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Classification of Elderly Patients with Comorbidities and Their Subtypes: A Data-Driven Cluster Analysis
Xiuqi Qiao1,2, Xinda Chen3, Weihao Wang1
1Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, People's Republic of China.
Clinical Interventions in Aging
|October 9, 2025
Summary
This study classified elderly patients with multiple chronic conditions into four distinct subtypes using key health metrics. These classifications reveal significant differences in disease prevalence, aiding in understanding complex health profiles in older adults.
Area of Science:
- Gerontology
- Internal Medicine
- Data Science
Background:
- Multimorbidity is common in elderly patients.
- Understanding disease heterogeneity is crucial for effective management.
Purpose of the Study:
- To classify elderly patients with multimorbidity into distinct subgroups.
- To identify subgroups with higher prevalence of specific related diseases.
Main Methods:
- K-means clustering was applied to individuals aged 60+ with comorbidities.
- Clustering variables included body mass index (BMI), intrinsic capacity (IC), low-density lipoprotein cholesterol (LDL-c), fasting plasma glucose (FPG), and systolic blood pressure (SBP).
- Logistic regression compared disease prevalence across identified clusters.
Main Results:
- Four distinct subtypes of elderly patients with multimorbidity were identified from 350 participants.
- Subtypes differed in BMI, IC, LDL-c, FPG, and SBP.
- Cluster 4 showed higher prevalence of hypertension, frailty, osteoporosis, and sarcopenia; Clusters 3 and 4 had higher coronary heart disease prevalence.
Conclusions:
- Significant pathophysiological heterogeneity exists among elderly patients with multimorbidity.
- This data-driven classification provides a foundation for understanding disease complexity.
- Further research is needed to evaluate the clinical utility of these classifications.
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