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001033997 005__ 20241212210729.0
001033997 0247_ $$2doi$$a10.1101/2024.11.27.24318057
001033997 037__ $$aFZJ-2024-06830
001033997 041__ $$aEnglish
001033997 1001_ $$0P:(DE-Juel1)188338$$aNazarzadeh, Kimia$$b0$$eCorresponding author
001033997 245__ $$aMachine Learning-Driven Correction of Handgrip Strength: A Novel Biomarker for Neurological and Health Outcomes in the UK Biobank
001033997 260__ $$c2024
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001033997 520__ $$aBackground: Handgrip strength (HGS) is a significant biomarker for overall health, offering a simple, cost-effective method for assessing muscle function. Lower HGS is linked to higher mortality, functional decline, cognitive impairments, and chronic diseases. Considering the influence of anthropometrics and demographics on HGS, this study aims to develop a corrected HGS score using machine learning (ML) models to enhance its utility in understanding brain health and disease. Methods: Using UK Biobank data, sex-specific ML models were developed to predict HGS based on three anthropometric variables and age. A novel biomarker, ∆HGS, was introduced as the difference between true HGS (i.e., directly measured HGS) and bias-free predicted HGS. The neural basis of true HGS and ∆HGS was investigated by correlating them to regional gray matter volume (GMV). Statistical analyses were performed to test their sensitivity to longitudinal changes in stroke and major depressive disorder (MDD) patients compared to matched healthy controls (HC).Results: HGS could be accurately predicted using anthropometric and demographic features, with linear support vector machine (SVM) demonstrating high accuracy. Compared to true HGS, ∆HGS showed high reassessment reliability and stronger, widespread associations with GMV, especially in motor-related regions. Longitudinal analysis revealed that neither HGS nor ∆HGS effectively differentiated patients from matched HC at post time-point.Conclusion: The proposed ∆HGS score exhibited stronger correlations with GMV compared to true HGS, suggesting it better represents the relationship between muscle strength and brain structure. While not effective in differentiating patients from HC at post time-point, the increase in ∆HGS from pre to post time-points in patient cohorts may indicate improved utility for monitoring disease progression, treatment efficacy, or rehabilitation effects, warranting further longitudinal validation.
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001033997 536__ $$0G:(GEPRIS)458640473$$aSFB 1451 B05 - Einzelfallvorhersagen der motorischen Fähigkeiten bei Gesunden und Patienten mit motorischen Störungen (B05) (458640473)$$c458640473$$x2
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001033997 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon B.$$b1
001033997 7001_ $$0P:(DE-Juel1)180946$$aAntonopoulos, Georgios$$b2
001033997 7001_ $$0P:(DE-HGF)0$$aHensel, Lukas$$b3
001033997 7001_ $$0P:(DE-HGF)0$$aTscherpel, Caroline$$b4
001033997 7001_ $$0P:(DE-Juel1)187351$$aKomeyer, Vera$$b5
001033997 7001_ $$0P:(DE-Juel1)185083$$aRaimondo, Federico$$b6
001033997 7001_ $$0P:(DE-Juel1)131699$$aMüller, Veronika$$b7
001033997 7001_ $$0P:(DE-Juel1)161406$$aGrefkes, Christian$$b8
001033997 7001_ $$0P:(DE-Juel1)172843$$aPatil, Kaustubh R.$$b9
001033997 773__ $$a10.1101/2024.11.27.24318057
001033997 8564_ $$uhttps://www.medrxiv.org/content/10.1101/2024.11.27.24318057v2.full
001033997 8564_ $$uhttps://juser.fz-juelich.de/record/1033997/files/Machine%20Learning-Driven%20Correction%20of%20Handgrip%20Strength%3A%20A%20Novel%20Biomarker%20for%20Neurological%20and%20Health%20Outcomes%20in%20the%20UK%20Biobank.pdf$$yRestricted
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001033997 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)161406$$a Goethe Universität Frankfurt$$b8
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001033997 9141_ $$y2024
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