These authors contributed equally to this work.
BDNF variability in opioid addicts and response to methadone treatment: preliminary findings
Article first published online: 26 DEC 2007
© 2008 The Authors Journal compilation © 2008 Blackwell Publishing Ltd
Genes, Brain and Behavior
Volume 7, Issue 5, pages 515–522, July 2008
How to Cite
De Cid, R., Fonseca, F., Gratacòs, M., Gutierrez, F., Martín-Santos, R., Estivill, X. and Torrens, M. (2008), BDNF variability in opioid addicts and response to methadone treatment: preliminary findings. Genes, Brain and Behavior, 7: 515–522. doi: 10.1111/j.1601-183X.2007.00386.x
- Issue published online: 9 JUL 2008
- Article first published online: 26 DEC 2007
- Received 17 September 2007, revised 14 December 2007, accepted for publication 16 December 2007
- opioid dependence;
- substance abuse;
Brain-derived neurotrophic factor (BDNF) signaling pathways have been shown to be essential for opioid-induced plasticity. We conducted an exploratory study to evaluate BDNF variability in opioid addict responders and nonresponders to methadone maintenance treatment (MMT). We analyzed 21 single nucleotide polymorphisms (SNPs) across the BDNF genomic region. Responders and nonresponders were classified by means of illicit opioid consumption detected in random urinalysis. Patients were assessed by a structured interview (Psychiatric Research Interview for Substance and Mental Disorders (PRISM)-DSM-IV) and personality was evaluated by the Cloninger’s Temperament and Character Inventory. No clinical, environmental and treatment characteristics were different between the groups, except for the Cooperativeness dimension (P < 0.001). Haplotype block analysis showed a low-frequency (2.7%) haplotype (13 SNPs) in block 1, which was more frequent in the nonresponder group than in the responder group (4/42 vs. 1/135; Pcorrected = 0.023). Fine mapping in block 1 allows us to identify a haplotype subset formed by only six SNPs (rs7127507, rs1967554, rs11030118, rs988748, rs2030324 and rs11030119) associated with differential response to MMT (global P sim = 0.011). Carriers of the CCGCCG haplotype had an increased risk of poorer response, even after adjusting for Cooperativeness score (OR = 20.25 95% CI 1.46–280.50, P = 0.025). These preliminary results might suggest the involvement of BDNF as a factor to be taken into account in the response to MMT independently of personality traits, environmental cues, methadone dosage and psychiatric comorbidity.
Opioid dependence disorder is a complex disease. The development of a drug addiction and the tendency to relapse are caused by a combination of both genetic and environmental factors (Kreek et al. 2005). Methadone maintenance treatment (MMT) is the most widely used treatment for opioid dependence and has been shown to be effective in opioid-dependent subjects who stay in treatment (Amato et al. 2005). If retention in treatment and/or illicit opioid use is considered as the main treatment outcomes, between 30% and 80% of treated patients respond poorly to MMT (GAO 1990). The provision of adequate doses of methadone and other psychosocial services are the main factors related to the success of MMT (Amato et al. 2005; Ward et al. 1999).
Personality characteristics of patients have also been related to MMT outcomes (Cacciola et al. 2001). From the dimensional approach, Cloninger’s model posits that personality encompasses partially inherited temperamental traits and acquired character traits (Cloninger et al. 1993). Temperamental dimensions may be correlated to specific brain systems and have been described as genetically independent from each other (Ebstein 2006; Gerra et al. 2005). In recent years, the study of the therapeutic response to MMT according to patients’ genetic backgrounds has become an issue of increasing interest (Barratt et al. 2006; Crettol et al. 2006; Lawford et al. 2000).
