Literature DB >> 36195638

Clinical, environmental, and genetic risk factors for substance use disorders: characterizing combined effects across multiple cohorts.

Peter B Barr1,2, Morgan N Driver3, Sally I-Chun Kuo4, Mallory Stephenson5, Fazil Aliev4,6, Richard Karlsson Linnér7, Jesse Marks8, Andrey P Anokhin9, Kathleen Bucholz9, Grace Chan10,11, Howard J Edenberg12,13, Alexis C Edwards5, Meredith W Francis9, Dana B Hancock8, K Paige Harden14,15, Chella Kamarajan16, Jaakko Kaprio17, Sivan Kinreich16, John R Kramer11, Samuel Kuperman11, Antti Latvala18, Jacquelyn L Meyers16,19, Abraham A Palmer20,21, Martin H Plawecki22, Bernice Porjesz16, Richard J Rose23, Marc A Schuckit20, Jessica E Salvatore4, Danielle M Dick4,6.   

Abstract

Substance use disorders (SUDs) incur serious social and personal costs. The risk for SUDs is complex, with risk factors ranging from social conditions to individual genetic variation. We examined whether models that include a clinical/environmental risk index (CERI) and polygenic scores (PGS) are able to identify individuals at increased risk of SUD in young adulthood across four longitudinal cohorts for a combined sample of N = 15,134. Our analyses included participants of European (NEUR = 12,659) and African (NAFR = 2475) ancestries. SUD outcomes included: (1) alcohol dependence, (2) nicotine dependence; (3) drug dependence, and (4) any substance dependence. In the models containing the PGS and CERI, the CERI was associated with all three outcomes (ORs = 01.37-1.67). PGS for problematic alcohol use, externalizing, and smoking quantity were associated with alcohol dependence, drug dependence, and nicotine dependence, respectively (OR = 1.11-1.33). PGS for problematic alcohol use and externalizing were also associated with any substance dependence (ORs = 1.09-1.18). The full model explained 6-13% of the variance in SUDs. Those in the top 10% of CERI and PGS had relative risk ratios of 3.86-8.04 for each SUD relative to the bottom 90%. Overall, the combined measures of clinical, environmental, and genetic risk demonstrated modest ability to distinguish between affected and unaffected individuals in young adulthood. PGS were significant but added little in addition to the clinical/environmental risk index. Results from our analysis demonstrate there is still considerable work to be done before tools such as these are ready for clinical applications.
© 2022. The Author(s), under exclusive licence to Springer Nature Limited.

Entities:  

Year:  2022        PMID: 36195638     DOI: 10.1038/s41380-022-01801-6

Source DB:  PubMed          Journal:  Mol Psychiatry        ISSN: 1359-4184            Impact factor:   13.437


  34 in total

1.  Specificity of genetic and environmental risk factors for use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates in male twins.

Authors:  Kenneth S Kendler; Kristen C Jacobson; Carol A Prescott; Michael C Neale
Journal:  Am J Psychiatry       Date:  2003-04       Impact factor: 18.112

Review 2.  The social epidemiology of substance use.

Authors:  Sandro Galea; Arijit Nandi; David Vlahov
Journal:  Epidemiol Rev       Date:  2004       Impact factor: 6.222

3.  Childhood socioeconomic status and longitudinal patterns of alcohol problems: Variation across etiological pathways in genetic risk.

Authors:  Peter B Barr; Judy Silberg; Danielle M Dick; Hermine H Maes
Journal:  Soc Sci Med       Date:  2018-05-14       Impact factor: 4.634

4.  Neighborhood conditions and trajectories of alcohol use and misuse across the early life course.

Authors:  Peter B Barr
Journal:  Health Place       Date:  2018-03-05       Impact factor: 4.078

Review 5.  Genetic and environmental influences on cannabis use initiation and problematic use: a meta-analysis of twin studies.

Authors:  Karin J H Verweij; Brendan P Zietsch; Michael T Lynskey; Sarah E Medland; Michael C Neale; Nicholas G Martin; Dorret I Boomsma; Jacqueline M Vink
Journal:  Addiction       Date:  2010-03       Impact factor: 6.526

6.  Alcohol and nicotine polygenic scores are associated with the development of alcohol and nicotine use problems from adolescence to young adulthood.

Authors:  Joseph D Deak; D Angus Clark; Mengzhen Liu; Jonathan D Schaefer; Seon Kyeong Jang; C Emily Durbin; William G Iacono; Matt McGue; Scott Vrieze; Brian M Hicks
Journal:  Addiction       Date:  2021-10-24       Impact factor: 6.526

7.  The heritability of alcohol use disorders: a meta-analysis of twin and adoption studies.

Authors:  B Verhulst; M C Neale; K S Kendler
Journal:  Psychol Med       Date:  2014-08-29       Impact factor: 7.723

8.  Predicting risk for Alcohol Use Disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study.

Authors:  Sivan Kinreich; Jacquelyn L Meyers; Adi Maron-Katz; Chella Kamarajan; Ashwini K Pandey; David B Chorlian; Jian Zhang; Gayathri Pandey; Stacey Subbie-Saenz de Viteri; Dan Pitti; Andrey P Anokhin; Lance Bauer; Victor Hesselbrock; Marc A Schuckit; Howard J Edenberg; Bernice Porjesz
Journal:  Mol Psychiatry       Date:  2019-10-08       Impact factor: 15.992

9.  Using polygenic scores for identifying individuals at increased risk of substance use disorders in clinical and population samples.

Authors:  Peter B Barr; Albert Ksinan; Jinni Su; Emma C Johnson; Jacquelyn L Meyers; Leah Wetherill; Antti Latvala; Fazil Aliev; Grace Chan; Samuel Kuperman; John Nurnberger; Chella Kamarajan; Andrey Anokhin; Arpana Agrawal; Richard J Rose; Howard J Edenberg; Marc Schuckit; Jaakko Kaprio; Danielle M Dick
Journal:  Transl Psychiatry       Date:  2020-06-18       Impact factor: 6.222

10.  Which adolescents develop persistent substance dependence in adulthood? Using population-representative longitudinal data to inform universal risk assessment.

Authors:  M H Meier; W Hall; A Caspi; D W Belsky; M Cerdá; H L Harrington; R Houts; R Poulton; T E Moffitt
Journal:  Psychol Med       Date:  2015-12-01       Impact factor: 7.723

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