Gaining muscle and losing fat at the same time: If I can do it, anyone can

The idea of gaining muscle and losing fat at the same time seems impossible because of three widely held misconceptions: (a) to gain muscle you need a calorie surplus; (b) to lose fat you need a calorie deficit; and (c) you cannot achieve a calorie surplus and deficit at the same time.

Not too long ago, unfortunately I was in the right position to do some self-experiments in order to try to gain muscle and concurrently lose fat, without steroids, keeping my weight essentially constant (within a range of a few lbs). This was because I was obese, and then reached a point in the fat loss stage where I could stop losing weight while attempting to lose fat. This is indeed difficult and slow, as muscle gain itself is slow, and it apparently becomes slower as one tries to restrict fat gain. Compounding that is the fact that self-experimentation invariably leads to some mistakes.

The photos below show how I looked toward the end of my transformation from obese to relatively lean (right), and then about 1.5 years after that (left). During this time I gained muscle and lost fat, in equal amounts. How do I know that? It is because my weight is the same in both photos, even though on the left my body fat percentage is approximately 5 points lower. I estimate it to be slightly over 12 percent (on the left). This translates into a difference of about 7.5 lbs, of “fat turning into muscle”, so to speak.

A previous post on my transformation from obese to relatively lean has more measurement details (). Interestingly, I am very close to being overweight, technically speaking, in both photos above! That is, in both photos I have a body mass index that is close to 25. In fact, after putting on even a small amount of muscle, like I did, it is very easy for someone to reach a body mass index of 25. See the table below, from the body mass index article on Wikipedia ().

As someone gains more muscle and remains lean, approaching his or her maximum natural muscular potential, that person will approach the limit between the overweight and obese areas on the figure above. This will happen even though the person may be fairly lean, say with a body fat percentage in the single digits for men and around 14-18 percent for women. This applies primarily to the 5’7’’ – 5’11’’ range; things get somewhat distorted toward the extremes.

Contrast this with true obesity, as in the photo below. This photo was taken when I was obese, at the beach. If I recall it properly, it was taken on the Atlantic City seashore, or a beach nearby. I was holding a bottle of regular soda, which is emblematic of the situation in which many people find themselves in today’s urban societies. It reminds me of a passage in Gary Taubes’s book “Good Calories, Bad Calories” (), where someone who had recently discovered the deliciousness of water sweetened with sugar wondered why anyone “of means” would drink plain water ever again.

Now, you may rightfully say that a body composition change of about 7.5 lbs in 1.5 years is pitiful. Indeed, there are some people, typically young men, who will achieve this in a few months without steroids. But they are relatively rare; Scooby has a good summary of muscle gain expectations (). As for me, I am almost 50 years old, an age where muscle gain is not supposed to happen at all. I tend to gain fat very easily, but not muscle. And I was obese not too long ago. My results should be at the very low end of the scale of accomplishment for most people doing the right things.

By the way, the idea that muscle gain cannot happen after 40 years of age or so is another misconception; even though aging seems to promote muscle loss and fat gain, in part due to natural hormonal changes. There is evidence that many men may experience of low point (i.e., a trough) in their growth hormone and testosterone levels in their mid-40s, possibly due to a combination of modern diet and lifestyle factors. Still, many men in their 50s and 60s have higher levels ().

And what are the right things to do if one wants to gain muscle and lose fat at the same time? In my next post I will discuss the misconceptions mentioned at the beginning of this post, and a simple approach for concurrently gaining muscle and losing fat. The discussion will be based on my own experience and that of several HCE () users. The approach relies heavily on individual customization; so it will probably be easier to understand than to implement. Strength training is part of this simple strategy.

One puzzling aspect of strength training, from an evolutionary perspective, is that people tend to be able to do a lot more of it than is optimal for them. And, when they do even a bit more than they should, muscle gain stalls or even regresses. The minimalists frequently have the best results.

