Social factors rival genes in predicting common diseases

Clinical takeaway: Social and behavioral context can flag elevated disease risk that genetic and demographic data miss, supporting its inclusion in risk-assessment models.

Genetic risk scores have given clinicians a way to estimate inherited disease risk, but they capture only part of what shapes who gets sick. Social, behavioral, and environmental factors are known to matter too, yet the two are rarely modeled together, partly because few datasets measure both well.

A new analysis of NIH All of Us biobank data brought these factors into the models for six common diseases. Across all six, adding social and behavioral context improved prediction, and for four it contributed more than genetic risk scores did.

The six conditions were chosen to span distinct disease types: asthma, chronic kidney disease, coronary heart disease, high cholesterol, and breast and prostate cancer. The variety here matters, because a method that improves prediction across immune, metabolic, cardiovascular, and cancer risk is more likely to generalize than one tuned to a single condition.

Prediction improved most for asthma and chronic kidney disease. For four of the six conditions (asthma, chronic kidney disease, coronary heart disease, and high cholesterol), social and behavioral context contributed more to predictive accuracy than the polygenic score, a single figure summing the effect of many common gene variants on disease risk. In ranked importance, those measures often trailed only age, and for asthma they outranked everything else.

When the researchers tested for interaction between genetic and social factors, the added predictive value was negligible, and genetic effect sizes barely changed when social context was added to the models. In other words, social context did not amplify genetic risk; it added a separate layer of signal. The two kinds of risk appeared to act independently.

Some of the social factors were expected: smoking and economic status surfaced as strong contributors. Others were less so. A measure tracking neighborhood deprivation was the leading social contributor to asthma, chronic kidney disease, and coronary heart disease, while loneliness stood out for breast and prostate cancer.

The team drew on the All of Us biobank, linking genomic data, electronic health records, and responses to detailed lifestyle and social-determinant surveys for 171,614 participants. They condensed more than 140 survey and neighborhood-level variables into a smaller set of composite measures, then tested how much those measures improved disease prediction when added to models already containing age, sex, genetic ancestry, and polygenic scores.

The practical use is risk stratification: models that account for social and behavioral context may identify individuals that genetic scores and demographics alone would miss.

"Genes are an important part of the equation, but they do not determine destiny," said Samira Asgari, PhD, senior corresponding author and assistant professor of genetics and genomic sciences at the Icahn School of Medicine at Mount Sinai. "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."

Source: Biji A, et al. Am J Hum Genet. 2026 Jul 2. Integrating social determinants of health and genetic risk in disease risk models