Lifeworld

I ran several analyses on the effects of fruit consumption on mortality on the China Study II dataset using WarpPLS. For other China Study analyses, many using WarpPLS as well as HCE, click here.

The results are pretty clear – fruit consumption has no significant effect on mortality.

The bar charts figure below shows what seems to be a slight downward trend in mortality, in the 35-69 and 70-79 age ranges, apparently due to fruit consumption.


As it turns out, that slight trend may be due to something else: in the China Study II dataset, fruit consumption is positively associated with both animal protein and fat consumption. And, as we have seen from previous analyses (e.g., this one), the latter two seem to be protective.

So, if you like to eat fruit, maybe you should also make sure that you eat animal protein and fat as well.

In this post on the China Study II data we have seen that wheat apparently displaces dietary fat a lot, primarily fat from animal sources. We have also seen in that post that wheat is strongly and positively associated with mortality in both the 35-69 and 70-79 age ranges, whereas dietary fat is strongly and negatively associated with mortality in those ranges.

This opens the door for the hypothesis that wheat increased mortality in the China Study II sample mainly by displacing dietary fat, and not necessarily by being a primary cause of health problems. In fact, given the strong displacement effect discussed in the previous post, I thought that this hypothesis was quite compelling. I was partly wrong, as you’ll see below.

A counterintuitive hypothesis no doubt, given that wheat is unlikely to have been part of the diet of our Paleolithic ancestors, and thus the modern human digestive tract may be maladapted to it. Moreover, wheat’s main protein (gluten) is implicated in celiac disease, and wheat contains plant toxins such as wheat germ agglutinin.

Still, we cannot completely ignore this hypothesis because: (a) the data points in its general direction; and (b) wheat-based foods are found in way more than trivial amounts in the diets of populations that have relatively high longevity, such as the French.

Testing the hypothesis essentially amounts to testing the significance of two mediating effects; of fat as a mediator of the effects of wheat on mortality, in both the 35-69 and 70-79 age ranges. There are two main approaches for doing this. One is the classic test discussed by Baron & Kenny (1986). The other is the modern test discussed by Preacher & Hayes (2004), and extended by Hayes & Preacher (2010) for nonlinear relationships.

I tested the meditating effects using both approaches, including the nonlinear variation. I used the software WarpPLS for this; the results below are from WarpPLS outputs. Other analyses of the China Study data using WarpPLS can be found here (calorie restriction and longevity), and here (wheat, rice, and cardiovascular disease). For yet other studies, click here.

The graphs below show the path coefficients and chance probabilities of two models. The one at the top-left suggests that wheat flour consumption seems to be associated with a statistically significant increase in mortality in the 70-79 age range (beta=0.23; P=0.04). The effect in the 35-69 age range is almost statistically significant (beta=0.22; P=0.09); the likelihood that it is due to chance is 9 percent (this is the meaning of the P=0.09=9/100=9%).


The graph at the bottom-right suggests that the variable “FatCal”, which is the percentage of calories coming from dietary fat, is indeed a significant mediator of the relationships above between wheat and mortality, in both ranges. But “FatCal” is only a partial mediator.

The reason why “FatCal” is not a “perfect” mediator is that the direct effects of wheat on mortality in both ranges are still relatively strong after “FatCal” is added to the model (i.e., controlled for). In fact, the effects of wheat on mortality don’t change that much with the introduction of the variable “FatCal”.

This analysis suggests that, in the China Study II sample, one of wheat’s main sins might indeed have been to displace dietary fat from animal sources. Wheat consumption is strongly and negatively associated with dietary fat (beta=-0.37; P<0.01), and dietary fat is relatively strongly and negatively associated with mortality in both ranges (more in the 70-79 age range).

Why is dietary fat more protective in the 70-79 than in the 35-69 age range, with the latter effect only being significant at the P=0.10 level (a 10 percent chance probability)? My interpretation is that, as with almost any dietary habit, it takes years for a chronically low fat diet to lead to problems. See graph below; fat was not a huge contributor to the total calorie intake in this sample.


The analysis suggests that wheat also caused problems via other paths. What are them? We can’t say for sure based on this dataset. Perhaps the paths involve lectins and/or gluten. One way or another, the relationship is complex. As you can see from the graph below, the relationship between wheat consumption and mortality is nonlinear for the 70-79 age range, most likely due to confounding factors. The effect size is small for the 35-69 age range, even though it looks linear or quasi-linear in that range.


As you might recall from this post, rice does NOT displace dietary fat, and it seems to be associated with increased longevity. Carbohydrate content per se does not appear to be the problem here. Both rice and wheat foods are rich in them, and have a high glycemic index. Wheat products tend to have a higher glycemic load though.

