Chen Chen, Wu Hongfeng, Yang Likun, Kan Ke, Zhang Xinping, Zhang Su, Jia Rufu, Li Xian
School of Nursing, Chengde Medical University, Chengde, China.
Department of Medical Development, Hebei General Hospital, Shijiazhuang, China.
Front Public Health. 2025 Apr 9;13:1551034. doi: 10.3389/fpubh.2025.1551034. eCollection 2025.
Given the rising prevalence of chronic diseases and multimorbidity among middle-aged and older individuals in China, it is crucial to explore the patterns of chronic disease multimorbidity and uncover the underlying mechanisms driving the co-existence of multiple chronic conditions.
This study analyzed data from 19,206 participants in the China Health and Retirement Longitudinal Study (CHARLS 2018). The IsingFit model was used to build the chronic disease co-morbidity network, where nodes represented diseases and edges reflected conditionally independent partial correlations. Community detection identified groups of closely related diseases using the Louvain algorithm. Multivariable linear regression with forward stepwise selection explored factors influencing chronic disease co-morbidity. A random forest model ranked these factors by importance, providing insights into relationships and key contributors.
This study identified the most frequent multimorbidity pairs in the middle-aged and older adult population as hypertension with arthritis, and digestive diseases with arthritis. Multimorbidities were classified into four subgroups: respiratory diseases, metabolic syndrome, neurological diseases, and digestive diseases. Heart disease showed centrality in the multimorbidity network, while memory-related diseases played a bridging role. Key factors associated with multimorbidity included age, gender, pain, sleep, physical activity, depression, and education. Random forest analysis revealed that age and pain had the greatest impact on multimorbidity development, offering insights for targeted prevention and management strategies.
This study systematically analyzed multimorbidity patterns and their influencing factors in the Chinese middle-aged and older adult population. The data were examined at three levels: overall network, key influencing factors, and individual characteristics. Cardio-metabolic diseases were identified as a core component of the multimorbidity network. Advanced age, pain, and depression were found to be independent risk factors affecting the number of multimorbidities, while healthy behaviors acted as significant protective factors. The study enhances understanding of multimorbidity mechanisms and provides a scientific basis for public health interventions, emphasizing the importance of behavioral modification, health education, and social support for high-risk groups.
鉴于中国中老年人群中慢性病和多重疾病的患病率不断上升，探索慢性病多重疾病模式并揭示驱动多种慢性病共存的潜在机制至关重要。
本研究分析了中国健康与养老追踪调查（CHARLS 2018）中19206名参与者的数据。使用伊辛拟合模型构建慢性病共病网络，其中节点代表疾病，边反映条件独立的偏相关。社区检测使用Louvain算法识别密切相关疾病的组。采用向前逐步选择的多变量线性回归探索影响慢性病共病的因素。随机森林模型按重要性对这些因素进行排序，深入了解关系和关键因素。
本研究确定中老年人群中最常见的多重疾病组合为高血压与关节炎，以及消化系统疾病与关节炎。多重疾病被分为四个亚组：呼吸系统疾病、代谢综合征、神经系统疾病和消化系统疾病。心脏病在多重疾病网络中具有中心性，而与记忆相关的疾病起桥梁作用。与多重疾病相关的关键因素包括年龄、性别、疼痛、睡眠、身体活动、抑郁和教育程度。随机森林分析显示年龄和疼痛对多重疾病发展的影响最大，为有针对性的预防和管理策略提供了见解。
本研究系统分析了中国中老年人群的多重疾病模式及其影响因素。数据在三个层面进行了考察：整体网络、关键影响因素和个体特征。心血管代谢疾病被确定为多重疾病网络的核心组成部分。高龄、疼痛和抑郁被发现是影响多重疾病数量的独立危险因素，而健康行为则是重要的保护因素。该研究增进了对多重疾病机制的理解，为公共卫生干预提供了科学依据，强调了行为改变、健康教育和对高危人群的社会支持的重要性。
