Ground meat treats: Zucchini and onion meatloaf

A cousin of the meatball (), the meatloaf is a traditional German dish. The recipe below is for a meal that feeds 4-8 people. The ground beef used has little fat, and thus a relatively low omega-6 content. Most of the fat comes from the 1 lb of ground grass-fed lamb in the recipe, which has a higher omega-3 to omega-6 ratio than the regular (i.e., non-grass-fed) ground beef. The egg acts as a binder. Leave the potato out if you want to decrease the carbohydrate content; it does not add much (nutrient numbers are provided at the end of the post).

- Prepare some dry seasoning powder by mixing salt, parsley flakes, garlic powder, chili powder, and a small amount of cayenne pepper.
- Grate one zucchini squash and one peeled potato. Cut half an onion into small pieces of similar size.
- Mix 2 lb of very lean ground beef (96/4) with 1 lb of ground grass-fed lamb.
- Add the dry seasoning, zucchini, potato, onion and a whole egg to the ground meat mix.
- Vigorously mix by hand until you get a homogeneous look.
- Place the mix into a buttered casserole dish with the shape of a loaf.
- Preheat the oven to 375 degrees Fahrenheit.
- Bake the meatloaf for about 1 hour and a half.

It is a good idea to place the casserole dish within a tray, as in the photo above. The meatloaf will give off some of its juices as it bakes, which may overflow from the casserole dish and make a mess in your oven. Below is a slice of meatloaf served with a side of vegetables. The green spots in the meatloaf are the baked zucchini squash pieces.

A thick slice like the one on the photo above will have about 52 g of protein, 15 g of fat, and 6 g of carbohydrates (mostly from the potato). That'll be about 1/5 of the whole meatloaf; the slice will weigh a little less then 1/2 lb (approximately 200 g). The fat will be primairly saturated and monounsaturated (both healthy), with a good balance of omega-3 and omega-6 fats. The slice of meatloaf will also be a good source of vitamins B12 and B6, niacin, zinc, selenium, and phosphorus.

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

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

(Source: Ecopaper.com)

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

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

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

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

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

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

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

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

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

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

A Sign of the Times

Every now and then, I venture out to go shopping at mainstream chain clothing stores.  Although I find it onerous, there are certain things I can't get at thrift stores.  For example, I can never find nice jeans.

The last time I set foot in these stores was about two years ago.  It was tough to find pants my size at that time-- many stores simply didn't sell pants with a 30 inch waist.  This year, it was even harder, since some of the stores that formerly carried 30W pants no longer did.  I managed to find my usual 30W 30L size in two stores, but I had a bizarre experience in both cases.   I put them on, and they were falling off my waist.  Since my waist size hasn't changed in two years, and my old 30W 30L pants of the same brand still fit the same as they did when I bought them two years ago, I have to conclude that both stores have changed their definition of "30 inches".  My new size is 28W 30L, which is tough to find these days.
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Finding your sweet spot for muscle gain with HCE

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

60 Minutes Report on the Flavorist Industry

A reader sent me a link to a recent CBS documentary titled "Tweaking Tastes and Creating Cravings", reported by Morley Safer.

Safer describes the "flavorist" industry, entirely dedicated to crafting irresistible odors for the purpose of selling processed and restaurant food.  They focused on the company Givaudin.  Dr. David Kessler, author of The End of Overeating, makes an appearance near the end.

Here are a few notable quotes:

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Want to make coffee less acidic? Add cream to it

The table below is from a 2008 article by Ehlen and colleagues (), showing the amount of erosion caused by various types of beverages, when teeth were exposed to them for 25 h in vitro. Erosion depth is measured in microns. The third row shows the chance probabilities (i.e., P values) associated with the differences in erosion of enamel and root.

As you can see, even diet drinks may cause tooth erosion. That is not to say that if you drink a diet soda occasionally you will destroy your teeth, but regular drinking may be a problem. I discussed this study in a previous post (). After that post was published here some folks asked me about coffee, so I decided to do some research.

Unfortunately coffee by itself can also cause some erosion, primarily because of its acidity. Generally speaking, you want a liquid substance that you are interested in drinking to have a pH as close to 7 as possible, as this pH is neutral (). Tap and mineral water have a pH that is very close to 7. Black coffee seems to have a pH of about 4.8.

