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

Psychology

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

D. H. M. Pelt, P. C. Habets, et al.

Discover groundbreaking insights from researchers Dirk H M Pelt, Philippe C Habets, and their team, who utilized longitudinal data from the Netherlands Twin Register to unveil how exposome factors like optimism and social support predict adult well-being. While genetic data fell short, understanding the psychosocial landscape proves essential in this compelling study.

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Playback language: English
Abstract
This study uses longitudinal data from the Netherlands Twin Register (1991–2022) to build machine learning prediction models for adult well-being using exposome and genome data. The specific exposome (psychosocial factors) proved highly predictive (R² = 0.702), while the general exposome (objective environmental exposures) showed modest predictive power (R² = 0.047). Genetic data (polygenic scores) were not predictive. Optimism, personality, social support, and neighborhood housing characteristics were the most predictive factors.
Publisher
Nature Mental Health
Published On
Aug 14, 2024
Authors
Dirk H M Pelt, Philippe C Habets, Christiaan H Vinkers, Lannie Ligthart, Catharina E M van Beijsterveldt, René Pool, Meike Bartels
Tags
well-being
exposome
psychosocial factors
genetic data
machine learning

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