Building machine learning prediction models for well-being using predictors from the exposome and genome in a population cohort.

Effective personalized well-being interventions require the ability to predict who will thrive or not, and the understanding of underlying mechanisms. Here, using longitudinal data of a large population cohort (the Netherlands Twin Register, collected 1991-2022), we aim to build machine learning prediction models for adult well-being from the exposome and genome, and identify the most predictive factors ( between 702 and 5874). The specific exposome was captured by parent and self-reports of psychosocial factors from childhood to adulthood, the genome was described by polygenic scores, and the general exposome was captured by linkage of participants' postal codes to objective, registry-based exposures. Not the genome (  = -0.007 [-0.026-0.010]), but the general exposome (  = 0.047 [0.015-0.076]) and especially the specific exposome (  = 0.702 [0.637-0.753]) were predictive of well-being in an independent test set. Adding the genome ( = 0.334) and general exposome ( = 0.695) independently or jointly ( = 0.029) beyond the specific exposome did not improve prediction. Risk/protective factors such as optimism, personality, social support and neighborhood housing characteristics were most predictive. Our findings highlight the importance of longitudinal monitoring and promises of different data modalities for well-being prediction.