Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach.
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McCutcheon, Vivia V
Meyers, Jacquelyn L
Pandey, Ashwini K
Chorlian, David B
Viteri, Stacey Subbie-Saenz de
Francis, Meredith W
Bourdon, Jessica L
Dick, Danielle M
Anokhin, Andrey P
Schuckit, Marc A
Nurnberger, John I
Foroud, Tatiana M
Salvatore, Jessica E
Bucholz, Kathleen K
Journal titleTranslational psychiatry
Publication Begin page166
MetadataShow full item record
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.
CitationKinreich S, McCutcheon VV, Aliev F, Meyers JL, Kamarajan C, Pandey AK, Chorlian DB, Zhang J, Kuang W, Pandey G, Viteri SS, Francis MW, Chan G, Bourdon JL, Dick DM, Anokhin AP, Bauer L, Hesselbrock V, Schuckit MA, Nurnberger JI Jr, Foroud TM, Salvatore JE, Bucholz KK, Porjesz B. Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach. Transl Psychiatry. 2021 Mar 15;11(1):166. doi: 10.1038/s41398-021-01281-2. PMID: 33723218; PMCID: PMC7960734.
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- Predicting risk for Alcohol Use Disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study.
- Authors: Kinreich S, Meyers JL, Maron-Katz A, Kamarajan C, Pandey AK, Chorlian DB, Zhang J, Pandey G, Subbie-Saenz de Viteri S, Pitti D, Anokhin AP, Bauer L, Hesselbrock V, Schuckit MA, Edenberg HJ, Porjesz B
- Issue date: 2021 Apr
- Evaluating risk for alcohol use disorder: Polygenic risk scores and family history.
- Authors: Lai D, Johnson EC, Colbert S, Pandey G, Chan G, Bauer L, Francis MW, Hesselbrock V, Kamarajan C, Kramer J, Kuang W, Kuo S, Kuperman S, Liu Y, McCutcheon V, Pang Z, Plawecki MH, Schuckit M, Tischfield J, Wetherill L, Zang Y, Edenberg HJ, Porjesz B, Agrawal A, Foroud T
- Issue date: 2022 Mar
- Alcohol use disorder, psychiatric comorbidities, marriage and divorce in a high-risk sample.
- Authors: Thomas NS, Kuo SI, Aliev F, McCutcheon VV, Meyers JM, Chan G, Hesselbrock V, Kamarajan C, Kinreich S, Kramer JR, Kuperman S, Lai D, Plawecki MH, Porjesz B, Schuckit MA, Dick DM, Bucholz KK, Salvatore JE
- Issue date: 2022 Jun
- Association of parental divorce, discord, and polygenic risk with children's alcohol initiation and lifetime risk for alcohol use disorder.
- Authors: Kuo SI, Thomas NS, Aliev F, Bucholz KK, Dick DM, McCutcheon VV, Meyers JL, Chan G, Kamarajan C, Kramer JR, Hesselbrock V, Plawecki MH, Porjesz B, Tischfield J, Salvatore JE
- Issue date: 2023 Apr
- Gene-based polygenic risk scores analysis of alcohol use disorder in African Americans.
- Authors: Lai D, Schwantes-An TH, Abreu M, Chan G, Hesselbrock V, Kamarajan C, Liu Y, Meyers JL, Nurnberger JI Jr, Plawecki MH, Wetherill L, Schuckit M, Zhang P, Edenberg HJ, Porjesz B, Agrawal A, Foroud T
- Issue date: 2022 Jul 5