Clinical, environmental, and genetic risk factors for substance use disorders: characterizing combined effects across multiple cohorts.
paper
Primary
Public
- Authors
- Barr, Peter B; Driver, Morgan N; Kuo, Sally I-Chun; Stephenson, Mallory; Aliev, Fazil; Linnér, Richard Karlsson; Marks, Jesse; Anokhin, Andrey P; Bucholz, Kathleen; Chan, Grace; Edenberg, Howard J; Edwards, Alexis C; Francis, Meredith W; Hancock, Dana B; Harden, K Paige; Kamarajan, Chella; Kaprio, Jaakko; Kinreich, Sivan; Kramer, John R; Kuperman, Samuel; Latvala, Antti; Meyers, Jacquelyn L; Palmer, Abraham A; Plawecki, Martin H; Porjesz, Bernice; Rose, Richard J; Schuckit, Marc A; Salvatore, Jessica E; Dick, Danielle M
- Year
- 2022
- Journal
- Molecular psychiatry
- PMID
- 36195638
- DOI
- 10.1038/s41380-022-01801-6
- PMCID
- PMC9938102
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 (N = 12,659) and African (N = 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.
SUD prevalence across genetic and environmental risk factors.A Prevalence (and 95% confidence intervals) of those who meet criteria for alcohol, nicotine, drug, or any substance dependence across counts for items in the risk index. B Prevalence (and 95% confidence intervals) of those who meet criteria for alcohol, nicotine, drug, or any substance dependence across four categories: (1) those below the 90th percentile for all PGS and the CERI; (2) those at or above the 90th percentile for the CERI; (3) those at or above the 90th percentile for all PGS; and (4) those at or above the 90th percentile for both the CERI and PGS. PGS and risk index were first residualized on sex, age, age2, cohort, sex * age, sex * age2, sex * cohort, cohort * age, cohort * age2, sex * cohort * age, and sex * cohort * age2. Dotted colored lines represent corresponding lifetime prevalence estimates for alcohol dependence (red), nicotine dependence (green), drug dependence (blue), and any substance use disorder (purple) from nationally representative data [58].
ROC Curves for combined and baseline models.Receiver operating characteristic (ROC) curves for baseline models (red line, covariates only) and the full models (blue line, PGS + CERI + covariates) for each substance use disorder. The area under the curve (AUC) is presented for the PGS model in each cell. Change in AUC represents value of the difference between AUC from the full model and AUC from the base model.
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| Citation | PMID | DOI | Status |
|---|---|---|---|
| BarrPB, DickDM. The genetics of externalizing problems. Curr Top Behav Neurosci. 2020;47:93–112.3184513210.1007/7854_2019_120 | — | — | — |
| BarrPB, KsinanA, SuJ, JohnsonEC, MeyersJL, WetherillL, Using polygenic scores for identifying individuals at increased risk of substance use disorders in clinical and population samples. Transl Psychiatry 2020;10:196.3255514710.1038/s41398-020-00865-8PMC7303212 | — | — | — |
| BarrPB, MallardTT, Sanchez-RoigeS, PooreHE, LinnérRK, CollaboratorsC, Parsing genetically influenced risk pathways: genetic loci impact problematic alcohol use via externalizing and specific risk. 2021. https://www.medrxiv.org/content/10.1101/2021.07.20.21260861v1.10.1038/s41398-022-02171-xPMC952564936180423 | — | — | — |
| BarrPB, SilbergJ, DickDM, MaesHH. Childhood socioeconomic status and longitudinal patterns of alcohol problems: variation across etiological pathways in genetic risk. Soc Sci Med. 2018;209:51–58.2979316410.1016/j.socscimed.2018.05.027PMC5997543 | — | — | — |
