Genetic risk, as well as the physical environment and psychosocial factors, affect susceptibility to common, complex diseases. Although portions of patients' social and exposure histories are captured during routine healthcare, these data are rarely collected in a standardized, quantitative format that supports cross-disease risk stratification. We analyzed data from the Personalized Environment and Genes Study (PEGS) for 2,344 participants with genome sequencing who completed three exposome surveys. We derived polyexposure (PXS) and polysocial (PSS) scores from 489 variables and compared disease-classification performance of these scores, polygenic scores (PGS), and classification based on age/sex across nine self‑reported, ever-diagnosed diseases. For cross-sectional classification of disease risk, PXS had greater improvement than PGS or PSS compared to models with common clinical factors and demographic variables such as sex and age. The combined PXS+PSS+PGS model had the best performance overall (mean ΔAUC 0.10; maximum ΔAUC 0.27 for asthma). Importantly, the score types identified different individuals in high-score groups, and there was minimal overlap among these groups (PGS-PXS 0.94%; PGS-PSS 1.0%; triple overlap 0.18%, averaged across diseases). Further, PGS-PXS and PGS-PSS correlations were weak (mean r=0.04 and 0.03). This independence may enable identification of different individuals at risk of disease compared to existing clinical assessments and could help tailor interventions and care. As a next step in determining clinical feasibility, we propose a 66‑question minimum exposure question set, with 61 questions based on PXS and five on PSS, as a pragmatic framework for clinical collection of standardized data for multiple diseases.