Prediction of the Risk of Comorbid Alcoholism in Schizophrenia by Interaction of Common Genetic Variants in the Corticotropin-Releasing Factor System

ORIGINAL ARTICLE

ONLINE FIRST Prediction of the Risk of Comorbid Alcoholism in Schizophrenia by Interaction of Common Genetic Variants in the Corticotropin-Releasing Factor System

Katja Ribbe, MSc; Verena Ackermann, MSc; Judith Schwitulla, MSc; Martin Begemann, MD; Sergi Papiol, PhD; Sabrina Grube, PhD; Swetlana Sperling, BSc; Heidi Friedrichs, PhD; Olaf Jahn, PhD; Inge Sillaber, PhD; Olaf Gefeller, PhD; Henning Krampe, PhD; Hannelore Ehrenreich, MD, DVM

Context: Stress plays a major role in the development of Patients: A total of 1037 schizophrenic patients (Go¨t- comorbid alcohol use disorder (AUD). In turn, AUD wors- tingen Research Association for Schizophrenia sample), ens the outcome of psychiatric patients with respect to global 80 nonschizophrenic psychiatric disease controls as a disease severity, social situation, and socioeconomic bur- small replicate sample, and a case-control study includ- den. Prediction of persons at risk for AUD is crucial for fu- ing 1141 healthy subjects. ture preventive and therapeutic strategies. Main Outcome Measures: Association of CRHR1 and Objective: To investigate whether genetic variants of CRHBP genotypes with the following: (1) AUD; (2) a the corticotropin-releasing factor system or their inter- newly developed alcoholism severity score comprising action influence the risk of developing AUD in chronic 5 AUD-relevant variables; and (3) quantitative CRHR1 disease populations. and CRHBP messenger RNA expression. Results: An interaction of CRHR1 rs110402 and CRHBP Design: Genotype analysis comprising selected single- rs3811939 predicts high risk of comorbid AUD in schizo- nucleotide polymorphisms within the CRHR1 and CRHBP phrenic patients (odds ratio=2.27; 95% confidence in- genes in patients with schizophrenia and in a nonschizo- terval, 1.56-3.30; PϽ.001) as well as psychiatric disease phrenic psychiatric disease control sample should allow controls (odds ratio=4.02; 95% confidence interval, 0.95- the extraction of predictors of comorbid AUD. Gene ex- 17.05; P=.06) and leads to the highest CRHR1/CRHBP pression (messenger RNA) analysis in peripheral blood messenger RNA ratio (P=.02; dysbalanced stress axis). mononuclear cells was performed to gain the first mechanistic insight. Conclusions: The high predictive value of a genetic interaction within the stress axis for the risk of comorbid Setting: An ideal setup for this study was the Go¨ttin- AUD may be used for novel preventive and individual- gen Research Association for Schizophrenia Data Col- ized therapeutic approaches. lection of schizophrenic patients, specifically intended to enable association of genetic information with quan- Arch Gen Psychiatry. 2011;68(12):1247-1256. tifiable phenotypes in a phenotype-based genetic asso- Published online August 1, 2011. ciation study. doi:10.1001/archgenpsychiatry.2011.100

LCOHOL USE DISORDERS is considered (34%), individuals with (AUDs) are severe, com- schizophrenia are at high risk for devel- plex illnesses with preva- oping AUD.7-10 Comorbid AUD in turn de- lence rates of up to 30%.1-3 teriorates the course of disease and out- Treatment of AUD is ham- come, eg, it causes a higher percentage of pered by high relapse rates after clinical housing problems,11 disability,12 and hos- A 13 detoxification and months of absti- pital admissions. nence.4-6 In psychiatric diseases like schizo- Any severe disease poses a tremen- phrenia, comorbid AUDs reach an even dous stress on the affected individual. The more dramatic prevalence. In the Epide- high amount of comorbid substance abuse miologic Catchment Area Study, 47% of in schizophrenia may be the result of a dys- schizophrenic patients fulfilled criteria of functional way of coping with this stress. Author Affiliations are listed at any substance use disorder.3 Even though The use of alcohol as an easily available the end of this article. numbers become smaller once AUD alone tool to reduce tension and handle nega-

