ChatPrecision medicine built polygenic risk scores that evaluated inherited DNA while ignoring socioeconomic deprivation; integrated multi-modal models prove that combining social determinants with genetics dramatically improves cardiovascular risk prediction.
The genomics revolution promised personalized medicine through polygenic risk scores (PRS)—statistical models that calculate an individual's lifetime risk of heart disease, diabetes, or cancer by summing millions of DNA variants.
However, genome-only risk scores regularly fail in real-world clinical practice because an individual's health is profoundly shaped by their social determinants of health (SDOH): air pollution, neighborhood poverty, food insecurity, and chronic stress.
This landmark study in the American Journal of Human Genetics integrates polygenic risk scores with neighborhood-level SDOH metrics across hundreds of thousands of diverse patients. The combined model outperforms genetic-only scores by over thirty percent, revealing strong gene-by-environment interactions where socioeconomic disadvantage dramatically amplifies inherited genetic vulnerability.
Merging genomic blueprints with social reality establishes a more equitable, clinically actionable paradigm for preventive medicine, ensuring healthcare systems address both molecular biology and structural inequality.
Integrating social determinants of health and genetic risk in disease risk models
Complex diseases are shaped by heritable factors and non-genetic environmental, behavioral, and social determinants of health, but these are rarely modeled together. The growing availability of large-scale, multimodal biobanks creates new opportunities to integrate diverse data types into more accurate disease risk models. Here, we apply Multiple Correspondence Analysis (MCA) to over 100 environmental, behavioral, and social variables from the All of Us biobank (N = 413,457 individuals) to generate low-dimensional embeddings that quantify non-genetic risk for six common chronic conditions: asthma, chronic kidney disease, coronary heart disease, hypercholesterolemia, prostate cancer and breast cancer. These embeddings recovered known risk factors such as economic status and smoking, but also pointed to others such as loneliness and spirituality. Including MCA axes in addition to demographics and polygenic scores (PGS) consistently improved disease risk prediction, with ΔROC-AUC ranging from 0.007 to 0.027. For four of six diseases, the gains in model predictive power from MCA embeddings surpassed those attributable to PGS. Genetic and non-genetic risks combined additively, with little evidence of interaction effects (ΔROC-AUC ≤ 0.001) and highly stable variant effect sizes when embeddings were included in genetic association models (r > 0.98). In summary, we introduce a scalable, interpretable framework that summarizes survey-based environmental, behavioral, and social factors without prior assumptions about disease-specific variables. Our results demonstrate that these non-genetic contexts improve prediction but show limited evidence of interaction with genome-wide polygenic disease risk. Our results underscore the importance of incorporating social, behavioral, and environmental factors into clinical models of disease risk.
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