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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...

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Autores principales: Sood, Meemansa, Suenkel, Ulrike, von Thaler, Anna-Katharina, Zacharias, Helena U., Brockmann, Kathrin, Eschweiler, Gerhard W., Maetzler, Walter, Berg, Daniela, Fröhlich, Holger, Heinzel, Sebastian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
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.
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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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