Also problematic are drinks containing fermentable carbohydrates, such as sucrose, fructose, glucose, and lactose. These are fermented by acid-producing bacteria. Interestingly, when fermentable carbohydrates are consumed as part of foods that require chewing, such as fruits, acidity is either neutralized or significantly reduced by large amounts of saliva being secreted as a result of the chewing process.

So what to do about coffee?

One possible solution is to add heavy cream to it. A small amount, such as a teaspoon, appears to bring the pH in a cup of coffee to a little over 6. Another advantage of heavy cream is that it has no fermentable carbohydrates; it has no carbohydrates, period. You will have to get over the habit of drinking sweet beverages, including sweet coffee, if you were unfortunate enough to develop that habit (like so many people living in cities today).

It is not easy to find reliable pH values for various foods. I guess dentistry researchers are more interested in ways of repairing damage already done, and there doesn't seem to be much funding available for preventive dentistry research. Some pH testing results from a University of Cincinnati college biology page were available at the time of this writing; they appeared to be reasonably reliable the last time I checked them ().

New Review Papers on Food Reward

As research on the role of reward/palatability in obesity continues to accelerate, interesting new papers are appearing weekly.  Here is a roundup of review papers I've encountered in the last three months.  These range from somewhat technical to very technical, but I think they should be mostly accessible to people with a background in the biological sciences. 

Food and Drug Reward: Overlapping Circuits in Human Obesity and Addiction
Written by Dr. Nora D. Volkow and colleagues.  This paper describes the similarities between the mechanisms of obesity and addiction, with a focus on human brain imaging studies.  Most researchers don't think obesity is an addiction per se, but the mechanisms (e.g., brain areas important for reward) do seem to overlap considerably.  This paper is well composed and got a lot of media attention.  Dr. Volkow is the director of the National Institute on Drug Abuse, a branch of the National Institutes of Health.  The NIH is the main source of biomedical research funding in the US, and also conducts its own research.

Here's a quote from the paper:

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Another Simple Food Weight Loss Experience

Whole Health Source reader Sarah Pugh recently went on a six-week simple food (low reward) diet to test its effectiveness as a weight loss strategy, and she was kind enough to describe her experience for me, and provide a link to her blog where she discussed it in more detail (1). 

Consistent with the scientific literature and a number of previous reader anecdotes (2), Sarah experienced a reduction in appetite on the simple food diet, losing 15 pounds in 6 weeks without hunger.  In contrast to her prior experiences with typical calorie restriction, her energy level and mood remained high over this period.  Here's a quote from her blog:

Well, it looks like the theory that in the absence of nice palatable food, the body will turn quite readily to fat stores and start munching them up, is holding up. At the moment, the majority of the energy I use is coming from my insides, and my body is using it without such quibbles as the increased hunger, low energy, crappy thermo-regulation or bitchiness normally associated with severe calorie restriction.

I can't promise that everyone will experience results like this, but this is basically what the food reward hypothesis suggests should be possible, and it seems to work this way for many people.  That's one of the reasons why this idea interests me so much.

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A Brief Response to Taubes's Food Reward Critique, and a Little Something Extra

It appears Gary Taubes has completed his series critiquing the food reward hypothesis of obesity (1).  I have to hand it to him, it takes some cojones to critique an entire field of research, particularly when you have no scientific background in it, and have evidently not read any of the scientific literature on it.  As of 2012, a Google Scholar search for the terms “food reward” and “obesity” turned up 2,790 papers.

The food reward hypothesis of obesity states that the reward and palatability value of food influence body fatness, and excess reward/palatability can promote body fat accumulation.  If we want to test the hypothesis, the most direct way is to find experiments in which 1) the nutritional qualities of the experimental diet groups are kept the same or at least very similar, 2) some aspect of diet reward/palatability differs, and 3) changes in body fat/weight are measured (for example, 2, 3, 4, 5, 6, 7, 8, 9).  In these experiments the hypothesis has both arms and one leg tied behind its back, because the most potent reward factors (energy density, sugar, fat) have nutritional value and therefore experiments that modify these cannot be tightly controlled for nutritional differences.  Yet even with this severe disadvantage, the hypothesis is consistently supported by the scientific evidence.  Taubes repeatedly stated in his series that controlled studies like these have not been conducted, apparently basing this belief on a 22-year-old review paper by Dr. Israel Ramirez and colleagues that does not contain the word 'reward' (10).

