ArticleClinical interventions in aging2025
Classification of Elderly Patients with Comorbidities and Their Subtypes: A Data-Driven Cluster Analysis.
Article in Clinical interventions in aging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Article
- Chronic kidney disease related mortality trends with and without valvular heart disease among adults in the United States: A retrospective analysis.BMC cardiovascular disorders · 2026Article
- Comprehensive evaluation of triglyceride glucose index-a body shape index (TyG-ABSI) for incident peripheral artery disease: data-driven phenotyping and machine learning-based risk prediction in the UK Biobank.Cardiovascular diabetology · 2026Article
- Latent profile analysis and analytical model construction based on heterogeneity of intrinsic capacity in Chinese older adults.Frontiers in aging neuroscience · 2026Article
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5 authors.
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Abstract
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.
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