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Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach

Predictive models for recovering from alcohol use disorder (AUD) and identifying related predisposition biomarkers can have a tremendous impact on addiction treatment outcomes and cost reduction. Our sample (N = 1376) included individuals of European (EA) and African (AA) ancestry from the Collabora...

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Autores principales: Kinreich, Sivan, McCutcheon, Vivia V., Aliev, Fazil, Meyers, Jacquelyn L., Kamarajan, Chella, Pandey, Ashwini K., Chorlian, David B., Zhang, Jian, Kuang, Weipeng, Pandey, Gayathri, Viteri, Stacey Subbie-Saenz de., Francis, Meredith W., Chan, Grace, Bourdon, Jessica L., Dick, Danielle M., Anokhin, Andrey P., Bauer, Lance, Hesselbrock, Victor, Schuckit, Marc A., Nurnberger, John I., Foroud, Tatiana M., Salvatore, Jessica E., Bucholz, Kathleen K., Porjesz, Bernice
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7960734/
https://www.ncbi.nlm.nih.gov/pubmed/33723218
http://dx.doi.org/10.1038/s41398-021-01281-2
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author Kinreich, Sivan
McCutcheon, Vivia V.
Aliev, Fazil
Meyers, Jacquelyn L.
Kamarajan, Chella
Pandey, Ashwini K.
Chorlian, David B.
Zhang, Jian
Kuang, Weipeng
Pandey, Gayathri
Viteri, Stacey Subbie-Saenz de.
Francis, Meredith W.
Chan, Grace
Bourdon, Jessica L.
Dick, Danielle M.
Anokhin, Andrey P.
Bauer, Lance
Hesselbrock, Victor
Schuckit, Marc A.
Nurnberger, John I.
Foroud, Tatiana M.
Salvatore, Jessica E.
Bucholz, Kathleen K.
Porjesz, Bernice
author_facet Kinreich, Sivan
McCutcheon, Vivia V.
Aliev, Fazil
Meyers, Jacquelyn L.
Kamarajan, Chella
Pandey, Ashwini K.
Chorlian, David B.
Zhang, Jian
Kuang, Weipeng
Pandey, Gayathri
Viteri, Stacey Subbie-Saenz de.
Francis, Meredith W.
Chan, Grace
Bourdon, Jessica L.
Dick, Danielle M.
Anokhin, Andrey P.
Bauer, Lance
Hesselbrock, Victor
Schuckit, Marc A.
Nurnberger, John I.
Foroud, Tatiana M.
Salvatore, Jessica E.
Bucholz, Kathleen K.
Porjesz, Bernice
author_sort Kinreich, Sivan
collection PubMed
description Predictive models for recovering from alcohol use disorder (AUD) and identifying related predisposition biomarkers can have a tremendous impact on addiction treatment outcomes and cost reduction. Our sample (N = 1376) included individuals of European (EA) and African (AA) ancestry from the Collaborative Study on the Genetics of Alcoholism (COGA) who were initially assessed as having AUD (DSM-5) and reassessed years later as either having AUD or in remission. To predict this difference in AUD recovery status, we analyzed the initial data using multimodal, multi-features machine learning applications including EEG source-level functional brain connectivity, Polygenic Risk Scores (PRS), medications, and demographic information. Sex and ancestry age-matched stratified analyses were performed with supervised linear Support Vector Machine application and were calculated twice, once when the ancestry was defined by self-report and once defined by genetic data. Multifeatured prediction models achieved higher accuracy scores than models based on a single domain and higher scores in male models when the ancestry was based on genetic data. The AA male group model with PRS, EEG functional connectivity, marital and employment status features achieved the highest accuracy of 86.04%. Several discriminative features were identified, including collections of PRS related to neuroticism, depression, aggression, years of education, and alcohol consumption phenotypes. Other discriminated features included being married, employed, medication, lower default mode network and fusiform connectivity, and higher insula connectivity. Results highlight the importance of increasing genetic homogeneity of analyzed groups, identifying sex, and ancestry-specific features to increase prediction scores revealing biomarkers related to AUD remission.
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spelling pubmed-79607342021-03-28 Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach Kinreich, Sivan McCutcheon, Vivia V. Aliev, Fazil Meyers, Jacquelyn L. Kamarajan, Chella Pandey, Ashwini K. Chorlian, David B. Zhang, Jian Kuang, Weipeng Pandey, Gayathri Viteri, Stacey Subbie-Saenz de. Francis, Meredith W. Chan, Grace Bourdon, Jessica L. Dick, Danielle M. Anokhin, Andrey P. Bauer, Lance Hesselbrock, Victor Schuckit, Marc A. Nurnberger, John I. Foroud, Tatiana M. Salvatore, Jessica E. Bucholz, Kathleen K. Porjesz, Bernice Transl Psychiatry Article Predictive models for recovering from alcohol use disorder (AUD) and identifying related predisposition biomarkers can have a tremendous impact on addiction treatment outcomes and cost reduction. Our sample (N = 1376) included individuals of European (EA) and African (AA) ancestry from the Collaborative Study on the Genetics of Alcoholism (COGA) who were initially assessed as having AUD (DSM-5) and reassessed years later as either having AUD or in remission. To predict this difference in AUD recovery status, we analyzed the initial data using multimodal, multi-features machine learning applications including EEG source-level functional brain connectivity, Polygenic Risk Scores (PRS), medications, and demographic information. Sex and ancestry age-matched stratified analyses were performed with supervised linear Support Vector Machine application and were calculated twice, once when the ancestry was defined by self-report and once defined by genetic data. Multifeatured prediction models achieved higher accuracy scores than models based on a single domain and higher scores in male models when the ancestry was based on genetic data. The AA male group model with PRS, EEG functional connectivity, marital and employment status features achieved the highest accuracy of 86.04%. Several discriminative features were identified, including collections of PRS related to neuroticism, depression, aggression, years of education, and alcohol consumption phenotypes. Other discriminated features included being married, employed, medication, lower default mode network and fusiform connectivity, and higher insula connectivity. Results highlight the importance of increasing genetic homogeneity of analyzed groups, identifying sex, and ancestry-specific features to increase prediction scores revealing biomarkers related to AUD remission. Nature Publishing Group UK 2021-03-15 /pmc/articles/PMC7960734/ /pubmed/33723218 http://dx.doi.org/10.1038/s41398-021-01281-2 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Kinreich, Sivan
McCutcheon, Vivia V.
Aliev, Fazil
Meyers, Jacquelyn L.
Kamarajan, Chella
Pandey, Ashwini K.
Chorlian, David B.
Zhang, Jian
Kuang, Weipeng
Pandey, Gayathri
Viteri, Stacey Subbie-Saenz de.
Francis, Meredith W.
Chan, Grace
Bourdon, Jessica L.
Dick, Danielle M.
Anokhin, Andrey P.
Bauer, Lance
Hesselbrock, Victor
Schuckit, Marc A.
Nurnberger, John I.
Foroud, Tatiana M.
Salvatore, Jessica E.
Bucholz, Kathleen K.
Porjesz, Bernice
Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach
title Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach
title_full Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach
title_fullStr Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach
title_full_unstemmed Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach
title_short Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach
title_sort predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7960734/
https://www.ncbi.nlm.nih.gov/pubmed/33723218
http://dx.doi.org/10.1038/s41398-021-01281-2
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