There are, undoubtedly, many factors that go into a person’s weight. A new report from the lab of Sekar Kathiresan, MD, director of the Center for Genomic Medicine at Massachusetts General Hospital (MGH), documents a clear biologic basis for the predisposition of obesity. More specifically, that genetics play a role in how heavy a person gets.

The study used polygenic risk scores (PRS) to stratify patients into risk categories based on their genetic mutations. It is unusual for a disease to be caused by mutations in a single gene. More commonly, diseases (including cardiovascular disease, type 2 diabetes, and some brain disorders) are mediated by a collection of common and low-frequency genetic variants, most of which remain unknown. Each variant has a small effect, but, taken together, they could indicate a person’s overall risk.

Severe obesity affects 8% of adults in the United States, and while this figure is much lower—about 1%—in countries such as India and China, the prevalence of severe obesity has increased more than 100-fold in these regions over the last 30 years. It also shows “no signs of slowing,” the authors stated.

Obesity is also known to have a heritable component, “suggesting that inborn DNA variation confers increased susceptibility in some individuals, and protection in others.” Inherited susceptibility to obesity may, in rare cases, result from mutations in a single gene, such as the melanocortin 4 receptor (MC4R) gene, which have a large effect, but for most people with severe obesity there is no single genetic cause. In these cases, and as with other complex diseases, the researchers pointed out, “genetic susceptibility may instead result from the cumulative effects of numerous variants with individually modest effects—a polygenic model.”

Kathiresan’s group developed the PRS for obesity using the same approach that the group has used previously for diseases such as coronary artery disease—distilling information from millions of sites in the genome where people vary in their genetic code into a single number that reflects inherited risk. The dataset used to build the predictor was based on a large GWAS published in a 2015 Nature paper titled, “Genetic studies of body mass index yield new insights for obesity biology.” The polygenic predictor quantified the relationship between each of over 2.1 million common genetic variants and BMI in more than 300,000 individuals.

The study using the obesity predictor revealed that some people are much more susceptible to obesity than others. Those scoring in the top 10% were 29 pounds heavier on average than those in the lowest 10%t and were 25 times as likely to develop severe obesity.

Interestingly, the data show that the impact of the score starts to manifest itself by the time children enroll in preschool, something that surprised Amit Khera, MD, associate director, precision medicine unit, Center for Genomics Medicine at MGH and the first author on the paper. In addition, their weight trajectories continue to diverge over the years with Kathiresan calling the ages of 0–8 the “golden period of susceptibility.”

“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,” said Kathiresan.

The research is published in Cell in a paper titled, “Polygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood.”

“The power to identify people who are at high risk for obesity at birth is striking” noted Ali Torkamani, PhD, director of genomics and genome informatics at the Scripps Research Translational Institute. “However,” he added, “the genetic risk for obesity seems to have mostly expressed itself by the time people reach the age of 8 years old.” Torkamani said that “it remains to be seen if and how people could use this information early in life to prevent lifelong obesity.” Torkamani’s accompanying commentary on the study appears in alongside the research article in Cell.

Khera told GEN that many people are stigmatized for severe obesity based on the assumption that it is due to a lack of willpower or poor choices. This result may begin to destigmatize obesity in society in addition to providing important health information to those with high polygenic obesity scores who are also at higher risk for lots of other health complications. It also suggests how some people’s DNA puts them at markedly increased risk for obesity, helping to answer the question: “Is there a genetic reason why I have so much trouble keeping the weight off?” Lastly, and prognostically, the score identifies a subset of the population who is at substantially increased risk for developing severe obesity—this raises the potential for a targeted intervention.

However, the score simply predicts a risk—not more than that. Indeed, 17% of people with high-risk scores maintained a normal weight. “What’s really important here is that we have a new approach to ‘genome interpretation’ that could be done anytime after birth,” noted Khera. He added that, for diseases like coronary artery disease and breast cancer, “we clearly showed that these high-risk individuals—who are at quadruple the normal risk—are flying under the radar in our clinical practice.” So, finding and helping these individuals is a major goal for coming years.

“The ability to identify high-risk individuals from the time of birth may facilitate targeted strategies for obesity prevention with increased impact or cost-effectiveness,” said Khera.

Khera noted that “the evidence that genetic starts to manifest itself very early in life highlights the critical importance of public health interventions that start at a young age. This will take a lot of work to optimize, but healthier foods in our schools, education, provision of a safe environment for kids to play—these will all be important pieces of the puzzle.”