Another way to test the hypothesis is to see if people with higher food reward sensitivity (due to genetics or other factors) tend to gain more fat over time (for example, 11, 12, 13, 14, 15, 16).  In addition, studies that have examined the effect of palatability/reward on food intake in a controlled manner are relevant (17, 18, 19, 20, 21, 22), as are studies that have identified some of the mechanisms by which these effects occur (reviewed in 23).  Even if not all of the studies are perfect, at some point, one has to acknowledge that there are a lot of mutually buttressing lines of evidence here.  It is notable that virtually none of these studies appeared in Taubes's posts, and he appeared unaware of them. 
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My transformation: How I looked 10 years ago next to a thin man called Royce Gracie

The photos below were taken about 10 years ago. The first is at a restaurant near Torrance, California. (As you can see, the restaurant was about to close; we were the last customers.) I am standing next to Royce Grace, who had by then become a sensation (). He became a sensation by easily defeating nearly every champion fighter that was placed in front of him. In case you are wondering, Royce is 6’1” and I am 5’8”. The second photo also has Royce’s manager in it – that is his wife. Their children’s names both start with the letter “K”. I wonder how big they are right now.

I think that at the time these photos were taken I weighed around 200-210 lbs. Even though I am much shorter than Royce, I outweighed him by around 40 lbs. Now I weigh 150 lbs, at about 11 percent body fat, and look like the photo on the top-right area of this blog - essentially like a thin guy who does some manual labor for a living, I guess. A post is available discussing the "how" part of this transformation (). I only put a shirtless photo here after several readers told me that my previous photo looked out of place in this blog.

My day job is not even remotely related to fitness instruction. I am a college professor, and like to think of myself as a scholar. I don’t care much about my personal appearance; never did. At least in my mind, putting up shirtless photos on the web should not be done gratuitously. If you are a fitness instructor, or an athlete, that is fine. In my case, it is acceptable in the context of telling people that a few minutes of mid-day sun exposure, avoiding sunburn, yields 10,000 IU of skin-produced vitamin D, which is about 20 times more than one can get through most "fortified" industrial foods.

Royce is such a nice guy that, after much insistence, he paid for the dinner, and then we drove to his house and talked until about midnight. He had told me of a flight the next morning to Chicago, so I ended the interview and thanked him for the wonderful time we had spent together. I had to talk him out of driving ahead of me to I-405; he wanted to make sure I was not going to get lost at that time of the night. This was someone who was considered a demigod at the time in some circles. A humble, wonderful person.

Royce helped launch what is today the mega-successful Ultimate Fighting Championship franchise (), which was then still a no holders barred mixed martial arts tournament. At the time the photos were taken I was interviewing him for my book Compensatory Adaptation, which came out in print soon after (). The book has a full chapter on the famous Gracie Family, including his father Helio and his brother Rickson.

I talked before about the notion of compensatory adaptation and how it applies to our understanding of how we respond to diet and lifestyle changes (). In this context, I believe that the compensatory adaptation notion is far superior to that of hormesis (), which I think is interesting but overused and overrated.

The notion of compensatory adaptation has been picked up in the field of information systems, my main field of academic research. In this field, which deals with how people respond to technologies, it is part of a broader theory called media naturalness theory (). There are already several people who have received doctorates by testing this theory from novel angles. There are also several people today who call themselves experts in compensatory adaptation and media naturalness theory.

The above creates an odd situation, and something funny that happened with me a few times already. I do some new empirical research on compensatory adaptation, looking at it from a new angle, write an academic paper about it (often with one or more co-authors who helped me collect empirical data), and submit it to a selective refereed journal. Then an "expert" reviewer, who does not know who the authors of the paper are (this is called a "blind" review), recommends rejection of the paper because “the authors of this paper clearly do not understand the notion of compensatory adaptation”. Sometimes something like this is added: “the authors should read the literature on compensatory adaptation more carefully, particularly Kock (2004)” - an article that has a good number of citations to it ().

