A sleep-based risk model for predicting dementia: Development and validation in a Korean cohort

Abstract

Background: Dementia is a major public health challenge, yet existing prediction models often overlook sleep-related symptoms, despite their known links to cognitive decline. Objective: To develop and validate a four-year Dementia Risk Score (DRS) incorporating self-reported sleep-related symptoms with demographic and clinical factors to predict all-cause dementia, including Alzheimer's disease. Methods: Data from 3082 Korean adults aged 60-79 years were analyzed. Predictors were selected using LASSO regression and included in a multivariate logistic regression model. A point-based scoring system, the DRS, was constructed from the model coefficients. Internal validation was conducted using bootstrapping and a separate dataset. Results: The DRS achieved robust predictive performance, with AUC values of 0.824 in the training set and 0.826 in the validation set. Key predictors included sleep disturbance, use of sleep medications, daytime dysfunction, leg discomfort, and urge to move legs. Conclusions: The DRS provides a practical, scalable tool for predicting dementia risk, supporting community-based screening and early intervention. External validation is needed to confirm its broader applicability.