion from animal foods went down, and vice-versa.

The more animal protein consumed, the more fat is also consumed in this dataset. And that is animal fat, which comes mostly in the form of saturated and monounsaturated fats, in roughly equal amounts. How do I know that it is animal fat? Because of the strong association with animal protein. By the way, with a few exceptions (e.g., some species of fatty fish) animal foods in general provide only small amounts of polyunsaturated fats – omega-3 and omega-6.

Individually, animal protein and wheat flour consumption have the strongest direct effects on mortality in both age ranges. Animal protein consumption is protective, and wheat flour consumption detrimental.

Does the connection between animal protein, animal fat, and longevity mean that a diet high in saturated and monounsaturated fats is healthy for most people? Not necessarily, at least without extrapolation, although the results do not suggest otherwise. Look at the amounts of fat consumed per day. They range from a little less than 20 g/d to a little over 90 g/d. By comparison, one steak of top sirloin (about 380 g of meat, cooked) trimmed to almost no visible fat gives you about 37 g of fat.

These results do suggest that consumption of animal fats, primarily saturated and monounsaturated fats, is likely to be particularly healthy in the context of a low fat diet. Or, said in a different way, these results suggest that longevity is decreased by diets that are low in animal fats.

How much fat should one eat? In this dataset, the more fat was consumed together with animal protein (i.e., the more animal fat was consumed), the better in terms of longevity. In other words, in this dataset the lowest levels of mortality were associated with the highest levels of animal fat consumption. The highest level of fat consumption in the dataset was a little over 90 g/d.

What about higher fat intake contexts? Well, we know that men on a high fat diet such as a variation of the Optimal Diet can consume on average a little over 170 g/d of animal fat (130 g/d for women), and their health markers remain generally good ().

One of the critical limiting factors, in terms of health, seems to be the amount of animal fat that one can eat and still remain relatively lean. Dietary saturated and monounsaturated fats are healthy. But when accumulated as excess body fat, beyond a certain level, they become pro-inflammatory.

Sunday, January 15, 2012

What Causes Insulin Resistance? Part V

Previously in this series, we've discussed the role of cellular energy excess, inflammation, brain insulin resistance, and micronutrient status in insulin resistance.  In this post, I'll explore the role of macronutrients and sugar in insulin sensitivity.

Carbohydrate and Fat

There are a number of studies on the effect of carbohydrate:fat ratios on insulin sensitivity, but many of them are confounded by fat loss (e.g., low-carbohydrate and low-fat weight loss studies), which almost invariably improves insulin sensitivity.  What interests me the most is to understand what effect different carbohydrate:fat ratios have on insulin sensitivity in healthy, weight stable people.  This will get at what causes insulin resistance in someone who does not already have it.

Read more »

Thursday, January 12, 2012

New Obesity Review Paper by Yours Truly

The Journal of Clinical Endocrinology and Metabolism just published a clinical review paper written by myself and my mentor Dr. Mike Schwartz, titled "Regulation of Food Intake, Energy Balance, and Body Fat Mass: Implications for the Pathogenesis and Treatment of Obesity" (1).  JCEM is one of the most cited peer-reviewed journals in the fields of endocrinology, obesity and diabetes, and I'm very pleased that it spans the gap between scientists and physicians.  Our paper takes a fresh and up-to-date look at the mechanisms by which food intake and body fat mass are regulated by the body, and how these mechanisms are altered in obesity.  We explain the obesity epidemic in terms of the mismatch between our genes and our current environment, a theme that is frequently invoked in ancestral health circles.

Read more »

Monday, January 9, 2012

What Causes Insulin Resistance? Part IV

So far, we've explored three interlinked causes of insulin resistance: cellular energy excess, inflammation, and insulin resistance in the brain.  In this post, I'll explore the effects on micronutrient status on insulin sensitivity.

Micronutrient Status

There is a large body of literature on the effects of nutrient intake/status on insulin action, and it's not my field, so I don't intend this to be a comprehensive post.  My intention is simply to demonstrate that it's important, and highlight a few major factors I'm aware of.

Read more »

Ground meat treats: Beef and bison meatza

At the time of this writing, there was no Wikipedia article for the term “meatza”, which surprised me a bit given the number of recipes on the web. In fact, I could not find anything concrete about the dish’s tradition or  history.

Another thing that surprised me about this dish is how much my family and I like it. It has become a regular weekend treat for us for quite some time now.

The recipe below is for a meal that feeds 4-8 people. Like in my previous recipe for a zucchini and onion meatloaf (), the ground beef used here has little fat, and thus a relatively low omega-6 content. Most of the fat comes from the ground bison, which has a higher omega-3 to omega-6 ratio.

- Prepare some dry seasoning powder by mixing sea salt, parsley flakes, garlic powder, chili powder, and a small amount of cayenne pepper.
- Mix 2 lb of very lean ground beef (96/4) with 1 lb of ground bison.
- Add the dry seasoning and a whole egg to the ground meat mix.
- Vigorously mix by hand until you get a homogeneous look.
- Place the mix into a sheet pan coated with olive oil. Richard’s suggestion of creating edges helps keep the sautéed vegetables on top, when they are added later ().
- Preheat oven to 375 degrees Fahrenheit.
- Bake the meatza for about 15 minutes.
- Grate 1 lb of aged cheese.
- Slice one tomato, half an onion, and one green bell pepper, and sauté them in olive oil.
- Drain the meatza after if comes out of the oven, and add the sautéed vegetables to the top, together with half a can of tomato sauce.
- Add the 1 lb of grated aged cheese on top of the vegetables and tomato sauce.
- Return meatza to the oven, still at 375 degrees Fahrenheit, and bake it for about 10 minutes.

