Table 1 Exposomic research related to mental health across the lifespan.

Childhood

Choi et al. [21]

Internalizing and externalizing symptoms

133 variables at the family, peer, school, neighborhood, life event, and broader environmental levels

Polyenvironmental and polygenic risk models were used to study Gene X Environment effects in the ABCD Study

Integrating genomic and exposomic data substantially improves prediction of youth mental health symptoms, with environmental influences explaining more variance than genetics alone.

Childhood

Moore et al. [22]

Overall psychopathology quantified by P-factor.

348 exposures spanning perinatal, familial, social, and physical environment

Bifactor measurement model in ABCD Study and generalization in independent data from the Philadelphia Neurodevelopmental Cohort.

A general exposome factor, summarizing broad environmental influences, can be reliably identified in adolescents and strongly explains health outcomes in two independent cohorts, demonstrating robust generalization despite differences in the specific exposomic variables measured.

Adolescence

Visoki et al. [23]

Suicide attempts

Between 36–2864 exposures in three different cohorts.

ExWAS in 3 independent youth cohorts

A weighted exposome score reliably identified youth at high risk for suicide attempts across three diverse cohorts, outperforming demographic and family history factors and generalizing across different sample types and exposure measures.

Adolescence

Paglaccio et al. [24]

Internalizing symptoms

52 digital and social media exposure variables.

ExWAS conducted on digital exposures captured at age 12 assessment of the ABCD Study.

A digital exposome risk score, aggregating diverse social media exposures such as usage patterns, cyberbullying, secret accounts, and problematic or addictive behaviors, was strongly associated with poorer mental health and suicide attempts in U.S. adolescents, explaining risk beyond traditional demographic factors, non-social screen time, and other environmental adversities.

Young Adulthood

Wang et al. [25]

Depressive symptoms

385 environmental exposures grouped them into 12 domains

ExWAS and twin models to tease G from E in late adolescence and early adulthood cohort

Diverse environmental exposures are linked to depressive symptoms, but these associations are strongly shaped by both genetic and environmental influences, highlighting the complex interplay between genes and environment in youth depression.

Adulthood

Lin et al. [26]

Psychotic experiences

247 environmental, lifestyle, behavioral, and economic variables

ExWAS in UK Biobank followed by MR to assess causality

Numerous environmental and lifestyle factors associated with psychotic experiences; Mendelian randomization showed both potential causal and consequence relationships, highlighting complex genetic and bidirectional links between environment and psychosis risk.

Adulthood

Arias Magnasco et al. [27]

Seven psychiatric diagnostic domains

294 environmental, lifestyle, behavioral, and economic variables were included

ExWAS in UK Biobank to identify shared and specific exposures to psychiatric disorders

Childhood adversities and traumatic experiences are the strongest and most consistent environmental risk factors across a wide range of mental health outcomes, while some exposures are uniquely linked to specific psychiatric symptoms, highlighting both shared and distinct environmental influences on mental health.

Older adulthood

Camacho et al. [28]

Dementia

128 exposome factors from six categories (trauma, sociodemographic, physical, lifestyle, environmental, and early life) were selected from the UK Biobank.

ML (XGBoost) prediction models in UK Biobank

Machine learning models using a wide range of low-cost exposome data, such as lifestyle and environmental factors, can accurately predict dementia risk. This approach outperformed traditional statistical methods and highlighted novel, easily accessible predictors, suggesting that broad exposomic screening could enable scalable, affordable dementia risk assessment.