Speaking at Wise Traditions 2010

I'm happy to announce that I'll be presenting at the Weston A. Price foundation's 2010 Wise Traditions conference. The conference will be held in King of Prussia, Pennsylvania, Nov 12-14. The theme is the politics of food.

Sally Fallon Morell has invited me to give a talk on the diet and health of Pacific islanders. The talk will be titled "Kakana Dina: Diet and Health in the Pacific Islands", and it will take place on Sunday, November 14th from 4:00 to 5:20 pm. In preparation for the talk, I've read eight books and countless journal articles. Although some of the material will be familiar to people who follow the blog, I will not be rehashing what I've already published. I have nearly an hour and a half to talk, so I'll be going into some depth on the natural history and traditional food habits of Pacific island populations. Not just macronutrient breakdowns... specific foods and traditional preparation methods.

Learn about the health of traditional Pacific island populations, and what has changed since Western contact. Learn about traditional cooking and fermentation techniques. See unpublished photos from the Kitava study, courtesy of Dr. Staffan Lindeberg. Learn about the nutritional and ceremonial role of mammals including pork... and the most gruesome food of all.

I hope to see you there!


Kitava photo courtesy of Dr. Staffan Lindeberg

The China Study II: Wheat flour, rice, and cardiovascular disease

In my last post on the China Study II, I analyzed the effect of total and HDL cholesterol on mortality from all cardiovascular diseases. The main conclusion was that total and HDL cholesterol were protective. Total and HDL cholesterol usually increase with intake of animal foods, and particularly of animal fat. The lowest mortality from all cardiovascular diseases was in the highest total cholesterol range, 172.5 to 180; and the highest mortality in the lowest total cholesterol range, 120 to 127.5. The difference was quite large; the mortality in the lowest range was approximately 3.3 times higher than in the highest.

This post focuses on the intake of two main plant foods, namely wheat flour and rice intake, and their relationships with mortality from all cardiovascular diseases. After many exploratory multivariate analyses, wheat flour and rice emerged as the plant foods with the strongest associations with mortality from all cardiovascular diseases. Moreover, wheat flour and rice have a strong and inverse relationship with each other, which suggests a “consumption divide”. Since the data is from China in the late 1980s, it is likely that consumption of wheat flour is even higher now. As you’ll see, this picture is alarming.

The main model and results

All of the results reported here are from analyses conducted using WarpPLS. Below is the model with the main results of the analyses. (Click on it to enlarge. Use the "CRTL" and "+" keys to zoom in, and CRTL" and "-" to zoom out.) The arrows explore associations between variables, which are shown within ovals. The meaning of each variable is the following: SexM1F2 = sex, with 1 assigned to males and 2 to females; MVASC = mortality from all cardiovascular diseases (ages 35-69); TKCAL = total calorie intake per day; WHTFLOUR = wheat flour intake (g/day); and RICE = and rice intake (g/day).

The variables to the left of MVASC are the main predictors of interest in the model. The one to the right is a control variable – SexM1F2. The path coefficients (indicated as beta coefficients) reflect the strength of the relationships. A negative beta means that the relationship is negative; i.e., an increase in a variable is associated with a decrease in the variable that it points to. The P values indicate the statistical significance of the relationship; a P lower than 0.05 generally means a significant relationship (95 percent or higher likelihood that the relationship is “real”).

In summary, the model above seems to be telling us that:

- As rice intake increases, wheat flour intake decreases significantly (beta=-0.84; P<0.01). This relationship would be the same if the arrow pointed in the opposite direction. It suggests that there is a sharp divide between rice-consuming and wheat flour-consuming regions.

- As wheat flour intake increases, mortality from all cardiovascular diseases increases significantly (beta=0.32; P<0.01). This is after controlling for the effects of rice and total calorie intake. That is, wheat flour seems to have some inherent properties that make it bad for one’s health, even if one doesn’t consume that many calories.

- As rice intake increases, mortality from all cardiovascular diseases decreases significantly (beta=-0.24; P<0.01). This is after controlling for the effects of wheat flour and total calorie intake. That is, this effect is not entirely due to rice being consumed in place of wheat flour. Still, as you’ll see later in this post, this relationship is nonlinear. Excessive rice intake does not seem to be very good for one’s health either.

- Increases in wheat flour and rice intake are significantly associated with increases in total calorie intake (betas=0.25, 0.33; P<0.01). This may be due to wheat flour and rice intake: (a) being themselves, in terms of their own caloric content, main contributors to the total calorie intake; or (b) causing an increase in calorie intake from other sources. The former is more likely, given the effect below.

