Is Obesity a Disease? Do the Math
A good friend who struggled all his life with severe obesity once confided to us: “Obesity isn’t really a disease, is it? I mean, you can’t catch it.” But, if you stop to do the math, it turns out that you can. Math models of disease transmission show that obesity can indeed spread through social networks.
These insights come from a very engaging session at the annual meeting of the American Association for the Advancement of Science in Boston Sunday. UAB’s David Allison led discussion of compelling data presented by three accomplished obesity researchers: Diana Thomas, Kevin Hall, and Keisuke Ejima. These are folks whose research has applied rigorous math models to understand the true nature of obesity.
Thomas presented data just published in the American Journal of Clinical Nutrition. She did the math on how people eat and came up with evidence that it’s not a simple voluntary activity. In many ways like breathing, eating follows predictable patterns driven by physiology. Tired old advice that choosing to eat slowly will magically cause you to eat less turns out to be wrong. The rate and amount that we eat follows a complex model that is consistent across many different animals. It’s basic to survival.
Hall summarized the impressive research he has published to model the way our bodies adapt to weight loss and resist it. Hunger increases and metabolic rate drops when people lose significant amounts of weight. Self reports of how much people eat become skewed because of the tremendous effort it takes to eat less. Again, simplistic prescriptions to eat less and move more are simply wrong. They contradict the complex numbers of obesity math.
Ejima presented the elegant modeling he has done to describe how the epidemic of obesity is moving through the population. Genetic, social, maternal, and individual factors all interact to govern obesity rates. Thomas, who collaborated with Ejima on this research, explained to Science magazine:
All the models are indicating that over time, obesity rates will level off at around 35% to 40% prevalence. It is believed that obesity rates are leveling off because of the great work we’ve been doing in intervention. But that may not be the case. All of these models are indicating that a plateau is just a natural evolution of the system.
Discussing these models, Allison explained that much work lies ahead. But further work promises to answer important questions:
Should we intervene with people who are already at the high end of the BMI continuum, because maybe they’ll benefit the most or need it the most? Or should we intervene more broadly because we’ll have a bigger bang for our buck with a bigger population? These kinds of models can help inform those decisions.
Simple answers to complex problems are seductive. In hindsight, sometimes the answers might appear to be simple. But finding the answers to a complex problem like obesity requires us first, do the math.
Click here for more from Science magazine and here for the new study in AJCN. Abstracts for the sessions at the AAAS annual meeting are here, here, here, and here. For more on modeling obesity prevalence, please refer to this research by Thomas et al.
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February 21, 2017