Nutrition and Infectious Disease

Dr. Edward Mellanby's book Nutrition and Disease contains a chapter titled "Nutrition and Infection". It begins:

There is general agreement among medical men that the susceptibility of mankind to many types of infection is closely related to the state of nutrition. The difficulty arises, when closer examination is given to this general proposition, as to what constitutes good and bad nutrition, and the problem is not rendered easier by recent advances in nutritional science.

Dr. Mellanby was primarily concerned with the effect of fat-soluble vitamins on infectious disease, particularly vitamins A and D. One of his earliest observations was that butter protected against pneumonia in his laboratory dogs. He eventually identified vitamin A as the primary protective factor. He found that by placing rats on a diet deficient in vitamin A, they developed numerous infectious lesions, most often in the urogenital tract, the eyes, the intestine, the middle ear and the lungs. This was prevented by adding vitamin A or cabbage (a source of beta-carotene, which the rats converted to vitamin A) to the diet. Mellanby and his colleagues subsequently dubbed vitamin A the "anti-infective vitamin".

Dr. Mellanby was unsure whether the animal results would apply to humans, due to "the difficulty in believing that diets even of poor people were as deficient in vitamin A and carotene as the experimental diets." However, their colleagues had previously noted marked differences in the infection rate of largely vegetarian African tribes versus their carnivorous counterparts. The following quote from Nutrition and Disease refers to two tribes which, by coincidence, Dr. Weston Price also described in Nutrition and Physical Degeneration:

The high incidence of bronchitis, pneumonia, tropical ulcers and phthisis among the Kikuyu tribe who live on a diet mainly of cereals as compared with the low incidence of these diseases among their neighbours the Masai who live on meat, milk and raw blood (Orr and Gilks), probably has a similar or related nutritional explanation. The differences in distribution of infective disease found by these workers in the two tribes are most impressive. Thus in the cereal-eating tribe, bronchitis and pneumonia accounted for 31 per cent of all cases of sickness, tropical ulcers for 33 per cent, and phthisis for 6 per cent. The corresponding figures for the meat, milk and raw blood tribe were 4 per cent, 3 per cent and 1 per cent.

So they set out to test the theory under controlled conditions. Their first target: puerperal sepsis. This is an infection of the uterus that occurs after childbirth. They divided 550 women into two groups: one received vitamins A and D during the last month of pregnancy, and the other received nothing. Neither group was given instructions to change diet, and neither group was given vitamins during their hospital stay. The result, quoted from Nutrition and Disease:

The morbidity rate in the puerperium using the [British Medical Association] standard was 1.1 per cent in the vitamin group and 4.7 in the control group, a difference of 3.6 per cent which is twice the standard error (1.4), and therefore statistically significant.

This experiment didn't differentiate between the effects of vitamin A and D, but it did establish that fat-soluble vitamins are important for resistance to bacterial infection. The next experiment Dr. Mellanby undertook was a more difficult one. This time, he targeted puerperal septicemia. This is a more advanced stage of puerperal sepsis, in which the infection spreads into the bloodstream. In this experiment, he treated women who had already contracted the infection. This trial was not as tightly controlled as the previous one. Here's a description of the intervention, from Nutrition and Disease:

...all patients received when possible a diet rich not only in vitamin A but also of high biological quality. This diet included much milk, eggs, green vegetables, etc., as well as the vitamin A supplement. For controls we had to use the cases treated in previous years by the same obstetricians and gynecologists as the test cases.

In the two years prior to this investigation, the mortality rate for puerperal septicemia in 18 patients was 92%. In 1929, Dr. Mellanby fed 18 patients in the same hospital his special diet, and the mortality rate was 22%. This is a remarkable treatment for an infection that was almost invariably fatal at the time.

Dr. Mellanby was a man with a lot of perspective. He was not a reductionist; he knew that a good diet is more than the sum of its parts. Here's another quote from Nutrition and Disease:

It is probable that, as in the case of vitamin D and rickets, the question is not simple and that it will ultimately be found that vitamin A works in harmony with some dietetic factors, such as milk proteins and other proteins of high biological value, to promote resistance of mucous membranes and epithelial cells to invasion by micro-organisms, while other factors such as cereals, antagonise its influence. The effect of increasing the green vegetable and reducing the cereal intake on the resistance of herbivorous animals to infection is undoubted (Glenny and Allen, Boock and Trevan) and may well indicate a reaction in which the increased carotene of the vegetable plays only a part, but an important part.

