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Convergent Evidence for Predispositional Effects of Brain Gray Matter Volume on Alcohol Consumption

Published:September 13, 2019DOI:https://doi.org/10.1016/j.biopsych.2019.08.029

      Abstract

      Background

      Alcohol use has been reliably associated with smaller subcortical and cortical regional gray matter volumes (GMVs). Whether these associations reflect shared predisposing risk factors or causal consequences of alcohol use remains poorly understood.

      Methods

      Data came from 3 neuroimaging samples (N = 2423), spanning childhood or adolescence to middle age, with prospective or family-based data. First, we identified replicable GMV correlates of alcohol use. Next, we used family-based and longitudinal data to test whether these associations may plausibly reflect a predispositional liability for alcohol use or a causal consequence of alcohol use. Finally, we used heritability, gene-set enrichment, and transcriptome-wide association study approaches to evaluate whether genome-wide association study–defined genomic risk for alcohol consumption is enriched for genes that are preferentially expressed in regions that were identified in our neuroimaging analyses.

      Results

      Smaller right dorsolateral prefrontal cortex (DLPFC) (i.e., middle and superior frontal gyri) and insula GMVs were associated with increased alcohol use across samples. Family-based and prospective longitudinal data suggest that these associations are genetically conferred and that DLPFC GMV prospectively predicts future use and initiation. Genomic risk for alcohol use was enriched in gene sets that were preferentially expressed in the DLPFC and was associated with replicable differential gene expression in the DLPFC.

      Conclusions

      These data suggest that smaller DLPFC and insula GMV plausibly represent genetically conferred predispositional risk factors for, as opposed to consequences of, alcohol use. DLPFC and insula GMV represent promising biomarkers for alcohol-consumption liability and related psychiatric and behavioral phenotypes.

