Endocrine, Metabolic & Immune Disorders Drug Targets · Oct 21, 2026

Exploring Association Rules for Chronic Comorbidities in Possible Sarcopenic Obesity among Middle-aged and Older Adults: Evidence from the CHARLS 2015

By Huang W, Zhang B, Wang C et al.

Introduction: Sarcopenic obesity (SO) commonly coexists with multiple chronic conditions, contributing to poorer prognosis and a greater burden of disease management. However, the patterns of multimorbidity and inter-disease relationships in individuals with possible SO remain unclear. This study aimed to examine the prevalence and comorbidity patterns of chronic diseases among Chinese middle-aged and older adults with possible SO using Apriori association rule mining and network analysis. Methods: Data for this study were obtained from the 2015 wave of the China Health and Retirement Longitudinal Study (CHARLS), and 1,203 participants with possible SO were included in the final analysis. Apriori association rule mining was performed using R software to identify chronic disease comorbidity patterns, with minimum support and confidence thresholds set at 5% and 60%, respectively. To further clarify the relationships among chronic diseases, a comorbidity network was constructed, and node importance was evaluated using centrality indices. Results: Among the 1,203 participants with possible SO, 729 (60.6%) were female and 474 (39.4%) were male. Overall, 77.56% of participants had at least one chronic disease. The most common chronic conditions were arthritis or rheumatism (42.8%), hypertension (38.7%), digestive disease (25.2%), heart disease (20.4%), and dyslipidemia (16.5%). Regarding associations among these conditions, Apriori association rule mining identified 11 rules with confidence values greater than 60%. Notably, the rule {dyslipidemia, heart disease} → {hypertension} showed the highest confidence (75.31%) and lift (1.95), while {digestive disease} → {arthritis or rheumatism} had the highest support (15.96%). Furthermore, gender-stratified analyses showed that the predominant comorbidity pattern was {heart disease, arthritis or rheumatism} → {hypertension} among males and {dyslipidemia, heart disease} → {hypertension} among females. Finally, network analysis further identified heart disease as the most central node, followed by hypertension and dyslipidemia. Discussion: Individuals with possible SO show a high burden of multimorbidity. Cardiometabolic diseases are a major comorbidity cluster. Heart disease, hypertension, and dyslipidemia are central in the comorbidity network, highlighting the need for integrated cardiovascular risk assessment in this group. Gender-related differences in comorbidity patterns suggest that gender-stratified characteristics should be considered in future screening, risk stratification, and integrated management. Conclusion: Chinese middle-aged and older adults with possible SO commonly present with multiple chronic diseases. Cardiometabolic conditions, particularly heart disease, hypertension, and dyslipidemia, represent core components of the comorbidity network. By identifying chronic disease comorbidity patterns and further examining gender-stratified differences, this study provides evidence to support early identification, risk stratification, and integrated management among individuals with possible SO.

doi.org/10.2174/0118715303488741260615151240PubMed 42973373Populations