Chen Ye-Xin, Lin Yuan-Yuan, Yang Qian-Wen, Ye Bei-Bei, Gao Zi-Heng, Hu Dong-Sen, Liu Jiang-Teng, Yang Dong-Chen, Dong Yi-Yu, Cui Gai-Wen, Huang Wei-Jun, Zhang Yao-Fu, Zhao Jin-Xi
Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China; Beijing University of Chinese Medicine, Beijing, China.
Beijing University of Chinese Medicine, Beijing, China.
J Affect Disord. 2025 Dec 1;390:119794. doi: 10.1016/j.jad.2025.119794. Epub 2025 Jul 6.
Metabolic diseases and psychiatric disorders comorbidity are increasingly becoming a hot topic of current research. This study aimed to explore the multidimensional associations and heterogeneity characteristics of metabolic abnormality combinations and depressive symptoms in overweight and obese populations.
Based on data from two multinational cohort studies, the Health and Retirement Study (HRS) in the United States and the English Longitudinal Study of Ageing (ELSA) in the United Kingdom, a total of 11,288 participants with a BMI ≥ 25 kg/m were included. Symptom network models were used to analyze the direct associations between metabolic indicators and depressive symptoms. Linear regression was employed to quantify the dose-response effect of different metabolic abnormality combinations on depression scores. Interaction analysis was extracted to examine the interactions between diabetes, hypertension, and central obesity on depression scores. Logistic regression was implemented to analyze the relationships between specific depressive symptoms and metabolic abnormality combinations. Latent class analysis (LCA) was conducted to identify subtypes of overweight/obese individuals with depressive symptoms and their corresponding metabolic abnormalities.
Symptom network analysis revealed that 'felt depressed' was the core symptom node. Linear regression found the group associated with central obesity increased Center for Epidemiologic Studies Depression Scale-8 items (CES-D8) score, and the combination of 'diabetes+hypertension+central obesity' had the most significant impact on somatic symptoms. The interaction analysis found no significant multiplicative or additive interaction effects of the three metabolic diseases on depression scores. On specific depressive symptoms, 'Everything an effort' was significantly associated with multiple metabolic abnormality combinations. LCA identified five subtypes of depression, among which the 'diabetes+hypertension' combination was significantly related to the Action-Dysregulation Group, while the Anhedonia Group was primarily driven by central obesity.
The conclusion suggests that metabolic abnormalities affect depressive symptom dimensions through specific pathways, highlighting the need for targeted psychological intervention strategies based on metabolic characteristics.
代谢性疾病与精神障碍的共病日益成为当前研究的热点话题。本研究旨在探讨超重和肥胖人群中代谢异常组合与抑郁症状的多维关联及异质性特征。
基于两项跨国队列研究的数据，即美国的健康与退休研究（HRS）和英国的英国老龄化纵向研究（ELSA），纳入了11288名体重指数（BMI）≥25kg/m的参与者。采用症状网络模型分析代谢指标与抑郁症状之间的直接关联。运用线性回归量化不同代谢异常组合对抑郁评分的剂量反应效应。提取交互作用分析以检验糖尿病、高血压和中心性肥胖对抑郁评分的交互作用。实施逻辑回归分析特定抑郁症状与代谢异常组合之间的关系。进行潜在类别分析（LCA）以识别有抑郁症状的超重/肥胖个体的亚型及其相应的代谢异常。
症状网络分析显示，“感到沮丧”是核心症状节点。线性回归发现，与中心性肥胖相关的组增加了流行病学研究中心抑郁量表8项（CES-D8）评分，“糖尿病+高血压+中心性肥胖”组合对躯体症状的影响最为显著。交互作用分析发现，这三种代谢疾病对抑郁评分没有显著的相乘或相加交互作用。在特定抑郁症状方面，“事事费力”与多种代谢异常组合显著相关。LCA识别出五种抑郁亚型，其中“糖尿病+高血压”组合与行动失调组显著相关，而快感缺失组主要由中心性肥胖驱动。
该结论表明，代谢异常通过特定途径影响抑郁症状维度，突出了基于代谢特征制定针对性心理干预策略的必要性。