Neurotrophins in the brain enhance the growth and maintenance of several neuronal systems, modulate neurotransmission and play a role in plasticity mechanisms such as long-term potentiation (Chao 2003). As a member of the nerve growth factor-related family of neurotrophins, we focused on brain-derived neurotrophic factor (BDNF) (Anderson et al. 1995; Thoenen 1995). Human BDNF is located on chromosome 11p14.1 and encodes a 247 amino acid (aa) preprotein that is proteolytically cleaved to form the 120 aa mature protein, which is 100% conserved between mice, rats, pigs and humans (Maisonpierre et al. 1991). Linkage and association studies with markers in the BDNF genomic region have been associated with personality traits (Lang et al. 2004, 2005) and some diseases, including Parkinson’s disease, schizophrenia, bipolar disorder, obsessive-compulsive disorder and eating disorders. Most studies have focused on a single polymorphism in the pre-domain of BDNF (Val66Met) (Gratacos et al. 2007a). There is also strong evidence showing that the Met66 allele of this functional Val66Met polymorphism is associated with substance abuse (Beuten et al. 2005; Itoh et al. 2005; Matsushita et al. 2004) and specifically with opioid addiction (Cheng et al. 2005).
We present the preliminary results of an exploratory study aimed to explore the role of genetic variability of BDNF in response to MMT among a cohort of opioid-addicted subjects.
Patients and methods
The study recruited participants who met criteria for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) opioid dependence from the MMT Program at a drug abuse outpatient center in Barcelona (CAS Barceloneta). The characteristics of the MMT provided were as follows: no upper limit on methadone dosages prescribed, no restriction on duration of treatment, forced discharge occurring only as a result of patients’ violent behavior or drug trafficking and clinical management that includes individual counseling and encouraged drug abstinence.
To be eligible for the study, patients had to be Caucasian, enrolled in MMT for at least 6 months and receiving a stable methadone dose for the last 2 months. Exclusion criteria were language-related barriers, severe cognitive impairment or any medical disorder that would interfere with the research assessments.
A close-ended questionnaire was used to record patients’ sociodemographic characteristics, serological status (human immunodeficiency virus, hepatitis C virus (HCV), history of substance use and previous psychiatric treatment. Substance and nonsubstance use psychiatric disorders were diagnosed according to DSM-IV criteria, using the Spanish version of the Psychiatric Research Interview for Substance and Mental Disorders IV (PRISM-IV) in axis I and II (borderline and antisocial personality disorders) (Hasin et al. 2006; Torrens et al. 2004). The degree of addiction-related impairment was assessed using the Spanish version of the Addiction Severity Index (ASI) (Gonzalez et al. 2002; McLellan et al. 1980). Personality characteristics were evaluated using the Spanish version of the self-administered Cloninger’s Temperament and Character Inventory (TCI) (Gutierrez et al. 2001). The temperament dimensions included Novelty Seeking, Harm Avoidance, Reward Dependence and Persistence, and the character dimensions included: Self-Directedness, Cooperativeness and Self-Transcendence. Urinalysis to detect heroin use was performed at the center randomly every 1 or 2 weeks, under the supervision of the nursing staff.
According to urinalyses, patients were classified as responders or nonresponders to MMT. Responders to MMT were those patients whose last four urinalysis tests were negative for illicit opioids, and nonresponders were those with two or more of their last four urinalysis tests positive for illicit opioids. Written informed consent was obtained from each subject after they had received a complete description of the study and been given the chance to discuss any questions or issues. The study was approved by the ethical and clinical research committee of the institution.
Single nucleotide polymorphisms selection and genotyping
Genetic variability in the BDNF genomic region (GenBank accession number NC_000011) was assessed by selecting single nucleotide polymorphisms (SNPs) in a 63.8 kb region including complete coding sequence, 3′ untranslated region (UTR) and 5′UTR regions. SNPs were selected from available databases at the time of experimental design (NCBI database, dbSNP 120; http://www.ncbi.nlm.nih.gov) and from Celera databases (http://www.celera.com). A main criterion for inclusion markers was the availability of validation data and uniform distribution of SNPs along BDNF reference sequence. Based on the NCBI database (NCBI Build 36.1;http://www.ncbi.nlm.nih.gov), the common complete coding region and most of the 3′UTR region, common for preprotein isoforms a, b and c, are covered, as well the 5′UTR region for most of the described transcripts (Table 1).
