Volatilomics in psychiatric biomarker research: from odor signatures to multiomic integration

Schizophrenia remains a major challenge in psychiatry, with diagnosis still relying largely on symptom-based assessments and lacking validated biomarkers for early detection, screening, or longitudinal monitoring. Although genomic, transcriptomic, proteomic, and metabolomic studies have revealed systemic biological dysregulation involving immune, metabolic, and neurodevelopmental pathways, conventional multiomic approaches face significant translational limitations. They often require blood or tissue sampling and can be costly; although they include both dynamic, state-sensitive and more stable, trait-like signals, their complexity and sampling burden can limit frequent or population-level use. Volatilomics, the study of volatile organic compounds (VOCs) emitted through breath, sweat, and skin, has emerged as a complementary molecular approach that may help address these gaps. VOCs represent dynamic downstream metabolic outputs and may capture rapid, state-dependent biochemical changes. Analytical and machine-learning studies have reported VOC pattern alterations in schizophrenia with promising classification accuracy in small, early-stage discovery cohorts, although independent external validation remains limited. This review examines volatilomics as a promising complementary approach with potential applications in early detection and longitudinal monitoring, and considers how integration with systemic multiomics could contribute to diagnosis, patient stratification, and precision psychiatry.