Oh well, the beauty of the academic refereeing process …

Two Recent Papers by Matt Metzgar

This is just a quick post to highlight two recent papers by the economist and fellow health writer Matt Metzgar.

The first paper is titled "The Feasibility of a Paleolithic Diet for Low-income Consumers", and is co-authored by Dr. Todd C. Rideout, Maelan Fontes-Villalba, and Dr. Remko S. Kuipers (1).  They found that a Paleolithic-type diet that meets all micronutrient requirements except calcium (which probably has an unnecessarily high RDA) costs slightly more money than a non-Paleolithic diet that fulfills the same requirements, but both are possible on a tight budget. 

The second paper is titled "Externalities From Grain Consumption: a Survey", with Matt Metzgar as the sole author (2).  He reviews certain positive and negative externalities due to the effects of grain consumption on health.  The take-home message is that refined grains are unhealthy and therefore costly to society, whole grains are better, but grains in general have certain healthcare-related economic costs that are difficult to deny, such as celiac disease.

There are a lot of ideas floating around on the blogosphere, some good and others questionable.  Composing a manuscript and submitting it to a reputable scientific journal is a good way to demonstrate that your idea holds water, and it's also a good way to communicate it to the scientific community.  The peer review process isn't perfect but it does encourage scientific rigor.  I think Metzgar is a good example of someone who has successfully put his ideas through this process.  Pedro Bastos, who also spoke at the Ancestral Health Symposium, is another example (3).

The China Study II: How gender takes us to the elusive and deadly factor X

The graph below shows the mortality in the 35-69 and 70-79 age ranges for men and women for the China Study II dataset. I discussed other results in my two previous posts () (), all taking us to this post. The full data for the China Study II study is publicly available (). The mortality numbers are actually averages of male and female deaths by 1,000 people in each of several counties, in each of the two age ranges.

Men do tend to die earlier than women, but the difference above is too large.

Generally speaking, when you look at a set time period that is long enough for a good number of deaths (not to be confused with “a number of good deaths”) to be observed, you tend to see around 5-10 percent more deaths among men than among women. This is when other variables are controlled for, or when men and women do not adopt dramatically different diets and lifestyles. One of many examples is a study in Finland (); you have to go beyond the abstract on this one.

As you can see from the graph above, in the China Study II dataset this difference in deaths is around 50 percent!

This huge difference could be caused by there being significantly more men than women per county included the dataset. But if you take a careful look at the description of the data collection methods employed (), this does not seem to be the case. In fact, the methodology descriptions suggest that the researchers tried to have approximately the same number of women and men studied in each county. The numbers reported also support this assumption.

As I said before, this is a well executed research project, for which Dr. Campbell and his collaborators should be commended. I may not agree with all of their conclusions, but this does not detract even a bit from the quality of the data they have compiled and made available to us all.

So there must be another factor X causing this enormous difference in mortality (and thus longevity) among men and women in the China Study II dataset.

What could be this factor X?

This situation helps me illustrate a point that I have made here before, mostly in the comments under other posts. Sometimes a variable, and its effects on other variables, are mostly a reflection of another unmeasured variable. Gender is a variable that is often involved in this type of situation. Frequently men and women do things very differently in a given population due to cultural reasons (as opposed to biological reasons), and those things can have a major effect on their health.

So, the search for our factor X is essentially a search for a health-relevant variable that is reflected by gender but that is not strictly due to the biological aspects that make men and women different (these can explain only a 5-10 percent difference in mortality). That is, we are looking for a variable that shows a lot of variation between men and women, that is behavioral, and that has a clear impact on health. Moreover, as it should be clear from my last post, we are looking for a variable that is unrelated to wheat flour and animal protein consumption.

As it turns out, the best candidate for the factor X is smoking, particularly cigarette smoking.

The second best candidate for factor X is alcohol abuse. Alcohol abuse can be just as bad for one’s health as smoking is, if not worse, but it may not be as good a candidate for factor X because the difference in prevalence between men and women does not appear to be just as large in China (). But it is still large enough for us to consider it a close second as a candidate for factor X, or a component of a more complex factor X – a composite of smoking, alcohol abuse and a few other coexisting factors that may be reflected by gender.