|SNP||SNP code||Reference ID||Chromosome position||Gene position||Transcript position||Protein position||Alleles||MAF||HWE*||Missing (%)|
|6||hcv9278624||rs11819808||27637964||Intron VIIIh /promoter IX||Intronic||C/T||0.5||ND||0|
|7||rs11030102||rs11030102||27638172||Intron VIIIh /promoter IX||Intronic||C/G||24.5||0.417||0|
Blood samples were collected from all subjects and genomic DNA was obtained from peripheral blood using standard procedures. SNPs were genotyped using the SNPlex™ platform (Applied Biosystems, Foster City, CA, USA) according to the manufacturer’s instructions and analyzed on an Applied Biosystems 3730xl DNA Analyzer. Allele calling was performed by clustering analysis using GeneMapper v.4.0 software. The genotype call rate was >95%. Genotyping quality was controlled in two ways. First, internal positive and negative controls provided by the manufacturer ABI (Applied Biosystems) were included in the reaction plates. Second, six duplicated samples of two HapMap reference trios were incorporated in the genotyping process. Both genotype concordance and correct Mendelian inheritance were verified.
Differences in sociodemographic and clinical characteristics between groups were examined using chi-squared and one-way analysis of variance tests, with Bonferroni post hoc analysis, using the SPSS software analysis package version 10.0. TCI scales values were converted into T scores (Population Norms in Cloninger et al. 1994).
Genotyped SNPs with a call rate of fewer than 80% were not considered for the association analysis. We tested each polymorphism in the whole group to ensure the fit with Hardy Weinberg equilibrium (HWE). Because of multiple testing, we used a threshold of P = 0.001.
Multivariate logistic regression was used to assess the genetic effect of the SNPs. To better determine the real effect of SNPs on MMT outcome, we first performed a logistic regression analysis including the following variables: sex, age, TCI scores, psychiatric comorbidity and methadone dosage; factors that have a priori a high probability to be related to MMT response. Using a stepwise procedure, we selected the statistically significant variables that were included in the final model as covariates. Intergroup comparisons of genotype frequency differences were performed by regression analysis for dominant, recessive, overdominant and log-additive models of inheritance. Unadjusted crude odds ratios (OR) and 95% confidence intervals (95% CI) were calculated. We then calculated the OR adjusted for the clinical variables that were selected in the stepwise logistic regression procedure. The best genetic model was selected using the Akaike’s information criteria. Analysis was carried out using the SNPassoc R library, from the Comprehensive R Archive Network.
As a high correlation exists between the assayed markers because of tight linkage disequilibrium in the region, the problem of multiple testing was solved using the Spectral Decomposition approach (Li & Ji 2005). Allelewise experiment-wide significance threshold to control type I error rate at 5% resulted in a P value of 0.002. An additional correction factor was introduced to account for the four inheritance models tested (dominant, recessive, overdominant and log-additive models of inheritance) in the genotype analysis.
Haplotype association analysis was performed using a two-step method; first, we used a structured approach to test for association based on block structure and then we performed fine mapping of specific haplotype subsets inside the block using a sliding window approach. All SNPs with a minor allele frequency (MAF) >0.001 and inferred haplotypes with frequencies higher than 1% were included to define the haploblock structure. The block definition was based on the Linkage Disequilibrium (LD) measure D′ confidence interval block partition algorithm (Gabriel et al. 2002). Haplotype association analysis, as implemented in haploview v.4.0 software (Barrett et al. 2005), was based on block structure definition and the P value was based on simulation procedures derived after 10 000 simulation steps. A P value of <0.05 was considered statistically significant.
The sliding window approach was undertaken to better define specific haplotype subsets inside the block. In order to provide a comprehensive assessment of the haplotype subsets within the gene, the size of the window varied from 3, 6 and 10 SNPs. Only haplotypes with frequencies higher than 1% were considered. Global significance of the associated haplotype was estimated using a permutation test implemented in the haplo.stats v.1.3.1 package (Lake et al. 2003). A P value based on simulation procedures was derived (P global simulation) and a P value of <0.05 was considered to be statistically significant.
Crude and estimated OR and 95% CI for associated haplotypes were calculated using the function haplo.glm implemented in haplo.stats v.1.3.1 in the R programming language (Lake et al. 2003).
