Abstract

Background: The heterogeneous performance of dementia and Alzheimer’s disease risk scores makes it difficult to identify tools suitable for supporting risk reduction and prevention strategies. Although multiple factors may contribute, we hypothesized that differences in age specification and study design are the primary contributors to this variability.

Objective: To evaluate how age specification and length of follow-up affect the performance of three midlife risk scores: CogDrisk-ML, CAIDE, and UKBDRS.

Methods: Longitudinal data were drawn from the ARIC, UK Biobank, and Whitehall II cohorts. Participants free of dementia at baseline were included. Baseline variables were used to calculate each risk score. Risk prediction was evaluated using Cox proportional hazards models with three age specifications: UKBDRS (continuous), CogDrisk-ML (categorical: 40–49, 50–54, 55–59, 60–64 years), and CAIDE (categorical age: <47, 47–53, >53 years). Predictive accuracy was assessed using Harrell’s C-statistics and 95% confidence intervals (CIs).

Results: Across all cohorts and risk scores, excluding age from risk scores resulted in similar C-statistics. In age- and sex-only models, continuous age produced the highest Harrell’s C-statistics (95% CI) [ARIC: 0.71 (0.68, 0.73); UK Biobank: 0.78 (0.77, 0.79), and Whitehall II: 0.75 (0.62, 0.88)], followed by categorical age specifications in CogDrisk-ML and CAIDE. Compared with continuous age, categorical specifications reduced C-statistics, with the largest reduction for CAIDE. Shorter follow-up yielded higher but less precise C-statistics.

Conclusions: Age specification strongly influences dementia risk model performance, with continuous age yielding the highest accuracy, especially with shorter follow-up. UKBDRS performance was largely driven by its age specification, whereas CogDrisk-ML better supports early prevention.

 

Key words: Risk tool, dementia risk prediction, role of age, comparative performance, Alzheimer’s disease.