The “pork paradox”? National pork consumption and obesity

In my previous post () I discussed some country data linking pork consumption and health, analyzed with WarpPLS (). One of the datasets used, the most complete, contained data from Nationmaster.com () for the following countries: Australia, Brazil, Canada, China, Denmark, France, Germany, Hong Kong, Hungary, Japan, Mexico, Poland, Russia, Singapore, Spain, Sweden, United Kingdom, and United States. That previous post also addressed a study by Bridges (), based on country-level data, suggesting that pork consumption may cause liver disease.

In this post we continue that analysis, but with a much more complex model containing the following country variables: wealth (PPP-adjusted GNP/person), pork consumption (lbs/person/year), alcohol consumption (liters/person/year), obesity (% of population), and life expectancy (years). The model and results, generated by WarpPLS, are shown on the figure below. (See notes at the end of this post.) These results are only for direct effects.

WarpPLS also calculates total effects, which are the effects of each variable on any other variable to which it is linked directly and/or indirectly. Two variables may be linked indirectly, through various paths, even if they are not linked directly (i.e., have an arrow directly connecting them). Another set of outputs generated by the software are effect sizes, which are calculated as Cohen’s f-squared coefficients. The figure below shows the total effects table. The values underlined in red are for total effects that are both statistically significant and also above the effect size threshold recommended by Cohen to be considered relevant (f-squared > 0.02).

As I predicted in my previous post, wealth is positively associated with pork consumption. So is alcohol consumption, and more strongly than wealth; which is consistent with a study by Jeanneret and colleagues showing a strong association between alcohol consumption and protein rich diets (). The inclusion of wealth in the model, compared with the model without wealth in the previous post, renders the direct and total effects of alcohol and pork consumption on life expectancy statistically indistinguishable from zero. (This often happens when a confounder is added to a model.)

Pork consumption is negatively associated with obesity, which is interesting. So is alcohol consumption, but much less strongly than pork consumption. This does not mean that if you eat 20 doughnuts every day, together with 1 lb of pork, you are not going to become obese. What this does suggest is that maybe countries where pork is consumed more heavily are somewhat more resistant to obesity. Here it should be noted that pork is very popular in Asian countries, which are becoming increasingly wealthy, but without the widespread obesity that we see in the USA.

But it is not the inclusion of Asian countries in the dataset that paints such a positive picture for pork consumption vis-à-vis obesity, and even weakens the association between wealth and obesity so much as to make it statistically non-significant. Denmark is a wealthy country that has very low levels of obesity. And it happens to have the highest level of pork consumption in the whole dataset: 142.6 lbs/person/year. So we are not talking about an “Asian paradox” here.

More like a “pork paradox”.

Finally, as far as life expectancy is concerned, the key factors seem to be wealth and obesity. Wealth has a major positive effect on life expectancy, while obesity has a much weaker negative effect. Well, access to sanitation, medical services, and other amenities of civilization, still trumps obesity in terms of prolonging life; however miserable life may turn out to be. The competing effects of these two variables (i.e., wealth and obesity) were taken into consideration, or controlled for, in the calculation of total effects and effect sizes.

The fact that pork consumption is negatively associated with obesity goes somewhat against the idea that pork is inherently unhealthy; even though pork certainly can cause disease if not properly prepared and/or cooked, which is true for many other plant and animal foods. The possible connection with liver problems, alluded to in the previous post, is particularly suspicious in light of these results. Liver diseases often impair that organ’s ability to make glycogen based on carbohydrates and protein; that is, liver diseases frequently lead to liver insulin resistance. And obesity frequently follows from liver insulin resistance.

Given that pork consumption appears to be negatively associated with obesity, it would be surprising if it was causing widespread liver disease, unless its relationship with liver disease was found to be nonlinear. (Alcohol consumption seems to be nonlinearly associated with liver disease.) Still, most studies that suggest the existence of a causal link between pork consumption and liver disease, like Bridges’s (), hint at a linear and dose-dependent relationship.