Sociodemographic and clinical characteristics of subjects
The clinical sample included 91 patients [66 (73%) male, age range 25–66 years, mean age 38 ± 8 years]. A total of 68 patients were classified as responders (49 males, mean age 38 ± 7 years) and 23 patients as nonresponders (17 males, mean age 37 ± 9 years). There were no differences between responder and nonresponder groups in terms of prescribed daily methadone dosage (106.27 ± 70.96 vs. 90.00 ± 49.86 mg/day, t = 1.014, df = 88, P = 0.313) and length of time in MMT (40 ± 43 vs. 29 ± 41 months, t = 1.057, df = 88, P = 0.293). The main sociodemographic, medical and psychopathological characteristics of patients are summarized in Table 2.
|Responders (n = 68)||Nonresponders (n = 23)||P*|
|Male (%)||49 (72)||17 (74)||1.000|
|Age in years, mean (SD)||38 (7)||37 (9)||0.395|
|Years at school, mean (SD)||9 (2)||9 (4)||0.522|
|Single (%)||32 (48)||10 (44)||0.389|
|Legal background (%)||38 (57)||11 (52)||0.804|
|Live with family (%)||48 (72)||16 (76)||0.541|
|Offspring, mean (SD)||0.9 (1.1)||0.8 (1.0)||0.774|
|Employed (%)||19 (28)||10 (48)||0.187|
|Human immunodeficiency virus + (%)||25 (37)||5 (22)||0.207|
|HCV + (%)||49 (73)||15 (65)||0.595|
|Illicit opioid consumption in months, mean (SD)||148 (86)||89 (56)||<0.001|
|Other substances dependence disorder – lifetime prevalence (%)|
|Alcohol||19 (29)||6 (26)||1.000|
|Sedatives||17 (26)||7 (30)||0.786|
|Stimulants||3 (5)||1 (4)||1.000|
|Cannabis||13 (20)||3 (13)||0.753|
|Cocaine||44 (67)||12 (52)||0.223|
|Days of heroin consumption in the last 30 days, mean (SD)||0.4 (1.26)||10.0 (11.8)||<0.001|
|Days of cocaine consumption in the last 30 days, mean (SD)||1.7 (5.0)||8.5 (11.)||0.012|
|Psychiatric comorbidity (lifetime prevalence) (%)||50 (75)||15 (65)||0.424|
|Months in MMT, mean (SD)||40 (43)||29 (41)||0.293|
|Methadone dosage, mean (SD)||106.27 (70.96)||90.00 (49.86)||0.313|
|ASI scores, mean (SD)|
|General health status||3.1 (2.3)||3.3 (2.3)||0.719|
|Working problems||4.3 (2.8)||3.3 (2.8)||0.144|
|Alcohol use||1.3 (1.7)||1.4 (1.4)||0.789|
|Substance use||4.9 (2.5)||6.6 (1.8)||0.001|
|Legal problems||1.4 (2.0)||2.0 (2.4)||0.219|
|Social relationships||3.3 (2.5)||3.0 (2.1)||0.629|
|Psychological status||3.0 (2.5)||2.3 (1.9)||0.242|
|TCI temperament scales, mean (SD)|
|Harm Avoidance||59.1 (9.9)||56.2 (7.9)||0.203|
|Novelty Seeking||52.6 (7.9)||55.0 (9.6)||0.244|
|Reward Dependence||45.0 (9.6)||47.1 (7.7)||0.351|
|Persistence||42.5 (8.8)||43.9 (9.8)||0.513|
|TCI character scales, mean (SD)|
|Self-Directness||41.3 (12.0)||45.3 (8.3)||0.085|
|Cooperativeness||41.4 (8.6)||47.4 (5.1)||<0.001|
|Self-Transcendence||41.6 (10.3)||40.7 (8.2)||0.682|
No differences in the proportions of responders and nonresponders in terms of lifetime and current prevalence of other substance use dependence disorders (cocaine, cannabis, alcohol, sedatives and stimulants) and lifetime psychiatric comorbidity in axes I and II (75% in responders vs. 65% in nonresponders, χ2 = 0.456, df = 1, P = 0.424) were found. Regarding the severity of addiction, there was a tendency in the ‘Other Substance Use’ dimension of the ASI for the nonresponder group to score higher. However, when substance use in the last 30 days was compared for responders and nonresponders, the only difference found was that as expected, nonresponders reported more days of heroin consumption in last 30 days. The mean number of days of cocaine use during the last 30 days was not significantly different between both groups (Table 2).