I have had some discussions about this with a few colleagues and doctoral students who are Chinese (thanks William and Wei), and they mentioned stress to me, based on anecdotal evidence. Moreover, they pointed out that stressful lifestyles, smoking, and alcohol abuse tend to happen together - with a much higher prevalence among men than women.

What an anti-climax for this series of posts eh?

With all the talk on the Internetz about safe and unsafe starches, animal protein, wheat bellies, and whatnot! C’mon Ned, give me a break! What about insulin!? What about leucine deficiency … or iron overload!? What about choline!? What about something truly mysterious, related to an obscure or emerging biochemistry topic; a hormone du jour like leptin perhaps? Whatever, something cool!

Smoking and alcohol abuse!? These are way too obvious. This is NOT cool at all!

Well, reality is often less mysterious than we want to believe it is.

Let me focus on smoking from here on, since it is the top candidate for factor X, although much of the following applies to alcohol abuse and a combination of the two as well.

One gets different statistics on cigarette smoking in China depending on the time period studied, but one thing seems to be a common denominator in these statistics. Men tend to smoke in much, much higher numbers than women in China. And this is not a recent phenomenon.

For example, a study conducted in 1996 () states that “smoking continues to be prevalent among more men (63%) than women (3.8%)”, and notes that these results are very similar to those in 1984, around the time when the China Study II data was collected.

A 1995 study () reports similar percentages: “A total of 2279 males (67%) but only 72 females (2%) smoke”. Another study () notes that in 1976 “56% of the men and 12% of the women were ever-smokers”, which together with other results suggest that the gap increased significantly in the 1980s, with many more men than women smoking. And, most importantly, smoking industrial cigarettes.

So we are possibly talking about a gigantic difference here; the prevalence of industrial cigarette smoking among men may have been over 30 times the prevalence among women in the China Study II dataset.

Given the above, it is reasonable to conclude that the variable “SexM1F2” reflects very strongly the variable “Smoking”, related to industrial cigarette smoking, and in an inverse way. I did something that, grossly speaking, made the mysterious factor X explicit in the WarpPLS model discussed in my previous post. I replaced the variable “SexM1F2” in the model with the variable “Smoking” by using a reverse scale (i.e., 1 and 2, but reversing the codes used for “SexM1F2”). The results of the new WarpPLS analysis are shown on the graph below. This is of course far from ideal, but gives a better picture to readers of what is going on than sticking with the variable “SexM1F2”.

With this revised model, the associations of smoking with mortality in the 35-69 and 70-79 age ranges are a lot stronger than those of animal protein and wheat flour consumption. The R-squared coefficients for mortality in both ranges are higher than 20 percent, which is a sign that this model has decent explanatory power. Animal protein and wheat flour consumption are still significantly associated with mortality, even after we control for smoking; animal protein seems protective and wheat flour detrimental. And smoking’s association with the amount of animal protein and wheat flour consumed is practically zero.

Replacing “SexM1F2” with “Smoking” would be particularly far from ideal if we were analyzing this data at the individual level. It could lead to some outlier-induced errors; for example, due to the possible existence of a minority of female chain smokers. But this variable replacement is not as harmful when we look at county-level data, as we are doing here.

In fact, this is as good and parsimonious model of mortality based on the China Study II data as I’ve ever seen based on county level data.

Now, here is an interesting thing. Does the original China Study II analysis of univariate correlations show smoking as a major problem in terms of mortality? Not really.

The table below, from the China Study II report (), shows ALL of the statistically significant (P<0.05) univariate correlations with mortality in 70-79 age range. I highlighted the only measure that is directly related to smoking; that is “dSMOKAGEm”, listed as “questionnaire AGE MALE SMOKERS STARTED SMOKING (years)”.

The high positive correlation with “dSMOKAGEm” does not even make a lot of sense, as one would expect a negative correlation here – i.e., the earlier in life folks start smoking, the higher should be the mortality. But this reverse-signed correlation may be due to smokers who get an early start dying in disproportionally high numbers before they reach age 70, and thus being captured by another age range mortality variable. The fact that other smoking-related variables are not showing up on the table above is likely due to distortions caused by inter-correlations, as well as measurement problems like the one just mentioned.

