| Title: | Cerebrospinal Fluid Genetics Enhance Risk Stratification in Bipolar Disorder |
| Journal: | MedComm |
| Published: | 26 Feb 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41768366/ |
| DOI: | https://doi.org/10.1002/mco2.70629 |
| URL: | http://dx.doi.org/10.1002/mco2.70629 |
Publication 17615
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Abstract
Bipolar disorder (BD) research confronts challenges: blood-based biomarkers offer limited insights into neurobiology, while cerebrospinal fluid (CSF) collection is clinically unusual. Linking genetic susceptibility to pathophysiology remains crucial for biologically informed risk stratification. We integrated cohort data and genome-wide association study (GWAS) summary statistics: the largest BD meta-analysis, CSF multi-omics profiles including 3107 proteomic and 2602 metabolomic participants, and a validation cohort of 247,834 UK Biobank participants. Unsupervised clustering revealed four single-nucleotide variant (SNV) clusters: metabolic-imbalance, metabolic-active, human leukocyte antigen (HLA)+immune, and HLA-immune. These clusters exhibited distinct clinical features, with the metabolic-imbalance cluster showing multi-directional associations with 21 psychiatric traits, while the HLA-immune cluster was associated with emotional instability in BD patients (odds ratio [OR] = 1.14, p = 0.027). The optimized multimodal cluster-specific polygenic risk scores (PRS) model significantly outperformed clinical-only prediction factors (C-index = 0.77), with the metabolic-imbalance PRS contributing a 22.6% incremental predictive value (hazard ratio [HR] = 1.23, 95% CI: 1.04-1.45, p = 0.016). Risk reclassification showed an 84% reduction in false-negative rates in the low-risk subgroup, identifying a high-risk layer with a 17.6-fold increased BD incidence. Altogether, genetically informed substitutes for CSF biomarkers emerged as a scalable tool for risk prediction, overcoming the barriers of CSF collection while capturing neurobiological heterogeneity.</p>
7 Authors
- Yu Feng
- Xiaonan Guo
- Peng Huang
- Xiaolong Ji
- Ningning Jia
- Sheng Yang
- Shaohua Hu
1 Application
| Application ID | Title |
|---|---|
| 144904 | Integrative Deep Learning Analysis of UKBB Data: Bridging Genotypes, Medical Imaging, and Polygenic Scores for Precision Health Insights |
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