And why is dietary fat so important as to be significantly associated with increased longevity? This is not a trivial question, because if too much of that fat is stored as body fat it will actually decrease longevity. Dietary fat is very calorie-dense, and can be easily stored as body fat.

Dietary fat is important for various reasons, and probably some that we don’t know about yet. It leads to the formation of body fat, which is not only found in adipocytes or used only as a store of energy. Fat is a key component of a number of important tissues, including 60 percent of our brain. Since fat in the human body undergoes constant turnover, more in some areas than others, lack of dietary fat may compromise the proper functioning of various organs.

Without dietary fat, the very important fat-soluble vitamins (A, D, E and K) cannot be properly absorbed. Taking these vitamins in supplemental form will not work if you don’t consume fat as well. A very low fat diet is almost by definition a diet deficient in fat-soluble vitamins, even if those vitamins are consumed in large amounts via supplements.

Moreover, animals store fat-soluble vitamins in their body fat (as well as in organs), so we get these vitamins in one of their most natural and potent forms when we consume animal fat. Consuming copious amounts of olive and/or coconut oil will not have just the same effect.

References

Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality & Social Psychology, 51(6), 1173-1182.

Preacher, K.J., & Hayes, A.F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36 (4), 717-731.

Hayes, A. F., & Preacher, K. J. (2010). Quantifying and testing indirect effects in simple mediation models when the constituent paths are nonlinear. Multivariate Behavioral Research, 45(4), 627-660.

This post is based on an analysis of a subset of the China Study II data, using HealthCorrelator for Excel (HCE), which is publicly available for download and use on a free trial basis. You can access the original data on the HCE web site, under “Sample datasets”.

HCE was designed to be used with small and individual personal datasets, but it can also be used with larger datasets for multiple individuals.

This analysis focuses on two main variables from the China Study II data: mortality in the 35-69 age range, and mortality in the 70-79 range. The table below shows the coefficients of association calculated by HCE for those two variables. The original variable labels are shown.


One advantage of looking at mortality in these ranges is that they are more likely to reflect the impact of degenerative diseases. Infectious diseases likely killed a lot of children in China at the time the data was being collected. Heart disease, on the other hand, is likely to have killed more people in the 35-69 and 70-79 ranges.

It is also good to have data for both ranges, because factors that likely increased longevity were those that were associated with decreased mortality in both ranges. For example, a factor that was strongly associated with mortality in the 35-69 range, but not the 70-79 range, might simply be very deadly in the former range.

The mortalities in both ranges are strongly correlated with each other, which is to be expected. Next, at the very top for both ranges, is sex. Being female is by far the variable with the strongest, and negative, association with mortality.

While I would expect females to live longer, the strengths of the associations make me think that there is something else going on here. Possibly different dietary or behavioral patterns displayed by females. Maybe smoking cigarettes or alcohol abuse was a lot less prevalent among them.

Markedly different lifestyle patterns between males and females may be a major confounding variable in the China Study sample.

Some of the variables are redundant; meaning that they are highly correlated and seem to measure the same thing. This is clear when one looks at the other coefficients of association generated by HCE.

For example, plant food consumption is strongly and negatively correlated with animal food consumption; so strongly that you could use either one of these two variables to measure the other, after inverting the scale. The same is true for consumption of rice and white flour.

Plant food consumption is not strongly correlated with plant protein consumption; many plant foods have little protein in them. The ones that have high protein content are typically industrialized and seed-based. The type of food most strongly associated with plant protein consumption is white flour, by far. The correlation is .645.

The figure below is based on the table above. I opened a separate instance of Excel, and copied the coefficients generated by HCE into it. Then I built two bar charts with them. The variable labels were replaced with more suggestive names, and some redundant variables were removed. Only the top 7 variables are shown, ordered from left to right on the bar charts in order of strength of association. The ones above the horizontal axis possibly increase mortality in each age range, whereas the ones at the bottom possibly decrease it.


When you look at these results as a whole, a few things come to mind.

White flour consumption doesn’t seem to be making people live longer; nor does plant food consumption in general. For white flour, it is quite the opposite. Plant food consumption reflects white flour consumption to a certain extent, especially in counties where rice consumption is low. These conclusions are consistent with previous analyses using more complex statistics.

Total food is positively associated with mortality in the 35-69 range, but not the 70-79 range. This may reflect the fact that folks who reach the age of 70 tend to naturally eat in moderation, so you don’t see wide variations in food consumption among those folks.

Eating in moderation does not mean practicing severe calorie restriction. This post suggests that calorie restriction doesn't seem to be associated with increased longevity in this sample. Eating well, but not too much, is.

The bar for rice (consumption) on the left chart is likely a mirror reflection of the white flour consumption, so it may appear to be good in the 35-69 range simply because it reflects reduced white flour consumption in that range.

