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Predicting obesity?
May 2019
by Mel J. Yeates  |  Email the author


BOSTON & NEW YORK—Researchers have created a scoring system based on genetic markers that predicts an individual’s inborn risk for obesity. Using data from the largest existing genome-wide study of obesity, they’ve applied new algorithms to integrate information from more than two million genetic variants affecting body mass index (BMI). The resulting score accurately predicted BMI and obesity in over 300,000 individuals spanning from birth to middle age. A paper on the research was published in April in Cell.
“A polygenic risk score represents the additive effect of many many gene variants across the genome. It’s a number to capture the genetic risk that comes from millions of spots in the genome, each having a small effect,” said senior author Sekar Kathiresan, director of the Center of Genomic Medicine at the Massachusetts General Hospital and the Cardiovascular Disease Initiative at the Broad Institute. “The score is associated with only minimal differences in birth weight, but it predicts clear differences in weight during early childhood and profound differences on weight trajectory and risk of developing severe obesity in subsequent years.”
The study revealed that some people are much more susceptible to obesity than others: those scoring in the top 10 percent were 29 pounds heavier than those in the lowest 10 percent on average, and were 25 times as likely to develop severe obesity. The impact of the score started becoming discernible around age 3.
In a short video, Kathiresan related that, “Individuals with high polygenic risk are more likely to develop obesity over a lifetime, but they still have control over that fate. So they can modify the risk that comes from inheritance. Right now, obesity is largely viewed by the public as a disease of willpower—as basically an individual’s fault—and what our research is showing is that some are inherently more predisposed to putting on weight. And what we’re hoping is that this research really destigmatizes obesity.”
“This research into the polygenic basis of obesity could lead to new insights into the biology of the disease. For example, some people who are highly predisposed to obesity based on polygenic risk managed to stay quite thin; why might this be?” queried Kathiresan. “It could be that they carry genetic mutations that protect them in the context of having this polygenic risk. If we could identify these protective mutations and understand how those genes work, then we might be able to develop new treatments to help the entire population avoid obesity.”
While the score is not a perfect predictor—some with genetic predispositions never become obese—the researchers contend that genetic profiles can help identify high-risk individuals. Prior studies suggest that the impact of unhealthy diet and sedentary lifestyle on BMI is most pronounced in those with a genetic predisposition.
“This is certainly the case for obesity, where a healthy diet and exercise can offset a genetic predisposition,” added co-author Amit Khera, a postdoctoral fellow in the Kathiresan lab. “But it is also likely true that those with a high genetic predisposition have to work much harder to maintain a normal weight. We have always had a hunch that some people may have been born with a genetic profile that predisposes them to obesity, and we now confirm that this is both true and quantifiable.”
Using a $50 microarray that detects variations and mutations among millions of genetic markers, researchers anticipate their scoring approach will one day predict genetic risk for a range of health conditions, such as heart disease, breast cancer, obesity and atrial fibrillation. Interventions might include preventive cholesterol-lowering medication, lifestyle counseling or using wearable technology, such as an Apple Watch, to detect irregular heartbeats.
“Over the last 10 years, we’ve been able to find many variants that affect peoples’ risk for diseases such as heart attack, but it’s important to note that any one variant typically increases your risk by maybe 2 or 3 percent,” Khera mentioned in a short video. “So its power of prediction is actually quite modest. If we combine all 6.6 million variants together and weight them appropriately, we’re able to get ... a polygenic risk score.”
“We now have been able to test and validate these scores in over half a million people. So what do I think is the real future? Well, I can imagine that at a very young age, people would get a genetic test ... for a whole range of diseases. And that information, almost an inherited susceptibility report card, could then be used with the patient and their physician in order to personalize their care,” Khera noted. “We are in the early days for figuring out how and when best to disclose genetic information and how we can best empower patients to overcome any genetic risks identified — but we are incredibly excited about the potential.”
But the idea of a scoring system to predict obesity may be controversial. Dr. Ruth Loos, a professor at The Charles Bronfman Institute for Personalized Medicine at the Icahn School of Medicine and director of the Genetics of Obesity and Related Metabolic Traits Program at Mount Sinai, says about the paper: “Obesity is a complex disease ... We would be fooling ourselves if we believed that a single genetic score would be the end-all and be-all to predict future risk of obesity.”   
Loos mentions that a scoring system like this could have potential benefits, but she goes on to add that “a score solely based on genetics would only capture 50 percent of obesity risk, and can never accurately predict obesity. Thus, the score presented has no predictive ability.”
“In fact, family history (having obese parents in childhood) is a better (not perfect) predictor of a child’s future risk of obesity. It would be irresponsible to claim—based on this paper—that scientist can now predict obesity. If we want to predict future obesity, we need to account for all contributors; i.e. we need to account for genetics, but also family history and lifestyle,” concludes Loos.    
Code: E051923



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