As one looks at these univariate correlations, most of them make sense, although several can be and probably are distorted by correlations with other variables, even unmeasured variables. And some unmeasured variables may turn out to be critical. Remember what I said in my previous post – the variable “SexM1F2” was introduced by me; it was not in the original dataset. “Smoking” is this variable, but reversed, to account for the fact that men are heavy smokers and women are not.

Univariate correlations are calculated without adjustments or control. To correct this problem one can adjust a variable based on other variables; as in “adjusting for age”. This is not such a good technique, in my opinion; it tends to be time-consuming to implement, and prone to errors. One can alternatively control for the effects of other variables; a better technique, employed in multivariate statistical analyses. This latter technique is the one employed in WarpPLS analyses ().

Why don’t more smoking-related variables show up on the univariate correlations table above? The reason is that the table summarizes associations calculated based on data for both sexes. Since the women in the dataset smoked very little, including them in the analysis together with men lowers the strength of smoking-related associations, which would probably be much stronger if only men were included. It lowers the strength of the associations to the point that their P values become higher than 0.05, leading to their exclusion from tables like the one above. This is where the aggregation process that may lead to ecological fallacy shows its ugly head.

No one can blame Dr. Campbell for not issuing warnings about smoking, even as they came mixed with warnings about animal food consumption (). The former warnings, about smoking, make a lot of sense based on the results of the analyses in this and the last two posts.

The latter warnings, about animal food consumption, seem increasingly ill-advised. Animal food consumption may actually be protective in regards to the factor X, as it seems to be protective in terms of wheat flour consumption ().

Does High Circulating Insulin Drive Body Fat Accumulation? Answers from Genetically Modified Mice

The house mouse Mus musculus is an incredible research tool in the biomedical sciences, due to its ease of care and its ability to be genetically manipulated.  Although mice aren't humans, they resemble us closely in many ways, including how insulin signaling works.  Genetic manipulation of mice allows researchers to identify biological mechanisms and cause-effect relationships in a very precise manner.  One way of doing this is to create "knockout" mice that lack a specific gene, in an attempt to determine that gene's importance in a particular process.  Another way is to create transgenic mice that express a gene of interest, often modified in some way.  A third method is to use an extraordinary (but now common) tool called "Cre-lox" recombination (1), which allows us to delete or add a single gene in a specific tissue or cell type. 

Studying the relationship between obesity and insulin resistance is challenging, because the two typically travel together, confounding efforts to determine which is the cause and which is the effect of the other (or neither).  Some have proposed the hypothesis that high levels of circulating insulin promote body fat accumulation*.  To truly address this question, we need to consider targeted experiments that increase circulating insulin over long periods of time without altering a number of other factors throughout the body.  This is where mice come in.  Scientists are able to perform precise genetic interventions in mice that increase circulating insulin over a long period of time.  These mice should gain fat mass if the hypothesis is correct. 

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The China Study II: Gender, mortality, and the mysterious factor X

WarpPLS and HealthCorrelator for Excel were used to do the analyses below. For other China Study analyses, many using WarpPLS as well as HealthCorrelator for Excel, click here. For the dataset used, visit the HealthCorrelator for Excel site and check under the sample datasets area. As always, I thank Dr. T. Colin Campbell and his collaborators for making the data publicly available for independent analyses.

In my previous post I mentioned some odd results that led me to additional analyses. Below is a screen snapshot summarizing one such analysis, of the ordered associations between mortality in the 35-69 and 70-79 age ranges and all of the other variables in the dataset. As I said before, this is a subset of the China Study II dataset, which does not include all of the variables for which data was collected. The associations shown below were generated by HealthCorrelator for Excel.

The top associations are positive and with mortality in the other range (the “M006 …” and “M005 …” variables). This is to be expected if ecological fallacy is not a big problem in terms of conclusions drawn from this dataset. In other words, the same things cause mortality to go up in the two age ranges, uniformly across counties. This is reassuring from a quantitative analysis perspective.

The second highest association in both age ranges is with the variable “SexM1F2”. This variable is a “dummy” variable coded as 1 for male sex and 2 for female, which I added to the dataset myself – it did not exist in the original dataset. The association in both age ranges is negative, meaning that being female is protective. They reflect in part the role of gender on mortality, more specifically the biological aspects of being female, since we have seen before in previous analyses that being female is generally health-protective.