- The effect of total calorie intake on mortality from all cardiovascular diseases is insignificant when we control for the effects of rice and wheat flour intakes (beta=0.08; P=0.35). This suggests that neither wheat flour nor rice exerts an effect on mortality from all cardiovascular diseases by increasing total calorie intake from other food sources.

- Being female is significantly associated with a reduction in mortality from all cardiovascular diseases (beta=-0.24; P=0.01). This is to be expected. In other words, men are women with a few design flaws, so to speak. (This situation reverses itself a bit after menopause.)

Wheat flour displaces rice

The graph below shows the shape of the association between wheat flour intake (WHTFLOUR) and rice intake (RICE). The values are provided in standardized format; e.g., 0 is the mean (a.k.a. average), 1 is one standard deviation above the mean, and so on. The curve is the best-fitting U curve obtained by the software. It actually has the shape of an exponential decay curve, which can be seen as a section of a U curve. This suggests that wheat flour consumption has strongly displaced rice consumption in several regions in China, and also that wherever rice consumption is high wheat flour consumption tends to be low.

As wheat flour intake goes up, so does cardiovascular disease mortality

The graphs below show the shapes of the association between wheat flour intake (WHTFLOUR) and mortality from all cardiovascular diseases (MVASC). In the first graph, the values are provided in standardized format; e.g., 0 is the mean (or average), 1 is one standard deviation above the mean, and so on. In the second graph, the values are provided in unstandardized format and organized in terciles (each of three equal intervals).

The curve in the first graph is the best-fitting U curve obtained by the software. It is a quasi-linear relationship. The higher the consumption of wheat flour in a county, the higher seems to be the mortality from all cardiovascular diseases. The second graph suggests that mortality in the third tercile, which represents a consumption of wheat flour of 501 to 751 g/day (a lot!), is 69 percent higher than mortality in the first tercile (0 to 251 g/day).

Rice seems to be protective, as long as intake is not too high

The graphs below show the shapes of the association between rice intake (RICE) and mortality from all cardiovascular diseases (MVASC). In the first graph, the values are provided in standardized format. In the second graph, the values are provided in unstandardized format and organized in terciles.

Here the relationship is more complex. The lowest mortality is clearly in the second tercile (206 to 412 g/day). There is a lot of variation in the first tercile, as suggested by the first graph with the U curve. (Remember, as rice intake goes down, wheat flour intake tends to go up.) The U curve here looks similar to the exponential decay curve shown earlier in the post, for the relationship between rice and wheat flour intake.

In fact, the shape of the association between rice intake and mortality from all cardiovascular diseases looks a bit like an “echo” of the shape of the relationship between rice and wheat flour intake. Here is what is creepy. This echo looks somewhat like the first curve (between rice and wheat flour intake), but with wheat flour intake replaced by “death” (i.e., mortality from all cardiovascular diseases).

What does this all mean?

- Wheat flour displacing rice does not look like a good thing. Wheat flour intake seems to have strongly displaced rice intake in the counties where it is heavily consumed. Generally speaking, that does not seem to have been a good thing. It looks like this is generally associated with increased mortality from all cardiovascular diseases.

- High glycemic index food consumption does not seem to be the problem here. Wheat flour and rice have very similar glycemic indices (but generally not glycemic loads; see below). Both lead to blood glucose and insulin spikes. Yet, rice consumption seems protective when it is not excessive. This is true in part (but not entirely) because it largely displaces wheat flour. Moreover, neither rice nor wheat flour consumption seems to be significantly associated with cardiovascular disease via an increase in total calorie consumption. This is a bit of a blow to the theory that high glycemic carbohydrates necessarily cause obesity, diabetes, and eventually cardiovascular disease.

- The problem with wheat flour is … hard to pinpoint, based on the results summarized here. Maybe it is the fact that it is an ultra-refined carbohydrate-rich food; less refined forms of wheat could be healthier. In fact, the glycemic loads of less refined carbohydrate-rich foods tend to be much lower than those of more refined ones. (Also, boiled brown rice has a glycemic load that is about three times lower than that of whole wheat bread; whereas the glycemic indices are about the same.) Maybe the problem is wheat flour's  gluten content. Maybe it is a combination of various factors, including these.

Reference

Kock, N. (2010). WarpPLS 1.0 User Manual. Laredo, Texas: ScriptWarp Systems.

Acknowledgment and notes

- Many thanks are due to Dr. Campbell and his collaborators for collecting and compiling the data used in this analysis. The data is from this site, created by those researchers to disseminate their work in connection with a study often referred to as the “China Study II”. It has already been analyzed by other bloggers. Notable analyses have been conducted by Ricardo at Canibais e Reis, Stan at Heretic, and Denise at Raw Food SOS.