Green vegetables seem to be good when you consider the 35-69 range, but not the 70-79 range.

Neither rice nor green vegetables seem to be bad either. For overall longevity they may well be neutral, with the benefits likely coming from their replacement of white flour in the diet.

Dietary fat seems protective overall, particularly together with animal foods in the 70-79 range. This may simply reflect a delayed protective effect of animal fat and protein consumption.

The protective effect of dietary fat becomes clear when we look at the relationship between carbohydrate calories and fat calories. Their correlation is -.957, which essentially means that carbohydrate intake seriously displaces fat intake.

Carbohydrates themselves may not be the problem, even if coming from high glycemic foods (except wheat flour, apparently). This post shows that they are relatively benign if coming from high glycemic rice, even at high intakes of 206 to 412 g/day. The problem seems to be caused by carbohydrates displacing nutrient-dense animal foods.

Interestingly, rice does not displace animal foods or fat in the diet. It is positively correlated with them. Wheat flour, on the other hand, displaces those foods. Wheat flour is negatively and somewhat strongly correlated with consumption of animal foods, as well as with animal fat and protein.

There are certainly several delayed effects here, which may be distorting the results somewhat.  Degenerative diseases don’t develop fast and kill folks right away. They often require many years of eating and doing the wrong things to be fatal.

Genes do not evolve, nor do traits that are coded for our genes. We say that they evolve to facilitate discourse, which is alright. Populations evolve. A new genotype appears in a population and then either spreads or disappears. If it spreads, then the population is said to be evolving with respect to that genotype. A genotype may spread to an entire population; in population genetics, this is called “fixation”.


Asexual reproduction is very uncommon among animals. The most accepted theory to explain this is that animal populations live in environments that change very quickly, and thus need a great deal of genetic diversity within them to cope with the change. Otherwise they disappear, and so do their genes. Asexual reproduction leads to dramatically less genetic diversity in populations than sexual reproduction.

Asexual reproduction is similar to cloning. Each new individual looks a lot like its single parent. This does not work well in populations where individuals live relatively long lives. And even 1 year may be too long in this respect. It is just too much time to wait for a possible new mutation that will bring in some genetic diversity. To complicate matters, genetic mutation does not occur very often, and most genetic mutations are neutral with respect to the phenotype (i.e., they don’t code for any trait).

This is not so much of a problem for species whose members reproduce extremely fast; e.g., produce a new generation in less than 1 hour. A fast-reproducing species usually has a short lifespan as well. Accordingly, asexual reproduction is common among short-lived and fast-reproducing unicellular organisms and pathogens that have no cell structure like viruses.

Bacteria and viruses, in particular, form a part of the environment in which animals live that require animal populations to have a large amount of genetic diversity. Animal populations with low genetic diversity are unlikely to be able to cope with the barrage of diseases caused by these fast-mutating parasites.

We make sex chiefly because of the parasites.

And what about death? What does it bring to the table for a population?

Let us look at the other extreme – immortality. Immortality is very problematic in evolutionary terms because a population of immortal individuals would quickly outgrow its resources. That would happen too fast for the population to evolve enough intelligence to be able to use resources beyond those that were locally available.

In this post I assume that immortality is not the same as indestructibility. Here immortality is equated to the absence of aging as we know it. In this sense, immortals can still die by accident or due to disease. They simply do not age. For immortals, susceptibility to disease does not go up with age.

One could argue that a population of immortal individuals who did not reproduce would have done just fine. But that is not correct, because in this case immortality would be akin to cloning, but worse. Genetic diversity would not grow, as no mutations would occur. The fixed population of immortals would be unable to cope with fast-mutating parasites.

There is so much selection pressure against immortality in nature that it is no surprise that animals of very few species live more than 60 years on average. Humans are at the high end of the longevity scale. They are there for a few reasons. One is that our ancestors had offspring that required extra care, which led to an increase in the parents’ longevity. The offspring required extra care chiefly because of their large brains.

That increase in longevity was likely due to genetic mutations that helped our ancestors extend a lifespan that was programmed to be relatively short. Immortality is not a sound strategy for population survival, and thus there are probably many mechanisms through which it is prevented.

Death is evolution’s main ally. Sex is a very good helper. Both increase genetic diversity in populations.

We can use our knowledge of evolution to live better today. The aging clock can be slowed significantly via evolutionarily sound diet and lifestyle changes, essentially because some of our modern diet and lifestyle choices accelerate aging a lot. But diet and lifestyle changes probably will not make people live to 150.

If we want to become immortal, as we understand it in our current human form, ultimately we may want to beat evolution. In this sense, only very intelligent beings can become immortal.

