A study from the Icahn School of Medicine at Mount Sinai suggests that social determinants of health — including environmental conditions, health behaviors, access to resources, and social well-being — can play an equally important or even greater role than genetics in predicting a person’s risk of developing common diseases.

Published in the June 22 online issue of the American Journal of Human Genetics, the study, titled "Integrating Social Determinants of Health and Genetic Risk in Disease Risk Models," examined how inherited genetic risk and social, behavioral, and environmental factors interact to influence disease risk across diverse populations.

Using data from the NIH All of Us Research Program, researchers analyzed genetic information, electronic health records, and survey responses from participants across the United States. They evaluated six common conditions: asthma, chronic kidney disease, coronary heart disease, high cholesterol, breast cancer, and prostate cancer.

The researchers found that incorporating social determinants of health significantly improved disease risk prediction beyond genetics alone. For four of the six diseases studied, social, behavioral, and environmental factors contributed as much as, or more than, commonly used genetic risk scores.

"Genes are an important part of the equation, but they do not determine destiny," says senior corresponding author Samira Asgari Ph.D., assistant professor of genetics and genomic sciences at the Icahn School of Medicine at Mount Sinai, in a statement. "We found that the circumstances of people's lives—their environments, behaviors, and social experiences—can contribute as much as genetics to predicting disease risk. To truly understand health, we have to look at the whole person, not just their DNA."

While advances in genetics have expanded researchers' ability to estimate inherited risk, the new findings suggest that combining genetic information with social and environmental context may provide a more complete understanding of disease risk and help inform future prevention strategies.

“This can support improved population-level risk stratification and enable more context-aware interventions. Achieving these goals requires closer collaborations between statistical geneticists, genetic epidemiologists, and epidemiologists,” the paper states. “Our work also highlights the value of large-scale, diverse, and multimodal biobanks in advancing our understanding of health and disease.”

The researchers suggest that future efforts may focus on harmonizing survey instruments to build transportable low-dimensional summaries of environmental, behavioral, or social determinants of health across cohorts and routinely reporting multiple correspondence analysis (MCA)-style embeddings for these factors alongside genetic PCs. "Additionally, incorporating longitudinal data to capture timing and accumulation of exposures and integrating other data modalities will be essential to fully understand how non-genetic factors shape genetic risk and disease manifestation,” the study said. 


"Our goal is to build a more complete understanding of health and disease," added Asgari. "By combining genetics with social and environmental context, we can move toward risk models that better reflect the realities of people's lives and help advance more personalized approaches to health.”