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©2011 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 10/01/2021 tive emotions in the sense of self-treatment plays an es- METHODS sential role in AUD etiology.14-18 Both inborn and ac- quired capacities to respond to stress are likely to influence STUDY SETTING AND PARTICIPANTS this process. Of central importance for a coordinated stress response in mammals is the hypothalamus-pituitary- Schizophrenic Patients adrenal axis, with the corticotropin-releasing factor system playing a dominant role. Components of this highly Study participants were enrolled in the cross-sectional field study interregulated system include corticotropin-releasing hor- of GRAS as described previously.38,39 The study was approved mone (CRH),19 CRH receptors,20 and CRH-binding pro- by the Ethics Committee of the Georg-August-University, Go¨t- tein.21 The latter represents a passive ligand trap that neu- tingen, Germany, and review boards of participating centers, and tralizes CRH by binding it, thereby terminating its it complies with the Declaration of Helsinki. The GRAS Data Col- biological actions, in contrast to its active receptor that lection comprises at present 1037 patients with confirmed DSM-IV initiates signal transduction on binding.22 diagnosis of schizophrenia (82.2%) or schizoaffective disorder (17.8%) examined between September 1, 2005, and November While long-term alcohol consumption can induce last- 38,39 ing alterations within the CRH system,23,24 less is known 1, 2010, in 23 collaborating centers across Germany (Table 1). Almost all of these patients were of European Caucasian de- about how genetic variation of respective genes influ- scent (95.6% Caucasian, 1.6% other ethnicities, and 2.8% un- ences development of AUD. A pivotal study in alcohol- known). European Caucasian persons are a genetically homo- naive mice demonstrated that Crhr1 null mutation was as- geneous group with low average levels of genetic differentiation sociated with augmented ethanol consumption on stress compared with other human populations (no strong influence exposure.25 Conversely, in ethanol-dependent mice, re- on association results to be expected).40-42 Specifically, the Ger- duction of Crhr1 activity by Crhr1 blockade or by Crhr1 man population is very homogeneous, with low genetic differ- null mutation led to decreased alcohol self-administra- entiation along a north-south gradient within Germany. In fact, tion.26 In rhesus macaques, higher alcohol intake was found population substructure within Germany is too low to be detectable without prior information on subpopulation member- in animals carrying a CRH genotype conferring increased 43 stress reactivity.27 ship. Therefore, for the purpose of our study, population strati- fication was not essential. In humans as well, the CRH system has been linked 28,29 30 not only to depression, suicidality, and panic dis- Psychiatric Disease Controls order31 but also to alcohol consumption and AUD.32-36 Specifically, associations were described for a North As an independent, nonschizophrenic disease control (repli- American Caucasian population between AUD and 3 cate) sample, 80 patients with mental disorders other than single-nucleotide polymorphisms (SNPs) in the CRHBP schizophrenia (57.5% affective disorder, 16.3% substance use gene.32 Similarly, the Mannheim Study of Children at Risk disorder [including multiple drug or cannabis use], 10.0% anxi- observed relationships between CRHR1 SNPs and alco- ety disorder, 6.3% personality disorder, 3.7% delusional dis- hol consumption patterns, eg, binge drinking and life- order, 3.7% organic mental disorders, and 2.5% mental retar- time prevalence of drunkenness.33 Based on DSM-IV37 di- dation—all diagnosed according to DSM-IV) were recruited in agnosis only, a recent study found an association of genetic Go¨ttingen (eTable 1, http://www.archgenpsychiatry.com). variants of the CRHR1 gene with AUD.35 Also, protective constellations of CRHR1 regarding stress-related AUD Healthy Controls exist.34,36 This study was designed to investigate associations of Healthy controls exclusively for the genetic case-control part genetic variants within the CRH system including their of the study were voluntary blood donors (n=1141) recruited according to national guidelines for blood donation. As such, interaction with the development of AUD in a chronic they widely fulfill health criteria, ensured by broad predona- disease population. Schizophrenia, a severe psychiatric tion screening containing standardized questionnaires, inter- disorder as a grave and persistent stressor shared by the views, and determinations of hemoglobin level, blood pres- cohort under study, should allow for defining predic- sure, pulse, and body temperature. Comparable to the patient tors of comorbid AUD. To test this hypothesis, the Go¨t- population, almost all control subjects were of European Cau- tingen Research Association for Schizophrenia (GRAS) casian descent (97.8% Caucasian, 2.0% other ethnicities, and Data Collection was used, specifically intended to en- 0.2% unknown).38,39 able association of genetic information with quantifi- able phenotypes in a procedure termed phenotype-based PHENOTYPING genetic association study (PGAS).38,39 Based on GRAS/ PGAS, we report here for the first time to our knowl- Comprehensive interviews, testing, and clinical ratings were edge an interactive genetic constellation within the CRH conducted by one and the same traveling team of trained ex- system comprising variants of CRHR1 and CRHBP with aminers (psychiatrists, psychologists) using the GRAS Manual.38,39 Additionally, records and discharge letters of ev- high predictive value to detect an increased risk of co- ery patient were used to validate and complement the pa- morbid AUD in schizophrenic patients. Importantly, we tient’s (and, if applicable, relative’s or caretaker’s) statements. simultaneously provide replication of this finding in a psychiatric disease control cohort purposely comprising pa- Sociodemographic and Clinical Variables tients with different psychiatric diagnoses excluding schizophrenia. This heterogeneous replicate sample un- Semistructured interviews delivered biographic data, family back- derscores the generalizability of the revealed risk con- ground, level of education, and occupational history. Diagnoses stellation for imperiled populations. of schizophrenia or schizoaffective disorders were based on the

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Mean (SD) [Range]