- The path coefficients (indicated as beta coefficients) reflect the strength of the relationships; they are a bit like standard univariate (or Pearson) correlation coefficients, except that they take into consideration multivariate relationships (they control for competing effects on each variable). Whenever nonlinear relationships were modeled, the path coefficients were automatically corrected by the software to account for nonlinearity.

- The software used here identifies non-cyclical and mono-cyclical relationships such as logarithmic, exponential, and hyperbolic decay relationships. Once a relationship is identified, data values are corrected and coefficients calculated. This is not the same as log-transforming data prior to analysis, which is widely used but only works if the underlying relationship is logarithmic. Otherwise, log-transforming data may distort the relationship even more than assuming that it is linear, which is what is done by most statistical software tools.

- The R-squared values reflect the percentage of explained variance for certain variables; the higher they are, the better the model fit with the data. In complex and multi-factorial phenomena such as health-related phenomena, many would consider an R-squared of 0.20 as acceptable. Still, such an R-squared would mean that 80 percent of the variance for a particularly variable is unexplained by the data.

- The P values have been calculated using a nonparametric technique, a form of resampling called jackknifing, which does not require the assumption that the data is normally distributed to be met. This and other related techniques also tend to yield more reliable results for small samples, and samples with outliers (as long as the outliers are “good” data, and are not the result of measurement error).

- Only two data points per county were used (for males and females). This increased the sample size of the dataset without artificially reducing variance, which is desirable since the dataset is relatively small. This also allowed for the test of commonsense assumptions (e.g., the protective effects of being female), which is always a good idea in a complex analysis because violation of commonsense assumptions may suggest data collection or analysis error. On the other hand, it required the inclusion of a sex variable as a control variable in the analysis, which is no big deal.

- Since all the data was collected around the same time (late 1980s), this analysis assumes a somewhat static pattern of consumption of rice and wheat flour. In other words, let us assume that variations in consumption of a particular food do lead to variations in mortality. Still, that effect will typically take years to manifest itself. This is a major limitation of this dataset and any related analyses.

- Mortality from schistosomiasis infection (MSCHIST) does not confound the results presented here. Only counties where no deaths from schistosomiasis infection were reported have been included in this analysis. Mortality from all cardiovascular diseases (MVASC) was measured using the variable M059 ALLVASCc (ages 35-69). See this post for other notes that apply here as well.

Dogen Zenji on Nutritionism

Dogen Zenji was the man who brought the Soto lineage of Zen Buddhism to Japan. He was a prolific writer, and many of his texts are respected both inside and outside the Soto Zen community. Last week, my Zen group was discussing the Genjo Koan, one of his works that is frequently used as a chant. Here's an excerpt. It may seem cryptic but bear with me:

...when you sail out in a boat to the middle of an ocean where no land is in sight, and view the four directions, the ocean looks circular, and does not look any other way. But the ocean is neither round or square; its features are infinite in variety... It only look circular as far as you can see at that time. All things are like this.

Though there are many features in the dusty world and the world beyond conditions, you see and understand only what your eye of practice can reach. In order to learn the nature of the myriad things, you must know that although they may look round or square, the other features of oceans and mountains are infinite in variety; whole worlds are there. It is so not only around you, but also directly beneath your feet, or in a drop of water.

What Dogen meant, among other things, is that the world is much more complex than what our conscious minds can perceive or understand. It was true in the 13th century, and it's still true today, despite our greatly expanded understanding of the natural world.

We can apply this principle to nutrition. For example, what is red palm oil? Two hundred years ago, perhaps we only knew a few basic facts about it. It's a fat, it's red, it comes from an African palm fruit and it has a particular melting point and flavor. Then we learned about vitamins, so we knew it contained vitamin E, carotenes (provitamin A), and vitamin K. Then fatty acid composition, so we found out it's mostly palmitic and oleic acids. Now we know red palm oil contains an array of polyphenols, sterols, coenzyme Q10 and many other non-essential constituents. We don't know much about the biological effects of most of these substances, particularly in combination with one another.

Add to that the fact that every batch of red palm oil is different, due to strain, terroir, processing, storage, et cetera. We know what the concept "red palm oil" means, roughly, but the details are infinitely complex. Now feed it to a human, who is not only incredibly complex himself, but genetically and epigenetically unique. How can we possibly guess the outcome of this encounter based on the chemical composition of red palm oil? That's essentially what nutritionism attempts to do.

