Category: epidemiology

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Seth Roberts2 Comments

Thanks to Anne Weiss and Dave Lull.

Fermented Dairy Intake Negatively Associated with Diabetes

Seth Roberts8 Comments

A new epidemiological study followed about 16,000 people in Europe for about 12 years and focused on their dairy intake. Did the ones who came down with diabetes eat differently from those who didn’t?

The paper begins:

Current dietary guidelines for prevention of diabetes aim at substituting SFAs [saturated fatty acids] with unsaturated fatty acids. However, conventionally held notions that all SFAs, including those from dairy products, are detrimental to health have recently been challenged.

The shift of evidence (dairy less bad than previously believed) supports my view that what’s good for the brain (I found butter was good for my brain) is likely to be good for the rest of the body. The paper’s main conclusion is the possible protective value of cheese and yogurt:

This large prospective study found no association between total dairy product intake and diabetes risk. An inverse association of cheese intake and combined fermented dairy product [= cheese, yogurt, and “thick fermented milk”] intake with diabetes is suggested.

The combined fermented dairy association was not large in size (a risk reduction of 12%) but was significant (barely). When your main finding is barely significant you have no hope of using your data to explain it so the new information essentially stops there. The results support my view that fermented foods are unusually healthy.

In response to these findings, the director of research at Diabetes UK said, “This study gives us no reason to believe that people should change their dairy intake in an attempt to avoid [diabetes].” Wow. It is as if a prominent physicist said the earth was flat.

Thanks to Elizabeth Molin.

Is Epidemiology Worthless? The Case of Calcium

Seth Roberts11 Comments

Epidemiology has lots of critics. In this article, for example, it is called “lying on a grand scale.” Every critique I have read has ignored history. Epidemiologists have been right about two major issues: 1. Heavy smoking causes lung cancer. 2. Folate deficiency causes birth defects. In both cases, the first evidence was epidemiological. Another example is John Snow’s conclusion about the value of clean water. In my experience, epidemiologists often overstate the strength of their evidence (as do most of us) but overstatement is quite different from having nothing worth saying.

Let’s look at an example. Many people think osteoporosis is due to lack of calcium. Bones are made of calcium, right? The epidemiology of hip fractures is clear. In spite of the conventional idea, the rate of hip fracture has been highest in places where people eat a lot of calcium, such as Sweden, and lowest in places where they eat little, such as Hong Kong. (For example.) In other words, the epidemiology flatly contradicted the conventional idea. This was apparently ignored by nutrition experts (everyone knows correlation does not equal causation) who advised millions of people, especially women, to take calcium supplements to avoid osteoporosis. Millions of people followed (and follow) that advice.

Thanks to a recent meta-analysis we now know that experiments and better data firmly support the earlier epidemiology, which suggested that calcium supplements are dangerous. Here are its main conclusions:

In meta-analyses of placebo controlled trials of calcium or calcium and vitamin D, complete trial-level data were available for 28,072 participants from eight trials of calcium supplements and the WHI CaD participants not taking personal calcium supplements. . . .Calcium or calcium and vitamin D increased the risk of myocardial infarction (relative risk 1.24 (1.07 to 1.45), P = 0.004) and the composite of myocardial infarction or stroke (1.15 (1.03 to 1.27), P = 0.009). . . . A reassessment of the role of calcium supplements in osteoporosis management is warranted.

If the epidemiology had been taken more seriously, many heart attacks might have been avoided.

Is this an “anecdote” — a single example — proving nothing? Here’s how you can check. Randomly select a meta-analysis of epidemiological studies. Thousands have been done. Then ask if the results summarized in the meta-analysis appear random. Better yet, randomly pick two meta-analyses. Suppose the first summarizes 5 studies and the second summarizes 6. If the 11 results were shuffled together, how well could you assign them correctly?