Total GRAS Sample No AUD AUD P Value Variable (n=1037)a (n=673)a (n=364)a (Z /␹2 Value)b Sociodemographic Age, y 39.52 (12.56) [17.49-79.49] 40.37 (13.02) [17.49-79.49] 37.96 (11.54) [18.08-66.90] .01 (Z=−2.45) Male, No. (%) 693 (66.8) 399 (59.3) 294 (80.8) Ͻ.001 (␹2=49.18) Caucasian, No. (%)c 990 (95.6) 648 (96.3) 342 (94.0) .18 (␹2=4.85) Education, yd 12.01 (3.05) [8-27] 12.25 (3.18) [8-27] 11.57 (2.73) [8-23] .001 (Z=−3.19) Unemployed, No. (%) 368 (38.3) 214 (34.0) 154 (46.2) Ͻ.001 (␹2=35.81) Inpatient at assessment, No. (%) 445 (42.9) 262 (38.9) 183 (50.3) .001 (␹2=21.03) Clinical Age at first episode, y 26.25 (8.79) [5.40-67.55] 26.74 (9.16) [7.95-67.55] 25.33 (8.01) [5.40-57.79] .02 (Z=−2.25) Duration of disease (first episode), y 13.24 (10.70) [0.01-58.44] 13.56 (11.22) [0.04-58.44] 12.64 (9.68) [0.01-49.28] .61 (Z=−0.51) Age at onset (prodrome), y 23.28 (8.69) [2.02-66.53] 23.84 (9.01) [2.02-66.53] 22.15 (7.92) [6.78-55.44] .005 (Z=−2.82) Duration of disease (prodrome), y 15.97 (11.12) [0.05-62.31] 16.17 (11.55) [0.05-62.31] 15.57 (10.26) [0.06-52.28] .82 (Z=−0.23) Chlorpromazine equivalents 690.11 (711.17) [0-7350.00] 688.99 (739.49) [0-7375.00] 692.17 (656.88) [0-6324.29] .28 (Z=−1.07) Diagnosis of schizophrenia, No. (%)e 852 (82.2) 584 (81.4) 304 (83.5) .40 (␹2=0.70) Hospitalizations, No. 8.60 (9.77) [0-97] 7.76 (9.20) [0-82] 10.16 (10.57) [0-97] Ͻ.001 (Z=−4.88) PANSS score Positive 13.76 (6.32) [7-38] 13.67 (6.47) [7-38] 13.93 (6.04) [7-36] .18 (Z=−1.35) Negative 18.23 (7.85) [7-46] 18.21 (7.85) [7-44] 18.26 (7.87) [7-46] .91 (Z=−0.11) General 33.73 (11.83) [16-82] 33.61 (11.91) [16-78] 33.96 (11.70) [16-82] .54 (Z=−0.61) Total 65.64 (23.40) [30-160] 65.33 (23.58) [30-148] 66.19 (23.09) [30-160] .48 (Z=−0.49) GAF score 45.76 (17.25) [5-90] 46.90 (17.89) [5-90] 43.67 (15.82) [8-90] .01 (Z=−2.44) CGI score 5.57 (1.08) [2-8] 5.52 (1.09) [2-8] 5.66 (1.06) [2-8] .03 (Z=−2.12) Addiction-specific Current smoking No. of cigarettes/d 16.90 (14.97) [0-80] 13.47 (14.18) [0-80] 23.17 (14.34) [0-80] Ͻ.001 (Z=−10.16) No. (%) 717 (70.5) 399 (60.7) 318 (88.3) Ͻ.001 (␹2=85.20) Multiple drug use according 101 (9.7) 24 (3.6) 77 (21.2) Ͻ.001 (␹2=83.12) to DSM-IV, No. (%) Cannabis use disorder according 338 (32.6) 143 (21.2) 195 (53.6) Ͻ.001 (␹2=112.34) to DSM-IV, No. (%) Benzodiazepine use disorder 39 (3.8) 17 (2.5) 22 (6.0) .004 (␹2=8.08) according to DSM-IV, No. (%) Alcohol in lifetime, g/df 57.79 (103.51) [0-1224.50] 5.41 (9.13) [0-70.56] 124.25 (129.12) [3.86-1224.50] Ͻ.001 (Z=−22.62) Alcohol-related detoxifications, No.f 0.19 (1.17) [0-20] 0 0.55 (1.94) [0-20] Ͻ.001 (Z=−10.55) Chronicity, problematic drinking, y/age, yf 0.14 (0.22) [0-0.79] .01 (0.06) [0-0.59] 0.41 (0.18) [0.03-0.79] Ͻ.001 (Z=−28.45) Daily drinking, No. (%)f 291 (32.7) 28 (5.0) 263 (79.5) Ͻ.001 (Z=−22.82) SCID yes answersf,g 3.12 (4.08) [0-14] 0.91 (1.72) [0-10] 6.64 (4.27) [0-14] Ͻ.001 (Z=−19.67)

Abbreviations: AUD, alcohol use disorder; CGI, Clinical Global Impression; GAF, Global Assessment of Functioning; GRAS, Go¨ttingen Research Association for Schizophrenia; PANSS, Positive and Negative Symptom Scale; SCID, Structured Clinical Interview for DSM-IV Disorders. a Due to missing data on phenotyping, sample sizes vary between 747 and 1037 in the total sample, between 424 and 673 in patients without AUDs, and between 296 and 364 in patients with AUDs. b For statistical methods, Mann-Whitney U or ␹2 tests were used. Bolded values, PϽ.05. c Exploratory exclusion of non-Caucasian subjects did not appreciably alter any of the main findings of the study. d Rating according to graduation/certificate; patients currently in school or in educational training are excluded. e Versus schizoaffective disorders. f Components of the alcoholism severity score. g Addiction section of the SCID (criteria for abuse and dependence).

Structured Clinical Interview for DSM-IV Disorders and substan- Alcohol-Related Variables tiated by information from medical records, which also con- veyed numbers and durations of hospital stays and age at onset Two main outcome measures were used: (a) the dichotomous of schizophrenia and prodrome. Psychopathological state, symp- DSM-IV AUD diagnosis summarizing alcohol abuse and de- tom severity, and functional outcome were evaluated by clinical 44 pendence; and (b) a newly developed quantitative alcoholism ratings (Positive and Negative Syndrome Scale, Clinical Global severity score. For assessing AUD, the Structured Clinical In- Impression scale,45 and Global Assessment of Functioning37) and terview for DSM-IV Disorders (addiction section) was ap- questionnaires (State-Trait Anxiety Inventory46 and Brief Symp- tom Inventory47). For appraising current depression, 2 items of plied. Patient statements were confirmed and supplemented with the Positive and Negative Syndrome Scale general psychopathol- longitudinal information (records and discharge letters). In the ogy subscale (guilt feelings and depression)48 were used, to- alcoholism severity score, 5 alcohol-relevant variables were in- Ն gether with 2 items of the Brief Symptom Inventory (guilt feel- tegrated ( 3 of 5 variables were required for calculating the ings and thoughts of death or dying) and the Brief Symptom score): (1) numbers of alcohol-related detoxifications; (2) high- Inventory depression subscale. For judging the degree of anxi- est amount of regular drinking (in grams per day for Ն6 months); ety, the Positive and Negative Syndrome Scale general items (anxi- (3) frequency of drinking (11-point scale from never to daily); ety, tension, and somatic concern),48 Brief Symptom Inventory (4) number of positive Structured Clinical Interview for DSM-IV (anxiety scale), and State-Trait Anxiety Inventory were used Disorders items; and (5) chronicity (years of problematic al- (eFigure 1). cohol use divided by age in years).49

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©2011 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 10/01/2021 A B CRHR1 0.2 < r < 0.4 (51.5 kb, 17q12) 0.4 < r < 0.6 Cronbach α = .878 0.6 < r < 0.7 rs110402 rs1396862 rs1876831 Positive SCID items r > 0.7 5′ 3′