P.S.- I have to apologize, I forgot to copy down the primary literature references for this post before returning the book to the library. So for the skeptics out there, you'll either have to take my word for it, or find a copy of the book yourself.

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Fructose vs. Glucose Showdown

As you've probably noticed, I believe sugar is one of the primary players in the diseases of civilization. It's one of the "big three" that I focus on: sugar, industrial vegetable oil and white flour. It's becoming increasingly clear that fructose, which constitutes half of table sugar and typically 55% of high-fructose corn syrup, is the problem. A reader pointed me to a brand new study (free full text!), published in the Journal of Clinical Investigation, comparing the effect of ingesting glucose vs. fructose.

The investigators divided 32 overweight men and women into two groups, and instructed each group to drink a sweetened beverage three times per day. They were told not to eat any other sugar. The drinks were designed to provide 25% of the participants' caloric intake. That might sound like a lot, but the average American actually gets about 25% of her calories from sugar! That's the average, so there are people who get a third or more of their calories from sugar. In one group, the drinks were sweetened with glucose, while in the other group they were sweetened with fructose.

After ten weeks, both groups had gained about three pounds. But they didn't gain it in the same place. The fructose group gained a disproportionate amount of visceral fat, which increased by 14%! Visceral fat is the most dangerous type; it's associated with and contributes to chronic disease, particularly metabolic syndrome, the quintessential modern metabolic disorder (see the end of the post for more information and references). You can bet their livers were fattening up too.

The good news doesn't end there. The fructose group saw a worsening of blood glucose control and insulin sensitivity. They also saw an increase in small, dense LDL particles and oxidized LDL, both factors that associate strongly with the risk of heart attack and may in fact contribute to it. Liver synthesis of fat after meals increased by 75%. If you look at table 4, it's clear that the fructose group experienced a major metabolic shift, and the glucose group didn't. Practically every parameter they measured in the fructose group changed significantly over the course of the 9 weeks. It's incredible.

25% of calories from fructose is a lot. The average American gets about 13%. But plenty of people exceed that, perhaps going up to 20% or more. Furthermore, the intervention was only 10 weeks. What would a lower intake of fructose, say 10% of calories, do to a person over a lifetime? Nothing good, in my opinion. Avoiding refined sugar is one of the best things you can do for your health.

U.S. Fructose Consumption Trends
Peripheral vs. Ectopic Fat
Visceral Fat
Visceral Fat and Dementia
How to Give a Rat Metabolic Syndrome
How to Fatten Your Liver

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A Testament to the Flexibility of the Human Mind

I'm sure you've heard that humans have five senses: sight, hearing, touch, taste and smell. But we actually have far more senses than that. The canonical list doesn't include equilibrioception-- our sense of balance-- the result of fluid sloshing around in the inner ear. It also doesn't include proprioception, the ability to detect the position of our limbs using nerve endings in our tendons and muscles.

Furthermore, the sense of touch is actually several different senses, each detected and transmitted by its own special type of neuron. The sense of touch includes vibration sense, pressure sense, heat sense, cold sense and pain sense. The sense of smell can be divided into roughly 400 senses in humans, each one tuned in to a different class of airborne molecules. Vision can be divided into cells maximally responsive to four different wavelengths of light. I could go on but the rest are less exciting.

This brings me to what I really want to write about, the development (or perhaps refinement) of a new human sense: echolocation. Echolocation is the ability to gather sensory information about your surroundings by bouncing sounds off of objects and listening to the echo that returns. It's what bats use to hunt in the dark, and dolphins use to navigate muddy water and find food under the sand. There are a number of blind people who have developed the ability to use clicking sounds to "see" their surroundings, and it's remarkably effective. This represents a new use of the human mind, or at least a refinement of a rudimentary sense. Here are a few links if you'd like to watch/read more about it:

Human echolocation- Wikipedia
Daniel Kish- You Tube
The boy who sees without eyes- You Tube

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Images of Tooth Decay Healing due to an Improved Diet

