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

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. and Brockmann, Kathrin 2024. Machine learning-based personalized composite score dissects risk and protective factors for cognitive and motor function in older participants. Frontiers in Aging Neuroscience 16 , 1447944. 10.3389/fnagi.2024.1447944

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Abstract

Introduction: 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, the economy, and the healthcare system. Therefore, it is important to identify factors that influence healthy aging, particularly ones that are potentially modifiable through lifestyle choices. However, large-scale studies investigating the influence of multi-modal factors on a global description of healthy aging measured by multiple clinical assessments are sparse. Methods: 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. Results: 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. Discussion: We conclude that our low parametric modeling approach successfully recovered known risk and protective factors of healthy aging on a personalized level while providing an interpretable composite score. We suggest validating this modeling approach in other cohorts.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Medicine
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/
Publisher:	Frontiers Media
Date of First Compliant Deposit:	30 October 2024
Date of Acceptance:	26 September 2024
Last Modified:	30 Oct 2024 10:15
URI:	https://orca.cardiff.ac.uk/id/eprint/173521

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