ArticleFrontiers in psychiatry2026
Integrating clinical proxies and metabolic data identifies and distinguishes high-risk depression subtypes in a real-world first-hospitalization cohort.
Article in Frontiers in psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Early identification of high-risk depression subtypes, specifically, recurrent (RD) and treatment-resistant (TRD) depression, is critical for improving long-term outcomes, yet practical stratification tools based on routinely available clinical and metabolic data remain limited. This study aimed to characterize these subtypes within a real-world, first-hospitalization cohort by integrating clinical proxy indicators with metabolic biomarkers. Methods: In a cross-sectional analysis of 1,436 first-hospitalized patients with first-episode depression (FED) and RD, we compared demographic, clinical, and metabolic characteristics. TRD was operationally defined by electroconvulsive therapy (ECT) exposure. Multivariable logistic regression identified factors associated with RD (vs. FED) and TRD (within RD). Results: Compared to FED patients, RD patients were older (47.1 vs. 42.4 years, p<0.001), had longer hospital stays, and exhibited a worse metabolic profile, including higher triglycerides (1.53 vs. 1.39 mmol/L, Conclusion: In first-hospitalized patients, RD is associated with adverse metabolic markers, while TRD is characterized by clinical indicators of failed adequate treatment and high acute risk. A prolonged illness duration in first-episode patients may signal significant treatment delay. An integrated assessment of these accessible clinical and metabolic proxies could facilitate early risk stratification in routine care.
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