A new analysis of earlier observational studies, published last week in PLOS Medicine, suggests that those who eat full-fat dairy products also experience better health.
Last summer, we reported on a study that showed a link between eating more dietary dairy fat and lower rates of stroke. This week’s analysis reveals an association between eating more dairy fat and lower rates of type 2 diabetes.
This was a large analysis, with over 63,000 participants. On average, the authors note 29% lower risk of type 2 diabetes for those with the highest level of dairy fat consumption versus those with the lowest.
Both this review, and the one showing lower rates of stroke, relied upon an objective measurement of dairy fat consumption: biomarkers in the blood. This is a big step up compared to relying on the standard metric for assessing diets — food frequency questionnaires — which are notoriously unreliable measurement tools. In the study authors’ words:
Most prior studies of dairy foods and T2D have relied on self-reported dietary questionnaires, which may have errors or bias in memory as well as challenges in assessing less apparent sources of dairy fat such as in creams, sauces, cheeses, and cooking fats in mixed meals and prepared foods.
Circulating and tissue biomarker concentrations… help capture multiple dietary sources without relying on memory or subjective reporting, and reflect a complementary approach to investigate associations with T2D.
The New York Times reported that another large cohort study, published in The Lancet this summer, found an association between eating more full-fat dairy and lower risk of mortality and cardiovascular events.
All of these studies mentioned are observational, so we cannot assume causation. In other words, it is not clear that the additional dairy fat in subjects’ diets caused improved health.
However, it is very hard to imagine how we could repeatedly see these healthful associations if dairy fat were instead causing the health problems studied. Observational studies normally can’t prove cause and effect, but when they repeatedly give the opposite results versus what a theory would predict, the theory is likely completely wrong.