Some health experts predict that the next big advance in helping overweight people achieve a healthier weight will be to use personal genetic data to customize diets and physical activity plans, an approach known as “precision weight loss.”

The biggest challenge toward that goal is a need for better analytical tools to discover the relationship between genetics, behavior, and weight-related diseases.

A new report, published in the journal Obesity, summarizes what scientists currently know about factors that influence weight loss and weight regain, and identifies how genetic information and data collection from noninvasive, portable devices may soon be incorporated into research and weight loss treatment.

“Obesity is one of the gravest problems of our times.”

“I think within five years, we’ll see people start to use a combination of genetic, behavioral, and other sophisticated data to develop individualized weight management plans,” says Molly Bray, a geneticist and professor of nutritional sciences at the University of Texas at Austin.

Researchers speculate that in the future, patients might submit saliva samples for gene sequencing, along with using automated sensors to collect information about factors such as their environment, diet, activity, and stress. A computer algorithm would then take the information and provide patients with specific recommendations to achieve their target weight.

The falling cost of genome sequencing, plus portable monitors (such as Fitbit) to track in real time people’s behavior and environment, mean that scientists already have the ability to collect the kinds of data they need to do the fundamental research behind precision weight loss.

The real challenge now is to develop the tools to analyze this flood of data.

“We are pretty good at helping people lose weight in the short term,” says Bray. “But the stats on long-term weight loss are pretty dismal. We still don’t understand the process of weight regain very well, either from a behavioral or a biological standpoint.”

More than an ‘obesity gene’

Scientists have uncovered some of the genetic basis for weight-related diseases, such as the discovery of a gene that appears to cause energy from food to be stored as fat rather than be burned. Some reporters have dubbed it the “obesity gene,” but Bray cautions that it’s not so simple.

“When you go back and see how much of the variation in this gene accounts for the variation in body size in the general population, it’s really small.

“So that highlights that there are going to be several genes involved with obesity, and they’re going to interact with each other in complicated ways. And that’s certainly true of weight loss and maintenance too.”

Obesity puts a large burden on the health care system and increases an individual’s risk for a number of other diseases including diabetes, cardiovascular disease, and cancer. It’s also rising at an alarming rate worldwide.

“Obesity is one of the gravest problems of our times,” says Bray. “Obviously prevention would be the best approach, but there are literally millions of individuals who are currently obese and are in dire need of more effective strategies for long-term weight loss that will ultimately improve overall health.”

Multiple research projects have shown that about half of the variation in people’s body mass index can be attributed to genetic factors, while the rest is due to environmental factors, including diet and exercise. For example, depending on a person’s specific genetic makeup, exercise might be less effective at reducing weight for some people compared with others.

“When people hear that genes may be playing a role in their weight loss success, they don’t say, ‘Oh great, I just won’t exercise any more,'” says Bray.

“They actually say ‘Oh thank you. Finally someone acknowledges that it’s harder work for me than it is for others.’ And then I think they’re a little more forgiving of themselves, and they’re more motivated to make a change.”

The National Cancer Institute and the National Institutes of Health funded the work.

Source: University of Texas at Austin

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