Machine learning to discover factors predicting volume of white matter hyperintensities: insights from the UK Biobank
Date
2025
Authors
Yeshaw, Y.
Madakkatel, I.
Mulugeta, A.
Lumsden, A.
Hypponen, E.
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Journal article
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Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring, 2025; 17(1, article no. e70090):1-12
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Abstract
Introduction: Brain white matter hyperintensities (WMHs) reflect the risks of stroke, dementia, and overall mortality.
Methods: We used a hypothesis-free gradient boosting decision tree (GBDT) approach and conventional statistical methods to discover risk factors associated with volume of WMHs. The GBDT models considered data on 2891 input features, collected similar to 10 years prior to volume of WMH measurements from 44,053 participants. Top 3% of features, ranked by Shapley values, were taken forward to epidemiological analyses using linear regression.
Results: Adiposity, lung function, and indicators of metabolic health (eg, glycated hemoglobin, hypertension, alkaline phosphatase, microalbumin, and urate) contribute to WMH prediction. Of lifestyle factors, smoking had the strongest association. Time spent outdoors, creatinine, and several red blood cell indices were among the identified less-known predictors of WMHs.
Conclusions: Obesity, high blood pressure, lung function, metabolic abnormalities, and lifestyle are key contributors to WMHs, providing opportunities to prevent or reduce their development.
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Copyright 2025 The Author(s). Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer’s Association. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License. (http://creativecommons.org/licenses/by-nc/4.0/)