To be fair, nutritionism does work sometimes. For example, we can pretty well guess that a handful of wild almonds containing a lot of cyanide won't be healthy to eat, due at least in part to the cyanide. But outside extreme examples like this, we're in a gray zone that needs to be informed by empirical observation. In other words, what happens when the person in question actually eats the red palm oil? What happened when a large group of people in West Africa ate red palm oil for thousands of years? Those questions are the reason why I'm so interested in understanding the lives of healthy non-industrial cultures.

I'm not criticizing reductionist science or controlled experiments (which I perform myself); I just think they need to be kept in context. I believe they should be interpreted within the framework of more basic empirical observations*.

One of the most important aspects of scientific maturity is learning to accept and manage uncertainty and your own ignorance. Some things are more certain than others, but most aren't set in stone. I think Dogen would tell us to be wary of nutritionism, and other forms of overconfidence.

* Wikipedia's definition of empirical: "information gained by means of observation, experience, or experiment." As opposed to inferences made from experiments not directly related to the question at hand.

The China Study II: Cholesterol seems to protect against cardiovascular disease

First of all, many thanks are due to Dr. Campbell and his collaborators for collecting and compiling the data used in this analysis. This data is from this site, created by those researchers to disseminate the data from a study often referred to as the “China Study II”. It has already been analyzed by other bloggers. Notable analyses have been conducted by Ricardo at Canibais e Reis, Stan at Heretic, and Denise at Raw Food SOS.

The analyses in this post differ from those other analyses in various aspects. One of them is that data for males and females were used separately for each county, instead of the totals per county. Only two data points per county were used (for males and females). This increased the sample size of the dataset without artificially reducing variance (for more details, see “Notes” at the end of the post), which is desirable since the dataset is relatively small. This also allowed for the test of commonsense assumptions (e.g., the protective effects of being female), which is always a good idea in a complex analysis because violation of commonsense assumption may suggest data collection or analysis error. On the other hand, it required the inclusion of a sex variable as a control variable in the analysis, which is no big deal.

The analysis was conducted using WarpPLS. Below is the model with the main results of the analysis. (Click on it to enlarge. Use the "CRTL" and "+" keys to zoom in, and CRTL" and "-" to zoom out.) The arrows explore associations between variables, which are shown within ovals. The meaning of each variable is the following: SexM1F2 = sex, with 1 assigned to males and 2 to females; HDLCHOL = HDL cholesterol; TOTCHOL = total cholesterol; MSCHIST = mortality from schistosomiasis infection; and MVASC = mortality from all cardiovascular diseases.

The variables to the left of MVASC are the main predictors of interest in the model – HDLCHOL and TOTCHOL. The ones to the right are control variables – SexM1F2 and MSCHIST. The path coefficients (indicated as beta coefficients) reflect the strength of the relationships. A negative beta means that the relationship is negative; i.e., an increase in a variable is associated with a decrease in the variable that it points to. The P values indicate the statistical significance of the relationship; a P lower than 0.05 generally means a significant relationship (95 percent or higher likelihood that the relationship is “real”).

In summary, this is what the model above is telling us:

- As HDL cholesterol increases, total cholesterol increases significantly (beta=0.48; P<0.01). This is to be expected, as HDL is a main component of total cholesterol, together with VLDL and LDL cholesterol.

- As total cholesterol increases, mortality from all cardiovascular diseases decreases significantly (beta=-0.25; P<0.01). This is to be expected if we assume that total cholesterol is in part an intervening variable between HDL cholesterol and mortality from all cardiovascular diseases. This assumption can be tested through a separate model (more below). Also, there is more to this story, as noted below.

- The effect of HDL cholesterol on mortality from all cardiovascular diseases is insignificant when we control for the effect of total cholesterol (beta=-0.08; P=0.26). This suggests that HDL’s protective role is subsumed by the variable total cholesterol, and also that it is possible that there is something else associated with total cholesterol that makes it protective. Otherwise the effect of total cholesterol might have been insignificant, and the effect of HDL cholesterol significant (the reverse of what we see here).

- Being female is significantly associated with a reduction in mortality from all cardiovascular diseases (beta=-0.16; P=0.01). This is to be expected. In other words, men are women with a few design flaws. (This situation reverses itself a bit after menopause.)

- Mortality from schistosomiasis infection is significantly and inversely associated with mortality from all cardiovascular diseases (beta=-0.28; P<0.01). This is probably due to those dying from schistosomiasis infection not being entered in the dataset as dying from cardiovascular diseases, and vice-versa.