There was a significant difference in TCI scores between groups only in the Cooperativeness dimension, with the nonresponder group scoring higher (Table 2).
Block structure of the BDNF region
A detailed description of assayed SNPs is summarized in Table 1. All selected SNPs were in HWE (P > 0.01). Nine out of 30 SNPs were not polymorphic and were not informative to block structure definition. Twenty-one SNPs were used for infer block structure. Extended LD defined one block over the entire genomic region in the whole patient sample. Block structure was then independently derived separately in both patient samples: responders (n = 68) and nonresponders (n = 23). The block definition was slightly different between both groups (Fig. 1). Two blocks were defined in the nonresponder group (47 and 1 kb) and only one in the responder group (57 kb). In the nonresponder group, the major block was conserved in the coding and 3′UTR region but was broken upstream of the 5′UTR region of transcript NM_170733, which codes for BDNF isoform. Detailed composition of haploblocks for each group is summarized in Fig. 1.
BDNF variability between responders and nonresponders to MMT
Multivariate logistic regression analysis was performed to identify confounding variables in a predictive model of response to MMT. We included TCI dimension scores, psychiatric comorbidity (in the last 12 months), methadone dosage, age and gender as independent variables in the model. After a stepwise procedure, only Cooperativeness scores remained significant in the model, as also found in the univariate analysis.
The genotype distribution in responders and nonresponders under different models of genetic action did not yield statistically significant differences in the crude analysis (P > 0.05) and only was nominal for rs7940188 after adjusted analysis for Cooperativeness, Puncorrected = 0.045.
Haplotype analysis revealed a low-frequency haplotype (2.7%) in block 1 associated with a poorer response to MMT (Table 3). AGCTGATCCCGAA haplotype (hcv1751792, rs2049045, hcv1751795, hcv1751796, rs7103873, rs10835211, hcv1751802, rs7127507, rs988748, rs2030324, rs11030119, rs7934165 and rs10767665) in block 1 was more frequent in the nonresponder group than in the responder group (4/42 vs. 1/135, Pcorrected = 0.023). In addition, we observed a haplotype more frequent in responders than in nonresponders (34/102 vs. 5/41, Puncorrected = 0.0435), but this result was no longer significant after the permutation validation procedure. The sliding window approach identified a haplotype subset associated with a differential response to MMT, in the 10 and 6 window analyses. The smaller multimarker with statistical significance was the haplotype formed by six SNPs (rs7127507, rs1967554, rs11030118, rs988748, rs2030324 and rs11030119). This haplotype was significantly associated with a differential response (global P sim = 0.011). Haplotype analysis showed that carriers of the CCGCCG haplotype (both homozygotes and heterozygotes) had an increased risk of poorer response (OR = 11.99, 95% CI 1.24–116.33, P = 0.032), even after adjusting for Cooperativeness, although in this case, a wider CI was obtained (OR = 20.25, 95% CI 1.46–280.50), P = 0.025).
|Haplotype*||Frequency||Nonresponders||Responders†||P value||P value_ 10 000|
This exploratory analysis suggests that BDNF variability confers a differential susceptibility to MMT response in opioid-dependent patients. Although association of BDNF with opioid dependence disorder has been previously reported (Cheng et al. 2005), as far as we are aware, this is the first time that BDNF variability has been linked to opioid therapeutic response. The relevance of BDNF variability to therapeutic response has been previously suggested with regard to the efficacy of other psychiatric treatments such as prophylaxis with lithium carbonate for bipolar mood disorders (Rybakowski et al. 2005) and antidepressants for unipolar depression (Gratacos et al. 2007b).