Highest amount of regular Frequency of drinking CRHBP drinking in life (16.6 kb, 5q13.3)

rs3811939 rs1875999 rs1715747

Detoxifications 5′ 3′ Chronicity

C 80 Correlation between AUD and No AUD alcoholism severity score: r = 0.85 Alcohol abuse (AUD) Alcohol dependence (AUD)

Patients, No. 40

–4.00 –3.00 –2.00 –1.00 0 1.00 2.00 3.00 4.00 Alcoholism Severity Score

Figure 1. Basics of genotyping and phenotyping strategies. A, Location of selected single-nucleotide polymorphisms on CRHR1 and CRHBP genes. Coding regions are shown in black; untranslated regions, gray. kb indicates kilobases. B, Variables composing the alcoholism severity score and their intercorrelations (statistics: Pearson correlation and Cronbach ␣ coefficient). SCID indicates Structured Clinical Interview for DSM-IV Disorders. C, Distribution of alcoholism severity score and DSM-IV alcohol use disorder (AUD) diagnoses for either alcohol abuse or alcohol dependence in the Go¨ttingen Research Association for Schizophrenia sample of schizophrenic patients (n=957; statistics: point-biserial correlation).

GENOTYPING Quantitative Reverse Transcription–Polymerase Chain Reaction Single-nucleotide polymorphisms in CRHR1 and CRHBP were selected, 3 of each gene, considering database information on We prepared RNA with the miRNeasy Mini Kit (Qiagen, Hilden, minor allele frequencies (NCBI,50 HapMap,51 UCSC Genome Germany) and used it to synthesize complementary DNA (Su- Browser52) and previous reports, which had identified these SNPs perScriptIII; Invitrogen, Karlsruhe, Germany). Quantitative re- to be informative32-36,53 (Figure 1A). Genotyping was per- verse transcription–polymerase chain reaction was performed formed with Simple Probes (TIB Molbiol, Berlin, Germany) on with SYBR Green detection on the LightCycler 480 system a Light Cycler 480 (Roche, Mannheim, Germany) (eAppen- (Roche). Cycle threshold values were standardized to ␤-actin dix). Successful genotyping of the GRAS sample (n=1037) (eAppendix). ranged from n=1016 to n=1030 (average Ͼ98%), accounting for some variation of respective n numbers. STATISTICAL ANALYSES

Group differences in categorical and continuous variables EXPRESSION ANALYSIS IN PERIPHERAL were assessed with nonparametric Mann-Whitney U and ␹2 BLOOD MONONUCLEAR CELLS tests, respectively. Blom transformation54 was used to yield standardized values being approximately normally distrib- Isolation of Peripheral Blood Mononuclear Cells uted with a mean of 0 and variance of 1. Intercorrelations and internal consistency of alcoholism severity score compo- Blood of a number of schizophrenic GRAS patients and psy- nents were assessed using Pearson correlation coefficient chiatric disease controls (n=104; 64.4% male; 51.0% schizo- and Cronbach ␣.55 To assess the association between alco- phrenic) was collected in tubes with a citrate phosphate dex- holism severity score and AUD (continuous and binary vari- trose adenine solution. Peripheral blood mononuclear cells were able), the point-biserial correlation was calculated. Analysis isolated with the Ficoll-Paque PLUS isolation procedure (GE of covariance (adjusted for age) was used to analyze the Healthcare, Mu¨ nchen, Germany). effect of SNPs on the standardized alcoholism severity score