Two other main components of total cholesterol, in addition to HDL cholesterol, are VLDL and LDL cholesterol. These are carried in particles, known as lipoproteins. VLDL cholesterol is usually represented as a fraction of triglycerides in cholesterol equations (e.g., the Friedewald and Iranian equations). It usually correlates inversely with HDL; that is, as HDL cholesterol increases, usually VLDL cholesterol decreases. Given this and the associations discussed above, it seems that LDL cholesterol is a good candidate for the possible “something else associated with total cholesterol that makes it protective”. But waidaminet! Is it possible that the demon particle, the LDL, serves any purpose other than giving us heart attacks?

The graph below shows the shape of the association between total cholesterol (TOTCHOL) and mortality from all cardiovascular diseases (MVASC). The values are provided in standardized format; e.g., 0 is the average, 1 is one standard deviation above the mean, and so on. The curve is the best-fitting S curve obtained by the software (an S curve is a slightly more complex curve than a U curve).

The graph below shows some of the data in unstandardized format, and organized differently. The data is grouped here in ranges of total cholesterol, which are shown on the horizontal axis. The lowest and highest ranges in the dataset are shown, to highlight the magnitude of the apparently protective effect. Here the two variables used to calculate mortality from all cardiovascular diseases (MVASC; see “Notes” at the end of this post) were added. Clearly the lowest mortality from all cardiovascular diseases is in the highest total cholesterol range, 172.5 to 180; and the highest mortality in the lowest total cholesterol range, 120 to 127.5. The difference is quite large; the mortality in the lowest range is approximately 3.3 times higher than in the highest.

The shape of the S-curve graph above suggests that there are other variables that are confounding the results a bit. Mortality from all cardiovascular diseases does seem to generally go down with increases in total cholesterol, but the smooth inflection point at the middle of the S-curve graph suggests a more complex variation pattern that may be influenced by other variables (e.g., smoking, dietary patterns, or even schistosomiasis infection; see “Notes” at the end of this post).

As mentioned before, total cholesterol is strongly influenced by HDL cholesterol, so below is the model with only HDL cholesterol (HDLCHOL) pointing at mortality from all cardiovascular diseases (MVASC), and the control variable sex (SexM1F2).

The graph above confirms the assumption that HDL’s protective role is subsumed by the variable total cholesterol. When the variable total cholesterol is removed from the model, as it was done above, the protective effect of HDL cholesterol becomes significant (beta=-0.27; P<0.01). The control variable sex (SexM1F2) was retained even in this targeted HDL effect model because of the expected confounding effect of sex; females generally tend to have higher HDL cholesterol and less cardiovascular disease than males.

Below, in the “Notes” section (after the “Reference”) are several notes, some of which are quite technical. Providing them separately hopefully has made the discussion above a bit easier to follow. The notes also point at some limitations of the analysis. This data needs to be analyzed from different angles, using multiple models, so that firmer conclusions can be reached. Still, the overall picture that seems to be emerging is at odds with previous beliefs based on the same dataset.

What could be increasing the apparently protective HDL and total cholesterol in this dataset? High consumption of animal foods, particularly foods rich in saturated fat and cholesterol, are strong candidates. Low consumption of vegetable oils rich in linoleic acid, and of foods rich in refined carbohydrates, are also good candidates. Maybe it is a combination of these.

We need more analyses!

Reference:

Kock, N. (2010). WarpPLS 1.0 User Manual. Laredo, Texas: ScriptWarp Systems.


Notes:

- The path coefficients (indicated as beta coefficients) reflect the strength of the relationships; they are a bit like standard univariate (or Pearson) correlation coefficients, except that they take into consideration multivariate relationships (they control for competing effects on each variable).

- The R-squared values reflect the percentage of explained variance for certain variables; the higher they are, the better the model fit with the data. In complex and multi-factorial phenomena such as health-related phenomena, many would consider an R-squared of 0.20 as acceptable. Still, such an R-squared would mean that 80 percent of the variance for a particularly variable is unexplained by the data.

- The P values have been calculated using a nonparametric technique, a form of resampling called jackknifing, which does not require the assumption that the data is normally distributed to be met. This and other related techniques also tend to yield more reliable results for small samples, and samples with outliers (as long as the outliers are “good” data, and are not the result of measurement error).

- Colinearity is an important consideration in models that analyze the effect of multiple predictors on one single variable. This is particularly true for multiple regression models, where there is a temptation of adding many predictors to the model to see which ones come out as the “winners”. This often backfires, as colinearity can severely distort the results. Some multiple regression techniques, such as automated stepwise regression with backward elimination, are particularly vulnerable to this problem. Colinearity is not the same as correlation, and thus is defined and measured differently. Two predictor variables may be significantly correlated and still have low colinearity. A reasonably reliable measure of colinearity is the variance inflation factor. Colinearity was tested in this model, and was found to be low.

