Tainá Schons1,2, Giovana Mezzomo1,2, Pedro Henrique Rosa1,2, Gabriel Rocha2, Luisa Soares Pedroso1,2, Adriane Ribeiro Rosa1,2
Bipolar disorder (BD) is a complex psychiatric condition characterized by mood fluctuations ranging from mania to depression. Its clinical heterogeneity and symptom overlap with other psychiatric disorders pose significant challenges to early diagnosis and effective treatment. The transdiagnostic framework, particularly the Research Domain Criteria (RDoC) approach, proposes that shared biological mechanisms underpin different mental illnesses, shifting the focus from categorical diagnoses to dimensional constructs. In this context, metabolomics based on mass spectrometry (MS) represents a powerful platform to identify biochemical alterations and potential biomarkers across disorders. This study aimed to uncover metabolic signatures associated with BD by conducting a systematic in silico analysis of previously published MS-based metabolomic studies comparing BD patients and healthy controls. Comprehensive literature searches were performed in PubMed and Web of Science following predefined eligibility criteria aligned with PRISMA guidelines. Thirteen studies from six countries were selected, totaling data from 232 BD cases and 907 controls. From each study, significantly altered metabolites were extracted, and study quality was assessed using the QUADOMICS framework, which evaluates methodological rigor in omics research. Identified metabolites were integrated and analyzed through MetaboAnalyst to generate metabolite-gene-disease interaction networks, providing insight into biological pathways and disease associations. A total of 261 metabolites exhibited differential abundance in BD compared to controls. Network analysis revealed strong associations between BD-related metabolites and schizophrenia, suggesting converging biological pathways underlying psychotic and mood symptoms. Associations with type 2 diabetes (DM2) further highlighted the metabolic dysregulation observed in BD, reinforcing the bidirectional relationship between mental and physical health. These findings emphasize the potential for metabolic profiling to aid in developing diagnostic tools, promote early identification of at-risk individuals, and guide personalized therapeutic strategies. Addressing metabolic dysfunctions alongside psychiatric symptoms could represent a crucial step toward improving outcomes and life expectancy in individuals with BD. Future studies are warranted to validate these candidate biomarkers in larger, independent cohorts and to explore their predictive value for treatment response and disease progression.
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