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Metabolic-inflammatory biomarkers and risk of severe and common mental disorders: a large prospective cohort study
Yixin Ouyang1, Xinru Guo1, Songyu Wu1
1Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, 1163 Xinmin Street, Changchun, Jilin Province, 130021, China.
Background:
Inflammation and metabolic dysregulation are closely linked to mental disorders. This study systematically compared three metabolic-inflammatory indices-remnant cholesterol inflammatory index (RCII), neutrophil-to-high-density lipoprotein cholesterol ratio (NHR), and C-reactive protein-triglyceride-glucose index (CTI)-with the risk of severe and common mental disorders.
Methods:
This prospective cohort study included 230,147 participants from the UK Biobank. Kaplan-Meier curves and log-rank tests were used to compare cumulative incidence across quartiles. Cox regression estimated associations between metabolic-inflammatory indices and incident mental disorders. The area under the curve (AUC) and Net Reclassification Index (NRI) were used to evaluate incremental predictive value.
Results:
Over a median follow-up of 14.27 years, 15,485 participants developed mental disorders. All indices were associated with increased risks of depression, anxiety, and overall mental disorders. Schizophrenia risk was elevated only in the highest quartile. Bipolar disorder was associated only with NHR (HR = 1.111, 95% CI: 1.004-1.230, P = 0.041). NHR showed superior predictive ability for depression (AUC: 0.622 to 0.627, P = 0.007) and overall mental disorders (AUC: 0.614 to 0.618, P = 0.004). RCII and CTI showed comparable predictive performance for anxiety and depression, with all indices significantly improving risk reclassification (NRI > 0, P < 0.05).
Conclusions:
All indices exhibited threshold effects for schizophrenia. NHR showed unique predictive value for bipolar disorder and superior predictive ability for depression and overall mental disorders. These findings suggest that distinct metabolic-inflammatory indices may serve as complementary tools for predicting different mental disorders.