We all have that friend who survives on beer and barbecue and somehow stays slim. 

Our weight—though of course influenced by factors such as diet and exercise—can be frustratingly genetic. Scads of gene variants, differences in single letters of the genetic code, have been associated with being overweight. 

Researchers from Massachusetts General Hospital and the Broad Institute have now developed a scoring system they believe can reliably predict people’s obesity risk through life based on their genome. The study behind the scale’s development was published April 18 in the journal Cell. The possibility of administering a routine test, the authors contend, could help destigmatize obesity as stemming from lack of willpower; all the while urging those at higher obesity risk to pay extra mind to overcoming their biological blueprint.

To develop their risk scoring system, the research team analyzed data from the largest genome wide association study (GWAS) in obesity, compiled by another research group in 2015. A GWAS entails scanning an entire genome for particular gene variants associated with a particular trait or condition. 

The authors looked at over two million common gene variants in over 300,000 people in the study, ranging from newborns to middle-aged adults. A computer algorithm then crunched the numbers to come up with a “polygenic” obesity risk score that predicted someone’s BMI, a score based on the collective influence of multiple genes. Such genetic risk scores are being studied in a host of chronic conditions beyond just obesity.  

The new GWAS confirms what many have assumed: certain people gain weight because they are biologically more destined by their genes than others to develop obesity. The 10 percent of people in the study with the highest risk scores were on average 29 pounds heavier than the group in the lowest decile. The elevated scorers were 25 times more likely to develop severe obesity than the bottom 10 percent.

What’s more, the risk score had a negligible correlation with birth weight, suggesting that a chunky baby doesn’t necessarily mean an overweight adult, and vice versa. The correlation between genetic risk and weight becomes apparent sometime around toddlerdom. “We expected to a see a link between the score and weight outcomes,” says Amit Khera, the study’s first author. “But we were surprised by the impact of the genetic predisposition had already started to emerge by the time children enroll in preschool.”

Whereas the traditional approaches to exploring genetic causes of obesity often focused on single genetic mutations, Khera explains that on an individual basis these mutations are quite uncommon, even in the most severely affected people. “Here we show that it’s is not one mutation—but the cumulative effect of many variants—that translates into increased risk.”

Genes, however, are not the whole story. In the past few decades obesity rates have soared beyond what could be explained by genes. Since 1980, obesity prevalence has more than doubled in American adults. In kids and adolescents, rates have tripled.

“Let’s face it, obesity is somewhere around 50 percent due to genetic factors that determine someone’s innate predisposition to gain weight,” says Ruth Loos, a professor of environmental medicine and public health at Mount Sinai’s Icahn School of Medicine in New York City. “However, the other 50 percent is due to environmental factors, such as lifestyle, diet and physical activity. I am concerned that the authors may have oversold the genetic score and that their claims of prediction are too bold.”

The relatively recent surge in obesity rates has been attributed to our culture’s “obesogenic” environment: highly processed, high-calorie foods; an increasingly sedentary, digital workforce. The modern lifestyle has taken its toll on our physiques.

Loos points out that while it’s an improvement over previous obesity scoring systems, the newly published method captures less than 10 percent of the genetic risk of obesity. “With 90 percent of the information missing, it’s impossible to accurately predict obesity,” she says.

Cecile Janssens, a professor of translational epidemiology at Emory University in Atlanta, shares the skepticism, particularly since Khera hasn’t yet specified any practical clinical applications of their scoring system. “The authors don’t ask about how the score could be used in practice,” she says. “Given that people don’t get obese overnight and can see themselves get heavier when they look in the mirror or step on the scale, it seems that there are many more relevant targets for obesity prevention than a genetic score that only explains less than 10 percent of the variance in weight.”

Still, Khera feels that while he and his colleagues haven’t fully addressed how their findings could influence patient care—something they plan to do with future research— he suggests that the realization that severe obesity has a strong biological basis could help prevent stigmatization. He also posits that knowing your obesity risk at a young age could encourage earlier treatment and lifestyle interventions that could help overcome—at least partially—a genetic tendency toward carrying excess weight.

“DNA is not destiny,” he says. “We know a healthy lifestyle can offset a genetic predisposition, so a key goal is to empower people to prevent onset of disease.”