Optimizing Cognitive Phenotyping for Genetic Discovery in Alzheimer's Disease: A Simulation-Based Evaluation of Mini-Mental State Examination-Based Decline Measures

Abstract

Background: Alzheimer's disease (AD) is a progressive neurodegenerative brain disorder characterized by cognitive decline. The Mini-Mental State Examination (MMSE) is a well-established screening tool that evaluates five cognitive domains with a total maximum score of 30. Although previous studies have suggested a genetic basis for cognitive decline in AD, inconsistent findings have been reported and may stem from variability in phenotypic definitions. Methods: We conducted a simulation study using parameters informed by real-world AD cohorts to model 10-year trajectories of MMSE scores in a sample of 500 individuals. Three types of cognitive decline patterns, including linear, quadratic, and sigmoid, were simulated. We incorporated realistic missing data mechanisms to reflect typical challenges in longitudinal data collection. From the simulated MMSE data, we derived seven distinct phenotypic measures of cognitive decline. Using statistical models that assumed additive genetic effects, we compared the power of these phenotypes across different missing data scenarios to identify the most effective strategies for phenotyping in genetic association studies. Results: Adjusting for baseline MMSE improved the statistical power of measures that do not incorporate it in the definition. Across all simulated scenarios, simpler non-repeated measures, such as "Slope", "dMMSEdt", and "Time-to-MMSE10", consistently outperformed the others. Conclusions: The phenotypic measures "Slope" and "dMMSE/dt" consistently showed strong performance across a range of data distributions, while "Time-to-MMSE10" was particularly effective in cohorts with a higher proportion of individuals experiencing severe cognitive decline. These findings highlight the importance of tailoring the choice of cognitive decline measures to the specific characteristics of the dataset, such as the severity and distribution of decline, to improve the power to detect genetic factors associated with Alzheimer's disease.