AUTHOR=Xiang Jin , Xiong Yan , Liang Heting , Mao Qingyun , Zhang Yumeng , Li Yunting , Jiang Zhixia , Yuan Xiaoli 

TITLE=Latent profiles and correlates factors of cognitive function in older adults: a cross-sectional study

JOURNAL=Frontiers in Aging Neuroscience

VOLUME=Volume 17 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2025.1622804

DOI=10.3389/fnagi.2025.1622804

ISSN=1663-4365

ABSTRACT=ObjectiveThis study aimed to identify the latent profiles of cognitive function among community-dwelling and institutionalized older adults, and to examine their associated influencing factors, in order to inform the development of targeted interventions.MethodsA convenience sampling method was used to select 6,708 elderly people aged 60 years and older from six communities and nine long-term care institutions across China, who were assessed using a general information questionnaire, Mini-Mental State Examination (MMSE), the Frailty Scale, the Anxiety Scale, the Depression Scale, and the Pittsburgh Sleep Quality Index. Latent profile analysis (LPA) was performed based on the MMSE scores, and multiple logistic regression was used to analyse the influencing factors of cognitive function categories.ResultsA total of three cognitive function profiles were identified: High cognitive Function group (41.2%), Moderate Cognitive Function Group (48.2%) and Low cognitive Function group (10.7%). Higher Frailty [odds ratio (ORs) = 1.070–1.246], higher depressive symptom scores (OR = 1.059–1.191) and poorer sleep quality (higher PSQI; OR = 1.088) were associated with higher odds of belonging to the Moderate/Low cognitive profiles, whereas adequate social support (Yes vs. No; OR = 0.530–0.696), selected middle-income categories versus ≥¥6,000 in per-capita monthly household income (OR = 0.462–0.735) and male sex (OR = 0.556–0.876) were associated with lower odds.ConclusionCognitive function among older adults can be classified into three distinct latent profiles, each associated with different influencing factors. These findings underscore the need for stratified and personalized interventions at the community level to support stratified screening and tailored community programs; given the cross-sectional design, these associations do not establish causality or intervention effects.