      Keywords

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      References

        • World Health Organization
        Global status report on alcohol and health.
        World Health Organization Press, Geneva, Switzerland2014
        • Grant B.F.
        • Goldstein R.B.
        • Saha T.D.
        • Chou S.P.
        • Jung J.
        • Zhang H.
        • et al.
        Epidemiology of DSM-5 alcohol use disorder results from the National Epidemiologic Survey on Alcohol and Related Conditions III.
        JAMA Psychiatry. 2015; 72: 757-766
        • Substance Abuse and Mental Health Services Administration
        Key Substance Use and Mental Health Indicators in the United States: Results from the 2017 National Survey on Drug Use and Health.
        Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration, Rockville, MD2018 (Available at:) (Accessed February 22, 2018)
        • Mackey S.
        • Allgaier N.
        • Chaarani B.
        • Spechler P.
        • Orr C.
        • Bunn J.
        • et al.
        Mega-analysis of gray matter volume in substance dependence: general and substance-specific regional effects.
        Am J Psychiatry. 2018; 17: 19-128
        • Lange E.H.H.
        • Nerland S.
        • Jørgensen K.N.N.
        • Mørch-Johnsen L.
        • Nesvåg R.
        • Hartberg C.B.B.
        • et al.
        Alcohol use is associated with thinner cerebral cortex and larger ventricles in schizophrenia, bipolar disorder and healthy controls.
        Psychol Med. 2017; 4: 55-668
        • Yang X.
        • Tian F.
        • Zhang H.
        • Zeng J.
        • Chen T.
        • Wang S.
        • et al.
        Cortical and subcortical gray matter shrinkage in alcohol-use disorders: A voxel-based meta-analysis.
        Neurosci Biobehav Rev. 2016; 66: 92-103
        • Thayer R.E.
        • YorkWilliams S.
        • Karoly H.C.
        • Sabbineni A.
        • Ewing S.F.
        • Bryan A.D.
        • Hutchison K.E.
        Structural neuroimaging correlates of alcohol and cannabis use in adolescents and adults.
        Addiction. 2017; 112: 2144-2154
        • Whelan R.
        • Watts R.
        • Orr C.A.
        • Althoff R.R.
        • Artiges E.
        • Banaschewski T.
        • et al.
        Neuropsychosocial profiles of current and future adolescent alcohol misusers.
        Nature. 2014; 512: 185-189
        • Pfefferbaum A.
        • Kwon D.
        • Brumback T.
        • Thompson W.K.
        • Cummins K.
        • Tapert S.F.
        • et al.
        Altered brain developmental trajectories in adolescents after initiating drinking.
        Am J Psychiatry. 2017; 175: 370-380
        • Holmes A.J.
        • Hollinshead M.O.
        • Roffman J.L.
        • Smoller J.W.
        • Buckner R.L.
        Individual differences in cognitive control circuit anatomy link sensation seeking, impulsivity, and substance use.
        J Neurosci. 2016; 36: 4038-4049
        • Taffe M.A.
        • Kotzebue R.W.
        • Crean R.D.
        • Crawford E.F.
        • Edwards S.
        • Mandyam C.D.
        Long-lasting reduction in hippocampal neurogenesis by alcohol consumption in adolescent nonhuman primates.
        Proc Natl Acad Sci U S A. 2010; 107: 11104-11109
        • Kühn S.
        • Gallinat J.
        Gray matter correlates of posttraumatic stress disorder: A quantitative meta-analysis.
        Biol Psychiatry. 2013; 73: 70-74
        • Shnitko T.A.
        • Liu Z.
        • Wang X.
        • Grant K.A.
        • Christopher D.
        Chronic alcohol drinking slows brain development in adolescent and young adult nonhuman primates.
        eNeuro. 2019; 6: 1-11
        • Zou X.
        • Durazzo T.C.
        • Meyerhoff D.J.
        Regional brain volume changes in alcohol-dependent individuals during short-term and long-term abstinence.
        Alcohol Clin Exp Res. 2018; 42: 1062-1072
        • Luciana M.
        • Collins P.F.
        • Muetzel R.L.
        • Lim K.O.
        Effects of alcohol use initiation on brain structure in typically developing adolescents.
        Am J Drug Alcohol Abuse. 2013; 39: 345-355
        • Squeglia L.M.
        • Tapert S.F.
        • Sullivan E.V.
        • Jacobus J.
        • Meloy M.J.
        • Rohlfing T.
        • Pfefferbaum A.
        Brain development in heavy-drinking adolescents.
        Am J Psychiatry. 2015; 172: 531-542
        • Seo S.
        • Beck A.
        • Matthis C.
        • Genauck A.
        • Banaschewski T.
        • Bokde A.L.W.
        • et al.
        Risk profiles for heavy drinking in adolescence: Differential effects of gender.
        Addict Biol. 2018; 21: 348-356
        • Windle M.
        • Gray J.C.
        • Mankit K.
        • Barton A.W.
        • Brody G.
        • Beach S.R.H.
        • et al.
        Age sensitive associations of adolescent substance use with amygdalar, ventral striatum, and frontal volumes in young adulthood.
