When you’re overweight and the fat is concentrated in your belly as opposed to your hips or thighs, there’s a greater risk for developing heart disease or type II diabetes.
But before you say, “old news!” the fact is that until this new research published in JAMA, those observations had only been anecdotal. There was no concrete, scientific evidence to back them up.
What makes this research different from observational studies is that the researchers examined genetic predispositions to people with large amounts of belly fat, also known as a “waist-to-hip ratio.”
Led by Dr. Sekar Kathiresan of Massachusetts General Hospital, Harvard Medical School in Boston, the researchers pored through data from four genetic association studies conducted from 2007 to 2015. The studies together included more than 444,000 people.
“These results permit several conclusions,” the authors wrote in their paper. “First, these findings lend human genetic support to previous observations associating abdominal adiposity with cardiometabolic disease. Second, these results suggest that body fat distribution, beyond simple measurement of BMI (body mass index, a measure of obesity) could explain part of the variation in risk of type 2 diabetes and CHD (coronary heart disease) noted across individuals and sub-populations.”
“Third, waist-to-hip ratio adjusted for BMI might prove as a biomarker for the development of therapies to prevent type 2 diabetes and CHD,” they continued.
The authors arrived at their conclusions using a method called Mendelian randomization analysis. This sort of research allows doctors and scientists to examine whether variations in genes directly impact disease function. Mendelian randomization trials serve to exclude potential confounding that is found in most studies of human disease. “Confounding” is simply a medical term used to describe uncertainties, or confusion, in studying disease. For example, is the disease process caused by a different pathology that could be present in a sample as opposed to the one being studied based on a research “hypothesis” (a fancy word for an idea, or a theory).
You may have heard the term “correlation does not mean causation.” This is a commonly cited cautionary phrase that medical reporters learn early on, and that consumers of medical information should always keep in mind. However, Mendelian randomization helps eliminate confounding and potentially inaccurate or overly generalized conclusions that come from much medical research.
In recent years, scientific advances have made Mendelian randomization analyses easier for scientists to conduct.
In an editorial accompanying Kathiresan’s research, Dr. George Davy Smith and colleagues from the University of Bristol in the UK pose the question, “When will Mendelian randomization become relevant for clinical practice and public health?”
The answer, basically, is that this era is finally dawning. “Mendelian randomization is slowly beginning to generate data of clear clinical and public health relevance,” the authors write. “Attention to Mendelian randomization data could have helped avoid several very expensive late-stage clinical trial failures and might improve prediction of what RCTs (randomized clinical trials, where medicines are tested with one group getting the medicine and the other placebo) will show. Indeed, the adoption of Mendelian randomization to prioritize (or deprioritize) major investment in RCTs before their inception should be actively encouraged.”