AUTHOR=Schalkamp Ann-Kathrin , Lerche Stefanie , Wurster Isabel , Roeben Benjamin , Zimmermann Milan , Fries Franca , von Thaler Anna-Katharina , Eschweiler Gerhard , Maetzler Walter , Berg Daniela , Sinz Fabian H. , Brockmann Kathrin 

TITLE=Machine learning-based personalized composite score dissects risk and protective factors for cognitive and motor function in older participants

JOURNAL=Frontiers in Aging Neuroscience

VOLUME=Volume 16 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2024.1447944

DOI=10.3389/fnagi.2024.1447944

ISSN=1663-4365

ABSTRACT=With age, sensory, cognitive and motor abilities decline and the risk for neurodegenerative disorders increases. These impairments influence the quality of life and increase the need for care, thus putting a high burden on society, economy, and the healthcare system. Therefore, it is important to identify factors that influence healthy aging, in particular ones that are potentially modifiable through choice of lifestyle. However, large-scale studies that investigate the influence of multi-modal factors on a global description of healthy aging measured by multiple clinical assessments are sparse. We propose a Machine Learning 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 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 classical flexible regression model specifically trained for each single clinical score. In contrast to the flexible regression model, our composite score model is able to identify factors that globally influence cognitive and motoric abilities as measured by multiple clinical scores. The model identified several risk and protective factors for healthy aging and recovered physical exercise as a major, modifiable, protective factor. We conclude that our low parametric modelling approach successfully recovered known risk and protective factors of healthy aging on a personalized level while providing an interpretable composite score. We suggest to validate this modeling approach in other cohorts.