A Nomogram Model for Predicting Cognitive Frailty in Community-Dwelling Older Adults Based on Mental and Physical Functional Indicators.

Background: Cognitive frailty (CF), characterised by the co-occurrence of physical frailty and mild cognitive impairment, poses significant risks for adverse health outcomes in community-dwelling older adults, yet effective prediction tools remain limited.

Objective: This study aimed to develop and validate a nomogram model for predicting CF risk in community-dwelling older adults based on multidimensional mental and physical functional markers.

Methods: A cross-sectional analysis included 481 participants (mean age 69.2 ± 7.3 years; 60.3% female) from Shanghai communities. Assessments encompassed cognitive function (MoCA), physical frailty (EFS), mental health (GDS-15, PSQI), nutritional status (MNA-SF), and physical performance (grip strength, TUG test, standing on one leg with eyes closed/open tests). Univariate and multivariate logistic regression identified predictors, followed by nomogram construction and internal validation via bootstrapping (500 resamples).

Results: CF prevalence was 41.4% (199/481). Six independent predictors were identified: chronic disease status (OR = 2.587), malnutrition (OR = 0.852), depressive symptoms (OR = 1.062), poor sleep quality (OR = 1.245), impaired mobility (TUG time: OR = 1.100), and balance deficits (Time to stand on one leg with eyes closed time: OR = 0.935). The nomogram demonstrated moderate discrimination (C-index = 0.761, 95% CI = 0.718-0.804) and excellent calibration (Hosmer-Lemeshow p = 0.19). Internal validation confirmed robustness (corrected C-index = 0.761).

Conclusion: This nomogram integrates easily accessible mental and physical functional markers, offering a practical tool for early CF risk stratification in community settings. Its application may guide personalised interventions to mitigate cognitive and functional decline in ageing populations.