I was able to add a gender-related variable to the model because the data was originally provided for each county separately for males and females, as well as through “totals” that were calculated by aggregating data from both males and females. So I essentially de-aggregated the data by using data from males and females separately, in which case the totals were not used (otherwise I would have artificially reduced the variance in all variables, also possibly adding uniformity where it did not belong). Using data from males and females separately is the reverse of the aggregation process that can lead to ecological fallacy problems.

Anyway, the associations with the variable “SexM1F2” got me thinking about a possibility. What if females consumed significantly less wheat flour and more animal protein in this dataset? This could be one of the reasons behind these strong associations between being female and living longer. So I built a more complex WarpPLS model than the one in my previous post, and ran a linear multivariate analysis on it. The results are shown below.

What do these results suggest? They suggest no strong associations between gender and wheat flour or animal protein consumption. That is, when you look at county averages, men and women consumed about the same amounts of wheat flour and animal protein. Also, the results suggest that animal protein is protective and wheat flour is detrimental, in terms of longevity, regardless of gender. The associations between animal protein and wheat flour are essentially the same as the ones in my previous post. The beta coefficients are a bit lower, but some P values improved (i.e., decreased); the latter most likely due to better resample set stability after including the gender-related variable.

Most importantly, there is a very strong protective effect associated with being female, and this effect is independent of what the participants ate.

Now, if you are a man, don’t rush to take hormones to become a woman with the goal of living longer just yet. This advice is not only due to the likely health problems related to becoming a transgender person; it is also due to a little problem with these associations. The problem is that the protective effect suggested by the coefficients of association between gender and mortality seems too strong to be due to men "being women with a few design flaws".

There is a mysterious factor X somewhere in there, and it is not gender per se. We need to find a better candidate.

One interesting thing to point out here is that the above model has good explanatory power in regards to mortality. I'd say unusually good explanatory power given that people die for a variety of reasons, and here we have a model explaining a lot of that variation. The model  explains 45 percent of the variance in mortality in the 35-69 age range, and 28 percent of the variance in the 70-79 age range.

In other words, the model above explains nearly half of the variance in mortality in the 35-69 age range. It could form the basis of a doctoral dissertation in nutrition or epidemiology with important  implications for public health policy in China. But first the factor X must be identified, and it must be somehow related to gender.

Next post coming up soon ...

The Brain Controls Insulin Action

Insulin regulates blood glucose primarily by two mechanisms:

1. Suppressing glucose production by the liver
2. Enhancing glucose uptake by other tissues, particularly muscle and liver

Since the cells contained in liver, muscle and other tissues respond directly to insulin stimulation, most people don't think about the role of the brain in this process.  An interesting paper just published in Diabetes reminds us of the central role of the brain in glucose metabolism as well as body fat regulation (1).  Investigators showed that by inhibiting insulin signaling in the brains of mice, they could diminish insulin's ability to suppress liver glucose production by 20%, and its ability to promote glucose uptake by muscle tissue by 59%.  In other words, the majority of insulin's ability to cause muscle to take up glucose is mediated by its effect on the brain. 

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The China Study II: Animal protein, wheat, and mortality … there is something odd here!

WarpPLS and HealthCorrelator for Excel were used in the analyses below. For other China Study analyses, many using WarpPLS and HealthCorrelator for Excel, click here. For the dataset used, visit the HealthCorrelator for Excel site and check under the sample datasets area. I thank Dr. T. Colin Campbell and his collaborators at the University of Oxford for making the data publicly available for independent analyses.

The graph below shows the results of a multivariate linear WarpPLS analysis including the following variables: Wheat (wheat flour consumption in g/d), Aprot (animal protein consumption in g/d), Mor35_69 (number of deaths per 1,000 people in the 35-69 age range), and Mor70_79 (number of deaths per 1,000 people in the 70-79 age range).

Just a technical comment here, regarding the possibility of ecological fallacy. I am not going to get into this in any depth now, but let me say that the patterns in the data suggest that, with the possible exception of some variables (e.g., blood glucose, gender; the latter will get us going in the next few posts), ecological fallacy due to county aggregation is not a big problem. The threat of ecological fallacy exists, here and in many other datasets, but it is generally overstated (often by those whose previous findings are contradicted by aggregated results).

