Blood-based proteomics for schizophrenia: candidate biomarkers for risk assessment and precision psychiatry

Schizophrenia (SCZ) is a severe mental disorder where diagnosis remains subjective, and treatment efficacy is limited by high heterogeneity, particularly for negative and cognitive symptoms. There is an urgent need for objective molecular biomarkers, especially for early diagnosis and for identifying individuals at clinical high risk (CHR) for psychosis. This review analyzes blood proteomic studies in SCZ, CHR states, and related disorders (2019-2025), categorizing them into mass-spectrometry (MS)-based approaches (panoramic and targeted) and affinity-based methods (Olink, SOMAscan). It synthesizes findings from global discovery projects, targeted validation studies, critical risk prediction models, and large-scale Mendelian randomization analyses using UK Biobank data. The field is transitioning from cataloging differentially expressed proteins to identifying causal biomarkers with therapeutic potential (druggability). We propose a consolidated panel of high-priority protein markers, including the ITIH family, CRP, DNER, CTSS, and complement proteins. A critical gap remains in the incomplete coverage of these markers in commercial assays for targeted MS. The future lies in integrating validated proteomic panels with genetic data to enable personalized therapeutic strategies. However, significant hurdles related to reproducibility, cohort heterogeneity, and the lack of standardized validation protocols must be overcome before these biomarkers can be translated into routine clinical practice. Ongoing debates include the choice between MS-based and affinity-based platforms for clinical translation, the interpretation of complement and inflammatory markers as either core pathophysiological drivers or non-specific epiphenomena, and the predictive value of proteomic signatures for psychosis transition. Recent large-scale studies provide a crucial methodological framework, demonstrating the power of rigorous validation and the importance of harmonized, multi-cohort analyses for advancing the field toward clinical implementation.