| BarrPB. Neighborhood conditions and trajectories of alcohol use and misuse across the early life course. Health Place. 2018;51:36–44.2951871610.1016/j.healthplace.2018.02.007 | — | — | — |
| BegleiterH. The collaborative study on the genetics of alcoholism. Alcohol Health Res World. 1995;19:228.31798102PMC6875768 | — | — | — |
| BigdeliTB, FanousAH, LiY, RajeevanN, SaywardF, GenoveseG, Genome-wide association studies of schizophrenia and bipolar disorder in a diverse cohort of US Veterans. Schizophr Bull. 2020. 10.1093/schbul/sbaa133.PMC796506333169155 | — | — | — |
| BoydA, GoldingJ, MacleodJ, LawlorDA, FraserA, HendersonJ, Cohort profile: The ‘Children of the 90s’-The index offspring of the avon longitudinal study of parents and children. Int J Epidemiol. 2013. 10.1093/ije/dys064.PMC360061822507743 | — | — | — |
| BucholzKK, McCutcheonVV, AgrawalA, DickDM, HesselbrockVM, KramerJR, Comparison of parent, peer, psychiatric, and cannabis use influences across stages of offspring alcohol involvement: evidence from the COGA prospective study. Alcohol Clin Exp Res. 2017;41:359–68.2807315710.1111/acer.13293PMC5272776 | — | — | — |
| CameronCA, GelbachJB, MillerDL. Robust inference with multiway clustering. J Bus Economic Stat. 2011;29:238–49. | — | — | — |
| ConrodPJ, O’Leary-BarrettM, NewtonN, TopperL, Castellanos-RyanN, MacKieC, Effectiveness of a selective, personality-targeted prevention program for adolescent alcohol use and misuse: a cluster randomized controlled trial. JAMA Psychiatry. 2013;70:334–42.2334413510.1001/jamapsychiatry.2013.651 | — | — | — |
| CurranPJ, HussongAM. Integrative data analysis: the simultaneous analysis of multiple data sets. Psychol Methods. 2009;14:81–100.1948562310.1037/a0015914PMC2777640 | — | — | — |
| DavidsonKW, McGinnT. Screening for social determinants of health: the known and unknown. JAMA. 2019;322:1037–8.3146509510.1001/jama.2019.10915 | — | — | — |
| DeakJD, ClarkDA, LiuM, SchaeferJD, JangSK, DurbinCE, Alcohol and nicotine polygenic scores are associated with the development of alcohol and nicotine use problems from adolescence to young adulthood. Addiction. 2022;117:1117–27.3459037610.1111/add.15697PMC8931861 | — | — | — |
| DegenhardtL, CharlsonF, FerrariA, SantomauroD, ErskineH, Mantilla-HerraraA, The global burden of disease attributable to alcohol and drug use in 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Psychiatry 2018;5:987–1012.3039273110.1016/S2215-0366(18)30337-7PMC6251968 | — | — | — |
| DickDM, BarrP, GuyM, NasimA, ScottD. Review: genetic research on alcohol use outcomes in African American populations: A review of the literature, associated challenges, and implications. Am J Addictions. 2017;26:486–93.10.1111/ajad.12495PMC588410228240821 | — | — | — |
| DuncanL, ShenH, GelayeB, MeijsenJ, ResslerK, FeldmanM, Analysis of polygenic risk score usage and performance in diverse human populations. Nat Commun. 2019. 10.1038/s41467-019-11112-0.PMC665847131346163 | — | — | — |
| EdenbergHJ. The collaborative study on the genetics of alcoholism: an update. Alcohol Res Health. 2002;26:214–8.12875050PMC6683843 | — | — | — |
| FraserA, Macdonald-wallisC, TillingK, BoydA, GoldingJ, Davey smithG, Cohort profile: the avon longitudinal study of parents and children: ALSPAC mothers cohort. Int J Epidemiol. 2013;42:97–110.2250774210.1093/ije/dys066PMC3600619 | — | — | — |