This one's for the skeptics out there. As I mentioned in my previous post, Drs. Edward and May Mellanby and Dr. Weston Price reported that under the right circumstances, tooth decay can be reversed by proper nutrition. Here are images taken from the book Nutrition and Disease, by Dr. Mellanby, showing the re-calcification of decayed human teeth due to the growth of tertiary dentin (formerly known as secondary dentin). These are sections (slices) of teeth that have been treated with a chemical that darkens decayed areas. They represent four different teeth at different stages of decay reversal. Click on the image for a larger view:


Here's the text that accompanies the figure:

The hardening of carious areas that takes place in the teeth of children fed on diets of high calcifying value indicates the arrest of the active process and may result in “healing” of the infected area. As might be surmised, this phenomenon is accompanied by a laying down of a thick barrier of well-formed secondary denture. Illustrations of this healing process can be seen in Figs. 21 (b), (c) and (d). Summing up these results it will be clear that the clinical deductions made on the basis of the animal experiments have been justified, and that it is now known how to diminish the spread of caries and even to stop the active carious process in many affected teeth.

The following reference contains a summary of Dr. May Mellanby's experiments on healing tooth decay in children using diet: Mellanby, M. et al. British Medical Journal. Issue 1, page 507. 1932. The diet they used was typically a combination of some source of vitamin D (cod liver oil or irradiated ergosterol), plus liberal full-fat dairy, meats, eggs, vegetables, potatoes and grains low in phytic acid such as white bread. The most effective version of his diet, however, did not include grains.

In the book Nutrition and Physical Degeneration, Dr. Price provides X-rays showing the re-calcification of a mouth full of cavities using a similar diet.

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Modern Diet-Health Epidemiology: a Self-Fulfilling Prophecy? Part II

Certain ideas about diet and health, for better or for worse, have worked their way deeply into the American psyche in the last few decades. We're advised by health authorities, the news media, food advertisements, our doctors and our friends to eat less saturated fat, red meat and sugar, and more fruit, vegetables and whole grains. This has been the mainstream message for roughly four decades. To some degree, people are listening. We've replaced animal fats with unsaturated vegetable oils, red meat with poultry, whole milk with low-fat milk, and we're eating more fruit and vegetables than in recent history. Here are two graphs of U.S. Department of Agriculture data to illustrate the point:Whole grains are a very instructive case. Dr. Dennis Burkitt was one of the originators of the idea that fiber is good for health. He spent a number of years in eastern Africa, where he observed that natives on their traditional high-grain-fiber diets were free of many modern degenerative conditions, particularly those involving the digestive system. He found that as these cultures began to rely on Western foods such as white flour and sugar, their health declined dramatically. This is similar to the observation Dr. Weston Price made, however the two men interpreted their findings differently. Price attributed the effect to a loss of micronutrients, while Burkitt attributed it to the loss of fiber.

There are a number of observational studies that have examined the relationship between whole grain intake and health. The massive Iowa Women's Health Study, for example, showed that women with a high intake of grain fiber had a 17% lower risk of death from all causes combined. In the same group, women in the top quintile (top 20%) of whole grain consumption had a 30% lower risk of heart attack than women in the lowest quintile. These two papers were published in 2000 and 1998. Here's where it starts to get interesting. From the second paper:

Higher whole-grain intake was associated with having more education, a lower body mass index and waist-to-hip ratio [and] being a non-smoker, doing more regular physical activity, and using vitamin supplements and hormone replacement therapy.

Do whole grains prevent smoking too? An alternative explanation is that the women who were eating whole grains were all-around more conscientious and concerned about their health than those eating refined grains. And why not? They "knew" from mainstream diet advice that whole grains are healthier than refined grains. When is the last time you saw someone smoking a cigarette while eating whole grain muesli with skim milk and half a grapefruit for breakfast? Is it easier to imagine someone smoking while eating a donut and sweetened coffee? Women who eat whole grains, on average, are those that care about their health and adopt patterns that they perceive as healthy throughout their lives. This includes behaviors large and small, both measurable and unmeasurable. The investigators factored smoking into their model, but you can't factor in things you didn't measure or don't understand.

