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已发表论文
老年共病患者及其亚型的分类：基于数据的聚类分析
Authors Qiao X , Chen X , Wang W , Guo L, Pan Q
Received 10 July 2025
Accepted for publication 30 September 2025
Published 3 October 2025 Volume 2025:20 Pages 1671—1680
DOI https://doi.org/10.2147/CIA.S549148
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Zhi-Ying Wu
Xiuqi Qiao,1,2 Xinda Chen,3 Weihao Wang,1 Lixin Guo,1,2 Qi Pan1,2
1Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, People’s Republic of China; 2Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, People’s Republic of China; 3Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Beijing, People’s Republic of China
Correspondence: Lixin Guo, Email glx1218@163.com Qi Pan, Email panqi621@126.com
Background: To explore the precise classification of elderly patients with multimorbidity and identify subgroups with an increased prevalence of related diseases.
Methods: A data-driven clustering analysis (K-means clustering) was conducted on individuals aged 60 years or older with comorbidities. The clustering was based on five essential and routinely measured variables: body mass index (BMI), intrinsic capacity (IC), low-density lipoprotein cholesterol (LDL-c), fasting plasma glucose (FPG), and systolic blood pressure (SBP). Logistic regression models were used to compare the prevalence of diabetes, coronary heart disease, hypertension, osteoporosis, sarcopenia, and frailty among the clusters.
Results: A total of 350 elderly patients with a mean age of 78.74 ± 8.27 years were included. Four subtypes of elderly patients with multimorbidity were identified, with significant differences in disease prevalence observed among the groups. Specifically, cluster 1 included 70 participants who exhibited the highest levels of LDL-c and BMI, as well as relatively higher IC scores. Cluster 2 consisted of 117 participants, who had the highest IC scores among all clusters and similar BMI levels to cluster 1. Cluster 3 included 77 participants and was distinguished by the highest SBP levels. Cluster 4, comprising 86 participants, had the lowest IC and BMI levels. Compared with cluster 2, cluster 4 had significantly higher prevalence of hypertension and frailty. Cluster 3 and 4 had higher prevalence of coronary heart disease compared with cluster 1, and cluster 4 had the highest prevalence of osteoporosis and sarcopenia.
Conclusion: There is significant pathophysiological heterogeneity among individuals with elderly multimorbidity. This classification method provides a crucial foundation for understanding disease complexity in this population. Future research, including intervention studies based on these classifications, is needed to evaluate their potential clinical utility.
Keywords: aged, multimorbidity, clustering analysis, intrinsic capacity
1Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, People’s Republic of China; 2Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, People’s Republic of China; 3Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Beijing, People’s Republic of China
Correspondence: Lixin Guo, Email glx1218@163.com Qi Pan, Email panqi621@126.com
Background: To explore the precise classification of elderly patients with multimorbidity and identify subgroups with an increased prevalence of related diseases.
Methods: A data-driven clustering analysis (K-means clustering) was conducted on individuals aged 60 years or older with comorbidities. The clustering was based on five essential and routinely measured variables: body mass index (BMI), intrinsic capacity (IC), low-density lipoprotein cholesterol (LDL-c), fasting plasma glucose (FPG), and systolic blood pressure (SBP). Logistic regression models were used to compare the prevalence of diabetes, coronary heart disease, hypertension, osteoporosis, sarcopenia, and frailty among the clusters.
Results: A total of 350 elderly patients with a mean age of 78.74 ± 8.27 years were included. Four subtypes of elderly patients with multimorbidity were identified, with significant differences in disease prevalence observed among the groups. Specifically, cluster 1 included 70 participants who exhibited the highest levels of LDL-c and BMI, as well as relatively higher IC scores. Cluster 2 consisted of 117 participants, who had the highest IC scores among all clusters and similar BMI levels to cluster 1. Cluster 3 included 77 participants and was distinguished by the highest SBP levels. Cluster 4, comprising 86 participants, had the lowest IC and BMI levels. Compared with cluster 2, cluster 4 had significantly higher prevalence of hypertension and frailty. Cluster 3 and 4 had higher prevalence of coronary heart disease compared with cluster 1, and cluster 4 had the highest prevalence of osteoporosis and sarcopenia.
Conclusion: There is significant pathophysiological heterogeneity among individuals with elderly multimorbidity. This classification method provides a crucial foundation for understanding disease complexity in this population. Future research, including intervention studies based on these classifications, is needed to evaluate their potential clinical utility.
Keywords: aged, multimorbidity, clustering analysis, intrinsic capacity