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©2011 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 10/01/2021 and its single items. For the dichotomous outcome AUD, PHENOTYPE-BASED GENETIC estimation of the odds ratio (OR) and its 95% confidence ASSOCIATION STUDY interval (CI) for the effect of SNPs was performed in a logistic regression model incorporating age as an additional con- GRAS Patients founder. For all analyses, statistical significance was set to the .05 level. Statistical analyses were performed using SPSS for Windows version 17.0 statistical software (SPSS Inc, Chi- The hypothesis-guided PGAS approach started with as- cago, Illinois) and R version 2.10.1 statistical software (R sociation analyses of alcohol-relevant readouts with se- Foundation, Vienna, Austria). lected SNPs (Table 2). Only CRHR1 SNP1 (rs110402) turned out to be associated with the alcoholism severity score (F2,938=5.60; P=.004), whereas all other markers RESULTS showed no associations or only tendencies, eg, SNP1 of CRHBP (rs3811939) (F2,950=2.32; P=.10). When consid- AUD DIAGNOSIS IN THE GRAS POPULATION ering associations of SNPs with individual target vari- OF SCHIZOPHRENIC PATIENTS ables, however, more hits arise: all 3 SNPs of CRHR1 and SNP1 of CRHBP are associated with consumed alcohol To prepare for genotype-phenotype analysis, patients had in grams per day. Genotype-related distributions of raw to be comprehensively evaluated regarding comorbid data are displayed in eFigure 2. No associations be- AUD. Of all 1037 GRAS patients, 364 (35.1%) fulfill AUD tween the 6 SNPs and disease-related or disease- criteria, ie, alcohol abuse (n=251 [24.2%]) or alcohol de- unrelated control variables were detected. Neither anxi- pendence (n=113 [10.9%]) according to DSM-IV. Basic ety nor depression score yielded significant results. Except sociodemographic information, general and schizophre- for numbers of cigarettes (F2,991=3.93; P=.02), no asso- nia-related clinical readouts, and addiction-specific vari- ciations between any of the SNPs and other drugs were ables in all patients and in AUD vs non-AUD subgroups uncovered. Importantly, after correction for alcoholism are presented in Table 1. The AUD group is younger, is severity score (as a covariate), the significant relation- predominantly male, has fewer years of education, and ship between smoking and CRHR1 SNP1 disappeared is characterized by a higher unemployment rate. The pa- (F2,926=2.18; P=.11). tients with AUD have an earlier age at prodrome and age We next checked, based on the known biological in- at onset of the first psychotic episode, more hospitaliza- terplay of CRHR1 and CRHBP, a potential interaction of tions (reflected also by a higher proportion assessed here the most prominently alcoholism severity score– as inpatients), and lower level of functioning (accord- associated SNP1 genotypes of each gene. Figure 2A il- ing to the Global Assessment of Functioning). In addi- lustrates the genotype-phenotype results for CRHR1 SNP1 tion to expected differences in AUD-related variables, (rs110402) and CRHBP SNP1 (rs3811939) separately: the groups differ regarding current smoking status, ciga- TT carriers and the GG carriers, respectively, have the rettes per day, and other drug use, including cannabis, highest association with the alcoholism severity score. benzodiazepines, or multiple drug disorders according On grouping for interaction, a high-risk genotype for co- to DSM-IV. morbid AUD as judged by the alcoholism severity score, consisting of homozygous T in CRHR1 SNP1 (rs110402) OPERATIONALIZATION OF THE ALCOHOLISM and homozygous G in CRHBP SNP1 (rs3811939), con- trasts a significantly lower risk of all other possible com- SEVERITY SCORE IN THE GRAS SAMPLE Ͻ binations (F1,512=15.13, P .001; F1,201=8.64, P=.004; F1,458=11.81, P=.001). The interaction between CRHR1 For more detailed genotype-phenotype associations, the SNP1 and CRHBP SNP1 in the analysis of covariance more refined alcoholism severity score on top of the di- model was strong (F1,935=6.34; P=.01) (Figure 2B). Also, chotomous AUD diagnosis was created. Intercorrela- on the level of AUD diagnosis, this interaction is obvi- tions between selected target variables are presented in ous. Risk genotype carriers have a higher proportion of Figure 1B. A high internal consistency56 of these vari- Ͻ ␣ AUD (OR=2.27; 95% CI, 1.56-3.30; P .001) (Figure 2C). ables (Cronbach =.878) justifies their handling as an For comparison, CRHR1 SNP1 alone as risk factor yields alcoholism severity score. However, whereas diagnosis an OR of 1.62 (95% CI, 1.20-2.20; P=.002), and CRHBP of AUD was available for all 1037 GRAS patients, the al- SNP1 has an OR of 0.89 (95% CI, 0.69-1.16; P=.40). coholism severity score could be determined for only 957 Ͻ patients (for 80 patients, 3 of 5 variables were obtain- Nonschizophrenic Psychiatric Disease able). Correlation between AUD and the alcoholism se- Controls/Replicate Sample verity score amounts to r=0.85 (Figure 1C). To explore whether a comparable risk vs nonrisk con- CASE-CONTROL STUDY stellation would be detectable in a nonschizophrenic population, we analyzed a small psychiatric disease con- To explore a potential role of the 3 CRHR1 and 3 CRHBP trol sample (n=80). This sample differs from the GRAS SNPs as genetic risk factors for schizophrenia, a case- population (n=1037) expectedly in several ways. Whereas control study was conducted. No significant difference the percentage of AUD is relatively comparable (40.0% in distribution of genotypes between cases (GRAS pa- vs 35.1% in the GRAS sample), disease controls have lower tients, n=1037) and healthy controls (n=1141) was found rates of unemployment, lower doses of antipsychotics, for any of the 6 SNPs (eTable 2). lower Positive and Negative Syndrome Scale subscale

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©2011 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 10/01/2021 Table 2. Association of Selected Single-Nucleotide Polymorphisms of CRHR1 and CRHBP With Alcoholism Target Variables and Control Variables in the Go¨ttingen Research Association for Schizophrenia Samplea

P Value (F score)b

CRHR1 SNP1, CRHR1 SNP2, CRHR1 SNP3, CRHBP SNP1, CRHBP SNP2, CRHBP SNP3, Variable rs110402 rs1396862 rs1876831 rs3811939 rs1875999 rs1715747 Target Variables Target variable, combined

Alcoholism severity score .004 (F2,938=5.60) .12 (F2,939=2.15) .14 (F2,937=1.95) .099 (F2,950=2.32) .16 (F2,947=1.80) .97 (F2,947=0.03) Target variable, individual

Alcohol, g/d .001 (F2,729=7.00) .03 (F2,732=3.56) .03 (F2,730=3.46) .03 (F2,739=3.64) .18 (F2,739=1.74) .93 (F2,739=0.07)

Detoxifications, No. .66 (F2,984=0.41) .28 (F2,989=1.27) .28 (F2,987=1.28) .88 (F2,996=0.12) .19 (F2,998=1.67) .75 (F2,998=0.29)

Frequency of drinking Ͻ.001 (F2,868=8.40) .06 (F2,873=2.83) .08 (F2,871=2.53) .04 (F2,881=3.31) .40 (F2,882=0.75) .94 (F2,882=0.07)

SCID addiction section .04 (F2,791=3.33) .16 (F2,796=1.84) .17 (F2,794=1.76) .65 (F2,800=0.44) .38 (F2,801=0.91) .95 (F2,801=0.06)

Chronicity, problematic .09 (F2,891=2.39) .24 (F2,895=1.44) .27 (F2,893=1.31) .80 (F2,902=0.23) .73 (F2,903=0.32) .92 (F2,903=0.08) drinking, y/age, y

AUD according to DSM-IV .006 (F2,1012=5.20) .12 (F2,1017=2.15) .14 (F2,1015=1.95) .70 (F2,1024=0.36) .65 (F2,1026=0.43) .59 (F2,1026=0.52) Control Variables Other drugs

No. of cigarettes/d .02 (F2,991=3.93) .14 (F2,992=1.93) .18 (F2,990=1.72) .85 (F2,1000=0.16) .32 (F2,1001=1.13) .10 (F2,1001=2.27)

