Can We Predict Future Weight Gain?

Eric Ravussin

This morning, at the XI International Congress of Obesity, I attended a review session on the issue of whether or not it is possible to predict weight gain.

In his introductory comments, Johannes Hebebrand from Essen, Germany, emphasized that even in carriers of mutations that are associated with obesity (for e.g. mutations in the MC4 receptor), excess weight is not necessarily seen in all or even the majority of carriers of such mutations. These findings suggest that current genetic markers are not sufficient to clinically predict an individual’s risk of weight gain.

Eric Ravussin (picture), from the Pennington Research Centre in Baton Rouge, Louisiana, the winner of the 2010 Willendorf Award for Clinical Research, pointed out that one of the strongest predictors of future weight gain may well be lower energy requirements, which in turn is closely related to fat-free mass.

This makes intuitive sense, as it would obviously be easier for someone with lesser energy requirements to move into positive energy balance than someone who has higher energy needs. Although energy needs are in part determined by the level of physical activity, the data supporting the notion that less physical activity is indeed a causal factor (rather than a mere association) in the obesity epidemic are far from conclusive.

He discussed the concept of the “energy flux gap”, which implies that in order to gain weight over time, one would need a substantial sustained surplus of calories to gain and maintain a higher body weight. According to his studies using double-labeled water to measure energy expenditure, he estimated that it would take a sustained 10% excess caloric intake to induce a 7% weight gain. His calculations clearly imply (and are consistent with the observational evidence from population studies) that the obesity epidemic is primarily driven by increased food intake and not by decreased physical activity.

Indeed, it appears that both in animal models and humans, there is an inverse relationship between metabolic rate and caloric intake, which means that paradoxically, individuals with lower metabolic needs tend to also have a stronger drive to eat more.

Other important determinants of obesity risk include fat oxidation rates, where individuals who are better at using fat as a fuel, are less prone to weight gain that people with low fat oxidation rates. Twin studies show that this differential substrate use is very much determined by genetic factors.

Overall his data suggests that perhaps the best (and only) way to determine obesity risk at a metabolic level is to actually challenge the system by doing short-term caloric restriction or feeding studies. These studies can not only show weight gain or weight loss, but also help determine metabolic flexibility or peoples’ ability to switch from fat storage to fat burning with increased fat intake.

Perhaps clinicians need to pay more attention to actually measuring metabolic rates and responses in metabolic rates and substrate utilisation to determine obesity risk and response to treatment.

Stockholm, Sweden