Xue Xinyu, Wang Ziyi, Qi Yana, Chen Ningsu, Zhao Kai, Zhao Mengnan, Shi Lei, Yu Jiajie
Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu, China.
Department of Hematology, West China Hospital, Sichuan University, Chengdu, China.
J Glob Health. 2025 Feb 21;15:04051. doi: 10.7189/jogh.15.04051.
This study aims to develop specific multimorbidity relationships among the elderly and to explore the association of multidimensional factors with these relationships, thereby facilitating the formulation of personalised strategies for multimorbidity management.
Cluster analysis identified chronic conditions that tend to cluster together, and then association rule mining was used to investigate relationships within these identified clusters more closely. Stepwise logistic regression analysis was conducted to explore the relationship between influencing factors and different health statuses in older adults. The results of this study were presented by network graph visualisation.
A total of 15 045 individuals were included in this study. The average age was 73.0 ± 6.8 years. The number of patients with multimorbidity was 7426 (49.4%). The most common binary disease combination was hypertension and depression. The four major multimorbidity clusters identified were the tumour-digestive disease cluster, the metabolic-circulatory disease cluster, the metal-psychological disease cluster, and the age-related degenerative disease cluster. Cluster analysis by sex and region revealed similar numbers and types of conditions in each cluster, with some variations. Gender and number of medications had a consistent effect across all disease clusters, while aging, body mass index (BMI), waist-to-hip ratio (WHR), cognitive impairment, plant-based foods, animal-based foods, highly processed foods and marital status had varying effects across different disease clusters.
Multimorbidity is highly prevalent in the older population. The impact of lifestyle varies between different clusters of multimorbidity, and there is a need to implement different strategies according to different clusters of multimorbidity rather than an integrated approach to multimorbidity management.
本研究旨在揭示老年人中特定的共病关系，并探索多维因素与这些关系之间的关联，从而为共病管理制定个性化策略提供便利。
聚类分析确定了倾向于聚集在一起的慢性病，然后使用关联规则挖掘更深入地研究这些已确定聚类中的关系。进行逐步逻辑回归分析以探索影响因素与老年人不同健康状况之间的关系。本研究结果通过网络图可视化呈现。
本研究共纳入15045名个体。平均年龄为73.0±6.8岁。患有多种疾病的患者有7426人（49.4%）。最常见的二元疾病组合是高血压和抑郁症。确定的四个主要共病聚类为肿瘤 - 消化系统疾病聚类、代谢 - 循环系统疾病聚类、精神 - 心理疾病聚类和与年龄相关的退行性疾病聚类。按性别和地区进行的聚类分析显示，每个聚类中的疾病数量和类型相似，但存在一些差异。性别和用药数量在所有疾病聚类中具有一致的影响，而年龄、体重指数（BMI）、腰臀比（WHR）、认知障碍、植物性食物、动物性食物、高加工食品和婚姻状况在不同疾病聚类中的影响各不相同。
共病在老年人群中非常普遍。生活方式的影响在不同的共病聚类之间有所不同，因此需要根据不同的共病聚类实施不同的策略，而不是采用综合的共病管理方法。
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