| GaleaS, NandiA, VlahovD. The social epidemiology of substance use. Epidemiol Rev. 2004;26:36–52.1523494610.1093/epirev/mxh007 | — | — | — |
| GargA, Boynton-JarrettR, DworkinPH. Avoiding the unintended consequences of screening for social determinants of health. JAMA. 2016;316:813–4.2736722610.1001/jama.2016.9282 | — | — | — |
| GuF, ChenTH, PfeifferRM, FargnoliMC, CalistaD, GhiorzoP, Combining common genetic variants and non-genetic risk factors to predict risk of cutaneous melanoma. Hum Mol Genet. 2018. 10.1093/hmg/ddy282.PMC624074230060076 | — | — | — |
| HarrisKM, HalpernCT, HaberstickBC, SmolenA. The National Longitudinal Study of Adolescent Health (Add Health) sibling pairs data. Twin Res Hum Genet. 2013;16:391–8.2323178010.1017/thg.2012.137PMC3574787 | — | — | — |
| HarrisPA, TaylorR, ThielkeR, PayneJ, GonzalezN, CondeJG. Research electronic data capture (REDCap)-A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inf. 2009;42:377–81.10.1016/j.jbi.2008.08.010PMC270003018929686 | — | — | — |
| HasinDS, GrantBF. The National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) Waves 1 and 2: review and summary of findings. Soc Psychiatry Psychiatr Epidemiol. 2015;50:1609–40.2621073910.1007/s00127-015-1088-0PMC4618096 | — | — | — |
| HeathertonTF, KozlowskiLT, FreckerRC, FagerstromKO. The Fagerstrom Test for Nicotine Dependence: a revision of the Fagerstrom Tolerance Questionnaire. Br J Addict. 1991;86:1119–27.193288310.1111/j.1360-0443.1991.tb01879.x | — | — | — |
| HughesK, BellisMA, HardcastleKA, SethiD, ButchartA, MiktonC, The effect of multiple adverse childhood experiences on health: a systematic review and meta-analysis. Lancet Public Health. 2017. 10.1016/S2468-2667(17)30118-4.29253477 | — | — | — |
| JohnsonEC, DemontisD, ThorgeirssonTE, WaltersRK, PolimantiR, HatoumAS, A large-scale genome-wide association study meta-analysis of cannabis use disorder. Lancet Psychiatry 2020. 10.1016/S2215-0366(20)30339-4.PMC767463133096046 | — | — | — |
| Karlsson LinnerR, MallardTT, BarrPB, Sanchez-RoigeS, MadoleJW, DriverMN, Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction. Nat Neurosci. 2021;24:1367–76.3444693510.1038/s41593-021-00908-3PMC8484054 | — | — | — |
| KendlerKS, GardnerC, DickDM. Predicting alcohol consumption in adolescence from alcohol- specific and general externalizing genetic risk factors, key environmental exposures and their interaction. Psychol Med. 2011;41:1507–16.2094299310.1017/S003329171000190XPMC3103618 | — | — | — |
| KendlerKS, JacobsonKC, PrescottCA, NealeMC. Specificity of genetic and environmental risk factors for use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates in male twins. Am J Psychiatry 2003;160:687–95.1266835710.1176/appi.ajp.160.4.687 | — | — | — |
| KendlerKS, MyersJ. The boundaries of the internalizing and externalizing genetic spectra in men and women. Psychol Med. 2014;44:647–55.2357468510.1017/S0033291713000585PMC3825740 | — | — | — |
| KesslerRC, BerglundP, DemlerO, JinR, MerikangasKR, WaltersEE, Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry 2005;62:593.1593983710.1001/archpsyc.62.6.593 | — | — | — |
| KinreichS, MeyersJL, Maron-KatzA, KamarajanC, PandeyAK, ChorlianDB, Predicting risk for Alcohol Use Disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study. Mol Psychiatry 2021;26:1133–41.3159503410.1038/s41380-019-0534-xPMC7138692 | — | — | — |
