Low bone mineral content in older Eskimos: Meat-eating or shrinking?

Mazess & Mather (1974) is probably the most widely cited article summarizing evidence that bone mineral content in older North Alaskan Eskimos was lower (10 to 15 percent) than that of United States whites. Their finding has been widely attributed to the diet of the Eskimos, which is very high in animal protein. Here is what they say:

“The sample consisted of 217 children, 89 adults, and 107 elderly (over 50 years). Eskimo children had a lower bone mineral content than United States whites by 5 to 10% but this was consistent with their smaller body and bone size. Young Eskimo adults (20 to 39 years) of both sexes were similar to whites, but after age 40 the Eskimos of both sexes had a deficit of from 10 to 15% relative to white standards.”

Note that their findings refer strictly to Eskimos older than 40, not Eskimo children or even young adults. If a diet very high in animal protein were to cause significant bone loss, one would expect that diet to cause significant bone loss in children and young adults as well. Not only in those older than 40.

So what may be the actual reason behind this reduced bone mineral content in older Eskimos?

Let me make a small digression here. If you want to meet quite a few anthropologists who are conducting, or have conducted, field research with isolated or semi-isolated hunter-gatherers, you should consider attending the annual Human Behavior and Evolution Society (HBES) conference. I have attended this conference in the past, several times, as a presenter. That gave me the opportunity to listen to some very interesting presentations and poster sessions, and talk with many anthropologists.

Often anthropologists will tell you that, as hunter-gatherers age, they sort of “shrink”. They lose lean body mass, frequently to the point of becoming quite frail in as early as their 60s and 70s. They tend to gain body fat, but not to the point of becoming obese, with that fat replacing lean body mass yet not forming major visceral deposits. Degenerative diseases are not a big problem when you “shrink” in this way; bigger problems are  accidents (e.g., falls) and opportunistic infections. Often older hunter-gatherers have low blood pressure, no sign of diabetes or cancer, and no heart disease. Still, they frequently die younger than one would expect in the absence of degenerative diseases.

A problem normally faced by older hunter-gatherers is poor nutrition, which is both partially caused and compounded by lack of exercise. Hunter-gatherers usually perceive the Western idea of exercise as plain stupidity. If older hunter-gatherers can get youngsters in their prime to do physically demanding work for them, they typically will not do it themselves. Appetite seems to be negatively affected, leading to poor nutrition; dehydration often is a problem as well.

Now, we know from this post that animal protein consumption does not lead to bone loss. In fact, it seems to increase bone mineral content. But there is something that decreases bone mineral content, as well as muscle mass, like nothing else – lack of physical activity. And there is something that increases bone mineral content, as well as muscle mass, in a significant way – vigorous weight-bearing exercise.

Take a look at the figure below, which I already discussed on a previous post. It shows a clear pattern of benign ventricular hypertrophy in Eskimos aged 30-39. That goes down dramatically after age 40. Remember what Mazess & Mather (1974) said in their article: “… after age 40 the Eskimos of both sexes had a deficit of from 10 to 15% relative to white standards”.

Benign ventricular hypertrophy is also known as athlete's heart, because it is common among athletes, and caused by vigorous physical activity. A prevalence of ventricular hypertrophy at a relatively young age, and declining with age, would suggest benign hypertrophy. The opposite would suggest pathological hypertrophy, which is normally induced by obesity and chronic hypertension.

So there you have it. The reason older Eskimos were found to have lower bone mineral content after 40 is likely not due to their diet.  It is likely due to the same reasons why they "shrink", and also in part because they "shrink". Not only does physical activity decrease dramatically as Eskimos age, but so does lean body mass.

Obese Westerners tend to have higher bone density on average, because they frequently have to carry their own excess body weight around, which can be seen as a form of weight-bearing exercise. They pay the price by having a higher incidence of degenerative diseases, which probably end up killing them earlier, on average, than osteoporosis complications.

Reference

Mazess R.B., & Mather, W.W. (1974). Bone mineral content of North Alaskan Eskimos. American Journal of Clinical Nutrition, 27(9), 916-925.

