Social Factors May Match, Outpace Genetic Risk in Disease Prediction

Social determinants of health (SDOH) that include environmental conditions, health behaviors, access to resources, and social well-being can rival or exceed genetics in predicting a person’s risk of developing common disease, according to a new study from the Icahn School of Medicine at Mount Sinai published in The American Journal of Human Genetics.¹

Using data from the National Institutes of Health’s All of Us Research Program, authors analyzed genetic information, electronic health records, and survey responses from participants across the US, evaluating 6 conditions: asthma, chronic kidney disease (CKD), coronary heart disease (CHD), high cholesterol, breast cancer, and prostate cancer. For asthma, CKD, CHD, and hypercholesterolemia, social, behavioral, and environmental factors contributed as much as, or more than, commonly used genetic risk scores.

As health care continues to wrestle with how to optimally integrate SDOH data into clinical and actuarial models, the findings potentially carry direct implications for how health systems, payers, and managed care organizations think about risk stratification and prevention.^1,2

What Did the Mount Sinai Researchers Find?

The investigators constructed risk-prediction models for the 6 conditions previously mentioned using multiple correspondence analysis (MCA) on survey responses—there were more than 100 survey questions—from 413,457 participants after linking the responses to electronic health records (EHRs) and genetic data. These models incorporated polygenic scores (PGS) alongside MCA-derived “embeddings” measures that compressed more than 100 social, behavioral, and environmental variables into interpretable composite dimensions. Smoking and economic status, more familiar risk signals, were captured along with less commonly modeled factors such as loneliness and spirituality.

Model performance, measured by area under the receiver operating characteristic (ROC) curve, improved by 0.007 to 0.027 across diseases when SDOH embeddings were added, a statistically and clinically meaningful gain.

“Genes are an important part of the equation, but they do not determine destiny,” said senior corresponding author Samira Asgari, PhD, assistant professor of genetics and genomic sciences at Icahn, in a statement.² “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.”

A key nuance in the findings is that genetic and social risk factors appear to operate independently. When the research team tested for gene-environment interactions, the predictive gains were negligible (change in area under the ROC curve ≤ .001). Genetic variant effect sizes also remained highly stable after adjusting for social factors.

This additive structure has significant policy implications. It suggests that interventions targeting such social and behavioral factors as housing support, care coordination, or social isolation can reduce disease risk regardless of a patient’s genetic background.

What Does the Path Forward Look Like?

The authors are candid about their findings’ limits. Because the survey responses were collected at a single point in time and genetic risk is present from birth, the analysis cannot determine whether social exposures preceded disease onset or proceeded diagnosis. Causal interpretation is therefore limited. There was also potential for selection bias from survey nonresponse. Still, the research team emphasizes that these limitations do not diminish their principal finding: that social, behavioral, and environmental context improves risk prediction in ways that genetics alone cannot capture and that models integrating both data types more accurately reflect the full burden of complex disease.

Future work should aim to incorporate longitudinal data to capture the timing and accumulation of exposures and to explore the biological mechanisms through which social experiences may become embedded in disease risk. The researchers also call for greater harmonization of survey instruments across biobanks, so that MCA-style social risk embeddings can eventually travel across cohorts the way genetic principal components already do.

“Incorporating longitudinal data to capture timing and accumulation of exposures and integrating other data modalities will be essential to fully understand how nongenetic factors shape genetic risk and disease manifestation,” they concluded.

References

1. Biji A, Ferar K, Pejaver V, Kenny EE, Liu B, Asgari S. Integrating social determinants of health and genetic risk in disease risk models. Am J Hum Genet. Published online June 22, 2026. doi:10.1016/j.ajhg.2026.05.014
2. Study finds social determinants of health can match or exceed genetic risk in predicting common diseases. News release. Mount Sinai. June 22, 2026. News release. Accessed June 22, 2026. https://www.mountsinai.org/about/newsroom/2026/study-finds-social-determinants-of-health-can-match-or-exceed-genetic-risk-in-predicting-common-diseases