Maybe it will come as no surprise, then, that the Diet and Reinfarction showed a trend toward increased mortality in the group that doubled its grain fiber intake. Here's the graph of survival in the two groups.  It's important to mention that the fiber group probably increased its grain fiber haphazardly, using bran and unfermented grains, rather than the traditional processing techniques of healthy grain-based cultures Burkitt described.

Here's the theory. When the public decides that a particular behavior is healthy, at that point it becomes difficult to accurately measure its impact on health using observational studies. This is due to the fact that healthy, conscientious people tend to gravitate toward the recommendation. If a theory manages to become implanted early on, it will become a self-fulfilling prophecy as healthy, conscientious people adopt the behavior and are detected by subsequent observational studies. People who don't care about their health or aren't motivated enough to make a change will keep living how they used to, and that will also be detected.

You can adjust for some of these factors if you measure them. Researchers commonly adjust for age, gender, smoking, exercise and sometimes other factors when they're trying to nail down the effect of a particular factor on health. But you can't measure all the little things that accompany a health-conscious lifestyle. Do the participants take the stairs or the elevator? Do they take supplements, and if so, which ones? How much sunlight do they get? Do they have positive relationships with their friends and family? How often do they shave (kidding)? What is the quality of the foods they buy? How often do they visit the doctor, and how often do they follow her advice? There are too many potential confounding factors to measure and correct for, and collectively they have the potential to be significant. In my opinion, this means that observational data gathered from populations that already have opinions about the factor you're trying to study may tend to reinforce prevailing notions regardless of their accuracy.

This brings us to the recent study on meat intake and mortality. It was a massive observational study that followed the diet and health of 617,119 elderly Americans for 10 years. Researchers found that the highest quintile of red meat intake was at an elevated risk of cancer and cardiovascular disease, and had an overall risk of dying about 1/3 greater than those in the lowest quintile. That's a pretty somber finding for those of us who love a juicy steak. But let's look at a few of the things that came along with red meat intake. I'm going to post a few graphs of factors that associated with red meat. They're organized by ascending quintiles of red meat intake; in other words, the people eating the least (left) through the most (right) red meat.As compared to men eating the least red meat, men eating the most were three times more likely to smoke, half as likely to exercise regularly, and 22% less likely to take vitamin supplements! These are clearly people who are less concerned about their health in general. The investigators adjusted their model for a number of potential confounding factors: education, marital status, family history of cancer, race, body mass index, smoking history, exercise, alcohol intake, vitamin supplementation, fruit and vegetable intake, and hormone replacement therapy. This adjustment weakened but did not eliminate the association between red meat intake and mortality.

But again, you can't adjust for variables you don't measure. How about vitamin D status? Sugar intake? Quality and frequency of doctor's visits? Mental health? Dental health? Quality of food? There's no way to measure all the little things a health-conscious person will do to take care of himself. These unmeasured (and sometimes unmeasurable) factors can add up to have a major impact on health. So in the end, what are these studies really measuring? The association between diet and health, or the association between a health-conscious lifestyle and health? There's no way to know without a controlled trial.

Here are a few other critiques of the study that are worth reading. Chris Masterjohn points out that the investigators' method of measuring meat intake was stunningly inaccurate, and they may have been measuring wishful thinking more than meat itself. Dr. Michael Eades points out that two other studies appeared at the same time, without fanfare, that contradicted the study's findings. And Jenny Ruhl discusses the implications of the bizarre finding that red meat intake also associates with the risk of accidental death.

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Modern Diet-Health Epidemiology: a Self-Fulfilling Prophecy? Part I

Epidemiology is the study of population statistics to learn about health. It can provide simple information such as the prevalence of hepatitis C in a particular region, or it can provide more complex information such as the association between dietary patterns and gout. It has brought us many great things, from its roots in understanding the transmission of communicable diseases, to the identification of smoking as the probable cause of lung cancer.

Observational studies are a mainstay of epidemiology. In observational studies, investigators gather data passively rather than manipulating variables. For example, if you want to know if people who wear tight shoes develop bunions, you would find a group of people who wear tight shoes and one that doesn't. You would try your best to make sure the groups are the same in every way besides shoe tightness: age, gender, weight, etc. Then you would follow them for 10 years to see how many people in each group develop bunions. You would then know whether or not wearing tight shoes is associated with bunions.