Notes

- Country-level data is inherently problematic, particularly when simple models are used (e.g., a model with only two variables). There are just too many possible confounders that may lead to the appearance of causal associations.

- More complex models ameliorate the above situation somewhat, but bump into another problem associated with country-level data – small sample sizes. We used data from 18 countries in this analysis, which is more than in the Bridges study. Still, the effective sample size here (N=18) is awfully small.

- There were some missing values in this dataset, which were handled by WarpPLS employing the most widely used approach in these cases – i.e., by replacing the missing values with the mean of each column. The percentages of missing values per variable (i.e., column) were: alcohol consumption: 27.78%; life expectancy: 5.56%; and obesity: 33.33%.

Does pork consumption cause cirrhosis? Perhaps, if people become obese from eating pork

The idea that pork consumption may cause cirrhosis has been around for a while. A fairly widely cited 1985 study by Nanji and French () provides one of the strongest indictments of pork: “In countries with low alcohol consumption, no correlation was obtained between alcohol consumption and cirrhosis. However, a significant correlation was obtained between cirrhosis and pork.”

Recently Paul Jaminet wrote a blog post on the possible link between pork consumption and cirrhosis (). Paul should be commended for bringing this topic to the fore, as the implications are far-reaching and very serious. One of the key studies mentioned in Paul’s post is a 2009 article by Bridges (), from which the graphs below were taken.

The graphs above show a correlation between cirrhosis and alcohol consumption of 0.71, and a correlation between cirrhosis and pork consumption of 0.83. That is, the correlation between cirrhosis and pork consumption is the stronger of the two! Combining this with the Nanji and French study, we have evidence that: (a) in countries with low alcohol consumption we can find a significant correlation between cirrhosis and pork consumption; and (b) in countries where both alcohol and pork are consumed, pork consumption has the strongest correlation with cirrhosis.

Do we need anything else to ban pork from our diets? Yes, we do, as there is more to this story.

Clearly alcohol and pork consumption are correlated as well, as we can see from the graphs above. That is, countries where alcohol is consumed more heavily also tend to have higher levels of pork consumption. If alcohol and pork consumption are correlated, then a multivariate analysis of their effects should be conducted, as one of the hypothesized effects (of alcohol or pork) on cirrhosis may even disappear after controlling for the other effect.

I created a dataset, as best as I could, based on the graphs from the Bridges article. (I could not get the data online.) I then entered it into WarpPLS (). I wanted to run a moderating effect analysis, which is a form of nonlinear multivariate analysis. This is important, because the association between alcohol consumption and disease in general is well known to be nonlinear.

In fact, the relationship between alcohol consumption and disease is often used as a classic example of hormesis (), and its characteristic J-curve shape. Since correlation is a measure of linear association, the lower correlation between alcohol consumption and cirrhosis, when compared with pork consumption, may be just a “mirage of linearity”. In multivariate analyses, this mirage of linearity may lead to what are known as type I and II errors, at the same time ().

I should note that the Bridges study did something akin to a moderating effect analysis; through an analysis of the interaction between alcohol and pork consumption. However, in that analysis the values of the variables that were multiplied to create a “dummy” interaction variable were on their original scales, which can be a major source of bias. A more advisable way to conduct an interaction effect analysis is to first make the variables dimensionless, by standardizing them, and then creating a dummy interaction variable as a product of the two variables. That is what WarpPLS does for moderating effects’ estimation.

One more detour, leading to an important implication, and then we will get to the results. In a 1988 article, Jeanneret and colleagues show evidence of a strong and possibly causal association between alcohol consumption and protein-rich diets (). One possible implication of this is that in countries where pork is a dietary staple, like Denmark and Germany, alcohol consumption should be strongly and causally associated with pork consumption. (I guess Anthony Bordain would agree with this eh?)

Below are the results of a multivariate analysis on a model that incorporates the above implication, by including a link between alcohol and pork consumption. The model also explores the role of pork consumption as a moderator of the relationship between alcohol and cirrhosis, as well as the direct effect of pork consumption on cirrhosis. Finally, the total effects of alcohol and pork consumption on cirrhosis are also investigated; they are shown on the left.