Cannabis use disorder .12 (F2,1012=2.10) .24 (F2,1017=1.43) .24 (F2,1015=1.45) .17 (F2,1024=1.79) .61 (F2,1026=0.49) .42 (F2,1026=0.87) according to DSM-IV

Benzodiazepine use disorder .66 (F2,1012=0.42) .50 (F2,1017=0.70) .50 (F2,1015=0.69) .72 (F2,1024=0.33) .42 (F2,1026=0.87) .60 (F2,1026=0.51) according to DSM-IV

Multiple drug use according .53 (F2,1012=0.63) .22 (F2,1017=1.53) .22 (F2,1015=1.50) .70 (F2,1024=0.35) .22 (F2,1026=1.52) .33 (F2,1026=1.10) to DSM-IV Previously published association

Anxiety score .96 (F2,995=0.04) .66 (F2,1000=0.41) .66 (F2,998=0.42) .47 (F2,1007=0.76) .34 (F2,1008=1.07) .17 (F2,1008=1.76)

Depression score .98 (F2,1011=0.02) .55 (F2,1016=0.60) .53 (F2,1014=0.64) .36 (F2,1024=1.01) .70 (F2,1025=0.36) .41 (F2,1025=0.88) Disease-related variable

CGI score .89 (F2,983=0.11) .58 (F2,987=0.54) .61 (F2,985=0.49) .70 (F2,994=0.35) .94 (F2,995=0.06) .63 (F2,995=0.46)

GAF score .58 (F2,978=0.54) .32 (F2,982=1.15) .30 (F2,980=1.19) .58 (F2,989=0.55) .69 (F2,990=0.37) .38 (F2,990=0.96) PANSS score

Positive .77 (F2,977=0.26) .58 (F2,981=0.55) .56 (F2,979=0.58) .93 (F2,988=0.08) .79 (F2,989=0.23) .14 (F2,989=1.95)

Negative .08 (F2,974=2.56) .92 (F2,978=0.08) .94 (F2,976=0.07) .62 (F2,986=0.48) .39 (F2,986=0.93) .85 (F2,986=0.16)

General .75 (F2,974=0.29) .72 (F2,978=0.33) .68 (F2,976=0.38) .98 (F2,985=0.02) .70 (F2,986=0.35) .25 (F2,986=1.40)

Total .51 (F2,961=0.68) .95 (F2,965=0.05) .93 (F2,963=0.08) .99 (F2,973=0.11) .49 (F2,973=0.72) .31 (F2,973=1.16) Disease-unrelated variable

Body length, m .29 (F2,978=1.23) .26 (F2,982=1.33) .28 (F2,980=1.28) .14 (F2,989=1.98) .74 (F2,990=0.30) .37 (F2,990=0.98)

Abbreviations: AUD, alcohol use disorder; CGI, Clinical Global Impression; GAF, Global Assessment of Functioning; PANSS, Positive and Negative Symptom Scale; SCID, Structured Clinical Interview for DSM-IV Disorders. a The sample sizes varied from 733 to 1037, due to missing values at the level of either genotyping or phenotyping (eg, incomplete score). The statistical analyses used analysis of covariance models adjusted for age; for the binary variables of AUD and other DSM-IV diagnoses, logistic regression was used with age as covariate. b Bolded values, PϽ.10; bolded, italicized values, PϽ.05.

scores, and better functional outcome (according to the phrenic GRAS patients and psychiatric disease controls Global Assessment of Functioning) and smoke fewer ciga- (total of n=104) available for blood sampling, and CRHR1 rettes per day (eTable 1). Despite the small number and and CRHBP messenger RNA (mRNA) levels were quan- somewhat different characteristics, a pattern very simi- tified. As a biological estimate of ligand efficiency or ac- lar to the GRAS sample becomes obvious in disease con- tivity of the CRH system, the mRNA expression ratio of trols with respect to both alcoholism severity score and CRHR1 (active receptor) and CRHBP (ligand trap) was used. AUD diagnosis (Figure 2D). Again, risk genotype carri- CRHR1 and CRHBP compete for binding of CRH. ers tend to have a substantially higher alcoholism sever- Whereas the former is the active receptor that mediates ity score compared with all other genotype combina- the effects of CRH, the latter acts as a ligand trap, catch- tions (F1,41=4.88, P=.03; F1,15=4.59, P=.05; F1,44=3.80, ing CRH and thereby preventing the bound molecule from P=.06). Owing to the small sample size, the interaction having a biological effect at its receptor. In other words, effect of CRHR1 SNP1 and CRHBP SNP1 on alcoholism the ratio of active receptor and binding protein is of high severity score just failed to reach statistical significance importance for the quantitative biological effect of a ma- (F1,80=2.48; P=.12). Also regarding AUD diagnosis, risk jor determinant of the stress axis. Indeed, patients car- genotype carriers tend to differ from noncarriers rying the risk genotype combination (TT/GG) have a sig- (OR=4.02; 95% CI, 0.95-17.05; P=.06) (Figure 2C). nificantly higher ratio of CRHR1 to CRHBP mRNA (Z=−2.31; P=.02), ie, a putative dysbalance of the CRH GENOTYPE-DEPENDENT EXPRESSION ANALYSES system (Figure 2E). Separating this subgroup into sub- IN PERIPHERAL BLOOD MONONUCLEAR CELLS jects with or without AUD, an almost identical pattern of genotype-dependent mRNA expression was ob- To gain the first mechanistic insight, peripheral blood tained, even though it failed to reach statistical signifi- mononuclear cells were isolated from a number of schizo- cance owing to the small sample numbers (Figure 2E).