        Drug Alcohol Depend. 2018; 186: 94-101
        • Squeglia L.M.
        • Gray K.M.
        Alcohol and drug use and the developing brain.
        Curr Psychiatry Rep. 2016; 18: 46
        • Dager A.D.
        • McKay D.R.
        • Kent J.W.
        • Curran J.E.
        • Knowles E.
        • Sprooten E.
        • et al.
        Shared genetic factors influence amygdala volumes and risk for alcoholism.
        Neuropsychopharmacology. 2015; 40: 412-420
        • Henderson K.E.
        • Vaidya J.G.
        • Kramer J.R.
        • Kuperman S.
        • Langbehn D.R.
        • O’Leary D.S.
        Cortical thickness in adolescents with a family history of alcohol use disorder.
        Alcohol Clin Exp Res. 2018; 42: 89-99
        • Wilson S.
        • Malone S.M.
        • Thomas K.M.
        • Iacono W.G.
        Adolescent drinking and brain morphometry: A co-twin control analysis.
        Dev Cogn Neurosci. 2015; 16: 130-138
        • Sharma V.K.
        • Hill S.Y.
        Differentiating the effects of familial risk for alcohol dependence and prenatal exposure to alcohol on offspring brain morphology.
        Alcohol Clin Exp Res. 2017; 41: 312-322
        • Van Essen D.C.
        • Smith S.M.
        • Barch D.M.
        • Behrens T.E.J.
        • Yacoub E.
        • Ugurbil K.
        The WU-Minn Human Connectome Project: An overview.
        Neuroimage. 2013; 80: 62-79
        • Swartz J.R.
        • Williamson D.E.
        • Hariri A.R.
        Developmental change in amygdala reactivity during adolescence: Effects of family history of depression and stressful life events.
        Am J Psychiatry. 2015; 172: 276-283
        • Nikolova Y.S.
        • Knodt A.R.
        • Radtke S.R.
        • Hariri A.R.
        Divergent responses of the amygdala and ventral striatum predict stress-related problem drinking in young adults: possible differential markers of affective and impulsive pathways of risk for alcohol use disorder.
        Mol Psychiatry. 2016; 21: 348-356
        • Gusev A.
        • Ko A.
        • Shi H.
        • Bhatia G.
        • Chung W.
        • Penninx B.W.J.H.
        • et al.
        Integrative approaches for large-scale transcriptome-wide association studies.
        Nat Genet. 2016; 48: 245-252
        • Clarke T.-K.
        • Adams M.J.
        • Davies G.
        • Howard D.M.
        • Hall L.S.
        • Padmanabhan S.
        • et al.
        Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N = 112 117).
        Mol Psychiatry. 2017; 22: 1376-1384
        • Schumann G.
        • Liu C.
        • O’Reilly P.
        • Gao H.
        • Song P.
        • Xu B.
        • et al.
        KLB is associated with alcohol drinking, and its gene product β-Klotho is necessary for FGF21 regulation of alcohol preference.
        Proc Natl Acad Sci U S A. 2016; 113: 14372-14377
        • Welter D.
        • MacArthur J.
        • Morales J.
        • Burdett T.
        • Hall P.
        • et al.
        • The GTEx Consortium
        The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans.
        Science. 2015; 348: 648-660
        • Fromer M.
        • Roussos P.
        • Sieberts S.K.
        • Johnson J.S.
        • Kavanagh D.H.
        • Perumal T.M.
        • et al.
        Gene expression elucidates functional impact of polygenic risk for schizophrenia.
        Nat Neurosci. 2016; 19: 1442-1453
        • Saunders J.B.
        • Aasland O.G.
        • Babor T.F.
        • de la Fuente J.R.
        • Grant M.
        Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO Collaborative Project on Early Detection of Persons With Harmful Alcohol Consumption—II.
        Addiction. 1993; 88: 791-804
        • Babor T.F.
        • Higgins-Biddle J.C.
        • Saunders J.B.
        • Monteiro M.G.
        The Alcohol Use Disorders Identification Test: Guidelines for Use in Primary Care.
        World Health Organization, Geneva, Switzerland2001
        • Bucholz K.K.
        • Cadoret R.
        • Cloninger C.R.
        • Dinwiddie S.H.
        • Hesselbrock V.M.
        • Nurnberger J.I.
        • et al.
        A new, semi-structured psychiatric interview for use in genetic linkage studies: A report on the reliability of the SSAGA.
        J Stud Alcohol. 1994; 55: 149-158
        • Molina B.S.G.
        • Flory K.
        • Hinshaw S.P.
        • Greiner A.R.
        • Arnold L.E.
        • Swanson J.M.
        • et al.
        Delinquent behavior and emerging substance use in the MTA at 36 months: Prevalence, course, and treatment effects.
        J Am Acad Child Adolesc Psychiatry. 2007; 46: 1028-1040
        • Kendler K.S.
        • Gardner C.O.
        • Hickman M.
        • Heron J.
        • Macleod J.
        • Lewis G.
        • Dick D.M.
        Socioeconomic status and alcohol-related behaviors in mid- to late adolescence in the Avon Longitudinal Study of Parents and Children.
