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Grosu, S.* ; Rospleszcz, S. ; Hartmann, F.* ; Habes, M.* ; Bamberg, F.* ; Schlett, C.L.* ; Galiè, F.* ; Lorbeer, R.* ; Auweter, S.* ; Selder, S.* ; Buelow, R.* ; Heier, M. ; Rathmann, W.* ; Mueller-Peltzer, K.* ; Ladwig, K.-H. ; Grabe, H.J.* ; Peters, A. ; Ertl-Wagner, B.B.* ; Stoecklein, S.*

Associated factors of white matter hyperintensity volume: A machine-learning approach.

Sci. Rep. 11:2325 (2021)
Postprint Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Vascular Risk-factors; Small Vessel Disease; Diastolic Blood-pressure; Cerebrovascular-disease; Variable Selection; Unselected Cohort; Alcohol Intake; Lesions; Mri; Population
Sprache englisch
Veröffentlichungsjahr 2021
HGF-Berichtsjahr 2021
ISSN (print) / ISBN 2045-2322
e-ISSN 2045-2322
Zeitschrift Scientific Reports
Quellenangaben Band: 11, Heft: 1, Seiten: , Artikelnummer: 2325 Supplement: ,
Verlag Nature Publishing Group
Verlagsort London
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Epidemiology (EPI)
POF Topic(s) 30202 - Environmental Health
Forschungsfeld(er) Genetics and Epidemiology
PSP-Element(e) G-504000-010
G-504000-006
G-504000-003
G-504090-001
Förderungen
Projekt DEAL
Siemens Healthcare
German Research Foundation (DFG, Deutsche Forschungsgemeinschaft)
Munich Center of Health Sciences (MC-Health), Ludwig-Maximilians-Universitat, as part of LMUinnovativ
State of Bavaria
Helmholtz Zentrum Munchen-German Research Center for Environmental Health - German Federal Ministry of Education and Research (BMBF)
DOI 10.1038/s41598-021-81883-4
WOS ID WOS:000668339700042
Scopus ID 85099851028
PubMed ID 33504924
Erfassungsdatum 2021-03-01
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