I have not included plant protein consumption in the analysis because plant protein consumption is very strongly and positively associated with wheat flour consumption. The reason is simple. Almost all of the plant protein consumed by the participants in this study was probably gluten, from wheat products. Fruits and vegetables have very small amounts of protein. Keeping that in mind, what the graph above tells us is that:

- Wheat flour consumption is significantly and negatively associated with animal protein consumption. This is probably due to those eating more wheat products tending to consume less animal protein.

- Wheat flour consumption is positively associated with mortality in the 35-69 age range. The P value (P=0.06) is just shy of the 5 percent (i.e., P=0.05) that most researchers would consider to be the threshold for statistical significance. More consumption of wheat in a county, more deaths in this age range.

- Wheat flour consumption is significantly and positively associated with mortality in the 70-79 age range. More consumption of wheat in a county, more deaths in this age range.

- Animal protein consumption is not significantly associated with mortality in the 35-69 age range.

- Animal protein consumption is significantly and negatively associated with mortality in the 70-79 age range. More consumption of animal protein in a county, fewer deaths in this age range.

Let me tell you, from my past experience analyzing health data (as well as other types of data, from different fields), that these coefficients of association do not suggest super-strong associations. Actually this is also indicated by the R-squared coefficients, which vary from 3 to 7 percent. These are the variances explained by the model on the variables above the R-squared coefficients. They are low, which means that the model has weak explanatory power.

R-squared coefficients of 20 percent and above would be more promising. I hate to disappoint hardcore carnivores and the fans of the “wheat is murder” theory, but these coefficients of association and variance explained are probably way less than what we would expect to see if animal protein was humanity's salvation and wheat its demise.

Moreover, the lack of association between animal protein consumption and mortality in the 35-69 age range is a bit strange, given that there is an association suggestive of a protective effect in the 70-79 age range.

Of course death happens for all kinds of reasons, not only what we eat. Still, let us take a look at some other graphs involving these foodstuffs to see if we can form a better picture of what is going on here. Below is a graph showing mortality at the two age ranges for different levels of animal protein consumption. The results are organized in quintiles.

As you can see, the participants in this study consumed relatively little animal protein. The lowest mortality in the 70-79 age range, arguably the range of higher vulnerability, was for the 28 to 35 g/d quintile of consumption. That was the highest consumption quintile. About a quarter to a third of 1 lb/d of beef, and less of seafood (in general), would give you that much animal protein.

Keep in mind that the unit of analysis here is the county, and that these results are based on county averages. I wish I had access to data on individual participants! Still I stand by my comment earlier on ecological fallacy. Don't worry too much about it just yet.

Clearly the above results and graphs contradict claims that animal protein consumption makes people die earlier, and go somewhat against the notion that animal protein consumption causes things that make people die earlier, such as cancer. But they do so in a messy way - that spike in mortality in the 70-79 age range for 21-28 g/d of animal protein is a bit strange.

Below is a graph showing mortality at the two age ranges (i.e., 35-69 and 70-79) for different levels of wheat flour consumption. Again, the results are shown in quintiles.

Without a doubt the participants in this study consumed a lot of wheat flour. The lowest mortality in the 70-79 age range, which is the range of higher vulnerability, was for the 300 to 450 g/d quintile of wheat flour consumption. The high end of this range is about 1 lb/d of wheat flour! How many slices of bread would this be equivalent to? I don’t know, but my guess is that it would be many.

Well, this is not exactly the smoking gun linking wheat with early death, a connection that has been reaching near mythical proportions on the Internetz lately. Overall, the linear trend seems to be one of decreased longevity associated with wheat flour consumption, as suggested by the WarpPLS results, but the relationship between these two variables is messy and somewhat weak. It is not even clearly nonlinear, at least in terms of the ubiquitous J-curve relationship.

Frankly, there is something odd about these results.

This oddity led to me to explore, using HealthCorrelator for Excel, all ordered associations between mortality in the 35-69 and 70-79 age ranges and all of the other variables in the dataset. That in turn led me to a more complex WarpPLS analysis, which I’ll talk about in my next post, which is still being written.

I can tell you right now that there will be more oddities there, which will eventually take us to what I refer to as the mysterious factor X. Ah, by the way, that factor X is not gender - but gender leads us to it.