抑郁症患者存在患其他疾病的风险增加的情况，然而这些患者的并发症聚集模式仍不清楚。本研究旨在识别潜在的并发症模式，并探索包括12种慢性疾病的抑郁症成年人的并发症网络结构。采用横断面研究，基于2017年行为风险因素监测系统（BRFSS）的二次数据，涵盖了美国所有50个州。使用探索性图形分析（EGA），一个包括算法的统计图形模型，用于在多变量网络关系系统中进行变量分组和打分。EGA结果显示网络呈现出3种潜在的并发症模式，即并发症被分为3个因素。第一组由7种并发症组成（肥胖、癌症、高血压、高血脂、关节炎、肾脏疾病和糖尿病）。潜在的并发症的第二个模式包括哮喘和呼吸道疾病的诊断。最后一组将3种情况（心脏病发作、冠心病和中风）归为一组。高血压报告了更高的网络中心度测量。报道了慢性病之间的关联；此外，它们被分为3个潜在的并发症维度，并报道了网络要素负载。建议针对表现出抑郁症状和多疾病的患者实施保健和治疗指南和协议。版权所有©2023 Cristian Ramos-Vera等。

People with depression are at increased risk for comorbidities; however, the clustering of comorbidity patterns in these patients is still unclear.The aim of the study was to identify latent comorbidity patterns and explore the comorbidity network structure that included 12 chronic conditions in adults diagnosed with depressive disorder.A cross-sectional study was conducted based on secondary data from the 2017 behavioral risk factor surveillance system (BRFSS) covering all 50 American states. A sample of 89,209 U.S. participants, 29,079 men and 60,063 women aged 18 years or older, was considered using exploratory graphical analysis (EGA), a statistical graphical model that includes algorithms for grouping and factoring variables in a multivariate system of network relationships.The EGA findings show that the network presents 3 latent comorbidity patterns, i.e., that comorbidities are grouped into 3 factors. The first group was composed of 7 comorbidities (obesity, cancer, high blood pressure, high blood cholesterol, arthritis, kidney disease, and diabetes). The second pattern of latent comorbidity included the diagnosis of asthma and respiratory diseases. The last factor grouped 3 conditions (heart attack, coronary heart disease, and stroke). Hypertension reported higher measures of network centrality.Associations between chronic conditions were reported; furthermore, they were grouped into 3 latent dimensions of comorbidity and reported network factor loadings. The implementation of care and treatment guidelines and protocols for patients with depressive symptomatology and multimorbidity is suggested.Copyright © 2023 Cristian Ramos-Vera et al.