Neurology; Neurology (clinical); Cellular and Molecular Neuroscience
Abstract :
[en] Parkinson's disease (PD) exhibits a variety of symptoms, with approximately 25% of patients experiencing mild cognitive impairment and 45% developing dementia within ten years of diagnosis. Predicting this progression and identifying its causes remains challenging. Our study utilizes machine learning and multimodal data from the UK Biobank to explore the predictability of Parkinson's dementia (PDD) post-diagnosis, further validated by data from the Parkinson's Progression Markers Initiative (PPMI) cohort. Using Shapley Additive Explanation (SHAP) and Bayesian Network structure learning, we analyzed interactions among genetic predisposition, comorbidities, lifestyle, and environmental factors. We concluded that genetic predisposition is the dominant factor, with significant influence from comorbidities. Additionally, we employed Mendelian randomization (MR) to establish potential causal links between hypertension, type 2 diabetes, and PDD, suggesting that managing blood pressure and glucose levels in Parkinson's patients may serve as a preventive strategy. This study identifies risk factors for PDD and proposes avenues for prevention.
Disciplines :
Human health sciences: Multidisciplinary, general & others Computer science
Author, co-author :
Aborageh, Mohamed; Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt, Augustin, Germany. mohamed.aborageh@scai.fraunhofer.de
Hähnel, Tom; Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt, Augustin, Germany ; Department of Neurology, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
MARTINS CONDE, Patricia ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Digital Medicine
KLUCKEN, Jochen ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Digital Medicine ; Centre Hospitalier de Luxembourg (CHL), Luxembourg, Luxembourg
Fröhlich, Holger; Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt, Augustin, Germany. holger.froehlich@scai.fraunhofer.de ; Bonn-Aachen International Center for Information Technology (B-IT), Rheinische Friedrich-Wilhelms-Universität Bonn, 53115, Bonn, Germany. holger.froehlich@scai.fraunhofer.de
External co-authors :
yes
Language :
English
Title :
Predicting dementia in people with Parkinson's disease.
This research has been conducted using the UK Biobank resource under application 67829. Data used in the preparation of this article were obtained from the Parkinson\u2019s Progression Markers Initiative (PPMI) database www.ppmi-info.org/access-dataspecimens/download-data , RRID:SCR_006431. For up-to-date information on the study, visit www.ppmi-info.org . PPMI \u2013 a public-private partnership \u2013 is funded by the Michael J. Fox Foundation for Parkinson\u2019s Research and funding partners. A list of names of all the PPMI funding partners can be found at www.ppmi-info.org/about-ppmi/who-we-are/study-sponsors/ .
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