Fig. 3

Heterogenous multi-disease patten and multi-symptom patten was latent-based categorized into different aging-associated clusters in the older adults using Latent clustering analysis (LCA). Multi-disease pattern and multi-disease patten was clustered from 2 to 10 classes using the Latent Cluster Analysis (LCA, grey pools) with optimization by lowest BIC. The four-class patten clustering was selected with the lowest BIC for both multi-disease patten and multi-symptom patten. Four outcome clusters of multi-disease patten A or multi-symptom patten B were further amplified, respectively. Each patten cluster was globally characterized by prevalence variation of each coexistent disorder compared to the average prevalence in the older Chinese adults evaluated in this study. Four multi-disease patten clusters were described as heavy aging-associated patten (D3), moderate-to-mild aging-associated patten (D4 and D1) or slight aging-associated patten (D2). Four multi-symptom patten were described as heavy aging-associated patten (S3), moderate-to-mild aging-associated patten (S4 and S1) or slight aging-associated patten (S2). Color palettes from light to dark indicate the increase of aging-associated aspect