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Abstract
To identify the most important parameters associated with cerebral white matter hyperintensities (WMH), in consideration of potential collinearity, we used a data-driven machine-learning approach. We analysed two independent cohorts (KORA and SHIP). WMH volumes were derived from cMRI-images (FLAIR). 90 (KORA) and 34 (SHIP) potential determinants of WMH including measures of diabetes, blood-pressure, medication-intake, sociodemographics, life-style factors, somatic/depressive-symptoms and sleep were collected. Elastic net regression was used to identify relevant predictor covariates associated with WMH volume. The ten most frequently selected variables in KORA were subsequently examined for robustness in SHIP. The final KORA sample consisted of 370 participants (58% male; age 55.7 ± 9.1 years), the SHIP sample comprised 854 participants (38% male; age 53.9 ± 9.3 years). The most often selected and highly replicable parameters associated with WMH volume were in descending order age, hypertension, components of the social environment (i.e. widowed, living alone) and prediabetes. A systematic machine-learning based analysis of two independent, population-based cohorts showed, that besides age and hypertension, prediabetes and components of the social environment might play important roles in the development of WMH. Our results enable personal risk assessment for the development of WMH and inform prevention strategies tailored to the individual patient.
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1 LMU Munich, Department of Radiology, University Hospital, Munich, Germany (GRID:grid.5252.0) (ISNI:0000 0004 1936 973X)
2 German Research Center for Environmental Health, Institute of Epidemiology, Helmholtz Zentrum München, Munich-Neuherberg, Germany (GRID:grid.4567.0) (ISNI:0000 0004 0483 2525); Ludwig-Maximilians-University München, Chair of Epidemiology, Munich, Germany (GRID:grid.5252.0) (ISNI:0000 0004 1936 973X)
3 University of Texas Health Science Center At San Antonio, Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Neurodegenerative Disorders, San Antonio, USA (GRID:grid.267309.9) (ISNI:0000 0001 0629 5880); University of Pennsylvania, Department of Radiology and Penn Memory Center, Center for Biomedical Image Computing and Analytics, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972); University of Greifswald, Institute of Community Medicine and Department of Psychiatry, Greifswald, Germany (GRID:grid.5603.0)
4 Medical Center - University of Freiburg, Department of Diagnostic and Interventional Radiology, Freiburg, Germany (GRID:grid.7708.8) (ISNI:0000 0000 9428 7911)
5 University of Greifswald, Institute of Diagnostic Radiology and Neuroradiology, Greifswald, Germany (GRID:grid.5603.0)
6 German Research Center for Environmental Health, Institute of Epidemiology, Helmholtz Zentrum München, Munich-Neuherberg, Germany (GRID:grid.4567.0) (ISNI:0000 0004 0483 2525); University Hospital of Augsburg, KORA Study Centre, Augsburg, Germany (GRID:grid.419801.5) (ISNI:0000 0000 9312 0220)
7 German Diabetes Center, Institute for Biometrics and Epidemiology, Duesseldorf, Germany (GRID:grid.429051.b) (ISNI:0000 0004 0492 602X); German Center for Diabetes Research (DZD), Munich-Neuherberg, Germany (GRID:grid.452622.5)
8 German Research Center for Environmental Health, Institute of Epidemiology, Helmholtz Zentrum München, Munich-Neuherberg, Germany (GRID:grid.4567.0) (ISNI:0000 0004 0483 2525); Technical University Munich, Department of Psychosomatic Medicine and Psychotherapy, Klinikum Rechts Der Isar, Munich, Germany (GRID:grid.6936.a) (ISNI:0000000123222966)
9 University of Greifswald, Institute of Community Medicine and Department of Psychiatry, Greifswald, Germany (GRID:grid.5603.0); University of Greifswald, Institute of Diagnostic Radiology and Neuroradiology, Greifswald, Germany (GRID:grid.5603.0); German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany (GRID:grid.424247.3) (ISNI:0000 0004 0438 0426)
10 German Research Center for Environmental Health, Institute of Epidemiology, Helmholtz Zentrum München, Munich-Neuherberg, Germany (GRID:grid.4567.0) (ISNI:0000 0004 0483 2525); German Centre for Cardiovascular Research (DZHK E.V.), Munich, Germany (GRID:grid.452396.f) (ISNI:0000 0004 5937 5237); Ludwig-Maximilians-University München, Chair of Epidemiology, Munich, Germany (GRID:grid.5252.0) (ISNI:0000 0004 1936 973X)
11 LMU Munich, Department of Radiology, University Hospital, Munich, Germany (GRID:grid.5252.0) (ISNI:0000 0004 1936 973X); University of Toronto, Department of Radiology, The Hospital for Sick Children, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938)