Machine learning-based personalized composite score dissects risk and protective factors for cognitive and motor function in elderly

Most nations worldwide have aging populations. With age, sensory, cognitive and motor abilities decline and the risk for neurodegenerative disorders increases. These multiple impairments influence the quality of life and increase the need for care, thus putting a high burden on society, the economy, and the healthcare system. Therefore, it is important to identify factors that influence healthy aging, in particular ones that are potentially modifiable by each subject through choice of lifestyle. However, large-scale studies that investigate the influence of multiple multi-modal factors on a global description of healthy aging measured by multiple clinical assessments are sparse. Here, we propose a Machine Learning (ML) model that simultaneously predicts multiple cognitive and motor outcome measurements on a personalized level recorded from one learned composite score. This personalized composite score is derived by the model from a large set of multi-modal components from the TREND cohort including genetic, biofluid, clinical, demographic and lifestyle factors. We found that a model based on a single composite score was able to predict cognitive and motor abilities almost as well as a flexible regression model specifically trained for each single clinical score. In contrast to the flexible regression model, our composite score-based model is able to identify factors that globally influence cognitive and motoric abilities as measured by multiple clinical scores. We used the model to identify several risk and protective factors for healthy aging and recover physical exercise as a major, modifiable, protective factor.