| KongA, ThorleifssonG, FriggeML, VilhjalmssonBJ, YoungAI, ThorgeirssonTE, The nature of nurture: Effects of parental genotypes. Science. 2018;359:424–8.2937146310.1126/science.aan6877 | — | — | — |
| KranzlerHR, ZhouH, KemberRL, Vickers SmithR, JusticeAC, DamrauerS, Genome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populations. Nat Commun. 2019;10:1499.3094081310.1038/s41467-019-09480-8PMC6445072 | — | — | — |
| KruegerRF, HicksBM, PatrickCJ, CarlsonSR, IaconoWGWG, McGueM. Etiological connections among substance dependence, antisocial behavior and personality: modeling the externalizing spectrum. J Abnorm Psychol. 2002;111:411–24.12150417 | — | — | — |
| LeveyDF, SteinMB, WendtFR, PathakGA, ZhouH, AslanM, Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions. Nat Neurosci. 2021. 10.1038/s41593-021-00860-2.PMC840430434045744 | — | — | — |
| LiuM, JiangY, WedowR, LiY, BrazelDM, ChenF, Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet. 2019;51:237–44.3064325110.1038/s41588-018-0307-5PMC6358542 | — | — | — |
| MartinAR, GignouxCR, WaltersRK, WojcikGL, NealeBM, GravelS, Human demographic history impacts genetic risk prediction across diverse populations. Am J Hum Genet. 2017;100:635–49.2836644210.1016/j.ajhg.2017.03.004PMC5384097 | — | — | — |
| MartinAR, KanaiM, KamataniY, OkadaY, NealeBM, DalyMJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019;51:584–91.3092696610.1038/s41588-019-0379-xPMC6563838 | — | — | — |
| MeierMH, HallW, CaspiA, BelskyDW, CerdaM, HarringtonHL, Which adolescents develop persistent substance dependence in adulthood? Using population-representative longitudinal data to inform universal risk assessment. Psychol Med. 2016;46:877–89.2662072010.1017/S0033291715002482PMC4752874 | — | — | — |
| MeyersJL, SalvatoreJE, VuoksimaaE, KorhonenT, PulkkinenL, RoseRJ, Genetic influences on alcohol use behaviors have diverging developmental trajectories: a prospective study among male and female twins. Alcohol Clin Exp Res. 2014;38:2869–77.2542152110.1111/acer.12560PMC4244647 | — | — | — |
| MillsMC, RahalC. A scientometric review of genome-wide association studies. Commun Biol. 2019;2:9.3062310510.1038/s42003-018-0261-xPMC6323052 | — | — | — |
| NagelkerkeNJD. A note on a general definition of the coefficient of determination. Biometrika. 1991;78:691–2. | — | — | — |
| National Drug Intelligence Center. National drug threat assessment. 2019. Washington, DC: United States Department of Justice; 2011. | — | — | — |
| NorthstoneK, LewcockM, GroomA, BoydA, MacleodJ, TimpsonN, The Avon Longitudinal Study of Parents and Children (ALSPAC): an update on the enrolled sample of index children in 2019 [version 1; peer review: 2 approved]. Wellcome Open Res. 2019;4:51.3102005010.12688/wellcomeopenres.15132.1PMC6464058 | — | — | — |
| O’SullivanJW, ShcherbinaA, JustesenJM, TurakhiaM, PerezM, WandH, Combining clinical and polygenic risk improves stroke prediction among individuals with atrial fibrillation. Circ Genom Precis Med. 2021;14:339–47.10.1161/CIRCGEN.120.003168PMC821257534029116 | — | — | — |
| PolimantiR, PetersonRE, OngJS, MacGregorS, EdwardsAC, ClarkeTK, Evidence of causal effect of major depression on alcohol dependence: findings from the psychiatric genomics consortium. Psychol Med. 2019. 10.1017/S0033291719000667.PMC656560130929657 | — | — | — |