Observational data can never tell us that one thing caused another, only that the two are associated. The tight shoes may not have caused the bunions; they may simply have been associated with a third factor that was the true cause. For example, maybe people who wear tight shoes also tend to eat corn flakes, and corn flakes are the real cause of bunions. Or perhaps bunions actually cause people to wear tight shoes, rather than the reverse. Observational data can't resolve these questions definitively.

To establish causality, you have to do a controlled trial. In the case of our example, we would select 2,000 people and assign them randomly to two groups of 1,000. One group would wear tight shoes while the other would wear roomy shoes. After 10 years, we would see how many people developed bunions in each group. If the tight shoe group had more bunions, we could rightly say that tight shoes cause bunions. The reason this works is the randomization process (ideally) eliminates all differences between the groups except for the one you're trying to study. You should have the same number of corn flake eaters in each group if the randomization process worked correctly.

A less convincing but still worthwhile alternative would be to put tight and loose shoes on mice to see if they develop bunions. That's what researchers did in the case of the tobacco-lung cancer link. Controlled studies in animals reinforced the strong suggestion from epidemiological studies that smoking increases the risk of lung cancer.

Finally, another factor in determining the likelihood of associations representing causation is plausibility. In other words, can you imagine a way in which one factor might cause another or is the idea ridiculous? For example, did you know that shaving infrequently is associated with a 30% increase in cardiovascular mortality and a 68% increase in stroke incidence in British men? That's a better association than you get with some blood lipid markers and most dietary factors! It turns out:

The one fifth (n = 521, 21.4%) of men who shaved less frequently than daily were shorter, were less likely to be married, had a lower frequency of orgasm, and were more likely to smoke, to have angina, and to work in manual occupations than other men.

So what actually caused the increase in disease incidence? That's where plausibility comes in. I think we can rule out a direct effect of shaving on heart attacks and stroke. The authors agree:

The association between infrequent shaving and all-cause and cardiovascular disease mortality is probably due to confounding by smoking and social factors, but a small hormonal effect may exist. The relation with stroke events remains unexplained by smoking or social factors.

In other words, they don't believe shaving influences heart attack and stroke directly, but none of the factors they measured explain the association. This implies that there are other factors they didn't measure that are the real cause of the increase. This is a critical point! You can't determine the impact of factors you didn't measure! And you can't measure everything. You just measure the factors you think are most likely to be important and hope the data make sense.

This leads us to another important point. Investigators can use math to estimate the relative contribution of different factors to an association. For example, imagine the real cause of the increased stroke incidence in the example above was donut intake, and it just so happens that donut lovers also tend to shave less often. Now imagine the investigators measured donut intake. They can then mathematically adjust the association between shaving and stroke to subtract out the contribution of donuts. If no association remains, then this suggests (but does not prove) that the association between shaving and stroke was entirely due to shaving's association with donuts. But the more math you apply, the further you get from the original data. Complex mathematical manipulation of observational data requires certain assumptions, and while it is useful for extracting more information from the dataset, it should be viewed with caution in my opinion.

Of course, you can't adjust for things you didn't measure, as the study I cited above demonstrates. If factors you didn't measure are influencing your association, you may be left thinking you're looking at a causal relationship when in fact your association is just a proxy for something else. This is a major pitfall when you're doing studies in the diet-health field, because so many lifestyle factors travel together. For example, shaving less travels with being unmarried and smoking more. Judging by the pattern, it also probably associates with lower income, a poorer diet, less frequent doctor visits, and many other potentially negative things.

If the investigators had been dense, they may have decided that shaving frequently actually prevents stroke, simply because none of the other factors they measured could account for the association. Then they would be puzzled when controlled trials show that shaving doesn't actually influence the risk of stroke, and shaving mice doesn't either. They would have to admit at that point that they had been tricked by a spurious association. Or stubbornly cling to their theory and defend it with tortuous logic and by selectively citing the evidence. This happens sometimes.

These are the pitfalls we have to keep in mind when interpreting epidemiology, especially as it pertains to something as complex as the relationship between diet and health. In the next post, I'll get to the meat of my argument: that modern diet-health epidemiology may in some cases be a self-fulfilling prophecy.

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