Multimorbidity in the Era of Multiple Chronic Conditions: From Methodological Advances to Precision Prevention

Abstract

Multimorbidity refers to the presence of two or more chronic diseases in a single individual. It has surpassed the single-disease model and become a major public health challenge worldwide. In recent years, notable progress has been made in disease clustering analyses based on large cohorts and electronic health records. However, significant challenges remain, including high methodological heterogeneity, limited clustering stability, and unclear pathways for clinical application. This paper systematically reviews the epidemiological transition of multimorbidity, compares the applicability and limitations of common clustering methods such as hierarchical clustering, latent class analysis, K-centroids clustering, and network analysis, summarizes the heterogeneous characteristics of clustering patterns across populations and regions, and explores translational pathways from disease clustering to precision prevention. Based on this, we propose future research directions, including methodological standardization, integration of longitudinal data, development of stratified precision prevention strategies, and establishment of policy translation mechanisms. The goal is to provide a theoretical framework and methodological guidance for precision prevention in the era of multimorbidity.