The overarching goal of this project is to improve dementia prevention strategies by developing and validating a machine learning-based personalized medicine method for detecting the earliest signs of impending cognitive decline, enabling early and personalized multidomain interventions. Findings from multidomain lifestyle trials have emphasized that intervention effectiveness may be dependent on a methodology that does not yet exist, i.e., the accurate identification of at-risk individuals who are most likely to benefit. This project will address this methodological gap by (1) developing methods for predicting future cognitive decline based on clinical data and distinguishing between healthy individuals at higher risk for mild cognitive impairment and dementia and those who remain healthy; and (2) testing the methodology in ongoing dementia prevention trials.

2022

2025