In a major advancement, an international team of researchers has developed a polygenic risk score (PGS)—a genetic test capable of predicting a child’s risk of developing obesity as early as age five. This tool could be instrumental in identifying children at higher genetic risk and enabling early, targeted prevention strategies such as lifestyle and dietary interventions.
“What makes this score so impactful is its ability to forecast obesity risk well before traditional factors come into play,” said lead author Roelof Smit, Assistant Professor at the Novo Nordisk Foundation Center for Basic Metabolic Research (CBMR), University of Copenhagen, in a study published in Nature Medicine. “Early intervention at this stage can yield significant long-term health benefits.”
Professor Ruth Loos, co-author of the study, added: “This is a substantial leap in our ability to genetically predict obesity. The improved predictive power brings us closer to integrating such tools into clinical practice.”
The World Obesity Federation projects that by 2035, more than half the global population will be affected by overweight or obesity—a known risk factor for conditions such as diabetes, hypertension, cardiovascular disease, and cancer.
Developed with data from over five million individuals and involving more than 600 scientists from 500 institutions worldwide, the new PGS functions as a genetic calculator. It assesses the cumulative effect of various genetic risk variants to deliver an overall obesity risk score.
Remarkably, the new test outperforms previous models—doubling their accuracy in predicting long-term obesity risk. The study also explored how genetic predisposition influences response to lifestyle-based weight management programs. Individuals with a higher genetic risk of obesity were found to respond better to interventions like diet and exercise—but also tended to regain weight more quickly once the interventions ended.
Despite its promise, the PGS still faces limitations. While the test incorporates a more diverse genetic dataset than previous versions, it remains significantly more accurate for individuals of European ancestry compared to those of African descent—highlighting the continued need for greater representation in genomic research.