Lifeworld

The idea that protein leaches calcium from the bones has been around for a while. It is related to the notion that protein, especially from animal foods, increases blood acidity. The body then uses its main reservoir of calcium, the bones, to reduce blood acidity. Chris Masterjohn does not agree with this idea. This post generally supports Chris’s view, and adds a twist to it, related to plant protein consumption.

The “eat-meat-lose-bone” idea has apparently become popular due to the position taken by Loren Cordain on the topic. Dr. Cordain has also made several important and invaluable contributions to our understanding of the diets of our Paleolithic ancestors. He has argued in his book, The Paleo Diet, and elsewhere (see, e.g., here) that to counter the acid load of protein one should eat fruits and vegetables. The latter are believed to have an alkaline load.

If the idea that protein leaches calcium from the bones is correct, one would expect to see a negative association between protein consumption and bone mineral density (BMD). This negative association should be particularly strong in people aged 50 and older, who are more vulnerable to BMD losses.

As it turns out, this idea appears to be correct only for plant protein. Animal protein seems to be associated with an increase in BMD, at least according to a study by Promislow et al. (2002). The study shows that there is a positive multivariate association between animal protein consumption and BMD; an association that becomes negative when plant protein consumption is considered.

The study focused on 572 women and 388 men aged 55–92 years living in Rancho Bernardo, California. Food frequency questionnaires were administered in the 1988–1992 period, and BMD was measured 4 years later. The bar chart below shows the approximate increases in BMD (in g/cm^2) for each 15 g/d increment in protein intake.


The authors reported increments in BMD for different increments of protein (15 and 5 g/d), so the results above are adjusted somewhat from the original values reported in the article. Keeping that in mind, the increment in BMD for men due to animal protein was not statistically significant (P=0.20). That is the smallest bar on the left.

Does protein leach calcium from the bones? Based on this study, the reasonable answers to this question are yes for plant protein, and no for animal protein. For animal protein, it seems to be quite the opposite.

Even more interesting, calcium intake did not seem to be much of a factor. BMD gains due to animal protein seemed to converge to similar values whether calcium intake was high, medium or low. The convergence occurred as animal protein intake increased, and the point of convergence was between 85-90 g/d of animal protein intake.

And high calcium intakes did not seem to protect those whose plant protein consumption was high.

The authors do not discuss specific foods, but one can guess the main plant protein that those folks likely consumed. It was likely gluten from wheat products.

Are the associations above due to: (a) the folks eating animal protein consuming more fruits and vegetables than the folks eating plant protein; or (b) something inherent to animal foods that stimulates an increase in the absorption of dietary calcium, even in small amounts?

This question cannot be answered based on this study; it should have controlled for fruit and vegetable consumption for that.

But if I were to bet, I would bet on (b).

Reference

Promislow, J.H.E., Goodman-Gruen, D., Slymen, D.J., & Barrett-Connor, E. (2002). Protein consumption and bone mineral density in the elderly. American Journal of Epidemiology, 155(7), 636–644.

Spending too much time sitting down is clearly unnatural, particularly if you sit down on very comfortable chairs. Sitting down per se is probably natural, given the human anatomy, but not sitting down for hours in the same position. Also, comfortable furniture is an apparently benign Neolithic invention, but over several years it may stealthily contributed to the metabolic syndrome and the diseases of civilization.

Getting an elevated workstation may be a bit expensive. At work, you may have to go through a bit of a battle with your employer to get it (unless you are "teh boz"), only to find out that having to work standing up all the time is not what you really wanted. That may not be very natural either. So what is one to do? One possible solution is to buy a small foldable plastic table (or chair) like the one on the figure below, which may cost you less than $10, and put it on your work desk. I have been doing this for quite a while now, and it works fine for me.



The photo above shows a laptop computer. Nevertheless, you can use this table-over-table approach with a desktop computer as well. And you still keep the space under the foldable table, which you can use to place other items. With a desktop computer this approach would probably require two foldable tables to elevate the screen, keyboard, and mouse. This approach also works for reading documents and writing with a pen or pencil; just put a thick sheet of paper on the foldable table to make a flat surface (if the foldable table’s surface is not flat already). And you don’t have to be standing up all the time; you can sit down as well after removing the foldable table. It takes me about 5 seconds to do or undo this setup.

When you sit down, you may want to consider using a pillow like the one on the photo to force yourself to sit upright. (You can use it as shown, or place the pillow flat on the chair and sit on its edge.) Sitting on a very comfy chair with back support prevents you from using the various abdominal and back muscles needed to maintain posture. As a result, you may find yourself unusually prone to low back injuries and suffering from “mysterious” abdominal discomfort. You will also very likely decrease your nonexercise activity thermogenesis (NEAT), which is a major calorie expenditure regulator.

With posture stabilization muscles, as with almost everything else in the human body, the reality is this: if you don’t use them, you lose them.

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.