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

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dc.authoridAnokhin, Andrey/0000-0001-8158-6346
dc.authoridBucholz, Kathleen/0000-0003-3794-0736
dc.authoridPandey, Ashwini Kumar/0000-0002-2688-7901
dc.authoridKinreich, Sivan/0000-0001-6480-1622
dc.authorid/0000-0003-2291-6880
dc.authoridFrancis, Meredith/0000-0003-0547-6836
dc.authoridKuang, Weipeng/0009-0008-2622-2556
dc.contributor.authorKinreich, Sivan
dc.contributor.authorMcCutcheon, Vivia V.
dc.contributor.authorAliev, Fazil
dc.contributor.authorMeyers, Jacquelyn L.
dc.contributor.authorKamarajan, Chella
dc.contributor.authorPandey, Ashwini K.
dc.contributor.authorChorlian, David B.
dc.date.accessioned2024-09-29T16:01:01Z
dc.date.available2024-09-29T16:01:01Z
dc.date.issued2021
dc.departmentKarabük Üniversitesien_US
dc.description.abstractPredictive 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.en_US
dc.description.sponsorshipNIAAA NIH HHS [U10 AA008401] Funding Source: Medline; NIDA NIH HHS [T32 DA015035] Funding Source: Medlineen_US
dc.identifier.doi10.1038/s41398-021-01281-2
dc.identifier.issn2158-3188
dc.identifier.issue1en_US
dc.identifier.pmid33723218en_US
dc.identifier.scopus2-s2.0-85102578415en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1038/s41398-021-01281-2
dc.identifier.urihttps://hdl.handle.net/20.500.14619/5493
dc.identifier.volume11en_US
dc.identifier.wosWOS:000629648400007en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringernatureen_US
dc.relation.ispartofTranslational Psychiatryen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titlePredicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approachen_US
dc.typeArticleen_US

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