Comorbidity patterns and immune-metabolic differences in patients with acute-episode of schizophrenia spectrum disorders.

IF 4.1 Q2 PSYCHIATRY
Guoping Wu, Zhe Dong, Zhongcai Li, Qiongxian Zhao, Song Chen, Qing Dong, Liqiong Huang, Yaru Zhang, Xuan Wang, Sai Chen, Hongbing Liu, Zanzong Sun, Shengmei Ban, Baopeng Tian, Yunlong Tan
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Abstract

Patients with schizophrenia (SCZ) face multiple health challenges due to the complication of chronic diseases and psychiatric disorders. Among these, cardiovascular comorbidities are the leading cause of their life expectancy being 15-20 years shorter than that of the general population. Identifying comorbidity patterns and uncovering differences in immune and metabolic function are crucial steps toward improving prevention and management strategies. A retrospective cross-sectional study was conducted using electronic medical records of inpatients discharged between 2015 and 2024 from a municipal psychiatric hospital in China. The study included patients diagnosed with Schizophrenia, Schizotypal, and Delusional Disorders (SSDs) (ICD-10: F20-F29). Comorbidity patterns were identified through latent class analysis (LCA) based on the 20 most common comorbid conditions among SSD patients. To investigate differences in peripheral blood metabolic and immune function, linear regression or generalized linear models were applied to 44 laboratory test indicators collected during the acute episode. The Benjamini-Hochberg method was used for p-value correction, and the false discovery rate (FDR) was calculated, with statistical significance set at FDR < 0.05. Among 3,697 inpatients with SSDs, four distinct comorbidity clusters were identified: SSDs only (Class 1), High-Risk Metabolic Multisystem Disorders (Class 2, n = 39), Low-Risk Metabolic Multisystem Disorders (Class 3, n = 573), and Sleep Disorders (Class 4, n = 205). Compared to Class 1, Class 2 exhibited significantly elevated levels of apolipoprotein A (ApoA; β = 90.62), apolipoprotein B (ApoB; β = 0.181), mean platelet volume (MPV; β = 0.994), red cell distribution width-coefficient of variation (RDW-CV; β = 1.182), antistreptolysin O (ASO; β = 276.80), and absolute lymphocyte count (ALC; β = 0.306), along with reduced apolipoprotein AI (ApoAI; β = -0.173) and hematocrit (HCT; β = -35.13). Class 3 showed moderate increases in low-density lipoprotein cholesterol (LDL-C; β = 0.113), MPV (β = 0.267), white blood cell count (WBC; β = 0.476), and absolute neutrophil count (ANC; β = 0.272), with decreased HCT (β = -9.81). Class 4 was characterized by elevated aggregate index of systemic inflammation (AISI; β = 81.07), neutrophil-to-lymphocyte ratio (NLR; β = 0.465), and systemic inflammation response index (SIRI; β = 0.346), indicating a heightened inflammatory state. The comorbidity patterns of patients with SCZ can be distinctly classified. During the acute episode, those with comorbid metabolic disorders exhibit a higher risk of cardiovascular diseases and immune system abnormalities, while patients with comorbid sleep disorders present a pronounced systemic inflammatory state and immune dysfunction. This study provides a basis for the chronic disease management and anti-inflammatory treatment, while also offering objective biomarker insights for transdiagnostic research.

精神分裂症谱系障碍急性发作患者的共病模式和免疫代谢差异。
精神分裂症患者由于慢性疾病和精神障碍的并发症而面临多重健康挑战。其中,心血管合并症是导致他们的预期寿命比一般人群短15-20年的主要原因。确定合并症模式并揭示免疫和代谢功能的差异是改善预防和管理策略的关键步骤。利用2015年至2024年中国一家市级精神病院出院住院患者的电子病历进行回顾性横断面研究。该研究包括被诊断为精神分裂症、分裂型和妄想障碍(ssd)的患者(ICD-10: F20-F29)。根据SSD患者中20种最常见的合并症,通过潜在分类分析(LCA)确定合并症模式。为了研究外周血代谢和免疫功能的差异,对急性发作期间收集的44项实验室检测指标应用线性回归或广义线性模型。采用Benjamini-Hochberg法进行p值校正,计算错误发现率(false discovery rate, FDR),设FDR为统计显著性
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