The total effects are both statistically significant, with the total effect of alcohol consumption being 94 percent stronger than the total effect of pork consumption on cirrhosis. Looking at the model, alcohol consumption is strongly associated with pork consumption (which is consistent with Jeanneret and colleagues’s study). Alcohol consumption is also strongly associated with cirrhosis, through a direct effect; much more so than pork. Finally, pork consumption seems to strengthen the relationship between alcohol consumption and cirrhosis (the moderating effect).

As we can see the relationship between pork consumption and cirrhosis is still there, in moderating and direct effects, even though it seems to be a lot weaker than that between alcohol consumption and cirrhosis. Why does pork seem to influence cirrhosis at all in this dataset?

Well, there is another factor that is strongly associated with cirrhosis, and that is obesity (). In fact, obesity is associated with just about any major disease, including various types of cancer ().

And in countries where pork is a dietary staple, isn’t it reasonable to assume that pork consumption will play a role in obesity? Often folks who consume a lot of addictive industrial foods (e.g., bread, candy, regular sodas) also eat plenty of foods with saturated fat; and the latter end up showing up in disease statistics, misleadingly supporting the lipid hypothesis. The phenomenon involving pork and cirrhosis may well be similar.

But you may find the above results and argument not convincing enough. Maybe you want to see some evidence that pork is actually good for one’s health. The results above suggest that it may not be bad at all, if you buy into the obesity angle, but not that it can be good.

So I downloaded the most recent data from Nationmaster.com () on the following variables: pork consumption, alcohol consumption, and life expectancy. The list of countries was a bit larger than and different from that in the Bridges study; the following countries were included: Australia, Brazil, Canada, China, Denmark, France, Germany, Hong Kong, Hungary, Japan, Mexico, Poland, Russia, Singapore, Spain, Sweden, United Kingdom, and United States. Below are the results of a simple multivariate analysis with WarpPLS.

As with the Bridges dataset, there is a strong multivariate association between alcohol and pork consumption (0.43). The multivariate association between alcohol consumption and life expectancy is negative (-0.14). The multivariate association between pork consumption and life expectancy is positive (0.36). Neither association is statistically significant, although the association involving pork consumption gets close to significance with a P=0.11 (a confidence level of 89 percent; calculated through jackknifing, a nonparametric technique). The graphs show the plots for the associations and the best-fitting lines; the blue dashed arrows indicate the multivariate associations to which the graphs refer. So, in this second dataset from Nationmaster.com, the more pork is consumed in a country, the longer is the life expectancy in that country.

In other words, for each 1 standard deviation variation in pork consumption, there is a 0.36 standard deviation variation in life expectancy, after we control for alcohol consumption. The standard deviation for pork consumption is 36.281 lbs/person/year, or 45.087 g/person/day; for life expectancy, it is 4.677 years. Working the numbers a bit more, the results above suggest that each extra gram of pork consumed per person per day is associated with approximately 13 additional days of overall life expectancy in a country! This is calculated as: 4.677/45.087*0.36*365 = 13.630.

Does this prove that eating pork will make you live longer? No single study will “prove” something like that. Pork consumption is also likely a marker for wealth in a country; and wealth is strongly and positively associated with life expectancy at the country level. Moreover, when you aggregate dietary and disease incidence data by country, often the statistical effects are caused by those people in the dietary extremes (e.g., alcohol abuse, not moderate consumption). Finally, if people avoid death from certain diseases, they will die in higher quantities from other diseases, which may bias statistical results toward what may look like a higher incidence of those other diseases.

What the results summarized in this post do suggest is that pork consumption may not be a problem at all, unless you become obese from eating it. How do you get obese from eating pork? Eating it together with industrial foods that are addictive would probably help.

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!

Notes

- 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 ().

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.

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.

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.