- An effort was made here to avoid multiple data points per county (even though this was available for some variables), because this could artificially reduce the variance for each variable, and potentially bias the results. The reason for this is that multiple answers from a single county would normally be somewhat correlated; a higher degree of intra-county correlation than inter-county correlation. The resulting bias would be difficult to control for, via one or more control variables. With only two data points per county, one for males and the other for females, one can control for intra-country correlation by adding a “dummy” sex variable to the analysis, as a control variable. This was done here.

- Mortality from schistosomiasis infection (MSCHIST) is a variable that tends to affect the results in a way that makes it more difficult to make sense of them. Generally this is true for any infectious diseases that significantly affect a population under study. The problem with infection is that people with otherwise good health or habits may get the infection, and people with bad health and habits may not. Since cholesterol is used by the human body to fight disease, it may go up, giving the impression that it is going up for some other reason. Perhaps instead of controlling for its effect, as done here, it would have been better to remove from the analysis those counties with deaths from schistosomiasis infection. (See also this post, and this one.)

- Different parts of the data were collected at different times. It seems that the mortality data is for the period 1986-88, and the rest of the data is for 1989. This may have biased the results somewhat, even though the time lag is not that long, especially if there were changes in certain health trends from one period to the other. For example, major migrations from one county to another could have significantly affected the results.

- The following measures were used, from this online dataset like the other measures. P002 HDLCHOL, for HDLCHOL; P001 TOTCHOL, for TOTCHOL; and M021 SCHISTOc, for MSCHIST.

- SexM1F2 is a “dummy” variable that was coded with 1 assigned to males and 2 to females. As such, it essentially measures the “degree of femaleness” of the respondents. Being female is generally protective against cardiovascular disease, a situation that reverts itself a bit after menopause.

- MVASC is a composite measure of the two following variables, provided as component measures of mortality from all cardiovascular diseases: M058 ALLVASCb (ages 0-34), and M059 ALLVASCc (ages 35-69). A couple of obvious problems: (a) they does not include data on people older than 69; and (b) they seem to capture a lot of diseases, including some that do not seem like typical cardiovascular diseases. A factor analysis was conducted, and the loadings and cross-loadings suggested good validity. Composite reliability was also good. So essentially MVASC is measured here as a “latent variable” with two “indicators”. Why do this? The reason is that it reduces the biasing effects of incomplete data and measurement error (e.g., exclusion of folks older than 69). By the way, there is always some measurement error in any dataset.

- This note is related to measurement error in connection with the indicators for MVASC. There is something odd about the variables M058 ALLVASCb (ages 0-34), and M059 ALLVASCc (ages 35-69). According to the dataset, mortality from cardiovascular diseases for ages 0-34 is typically higher than for 35-69, for many counties. Given the good validity and reliability for MVASC as a latent variable, it is possible that the values for these two indicator variables were simply swapped by mistake.

Low omega-6 to omega-3 ratio: Grain-fed meats or industrial vegetable oils?

Just a little note on the use of language. Clearly there is no such a thing as grain-fed or grass-fed beef, because one does not feed beef anything. One feeds cattle grain or grass, and then the resulting beef is said to be “grain-fed” or “grass-fed”. It is a manner of speaking that facilitates discourse, which is why it is used here.

To compensate for this digression, let me show you a graph, which pretty much summarizes the "punch line" of this post. The graph below shows the omega-6 fat contents of 1 lb (454 g) of grain-fed beef and 1 tablespoon (roughly 14 g) of a typical industrial vegetable oil (safflower oil). As you can see, there is a lot more omega-6 in the much smaller amount of industrial vegetable oil. A gram-for-gram comparison would practically make the beef content bar disappear.

It has been estimated that our Paleolithic ancestors consumed a diet with an omega-6 to omega-3 ratio of about 1. While other estimates exist, the general consensus seems to be that that ratio was not much greater than 5. Western diets, in contrast, typically have omega-6 to omega-3 ratios of between 15 and 40. In some cases, the ratio is even higher.

Omega-6 fats are essential fats, meaning that they must be part of one’s diet. Fats make up about 60 percent of our brain. About 20 percent is made up of omega-6 and omega-3 fats. The primary omega-6 fat found in our brain is arachidonic acid, which is either synthesized by our body based on linoleic acid from plant foods or obtained directly from animal foods such as meat and eggs. The predominant omega-3 fat found in our brain is docosahexaenoic acid (DHA), of which certain types of fish and algae are rich sources.

Inflammation is an important process in the human body, without which wounds would never heal. Incidentally, muscle gain would not occur without inflammation either. Strength training causes muscle damage and inflammation, after which recovery leads to muscle gain. Omega-6 fats play an important role in inflammation. Generally, they are pro-inflammatory.

