The exposome in schizophrenia: A systematic review of environmental risk indices

Schizophrenia is a complex psychiatric disorder resulting from the dynamic interaction between genetic vulnerability and multiple environmental exposures acting across the lifespan. The exposome framework has emerged as a comprehensive approach to capture cumulative and interdependent environmental risk, moving beyond traditional single-factor models.

A systematic review was conducted in accordance with PRISMA guidelines to synthesize current evidence on the development, validation, and application of exposome-based indices in schizophrenia and psychotic spectrum disorders. The search was performed in PubMed, ScienceDirect and SciELO for studies published between 2005 and 2026. Eligible studies included original research and reviews integrating multiple environmental exposures into cumulative or latent risk models. Methodological quality and risk of bias were independently assessed using the ROBINS-E tool. The protocol was prospectively registered in PROSPERO (CRD420261286786).

Thirty-three studies were included, identifying five primary composite indices such as the Exposome Score for Schizophrenia (ES-SCZ), Maudsley Environmental Risk Score (ERS), Early Adversity Scale for Schizophrenia (EAS-Sz), Polyenviromic Risk Score (PERS), and Korea-Polyenvironmental Risk Score (K-PERS-I). Across studies, higher cumulative environmental burden was consistently associated with increased psychosis risk, greater symptom severity, cognitive impairment, and poorer functional outcomes. Models using empirical weighting or context-specific calibration generally showed better performance than simple additive approaches.

Cumulative environmental burden can be operationalized through structured exposome-based indices, several of which demonstrate moderate discriminatory capacity for schizophrenia and psychosis-related outcomes. However, substantial methodological heterogeneity and limited external validation currently restrict clinical applicability, underscoring the need for harmonization and rigorous cross-cohort validation to advance translational utility.