Beef meatballs, with no spaghetti

There are pizza restaurants, whose specialty is pizza, even though they usually have a few side dishes. Not healthy enough?

Well, don’t despair, there are meatball restaurants too. I know of at least one, The Meatball Shop, on 84 Stanton Street, in New York City.

Finally a restaurant that elevates the "lowly" meatball to its well deserved place!

Meatballs are delicious, easy to prepare, and you can use quite a variety of meats to do them. Below is a simple recipe. We used ground grass-fed beef, not because of omega-6 concerns (see this post), but because of the different taste.

- Prepare some dry seasoning powder by mixing sea salt, parsley, garlic power, chili powder, and a small amount of cayenne pepper.
- Thoroughly mix 1 pound of ground beef, one or two eggs, and the seasoning powder.
- Make about 10 meatballs, and place them in a frying pan with a small amount of water (see picture below).
- Cover the pan and cook on low fire for about 1 hour.

There is no need for any oil in the pan. On a low fire the small amount of water at the bottom will heat up, circulate, and essentially steam the meatballs. The water will also prevent the meatballs from sticking to the pan. Some moisture will also be released by the meat.

Part of the fat from the meat will be released and accumulate at the bottom of the pan. If you add tomato sauce and mix, the fat will become part of the resulting red sauce. This sauce will add moisture back to the dish, as the meatballs sometimes get a bit dry from the cooking.

Five meatballs of the type that we used (about 15 percent fat) will have about 57 g of protein and 32 g of fat; the latter mostly saturated and monounsaturated (both healthy). They will also be a good source of vitamins B12 and B6, niacin, zinc, selenium, and phosphorus.

Add a fruit or a sweet potato as a side dish to 3-5 meatballs and you have a delicious and nutritious meal that may eve impress some people!

The China Study II: Carbohydrates, fat, calories, insulin, and obesity

The “great blogosphere debate” rages on regarding the effects of carbohydrates and insulin on health. A lot of action has been happening recently on Peter’s blog, with knowledgeable folks chiming in, such as Peter himself, Dr. Harris, Dr. B.G. (my sista from anotha mista), John, Nigel, CarbSane, Gunther G., Ed, and many others.

I like to see open debate among people who hold different views consistently, are willing to back them up with at least some evidence, and keep on challenging each other’s views. It is very unlikely that any one person holds the whole truth regarding health matters. Unfortunately this type of debate also confuses a lot of people, particularly those blog lurkers who want to get all of their health information from one single source.

Part of that “great blogosphere debate” debate hinges on the effect of low or high carbohydrate dieting on total calorie consumption. Well, let us see what the China Study II data can tell us about that, and about a few other things.

WarpPLS was 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 here, visit the HealthCorrelator for Excel site and check under the sample datasets area.

The two graphs below show the relationships between various foods, carbohydrates as a percentage of total calories, and total calorie consumption. A basic linear analysis was employed here. As carbohydrates as a percentage of total calories go up, the diet generally becomes a high carbohydrate diet. As it goes down, we see a move to the low carbohydrate end of the scale.

The left parts of the two graphs above are very similar. They tell us that wheat flour consumption is very strongly and negatively associated with rice consumption; i.e., wheat flour displaces rice. They tell us that fruit consumption is positively associated with rice consumption. They also tell us that high wheat flour consumption is strongly and positively associated with being on a high carbohydrate diet.

Neither rice nor fruit consumption has a statistically significant influence on whether the diet is high or low in carbohydrates, with rice having some effect and fruit practically none. But wheat flour consumption does. Increases in wheat flour consumption lead to a clear move toward the high carbohydrate diet end of the scale.

People may find the above results odd, but they should realize that white glutinous rice is only 20 percent carbohydrate, whereas wheat flour products are usually 50 percent carbohydrate or more. Someone consuming 400 g of white rice per day, and no other carbohydrates, will be consuming only 80 g of carbohydrates per day. Someone consuming 400 g of wheat flour products will be consuming 200 g of carbohydrates per day or more.