Too much inflammation, particularly in a chronic fashion, is believed to be very detrimental to our health. A very high omega-6 to omega-3 ratio seems to cause excessive and chronic inflammation. The reason is that omega-3 fats are generally anti-inflammatory, counteracting the pro-inflammatory action of omega-6 fats. Over time, a very high omega-6 to omega-3 ratio is believed to cause a number of Western diseases. Among them are cardiovascular complications, cancer, and various autoimmune diseases.

So, should you worry about too much omega-6 from grain-fed meats?

If you think that the answer is “yes”, consider this. Apparently the (arguably) longest-living group in the world, the non-Westernized Okinawans, consume plenty of pork. Pork is a staple of their traditional diet. It is true that the average cut will have an omega-6 to omega-3 ratio of more than 7, which is not very favorable. Pork in general, whether grain-fed or not, is relatively high in omega-6 fats. As a side note, pork is not a good source of linoleic acid (found in plants), even though it is a rich source of arachidonic acid, the omega-6 fat synthesized from linoleic acid by various animals.

It is difficult to estimate the exact amounts of omega-6 and omega-3 fats from grain-fed cuts of meat; different sources provide different estimates. Here are some reasonable estimates based on various sources, including Nutritiondata.com. A typical 100 g portion of grain-fed pork should contain about 690 mg of omega-6 fats, and 120 mg of omega-3 fats. A typical 100 g portion of grain-fed beef should have about 234 mg of omega-6 fats, and 12 mg of omega-3 fats. It does not take that much omega-3 to counterbalance the omega-6 obtained from grain-fed pork or beef, even if one eats a lot of them. Two softgels of fish oil will normally contain about 720 mg of omega-3 fats (they will also come with 280 mg of omega-6 fats). Three sardines will have over 2 g of omega-3 fats, and less than 200 mg of omega-6 fats.

Industrial vegetable oils (made from, e.g., safflower seeds, soybean, and sunflower seeds) are very, very rich sources of omega-6 fats, in the form of linoleic acid. There is a lot more omega-6 in them than in grain-fed meats. One tablespoon of safflower oil contains over 10 g of omega-6 fats, in the form of linoleic acid, and virtually zero omega-3 fats. About 2 kg (4.4 lbs) of grain-fed pork, and 5 kg (11 lbs) of grain-fed beef will give you that much omega-6; but they will also come with omega-3.

How much fish oil does one need to neutralize 10 g of pure omega-6 fats? A lot! And there is a problem. Excessive fish oil consumption may be toxic to the liver.

If you cook with industrial vegetable oils rich in linoleic acid (this excludes olive and coconut oils), or eat out a lot in restaurants that use them (the vast majority), you will probably be consuming significantly more than 10 g of omega-6 fats per day. The likely negative health effects of eating grain-fed meats pales in comparison with the likely negative health effects of this much omega-6 fats from industrial vegetable oils.

You should reduce as much as possible your consumption of industrial vegetable oils rich in linoleic acid, as well as other products that use them (e.g., margarine). Keep in mind that industrial vegetable oils are in many, many industrialized foods; even canned sardines, if they are canned with soybean oil.

It is also advisable to couple this with moderate consumption of fish rich in omega-3, such as sardines and salmon. (See this post for a sardine recipe.) Taking large doses of fish oil every day may not be such a good idea.

Should you also consume only grass-fed meat? Do it if you can. But, if you cannot, maybe you shouldn’t worry too much about it. This also applies to eggs, dairy, and other animal products.

References:

Elliott, W.H., & Elliott, D.C. (2009). Biochemistry and molecular biology. New York: NY: Oxford University Press.

Ramsden, C.E., Faurot, K.R., Carrera-Bastos, P., Cordain, L., De Lorgeril, M., & Sperling (2009). Dietary fat quality and coronary heart disease prevention: A unified theory based on evolutionary, historical, global, and modern perspectives. Current Treatment Options in Cardiovascular Medicine, 11(4), 289-301.

Schmidt, M.A. (1997). Smart fats: How dietary fats and oils affect mental, physical and emotional intelligence. Berkeley, CA: North Atlantic Books.

The China Study on Wheat

Denise Minger has just put up another great China Study post that's worth reading if you haven't already. Denise has been busy applying her statistics skills to the mountain of data the study collected. She noted in a previous post that wheat intake was strongly associated with coronary heart disease (CHD), the quintessential modern cardiovascular disease. I, and several other people, requested that she work her mathmagic to see if the association could be due to some other factor. For example, wheat is eaten mostly in the Northern regions of China, and CHD rates are generally higher at higher latitudes (vitamin D insufficiency?). This is true in Europe as well, and may be partly responsible for the purported benefits of the Mediterranean diet. You can mathematically determine if the association between wheat and CHD is simply due to the fact that wheat eaters live further North.