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©2011 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 10/01/2021 A GRAS sampleB Combination of CRHR1 and CRHBP D Combination of CRHR1 and CRHBP genotypes in the GRAS sample (n = 935) genotypes in psychiatric disease controls (n = 80)

Risk Risk 0.50 0.50 0.50 0.80 genotype CRHR1 SNP1 CRHBP SNP1 P < .001 genotype P = .001 (rs110402) (rs3811939) P = .004 0.70 P = .03 P = .06 0.40 0.40 0.40 P = .05 0.60

0.30 0.30 0.30 0.50 P = .004 P = .10 0.40 Interaction effect: P = .01 Interaction effect: 0.20 0.20 0.20 0.30 P = .12

0.20 0.10 0.10 0.10 0.10 Alcoholism Severity Score Alcoholism Severity Score Alcoholism Severity Score 0.00 0.00 0.00 0.00

–0.10 –0.10 –0.10 –0.10 CC CT TT AA GA GG CC/CT TT CC/CT TT CRHR1 SNP1 CC/CT TT CC/CT TT n = 296 n = 440 n = 202 n = 69 n = 363 n = 518 GG GG GA/AA GA/AA CRHBP SNP1 GG GG GA/AA GA/AA n = 394 n = 118 n = 340 n = 83 n = 31 n = 10 n = 34 n = 5

C E GRAS sample (n = 1012) Risk combination of CRHR1 Risk combination of CRHR1 and CRHBP genotypes and CRHBP genotypes OR = 2.27 All other combinations of CRHR1 and CRHBP genotypes (no risk) n = 129 (95% CI, 1.56-3.30) P < .001 All patients with available Patients with AUD Patients without AUD All other combinations of CRHR1 PBMCs (n = 104) (n = 34) (n = 70) and CRHBP genotypes (no risk) 16 16 16 n = 883 14 14 14 (Median) 12 P = .02 12 P = .08 12 P = .12 0 20 40 60 80 10 10 10 8 8 8 Patients, % CRHBP / 6 6 6 4 4 4 AUD

CRHR1 2 2 2 No AUD 0 0 0 n = 17 n = 87 n = 8n = 26 n = 9n = 61 Ratio Psychiatric disease controls (n = 80) Risk combination of CRHR1 Genotype OR = 4.02 mRNA and CRHBP genotypes (95% CI, 0.95-17.05) Expression CC/CT + GG Risk: TT + GG CC/CT + GA/AA TT + GA/AA n = 10 P = .06 (n = 34) (n = 17) (n = 36) (n = 17) All other combinations of CRHR1 CRHR1, 58.00 82.04 36.09 22.11 and CRHBP genotypes (no risk) median (range) (1.00-265.03) (6.65-447.79) (1.13-380.92) (1.66-88.24) n = 70 CRHBP, 4.30 4.62 4.82 3.51 0 20 40 60 80 median (range) (1.50-10.98) (2.27-11.90) (1.00-8.85) (1.68-7.55) Controls % Ratio, 10.81 13.64 8.07 4.87 median (range) (0.22-63.05) (2.55-94.35) (0.35-73.86) (0.55-52.59)

Figure 2. Phenotype-based genetic association study. A, Distribution of alcoholism severity scores in CRHR1 SNP1 genotypes and CRHBP SNP1 genotypes using analysis of covariance adjusted for age. GRAS indicates Go¨ttingen Research Association for Schizophrenia. Data are presented as mean (SEM). B, Interaction effect between CRHR1 SNP1 and CRHBP SNP1 genotypes with respect to alcoholism severity score in the GRAS sample using analysis of covariance adjusted for age. Data are presented as mean (SEM). C, Interaction effect between CRHR1 SNP1 and CRHBP SNP1 genotypes with respect to the diagnosis of alcohol use disorder (AUD) according to DSM-IV using logistic regression analyses with age as confounder for estimating odds ratios (ORs) and 95% confidence intervals (CIs). D, Interaction effect between CRHR1 SNP1 and CRHBP SNP1 genotypes with respect to alcoholism severity score in psychiatric disease controls using analysis of covariance adjusted for age. Data are presented as mean (SEM). E, Ratio of CRHR1 and CRHBP messenger RNA (mRNA) expression in peripheral blood mononuclear cells (PBMCs) dependent on genotypes (risk genotype against all others) in a total of 104 patients as well as on separation of these patients according to the diagnosis of AUD (n=34) and non-AUD (n=70) using Mann-Whitney U tests. Raw data of mRNA levels (normalized to ␤-actin) dependent on genotype combinations are presented in the table below.

These data underscore a genotype-related rather than all other possible genotype combinations (which may even purely alcohol-induced mRNA expression difference. be seen as protective regarding AUD). Moreover, we al- ready replicated this finding in a smaller nonschizo- COMMENT phrenic psychiatric disease control group, emphasizing the general importance of this observation for popula- We identified a prominent interaction of distinct vari- tions under chronic stress. In the case-control study, com- ants of 2 genes of the CRH system, CRHR1 and CRHBP, paring frequencies of genotype distribution in healthy and predicting the risk of comorbid AUD in a chronically schizophrenic individuals, none of these genotypes plays stressed population. In more than 1000 schizophrenic a role as schizophrenia risk factor. subjects, we showed that carriers of a homozygous T al- An experimental approach to chronic stress– lele in CRHR1 rs110402 combined with a homozygous induced alcoholism in humans is very difficult to take. G allele in CRHBP rs3811939 are more than twice as likely In this study, schizophrenia was used as a model of se- to develop comorbid AUD (OR=2.27) than carriers of vere chronic stress in a field-study-type design. Patients