Fruits generally have much less carbohydrate than white rice, even very sweet fruits. For example, an apple is about 12 percent carbohydrate.

There is a measure that reflects the above differences somewhat. That measure is the glycemic load of a food; not to be confused with the glycemic index.

The right parts of the graphs above tell us something else. They tell us that the percentage of carbohydrates in one’s diet is strongly associated with total calorie consumption, and that this is not the case with percentage of fat in one’s diet.

Given the above, one may be interested in looking at the contribution of individual foods to total calorie consumption. The graph below focuses on that. The results take nonlinearity into consideration; they were generated using the Warp3 algorithm option of WarpPLS.

As you can see, wheat flour consumption is more strongly associated with total calories than rice; both associations being positive. Animal food consumption is negatively associated, somewhat weakly but statistically significantly, with total calories. Let me repeat for emphasis: negatively associated. This means that, as animal food consumption goes up, total calories consumed go down.

These results may seem paradoxical, but keep in mind that animal foods displace wheat flour in this dataset. Note that I am not saying that wheat flour consumption is a confounder; it is controlled for in the model above.

What does this all mean?

Increases in both wheat flour and rice consumption lead to increases in total caloric intake in this dataset. Wheat has a stronger effect. One plausible mechanism for this is abnormally high blood glucose elevations promoting abnormally high insulin responses. Refined carbohydrate-rich foods are particularly good at raising blood glucose fast and keeping it elevated, because they usually contain a lot of easily digestible carbohydrates. The amounts here are significantly higher than anything our body is “designed” to handle.

In normoglycemic folks, that could lead to a “lite” version of reactive hypoglycemia, leading to hunger again after a few hours following food consumption. Insulin drives calories, as fat, into adipocytes. It also keeps those calories there. If insulin is abnormally elevated for longer than it should be, one becomes hungry while storing fat; the fat that should have been released to meet the energy needs of the body. Over time, more calories are consumed; and they add up.

The above interpretation is consistent with the result that the percentage of fat in one’s diet has a statistically non-significant effect on total calorie consumption. That association, although non-significant, is negative. Again, this looks paradoxical, but in this sample animal fat displaces wheat flour.

Moreover, fat leads to no insulin response. If it comes from animals foods, fat is satiating not only because so much in our body is made of fat and/or requires fat to run properly; but also because animal fat contains micronutrients, and helps with the absorption of those micronutrients.

Fats from oils, even the healthy ones like coconut oil, just do not have the latter properties to the same extent as unprocessed fats from animal foods. Think slow-cooking meat with some water, making it release its fat, and then consuming all that fat as a sauce together with the meat.

In the absence of industrialized foods, typically we feel hungry for those foods that contain nutrients that our body needs at a particular point in time. This is a subconscious mechanism, which I believe relies in part on past experience; the reason why we have “acquired tastes”.

Incidentally, fructose leads to no insulin response either. Fructose is naturally found mostly in fruits, in relatively small amounts when compared with industrial foods rich in refined sugars.

And no, the pancreas does not get “tired” from secreting insulin.

The more refined a carbohydrate-rich food is, the more carbohydrates it tends to pack per unit of weight. Carbohydrates also contribute calories; about 4 calories per g. Thus more carbohydrates should translate into more calories.

If someone consumes 50 g of carbohydrates per day in excess of caloric needs, that will translate into about 22.2 g of body fat being stored. Over a month, that will be approximately 666.7 g. Over a year, that will be 8 kg, or 17.6 lbs. Over 5 years, that will be 40 kg, or 88 lbs. This is only from carbohydrates; it does not consider other macronutrients.

There is no need to resort to the “tired pancreas” theory of late-onset insulin resistance to explain obesity in this context. Insulin resistance is, more often than not, a direct result of obesity. Type 2 diabetes is by far the most common type of diabetes; and most type 2 diabetics become obese or overweight before they become diabetic. There is clearly a genetic effect here as well, which seems to moderate the relationship between body fat gain and liver as well as pancreas dysfunction.

It is not that hard to become obese consuming refined carbohydrate-rich foods. It seems to be much harder to become obese consuming animal foods, or fruits.