        J Stud Alcohol Drugs. 2014; 75: 541-545
        • Meng Y.
        • Holmes J.
        • Hill-Mcmanus D.
        • Brennan A.
        • Meier P.S.
        Trend analysis and modelling of gender-specific age, period and birth cohort effects on alcohol abstention and consumption level for drinkers in Great Britain using the General Lifestyle Survey 1984–2009.
        Addiction. 2014; 109: 206-215
        • Collins S.E.
        Associations between socioeconomic factors and alcohol outcomes.
        Alcohol Res. 2016; 38: 83-94
        • Grittner U.
        • Kuntsche S.
        • Gmel G.
        • Bloomfield K.
        Alcohol consumption and social inequality at the individual and country levels—Results from an international study.
        Eur J Public Health. 2013; 23: 332-339
        • Delker E.
        • Brown Q.
        • Hasin D.S.
        Alcohol consumption in demographic subpopulations: An epidemiologic overview.
        Alcohol Res. 2016; 38: 7-15
        • Cacciola E.E.T.
        • Nevid J.S.
        Alcohol consumption in relation to residence status and ethnicity in college students.
        Psychol Addict Behav. 2014; 28: 1278-1283
        • Keyes K.M.
        • Hatzenbuehler M.L.
        • Grant B.F.
        • Hasin D.S.
        Stress and alcohol: Epidemiologic evidence.
        Alcohol Res. 2012; 34: 391-400
        • Enoch M.-A.
        The role of early life stress as a predictor for alcohol and drug dependence.
        Psychopharmacology (Berl). 2011; 214: 17-31
        • Mclaughlin K.A.
        • Green J.G.
        • Gruber M.J.
        • Sampson N.A.
        • Zaslavsky A.M.
        • Kessler R.C.
        Childhood adversities and adult psychiatric disorders in the National Comorbidity Survey Replication II.
        . 2013; 67: 124-132
        • Tzourio-Mazoyer N.
        • Landeau B.
        • Papathanassiou D.
        • Crivello F.
        • Etard O.
        • Delcroix N.
        • et al.
        Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.
        Neuroimage. 2002; 15: 273-289
        • Winkler A.M.
        • Webster M.A.
        • Vidaurre D.
        • Nichols T.E.
        • Smith S.M.
        Multi-level block permutation.
        Neuroimage. 2015; 123: 253-268
        • Winkler A.M.
        • Ridgway G.R.
        • Webster M.A.
        • Smith S.M.
        • Nichols T.E.
        Permutation inference for the general linear model.
        Neuroimage. 2014; 92: 381-397
        • Winkler A.M.
        • Ridgway G.R.
        • Douaud G.
        • Nichols T.E.
        • Smith S.M.
        Faster permutation inference in brain imaging.
        Neuroimage. 2016; 141: 502-516
        • Vul E.
        • Harris C.
        • Winkielman P.
        • Pashler H.
        Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, and Social Cognition1.
        Perspect Psychol Sci. 2009; 4: 274-290
        • Kochunov P.
        • Jahanshad N.
        • Marcus D.
        • Winkler A.
        • Sprooten E.
        • Nichols T.E.
        • et al.
        Heritability of fractional anisotropy in human white matter: A comparison of Human Connectome Project and ENIGMA-DTI data.
        Neuroimage. 2015; 111: 300-301
        • Ziyatdinov A.
        • Brunel H.
        • Martinez-Perez A.
        • Buil A.
        • Perera A.
        • Soria J.M.
        Solarius: An R interface to SOLAR for variance component analysis in pedigrees.
        Bioinformatics. 2016; 32: 1901-1902
        • Revelle W.
        Package ‘psych’: Procedures for Psychological, Psychometric and Personality Research.
        (Version 1.8.10. Available at:)
        • Bates D.
        • Maechler Martin
        • Walker S.
        Package ‘lme4’: Linear Mixed-Effects Models Using ‘Eigen’ and S4.
        (Version 1.1-19. Available at:)
        • R Core Team
        R: A language and environment for statistical computing.
        R Foundation for Statistical Computing, Vienna, Austria2014 (Version 3.5.1. Available at:)
        • Hahsler M.
        • Piekenbrock M.
        • Arya S.
        • Mount D.
        dbscan: Density Based Clustering of Applications with Noise (DBSCAN) and Related Algorithms.
        (Version 1.1-3. Available at:)
        • Pinheiro J.
        • DebRoy S.
        • Bates D.
        • Sarkar D.
        • R Core Team
        nlme: Linear and Nonlinear Mixed Effects Models.
        (Version 3.1-137. Available at:)
        • Keller M.C.
        Gene × environment interaction studies have not properly controlled for potential confounders: The problem and the (simple) solution.
        Biol Psychiatry. 2014; 75: 18-24
        • Baranger D.A.A.
        • Ifrah C.
        • Prather A.A.
        • Carey C.E.
        • Corral-Frías N.S.
        • Drabant Conley E.
        • et al.
        PER1 rs3027172 genotype interacts with early life stress to predict problematic alcohol use, but not reward-related ventral striatum activity.