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©2011 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 10/01/2021 with schizophrenia (as heterogeneous as this disease may disorders in patients with AUD because cannabis but not be) and its social and personal consequences certainly alcohol is clearly associated with earlier age at onset.63,64 belong to an endangered population with respect to their Accordingly, correcting for cannabis use eliminates the chronic stress level. Nevertheless, the fact that stress might association of AUD with age at onset in the GRAS popu- be a causative factor of alcoholism in schizophrenia (and lation. other chronic diseases) does not exclude further mecha- For decades, it has been known that AUD has a heri- nisms leading to increased alcohol consumption in this tability of 50% to 60%.65 Regarding genetic risk factors, and other disease populations. Also, we have to be aware the genes of the CRH system and their interaction be- that stress is a very complex biological system and the long to a whole group of genes that predict the risk of corticotropin system is only part of it. Therefore, the in- developing alcoholism. For instance, genome-wide as- teraction detected here between CRHR1 and CRHBP will sociation studies on AUD including up to 2000 pa- not explain each and every aspect of stress-induced al- tients66-71 have identified genes encoding alcohol dehy- coholism; however, it will provide clinically and pro- drogenase,68,71 ␥-aminobutyric acid A receptor,67,68 phylactically important information for those who carry dopamine receptor,72 and serotonin receptor70 to be as- the risk constellation of genotypes. sociated (ORϽ2) with the risk of alcoholism. However, Except for SNP1 of CRHR1, single markers of as with other complex psychiatric diseases, the diagno- CRHR1 and CRHBP show no or only the tendency of sis alone (as used in genome-wide association studies) an association with AUD or the alcoholism severity is of limited value in spotting relevant genetic risk con- score. The most prominent effect is obtained by com- stellations and even less helpful for identifying impor- bining genotypes CRHR1 rs110402 and CRHBP tant biological subgroups of the disorder. rs3811939. Importantly, gene expression levels in Interplay between AUD and stress has been demon- peripheral blood mononuclear cells reveal quantitative strated in several animal experiments.27 First human stud- differences that may explain biological consequences ies indicated that CRH receptor antagonists may reduce of respective genotype combinations: the ratio of symptom severity of depression and anxiety73 and im- CRHR1 to CRHBP mRNA is highest in the risk constel- prove resistance against psychosocial stress.74 Treat- lation, independent of the presence or absence of ment of AUD with CRH receptor ligands is presently un- AUD. This finding supports the primary (genetic) der study in clinical trials (eg, ClinicalTrials.gov identifier influence on basal gene expression in the sense of an NCT01187511). Such an approach should be specifi- innate dysbalanced (hyperactive) stress axis, which cally tested in the herein delineated subgroup of at-risk may be additionally challenged by long-term alcohol individuals. Contrasting reports on other disease popu- consumption.23,24 lations,28,31 no associations of the herein described risk We note that the detected associations are restricted genotype with depression and anxiety were found in to AUD and do not extend to other drug use disorders, schizophrenic patients. This negative finding may be re- eg, cannabis use, benzodiazepine use, or polytoxicoma- lated to the underlying disease phenotype, the instru- nia. An apparent association of CRHR1 rs110402 with ciga- ments used, or the fact that respective symptoms were rette smoking disappears after correction for AUD, un- determined cross-sectionally. derscoring the high comorbidity of smoking and AUD.57,58 To conclude, our data suggest that a distinct geno- Nevertheless, because nicotine is a stimulator of the stress type constellation comprising 2 determinants of the CRH axis59 and alcohol and nicotine dependence have an over- system has high power to predict the risk of comorbid lapping genetic background,60 this association may de- AUD in endangered populations. This knowledge should serve further elucidation. be used for preventive strategies in patients with severe The finding of a high predictive power of 2 geno- psychiatric disease to avert further individual health, so- types within the CRH system not only substantiates cial, or economic decline. Moreover, it could deliver a the biological role of stress in AUD development but basis for novel individualized treatment approaches with, may also serve as an indicator of persons at risk. for example, CRH antagonists. Comorbid AUD adds to the negative outcome of psychiatric patients with respect to global disease severity, Submitted for Publication: March 21, 2011; final revi- social situation, and socioeconomic burden.11-13 Thus, sion received March 28, 2011; accepted June 4, 2011. intensified preventive measures for high-risk subjects Published Online: August 1, 2011. doi:10.1001 and perhaps even personalized genotype-based treat- /archgenpsychiatry.2011.100 ment strategies61 might be desirable. Author Affiliations: Division of Clinical Neuroscience, The presence of comorbid AUD in as many as 35% of Max Planck Institute of Experimental Medicine (Mss the GRAS population and the devastating psychosocial Ribbe, Ackermann, and Sperling and Drs Begemann, Pa- and clinical consequences agree well with other reports piol, Grube, Friedrichs, Jahn, Sillaber, and Ehrenreich) on schizophrenic patients.11-13,62 The causal role of AUD and DFG Research Center for Molecular Physiology of in this overall aggravated situation is emphasized by the the Brain (Drs Papiol, Jahn, and Ehrenreich), Go¨ttin- fact that schizophrenia-typical symptoms are not differ- gen, Department of Medical Informatics, Biometry, and ent between AUD and non-AUD groups. Surprisingly, at Epidemiology, University of Erlangen-Nuremberg, Er- first view, patients with AUD have an earlier age at on- langen (Ms Schwitulla and Dr Gefeller), and Depart- set of schizophrenic prodrome and first schizophrenic ment of Anaesthesiology and Intensive Care Medicine, episode compared with patients without AUD. This find- Charite´, Campus Virchow-Klinikum and Campus Charite´ ing, however, may be explained by more cannabis use Mitte, Berlin (Dr Krampe), Germany.

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©2011 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 10/01/2021 Correspondence: Hannelore Ehrenreich, MD, DVM, Di- 15. Carpenter KM, Hasin D. A prospective evaluation of the relationship between rea- vision of Clinical Neuroscience, Max Planck Institute of sons for drinking and DSM-IV alcohol-use disorders. Addict Behav. 1998;23 (1):41-46. Experimental Medicine, Hermann-Rein-Strasse 3, 37075 16. Hasking PA, Oei TP. Alcohol expectancies, self-efficacy and coping in an alcohol- Go¨ttingen, Germany ([email protected]). dependent sample. Addict Behav. 2007;32(1):99-113. Author Contributions: Mss Ribbe and Ackermann con- 17. Tyssen R, Vaglum P, Aasland OG, Grønvold NT, Ekeberg O. Use of alcohol to tributed equally to this work. 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