Sci Rep. 2026 Jun 26. doi: 10.1038/s41598-026-56973-w. Online ahead of print.
ABSTRACT
To identify multimorbidity patterns using latent class analysis (LCA) and examine their associations with cognitive trajectories among older Chinese adults. This longitudinal study utilized data from the China Health and Retirement Longitudinal Study (CHARLS) conducted between 2011 and 2015. Participants aged ≥ 45 years with at least one of three target chronic conditions (cardiovascular disease, diabetes, stroke) and complete cognitive function data were included. LCA identified distinct multimorbidity patterns. Linear mixed-effects models examined cognitive trajectories across multimorbidity patterns and disease burden (defined as single disease versus multimorbidity) categories, adjusting for age, sex, education, and residential location. A total of 3,155 participants were included in the final analysis. Three multimorbidity patterns were identified: Heart Disease-Dominant (57.1%), Stroke-Dominant (9.8%), and Diabetes-Dominant (33.2%). The Stroke-Dominant group exhibited significantly lower baseline cognitive function (β=-2.055, p < 0.001) compared to the Heart Disease-Dominant group. Compared to the single disease group, multimorbidity (≥ 2 conditions) was associated with accelerated cognitive decline, operationally defined as a significantly negative Time × Group interaction in the linear mixed-effects model (Time × Multimorbidity: β=-0.411, p = 0.017), with the multimorbidity group declining by 0.296 points biennially versus slight improvement (+ 0.115 points) in the single disease group. Specific multimorbidity patterns and disease burden demonstrate differential associations with cognitive trajectories among older adults. Stroke-dominant patterns and multimorbidity represent high-risk groups requiring targeted cognitive screening and integrated chronic disease management interventions.
PMID:42362608 | DOI:10.1038/s41598-026-56973-w