To make a long story short, nothing could explain the association except wheat itself, even latitude. Furthermore, she found a strong association between wheat intake and body mass index, typically a predictor of fat mass although we can't say that for sure. That finding echos a previous study in China where wheat eaters were more likely to be overweight than rice eaters (1, 2). Head over to Denise's post for the full story.

The China Study has major limitations built into its basic design, due to the fact that it was observational and pooled the blood samples of many individuals. Therefore, its findings can never prove anything, they can only suggest or be consistent with hypotheses. However, the study also has some unique advantages, such as a diversity of diets and regions, and the fact that people had presumably been eating a similar diet for a long time. I feel that Denise's efforts are really teasing out some useful information from the study that have been de-emphasized by other investigators.

There has been very little serious investigation into the health effects of wheat in the general population. Researchers studying celiac disease and other forms of gluten allergy, and the efforts of the paleolithic diet community in spreading that information (for example, Loren Cordain and Pedro Bastos), have been major contributors to understanding the health effects of wheat. Denise's analysis is one of the strongest pieces of evidence I've come by so far. I think there's enough indirect evidence that investigators should begin taking the idea seriously that wheat, particularly in the form of industrial flour products, may contribute to chronic disease in more than just a small subset of the population.

How to lose fat and gain muscle at the same time? Strength training plus a mild caloric deficit

Ballor et al. (1996) conducted a classic and interesting study on body composition changes induced by aerobic and strength training. This study gets cited a lot, but apparently for the wrong reasons. One of these reasons can be gleaned from this sentence in the abstract:

    “During the exercise training period, the aerobic training group … had a significant … reduction in body weight … as compared with the [strength] training group ...”

That is, one of the key conclusions of this study was that aerobic training was more effective than strength training as far as weight loss is concerned. (The authors refer to the strength training group as the “weight training group”.)

Prior to starting the exercise programs, the 18 participants had lost a significant amount of weight through dieting, for a period of 11 weeks. The authors do not provide details on the diet, other than that it was based on “healthy” food choices. What this means exactly I am not sure, but my guess is that it was probably not particularly high or low in carbs/fat, included a reasonable amount of protein, and led to a caloric deficit.

The participants were older adults (mean age of 61; range, 56 to 70), who were also obese (mean body fat of 45 percent), but otherwise healthy. They managed to lose an average of 9 kg (about 20 lbs) during that 11-week period.

Following the weight loss period, the participants were randomly assigned to either a 12-week aerobic training (four men, five women) or weight training (four men, five women) exercise program. They exercised 3 days per week. These were whole-body workouts, with emphasis on compound (i.e., multiple-muscle) exercises. The figure below shows what actually happened with the participants.

As you can see, the strength training group (WT) gained about 1.5 kg of lean mass, lost 1.2 kg of fat, and thus gained some weight. The aerobic training group (AT) lost about 0.6 kg of lean mass and 1.8 kg of fat, and thus lost some weight.

Which group fared better? In terms of body composition changes, clearly the strength training group fared better. But my guess is that the participants in the strength training group did not like seeing their weight going up after losing a significant amount of weight through dieting. (An analysis of the possible psychological effects of this would be interesting; a discussion for another blog post.)

The changes in the aerobic training group were predictable, and were the result of compensatory adaptation. Their bodies changed to become better adapted to aerobic exercise, for which a lot of lean mass is a burden, as is a lot of fat mass.

So, essentially the participants in the strength training group lost fat and gained muscle at the same time. The authors say that the participants generally stuck with their weight-loss diet during the 12-week exercise period, but not a very strict away. It is reasonable to conclude that this induced a mild caloric deficit in the participants.

Exercise probably induced hunger, and possibly a caloric surplus on exercise days. If that happened, the caloric deficit must have occurred on non-exercise days. Without some caloric deficit there would not have been fat loss, as extra calories are stored as fat.

There are many self-help books and programs online whose main claim is to have a “revolutionary” prescription for concurrent fat loss and muscle gain – the “holy grail” of body composition change.

Well, it may be as simple as combining strength training with a mild caloric deficit, in the context of a nutritious diet focused on unprocessed foods.

Reference:

Ballor, D.L., Harvey-Berino, J.R., Ades, P.A., Cryan, J., & Calles-Escandon, J. (1996). Contrasting effects of resistance and aerobic training on body composition and metabolism after diet-induced weight loss. Metabolism, 45(2), 179-183.