Revealing multiple biological subtypes of schizophrenia through a data-driven approach

Abstract

Introduction: The brain imaging subtypes of schizophrenia have been widely investigated using data-driven approaches. However, the heterogeneity of SZ in multiple biological data is largely unknown. Methods: A data-driven model was used to classify brain imaging, gut microbiota, and brain-gut fusion data obtained through a dot product fusion method, identifying significant subtypes and calculating their correlations with clinical symptoms and cognitive performance. Results: These subtypes remain relatively independent and demonstrate typical features and biomarkers, which are significantly associated with clinical symptoms and cognitive performance. Two brain subtypes with opposite structural and functional changes are identified: (1) a structural variant-dominant brain subtype with negative symptoms and cognitive deficits and (2) a functional alteration-dominant brain subtype with positive symptoms. The three gut subtypes include the following: (1) Collinsella-dominant; (2) Prevotella-dominant with positive symptoms; and (3) Streptococcus-dominant. Two brain-gut subtypes show different abnormalities in brain‒genus linkages: (1) strong connectivity of “brain function in the temporal and parietal lobes–Prevotella” with reduced attention scores and (2) strong connectivity of “brain structure and function in the frontal and parietal lobes–multiple genera” with positive symptoms. Notably, brain subtypes and brain-gut subtypes are most relevant to clinical symptoms, whereas gut subtypes reveal more cognitive biomarkers. Conclusion: These findings show the potential to identify multiple biological subtypes with distinct biomarkers, thereby suggesting the possibility of personalized and precise treatment for SZ patients.