        Front Psychol. 2016; 7: 464
        • Bates D.
        • Mächler M.
        • Bolker B.M.
        • Walker S.C.
        • Maechler Martin
        • Walker S.C.
        Fitting linear mixed-effects models using lme4.
        J Stat Softw. 2015; 67: 1-48
        • Finucane H.K.
        • Bulik-Sullivan B.
        • Gusev A.
        • Trynka G.
        • Reshef Y.
        • Loh P.R.
        • et al.
        Partitioning heritability by functional annotation using genome-wide association summary statistics.
        Nat Genet. 2015; 47: 1228-1235
        • Finucane H.K.
        • Reshef Y.
        • Anttila V.
        • Slowikowski K.
        • Gusev A.
        • Byrnes A.
        • et al.
        Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types.
        Nat Genet. 2018; 50: 621-629
        • Bulik-Sullivan B.
        • Loh P.R.
        • Finucane H.K.
        • Ripke S.
        • Yang J.
        • Patterson N.
        • et al.
        LD score regression distinguishes confounding from polygenicity in genome-wide association studies.
        Nat Genet. 2015; 47: 291-295
        • de Leeuw C.A.
        • Mooij J.M.
        • Heskes T.
        • Posthuma D.
        MAGMA: Generalized gene-set analysis of GWAS data.
        PLoS Comput Biol. 2015; 11e1004219
        • Watanabe K.
        • Taskesen E.
        • Van Bochoven A.
        • Posthuma D.
        Functional mapping and annotation of genetic associations with FUMA.
        Nat Commun. 2017; 8: 1826
        • Watanabe K.
        • Stringer S.
        • Frei O.
        • Umićević Mirkov M.
        • de Leeuw C.
        • Polderman T.J.C.
        • et al.
        A global overview of pleiotropy and genetic architecture in complex traits.
        Nat Genet. 2018; 51: 1339-1348
        • Young-Wolff K.C.
        • Enoch M.A.
        • Prescott C.A.
        The influence of gene-environment interactions on alcohol consumption and alcohol use disorders: A comprehensive review.
        Clin Psychol Rev. 2011; 31: 800-816
        • Carey C.E.
        • Agrawal A.
        • Bucholz K.K.
        • Hartz S.M.
        • Lynskey M.T.
        • Nelson E.C.
        • et al.
        Associations between polygenic risk for psychiatric disorders and substance involvement.
        Front Genet. 2016; 7: 149
        • Smoller J.W.
        • Craddock N.
        • Kendler K.
        • Lee P.H.
        • Neale B.M.
        • Nurnberger J.I.
        • et al.
        Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis.
        Lancet. 2013; 381: 1371-1379
        • Dick D.M.
        • Agrawal A.
        The genetics of alcohol and other drug dependence.
        Alcohol Res Health. 2008; 31: 111-118
        • Droutman V.
        • Read S.J.
        • Bechara A.
        Revisiting the role of the insula in addiction.
        Trends Cogn Sci. 2015; 19: 414-420
        • Rijsdijk F.V.
        • Sham P.C.
        Analytic approaches to twin data using structural equation models.
        Brief Bioinform. 2002; 3: 119-133
        • Agrawal A.
        • Lynskey M.T.
        Are there genetic influences on addiction: Evidence from family, adoption and twin studies.
        Addiction. 2008; 103: 1069-1081
        • LaBuda M.C.
        • Svikis D.S.
        • Pickens R.W.
        Twin closeness and co-twin risk for substance use disorders: Assessing the impact of the equal environment assumption.
        Psychiatry Res. 1997; 70: 155-164
        • Felson J.
        What can we learn from twin studies? A comprehensive evaluation of the equal environments assumption.
        Soc Sci Res. 2014; 43: 184-199
        • Munafò M.R.
        • Davey Smith G.
        Robust research needs many lines of evidence.
        Nature. 2018; 553: 399-401
        • Substance Abuse and Mental Health Services Administration
        Results from the 2015 National Survey on Drug Use and Health: Detailed Tables.
        (Available at:) (Accessed February 22, 2018)
        • Liu J.
        • Lewohl J.M.
        • Harris R.A.
        • Iyer V.R.
        • Dodd P.R.
        • Randall P.K.
        • Mayfield R.D.
        Patterns of gene expression in the frontal cortex discriminate alcoholic from nonalcoholic individuals.
        Neuropsychopharmacology. 2006; 31: 1574-1582
        • Kapoor M.
        • Wang J.
        • Farris S.P.
        • Liu Y.
        • Mcclintick J.
        • Gupta I.
        • et al.
        Analysis of whole genome-transcriptomic organization in brain to identify genes associated with alcoholism.
        Transl Psychiatry. 2019; 9: 89
        • Volkow N.D.
        • Koob G.F.
        • Croyle R.T.
        • Bianchi D.W.
        • Gordon J.A.
        • Koroshetz W.J.
        • et al.
        The conception of the ABCD study: From substance use to a broad NIH collaboration.
        Dev Cogn Neurosci. 2018; 32: 4-7