Maybe we can achieve that by changing our genes, or by learning how to transfer our consciousness “software” into robots. In doing so, however, we may become something different; something that is not human and thus doesn’t see things in the same way as a human does. A conscious robot, without the hormones that so heavily influence human behavior, may find that being alive is pointless.

There is another problem. What if the only natural way to achieve some form of immortality is through organic death, but in a way that we don’t understand? This is not a matter of faith or religion. There are many things that we don’t know for sure. This is probably the biggest mystery of all; one that we cannot unravel in our current human state.

The idea that calorie restriction extends human life comes largely from studies of other species. The most relevant of those studies have been conducted with primates, where it has been shown that primates that eat a restricted calorie diet live longer and healthier lives than those that are allowed to eat as much as they want.

There are two main problems with many of the animal studies of calorie restriction. One is that, as natural lifespan decreases, it becomes progressively easier to experimentally obtain major relative lifespan extensions. (That is, it seems much easier to double the lifespan of an organism whose natural lifespan is one day than an organism whose natural lifespan is 80 years.) The second, and main problem in my mind, is that the studies often compare obese with lean animals.

Obesity clearly reduces lifespan in humans, but that is a different claim than the one that calorie restriction increases lifespan. It has often been claimed that Asian countries and regions where calorie intake is reduced display increased lifespan. And this may well be true, but the question remains as to whether this is due to calorie restriction increasing lifespan, or because the rates of obesity are much lower in countries and regions where calorie intake is reduced.

So, what can the China Study II data tell us about the hypothesis that calorie restriction increases longevity?

As it turns out, we can conduct a preliminary test of this hypothesis based on a key assumption. Let us say we compared two populations (e.g., counties in China), based on the following ratio: number of deaths at or after age 70 divided by number deaths before age 70. Let us call this the “ratio of longevity” of a population, or RLONGEV. The assumption is that the population with the highest RLONGEV would be the population with the highest longevity of the two. The reason is that, as longevity goes up, one would expect to see a shift in death patterns, with progressively more people dying old and fewer people dying young.

The 1989 China Study II dataset has two variables that we can use to estimate RLONGEV. They are coded as M005 and M006, and refer to the mortality rates from 35 to 69 and 70 to 79 years of age, respectively. Unfortunately there is no variable for mortality after 79 years of age, which limits the scope of our results somewhat. (This does not totally invalidate the results because we are using a ratio as our measure of longevity, not the absolute number of deaths from 70 to 79 years of age.) Take a look at these two previous China Study II posts (here, and here) for other notes, most of which apply here as well. The notes are at the end of the posts.

All of the results reported here are from analyses conducted using WarpPLS. Below is a model with coefficients of association; it is a simple model, since the hypothesis that we are testing is also simple. (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: TKCAL = total calorie intake per day; RLONGEV = ratio of longevity; SexM1F2 = sex, with 1 assigned to males and 2 to females.



As one would expect, being female is associated with increased longevity, but the association is just shy of being statistically significant in this dataset (beta=0.14; P=0.07). The association between total calorie intake and longevity is trivial, and statistically indistinguishable from zero (beta=-0.04; P=0.39). Moreover, even though this very weak association is overall negative (or inverse), the sign of the association here does not fully reflect the shape of the association. The shape is that of an inverted J-curve; a.k.a. U-curve. When we split the data into total calorie intake terciles we get a better picture:


The second tercile, which refers to a total daily calorie intake of 2193 to 2844 calories, is the one associated with the highest longevity. The first tercile (with the lowest range of calories) is associated with a higher longevity than the third tercile (with the highest range of calories). These results need to be viewed in context. The average weight in this dataset was about 116 lbs. A conservative estimate of the number of calories needed to maintain this weight without any physical activity would be about 1740. Add about 700 calories to that, for a reasonable and healthy level of physical activity, and you get 2440 calories needed daily for weight maintenance. That is right in the middle of the second tercile.

In simple terms, the China Study II data seems to suggest that those who eat well, but not too much, live the longest. Those who eat little have slightly lower longevity. Those who eat too much seem to have the lowest longevity, perhaps because of the negative effects of excessive body fat.

Because these trends are all very weak from a statistical standpoint, we have to take them with caution. What we can say with more confidence is that the China Study II data does not seem to support the hypothesis that calorie restriction increases longevity.

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). Whenever nonlinear relationships were modeled, the path coefficients were automatically corrected by the software to account for nonlinearity.

- 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 (each county, not individual, is a separate data point is this dataset). 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 multivariate analyses 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.

- 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. The reason for this is that mortality from schistosomiasis infection can severely distort the results in the age ranges considered here. On the other hand, removal of counties with deaths from schistosomiasis infection reduced the sample size, and thus decreased the statistical power of the analysis.