Charting the proteome landscape in major psychiatric disorders: From biomarkers to biological pathways towards drug discovery.
Level 3 - non-randomized controlled study
Systematic review of non-randomized, observational case-control biomarker studies
PubMed 35763977 · doi:10.1016/j.euroneuro.2022.06.001
What was done
The authors conducted a systematic review of published literature on peripheral blood proteomics in schizophrenia, bipolar disorder, and major depressive disorder. Differentially expressed proteins were extracted and analyzed using pathway and network analyses to identify shared and disorder-specific biological pathways.
What was found
Across 51 studies involving 9,423 participants, 486 differentially expressed proteins were identified. Most biological pathways were common to all three disorders, predominantly involving immune system processes (interleukin, Toll-like receptor, and complement signaling) and signal transduction pathways (MAPK1/MAPK3, PI3K-Akt, Focal Adhesion-PI3K-Akt-mTOR, GPCR, and JAK-STAT). Other shared pathways included advanced glycosylation end-product receptor signaling, insulin-like growth factor regulation, cholesterol metabolism, and IL-17 signaling. Schizophrenia and bipolar disorder specifically shared integrin interactions, GRB2:SOS-MAPK linkage, and syndecan interactions, while bipolar disorder and major depressive disorder shared NRF2 and EGFR signaling pathways. No numerical effect sizes, fold-changes, or diagnostic accuracy metrics were reported in the abstract.
Why it matters
This review demonstrates that peripheral proteomic alterations across major psychiatric disorders heavily overlap in immune and intracellular signaling cascades, highlighting shared systemic biology rather than disease-specific blood biomarkers.
Limits
Peripheral blood markers may not reflect central nervous system pathology. The abstract lacks quantitative effect sizes, measures of statistical heterogeneity, and controls for crucial confounders such as medication exposure, disease stage, and assay platform variability.
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