| QuachBC, BrayMJ, GaddisNC, LiuM, PalviainenT, MinicaCC, Expanding the genetic architecture of nicotine dependence and its shared genetics with multiple traits. Nat Commun. 2020;11:5562.3314456810.1038/s41467-020-19265-zPMC7642344 | — | — | — |
| RoseRJ, SalvatoreJE, AaltonenS, BarrPB, BoglLH, ByersHA FinnTwin12 Cohort: An Updated Review. Twin Res Hum Genet. 2019;22:302–11.3164083910.1017/thg.2019.83PMC7108792 | — | — | — |
| RuanY, LinY-F, FengY-CA, ChenC-Y, LamM, GuoZ, Improving polygenic prediction in ancestrally diverse populations. Nat Genet. 2022. 10.1038/s41588-022-01054-7.PMC911745535513724 | — | — | — |
| SacksJJ, GonzalesKR, BoucheryEE, TomediLE, BrewerRD. National and state costs of excessive alcohol consumption. Am J Prev Med. 2010;2015:e73–e79.10.1016/j.amepre.2015.05.03126477807 | — | — | — |
| Sanchez-RoigeS, PalmerAA, ClarkeTK. Recent efforts to dissect the genetic basis of alcohol use and abuse. Biol Psychiatry 2020;87:609–18.3173378910.1016/j.biopsych.2019.09.011PMC7071963 | — | — | — |
| SchaeferJD, JangSK, ClarkDA, DeakJD, HicksBM, IaconoWG, Associations between polygenic risk of substance use and use disorder and alcohol, cannabis, and nicotine use in adolescence and young adulthood in a longitudinal twin study. Psychol Med. 2021:1–11. (Online available ahead of printing).10.1017/S0033291721004116PMC1012383337310313 | — | — | — |
| SherKJ, GrekinER, WilliamsNA. The development of alcohol use disorders. Annu Rev Clin Psychol. 2005;1:493–523.1771609710.1146/annurev.clinpsy.1.102803.144107 | — | — | — |
| TrubetskoyV, PardiñasAF, QiT, PanagiotaropoulouG, AwasthiS, BigdeliTB, Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature. 2022;2022:1–13.10.1038/s41586-022-04434-5PMC939246635396580 | — | — | — |
| U.S. Overdose deaths in 2021 increased half as much as in 2020 - but are still up 15%. https://www.cdc.gov/nchs/pressroom/nchs_press_releases/2022/202205.htm. Accessed 15 May 2022. | — | — | — |
| VerhulstB, NealeMC, KendlerKS. The heritability of alcohol use disorders: a meta-analysis of twin and adoption studies. Psychol Med. 2015;45:1061–72.2517159610.1017/S0033291714002165PMC4345133 | — | — | — |
| VerweijKJH, ZietschBP, LynskeyMT, MedlandSE, NealeMC, MartinNG, Genetic and environmental influences on cannabis use initiation and problematic use: a meta-analysis of twin studies. Addiction. 2010;105:417–30.2040298510.1111/j.1360-0443.2009.02831.xPMC2858354 | — | — | — |
| WaltersRK, PolimantiR, JohnsonEOECEO, McClintickJN, AdamsMJ, AdkinsAE, Trans-ancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. Nat Neurosci. 2018;21:1656–69.3048294810.1038/s41593-018-0275-1PMC6430207 | — | — | — |
| WilliamsDR, MohammedSA, LeavellJ, CollinsC. Race, socioeconomic status, and health: complexities, ongoing challenges, and research opportunities. Ann N Y Acad Sci. 2010;1186:69–101.2020186910.1111/j.1749-6632.2009.05339.xPMC3442603 | — | — | — |
| ZhouH, RentschCT, ChengZ, KemberRL, NunezYZ, ShervaRM, Association of OPRM1 Functional Coding Variant With Opioid Use Disorder: A Genome-Wide Association Study. JAMA Psychiatry 2020. 10.1001/jamapsychiatry.2020.1206.PMC727088632492095 | — | — | — |
| ZhouH, SealockJM, Sanchez-RoigeS, ClarkeTK, LeveyDF, ChengZ, Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits. Nat Neurosci. 2020. 10.1038/s41593-020-0643-5.PMC748555632451486 | — | — | — |
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