Association of central adiposity indices with cognitive impairment in elderly populations: Development and validation of a risk prediction nomogram using NHANES and CHARLS cohorts

BackgroundCognitive impairment is a primary contributor to disability among older adults, with growing evidence identifying central adiposity as an adjustable risk factor for neurodegeneration, but comprehensive predictive models integrating central adiposity indices for mild cognitive impairment (MCI) in aging individuals remain underexplored.ObjectiveThis study aimed to establish and verify a model for predicting the risk of MCI in elderly population by incorporating anthropometric indices.MethodsWe calculated five central adiposity indices using anthropometric measurements from 2464 United States adults aged 60 years or older (National Health and Nutrition Examination Survey, 2011-2014). Cognitive performance was assessed using three standardized neuropsychological tests. A random assignment placed participants into either a training (n = 1725) or a validation (n = 739) set. Furthermore, the data from participants in the 2011 wave of the China Health and Retirement Longitudinal Study served as an external validation cohort (n = 536). LASSO-selected predictors were employed to inform multivariable logistic regression modeling.ResultsPositive linear relationships were found between three anthropometric indices-A Body Shape Index (ABSI), Conicity Index (CoI) and Weight-Adjusted-Waist Index (WWI)-with MCI risk (p < 0.05, p for nonlinearity > 0.05). The nomogram incorporating ABSI demonstrated strong discriminative capacity (training AUC = 0.861; internal validation AUC = 0.826; external validation AUC = 0.798), precise calibration, and good clinical utility.ConclusionsThe risk of MCI was independently linked to central adiposity indices (ABSI, WWI, and CoI). The nomogram incorporating ABSI provided a validated, clinically applicable prediction model for initial screening of MCI in older populations.