The photo montage above shows a side dish of baked potatoes and zucchini. That is optional, as the meatza has vegetables added to it. I usually cut the meatza into 8 rectangular pieces. Each rectangle will have about 50 g of protein and 20 g of fat. The fat will be primarily saturated and monounsaturated (both healthy), with a good balance of omega-3 and omega-6 fats. Each piece of meatza will also be a good source of vitamins B12 and B6, niacin, calcium, zinc, selenium, and phosphorus.

Sunday, January 8, 2012

What Causes Insulin Resistance? Part III

As discussed in previous posts, cellular energy excess and inflammation are two important and interlinked causes of insulin resistance.  Continuing our exploration of insulin resistance, let's turn our attention to the brain.

The brain influences every tissue in the body, in many instances managing tissue processes to react to changing environmental or internal conditions.  It is intimately involved in insulin signaling in various tissues, for example by:
  • regulating insulin secretion by the pancreas (1)
  • regulating glucose absorption by tissues in response to insulin (2)
  • regulating the suppression of glucose production by the liver in response to insulin (3)
  • regulating the trafficking of fatty acids in and out of fat cells in response to insulin (4, 5)
Because of its important role in insulin signaling, the brain is a candidate mechanism of insulin resistance.

Read more »

Saturday, January 7, 2012

What Causes Insulin Resistance? Part II

In the last post, I described how cellular energy excess causes insulin resistance, and how this is triggered by whole-body energy imbalance.  In this post, I'll describe another major cause of insulin resistance: inflammation. 


In 1876, a German physician named W Ebstein reported that high doses of sodium salicylate could totally eliminate the signs and symptoms of diabetes in certain patients (Berliner Klinische Wochenschrift. 13:337. 1876). Following up on this work in 1901, the British physician RT Williamson reported that treating diabetic patients with sodium salicylate caused a striking decrease in the amount of glucose contained in the patients' urine, also indicating an apparent improvement in diabetes (2).  This effect was essentially forgotten until 1957, when it was rediscovered.

Read more »

Friday, January 6, 2012

What Causes Insulin Resistance? Part I

Insulin is an ancient hormone that influences many processes in the body.  Its main role is to manage circulating concentrations of nutrients (principally glucose and fatty acids, the body's two main fuels), keeping them within a fairly narrow range*.  It does this by encouraging the transport of nutrients into cells from the circulation, and discouraging the export of nutrients out of storage sites, in response to an increase in circulating nutrients (glucose or fatty acids). It therefore operates a negative feedback loop that constrains circulating nutrient concentrations.  It also has many other functions that are tissue-specific.

Insulin resistance is a state in which cells lose sensitivity to the effects of insulin, eventually leading to a diminished ability to control circulating nutrients (glucose and fatty acids).  It is a major contributor to diabetes risk, and probably a contributor to the risk of cardiovascular disease, certain cancers and a number of other disorders. 

Why is it important to manage the concentration of circulating nutrients to keep them within a narrow range?  The answer to that question is the crux of this post. 

Read more »

Wednesday, January 4, 2012

New York Times Magazine Article on Obesity

For those of you who haven't seen it, Tara Parker-Pope write a nice article on obesity in the latest issue of NY Times Magazine (1).  She discusses  research showing  that the body "resists" fat loss attempts, making it difficult to lose fat and maintain fat loss once obesity is established.
Read more »

Monday, January 2, 2012

High-Fat Diets, Obesity and Brain Damage

Many of you have probably heard the news this week:

High-fat diet may damage the brain
Eating a high-fat diet may rapidly injure brain cells
High fat diet injures the brain
Brain injury from high-fat foods

Your brain cells are exploding with every bite of butter!  Just kidding.  The study in question is titled "Obesity is Associated with Hypothalamic Injury in Rodents and Humans", by Dr. Josh Thaler and colleagues, with my mentor Dr. Mike Schwartz as senior author (1).  We collaborated with the labs of Drs. Tamas Horvath and Matthias Tschop.  I'm fourth author on the paper, so let me explain what we found and why it's important.  

The Questions

Among the many questions that interest obesity researchers, two stand out:
  1. What causes obesity?
  2. Once obesity is established, why is it so difficult to treat?
Our study expands on the efforts of many other labs to answer the first question, and takes a stab at the second one as well.  Dr. Licio Velloso and collaborators were the first to show in 2005 that inflammation in a part of the brain called the hypothalamus contributes to the development of obesity in rodents (2), and this has been independently confirmed several times since then.  The hypothalamus is an important brain region for the regulation of body fatness, and inflammation keeps it from doing its job correctly.

The Findings

Read more »

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

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

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

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

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

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

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

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

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

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

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

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

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

Sunday, January 1, 2012

Junk Free January

Last year, Matt Lentzner organized a project called Gluten Free January, in which 546 people from around the world gave up gluten for one month.  The results were striking: a surprisingly large proportion of participants lost weight, experienced improved energy, better digestion and other benefits (1, 2).  This January, Lentzner organized a similar project called Junk Free January.  Participants can choose between four different diet styles:
  1. Gluten free
  2. Seed oil free (soybean, sunflower, corn oil, etc.)
  3. Sugar free
  4. Gluten, seed oil and sugar free
Wheat, seed oils and added sugar are three factors that, in my opinion, are probably linked to the modern "diseases of affluence" such as obesity, diabetes and coronary heart disease.  This is particularly true if the wheat is eaten in the form of white flour products, and the seed oils are industrially refined and used in high-heat cooking applications.

If you've been waiting for an excuse to improve your diet, why not join Junk Free January?