Multidomain prediction of education-stratified MoCA-defined mild cognitive impairment in community-dwelling older adults in urban China.

BACKGROUND: Mild cognitive impairment (MCI) is an important public health concern in ageing populations, yet scalable approaches for early identification in community settings remain limited. Existing prediction studies in China have often relied on questionnaire-based or conventional epidemiological variables, whereas multidomain models incorporating objective behavioural and environmental measures remain less common. This study examined whether multidomain indicators could improve the identification of education-stratified MoCA-defined MCI in community-dwelling older adults in urban China. METHODS: We conducted a cross-sectional analysis of community-dwelling older adults aged 60 years or above from Nanjing, China (analytic sample: n = 309). MCI status was defined using education-stratified Montreal Cognitive Assessment (MoCA) cut-offs (MCI n = 110; non-MCI n = 199). Candidate predictors included accelerometer-derived movement behaviours, anthropometric and bioelectrical-impedance-derived body-composition measures, and objective and perceived built-environment indicators. To reduce potential circularity, education-related variables were excluded from the primary predictor set. Data were divided into a stratified training set (70%, n = 216) and an independent held-out test set (30%, n = 93). Six machine-learning algorithms were trained and evaluated. Model performance was assessed using discrimination, accuracy, calibration, decision curve analysis, and model-interpretation methods. RESULTS: In the independent held-out test set, LightGBM showed the best overall performance, with an AUC of 0.854 (95% CI 0.762-0.946) and an accuracy of 0.822 (95% CI 0.701-0.913). XGBoost also performed well (AUC 0.835, 95% CI 0.741-0.929), followed by random forest (AUC 0.810, 95% CI 0.712-0.908). LightGBM had the lowest Brier score (0.139, 95% CI 0.092-0.186), and both LightGBM and XGBoost showed more favourable calibration than the other classifiers. Decision curve analysis suggested greater net benefit for the better-performing models than the default treat-all and treat-none strategies across clinically relevant threshold probabilities. Model-interpretation analyses indicated that movement behaviours, central adiposity/body-composition measures, age, and neighbourhood-context variables contributed importantly to prediction. CONCLUSIONS: Multidomain predictors spanning objective movement behaviours, body-composition indicators, and built-environment measures showed value for identifying education-stratified MoCA-defined MCI in community-dwelling older adults. LightGBM achieved the best overall performance among the candidate models. These findings support the potential utility of integrating behavioural, biological, and environmental information for community-based cognitive-risk stratification, although external validation in independent and longitudinal cohorts is required before routine implementation.