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Bayesian network modeling of risk and prodromal markers of Parkinson’s disease
Parkinson’s disease (PD) is characterized by a long prodromal phase with a multitude of markers indicating an increased PD risk prior to clinical diagnosis based on motor symptoms. Current PD prediction models do not consider interdependencies of single predictors, lack differentiation by subtypes o...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9955606/ https://www.ncbi.nlm.nih.gov/pubmed/36827273 http://dx.doi.org/10.1371/journal.pone.0280609 |
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author | Sood, Meemansa Suenkel, Ulrike von Thaler, Anna-Katharina Zacharias, Helena U. Brockmann, Kathrin Eschweiler, Gerhard W. Maetzler, Walter Berg, Daniela Fröhlich, Holger Heinzel, Sebastian |
author_facet | Sood, Meemansa Suenkel, Ulrike von Thaler, Anna-Katharina Zacharias, Helena U. Brockmann, Kathrin Eschweiler, Gerhard W. Maetzler, Walter Berg, Daniela Fröhlich, Holger Heinzel, Sebastian |
author_sort | Sood, Meemansa |
collection | PubMed |
description | Parkinson’s disease (PD) is characterized by a long prodromal phase with a multitude of markers indicating an increased PD risk prior to clinical diagnosis based on motor symptoms. Current PD prediction models do not consider interdependencies of single predictors, lack differentiation by subtypes of prodromal PD, and may be limited and potentially biased by confounding factors, unspecific assessment methods and restricted access to comprehensive marker data of prospective cohorts. We used prospective data of 18 established risk and prodromal markers of PD in 1178 healthy, PD-free individuals and 24 incident PD cases collected longitudinally in the Tübingen evaluation of Risk factors for Early detection of NeuroDegeneration (TREND) study at 4 visits over up to 10 years. We employed artificial intelligence (AI) to learn and quantify PD marker interdependencies via a Bayesian network (BN) with probabilistic confidence estimation using bootstrapping. The BN was employed to generate a synthetic cohort and individual marker profiles. Robust interdependencies were observed for BN edges from age to subthreshold parkinsonism and urinary dysfunction, sex to substantia nigra hyperechogenicity, depression, non-smoking and to constipation; depression to symptomatic hypotension and excessive daytime somnolence; solvent exposure to cognitive deficits and to physical inactivity; and non-smoking to physical inactivity. Conversion to PD was interdependent with prior subthreshold parkinsonism, sex and substantia nigra hyperechogenicity. Several additional interdependencies with lower probabilistic confidence were identified. Synthetic subjects generated via the BN based representation of the TREND study were realistic as assessed through multiple comparison approaches of real and synthetic data. Altogether our work demonstrates the potential of modern AI approaches (specifically BNs) both for modelling and understanding interdependencies between PD risk and prodromal markers, which are so far not accounted for in PD prediction models, as well as for generating realistic synthetic data. |
format | Online Article Text |
id | pubmed-9955606 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-99556062023-02-25 Bayesian network modeling of risk and prodromal markers of Parkinson’s disease Sood, Meemansa Suenkel, Ulrike von Thaler, Anna-Katharina Zacharias, Helena U. Brockmann, Kathrin Eschweiler, Gerhard W. Maetzler, Walter Berg, Daniela Fröhlich, Holger Heinzel, Sebastian PLoS One Research Article Parkinson’s disease (PD) is characterized by a long prodromal phase with a multitude of markers indicating an increased PD risk prior to clinical diagnosis based on motor symptoms. Current PD prediction models do not consider interdependencies of single predictors, lack differentiation by subtypes of prodromal PD, and may be limited and potentially biased by confounding factors, unspecific assessment methods and restricted access to comprehensive marker data of prospective cohorts. We used prospective data of 18 established risk and prodromal markers of PD in 1178 healthy, PD-free individuals and 24 incident PD cases collected longitudinally in the Tübingen evaluation of Risk factors for Early detection of NeuroDegeneration (TREND) study at 4 visits over up to 10 years. We employed artificial intelligence (AI) to learn and quantify PD marker interdependencies via a Bayesian network (BN) with probabilistic confidence estimation using bootstrapping. The BN was employed to generate a synthetic cohort and individual marker profiles. Robust interdependencies were observed for BN edges from age to subthreshold parkinsonism and urinary dysfunction, sex to substantia nigra hyperechogenicity, depression, non-smoking and to constipation; depression to symptomatic hypotension and excessive daytime somnolence; solvent exposure to cognitive deficits and to physical inactivity; and non-smoking to physical inactivity. Conversion to PD was interdependent with prior subthreshold parkinsonism, sex and substantia nigra hyperechogenicity. Several additional interdependencies with lower probabilistic confidence were identified. Synthetic subjects generated via the BN based representation of the TREND study were realistic as assessed through multiple comparison approaches of real and synthetic data. Altogether our work demonstrates the potential of modern AI approaches (specifically BNs) both for modelling and understanding interdependencies between PD risk and prodromal markers, which are so far not accounted for in PD prediction models, as well as for generating realistic synthetic data. Public Library of Science 2023-02-24 /pmc/articles/PMC9955606/ /pubmed/36827273 http://dx.doi.org/10.1371/journal.pone.0280609 Text en © 2023 Sood et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Sood, Meemansa Suenkel, Ulrike von Thaler, Anna-Katharina Zacharias, Helena U. Brockmann, Kathrin Eschweiler, Gerhard W. Maetzler, Walter Berg, Daniela Fröhlich, Holger Heinzel, Sebastian Bayesian network modeling of risk and prodromal markers of Parkinson’s disease |
title | Bayesian network modeling of risk and prodromal markers of Parkinson’s disease |
title_full | Bayesian network modeling of risk and prodromal markers of Parkinson’s disease |
title_fullStr | Bayesian network modeling of risk and prodromal markers of Parkinson’s disease |
title_full_unstemmed | Bayesian network modeling of risk and prodromal markers of Parkinson’s disease |
title_short | Bayesian network modeling of risk and prodromal markers of Parkinson’s disease |
title_sort | bayesian network modeling of risk and prodromal markers of parkinson’s disease |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9955606/ https://www.ncbi.nlm.nih.gov/pubmed/36827273 http://dx.doi.org/10.1371/journal.pone.0280609 |
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