Sociodemographics, medical status and psychiatric comorbidity were not associated with a differential response to MMT, as reported by others (Kellogg et al. 2006). Psychiatric disorders such as mood disorders, anxiety disorders, eating disorders and schizophrenia have been associated with both opioid dependence disorder (Brooner et al. 1997) and BDNF variability (Gratacos et al. 2007a). However, our results suggest that the effect of BDNF on MMT response is independent of the presence of psychiatric comorbidity. Similar to pharmacological treatment studies on eating disorders (Klump et al. 2004), lower Cooperativeness scores were observed in responder group. However, other studies on major depression and panic disorder showed opposite results (Hirano et al. 2002; Marchesi et al. 2006). Although a sample size bias cannot be discarded, these differences could be diagnosis specific or might be related to the TCI assessment time (Marchesi et al. 2006).
Genetic variability in BDNF plays a role in patients undergoing MMT, as demonstrated by our findings. From the adjusted analysis, we identified one haplotype with a low frequency that confers a poorer response in patients undergoing MMT. Nevertheless, given the low frequency of this haplotype, it seems clear that no single effect is present and that other factors should be considered. Because BDNF has been linked to both the dopaminergic system (Seroogy et al. 1994) and the noradrenergic system (Akbarian et al. 2002) that play a relevant role in opioid dependence disorder, it is not unlikely that BDNF could affect the response to MMT. Differential response to MMT could be a consequence of a reduction of brain plasticity arising from an altered expression and functionality of BDNF.
From our data, we cannot infer any biological effect on BDNF protein levels. To date, the most widely reported BNDF variant that has been functionally tested is Val66Met (rs6265), which has been shown to affect intracellular trafficking and activity-dependent secretion of BDNF protein (Egan et al. 2003). Strong evidence has been reported of the role of Val66Met in substance abuse (Cheng et al. 2005; Itoh et al. 2005; Matsushita et al. 2004), but we were not able to find evidence of the effect of this single polymorphism in terms of response to MMT in our analyses. Moreover, in accordance with observed haploblock structure differences between groups, informative SNPs that define the associated haplotype are located in the 5′ region of BDNF. Other undetermined functional or regulatory unknown variants could be carried for the associated haplotype. Promoter variants not included in this study have been associated with BDNF levels, in addition to Val66Met (Jiang et al. 2005). Furthermore, there may be an alteration of silencing mechanisms regulated by fine control of messenger RNA expression. Specific patterns of expression in different brain regions and peripheral tissues have been reported in rats (Liu et al. 2006; Pattabiraman et al. 2005) and mice (Dennis & Levitt 2005), and a complex genomic structure has been reported in the human region (Liu et al. 2005; Pruunsild et al. 2007). Liu et al. have characterized a new gene (BDNFos) in the antisense complementary chain (Liu et al. 2005, 2006). BDNFos is transcribed to produce alternatively spliced natural antisense transcripts, and its fifth exon overlaps with the coding exon of human BDNF, suggesting a role as regulatory RNA. None of the associated SNPs in our study is located in the coding exon, but the risk haplotype could carry some of these functional variants, not directly related to the expression or function of BDNF but perhaps to a regulatory effect by BDNFos.
While the small sample size of our study may limit the generalizability of our results, the careful phenotypic assessment is a point of strength of the study. Results of the present exploratory study suggest involvement of BDNF as a factor to be considered in the response to MMT independently of personality traits, environmental cues, methadone dosage and the presence of medical and psychiatric comorbidity. Moreover, taking into account preclinical and clinical studies (Graham et al. 2007; Williamson et al. 2007), the role of BDNF and cocaine use in MMT response has yet to be determined. Future studies should confirm our results in a larger sample and in addition should account for pharmacokinetic factors that influence the response to methadone treatment.
- 2002) Brain-derived neurotrophic factor is essential for opiate-induced plasticity of noradrenergic neurons. J Neurosci 22, 4153–4162. , , , , , , , , , , & . (
- 2005) An overview of systematic reviews of the effectiveness of opiate maintenance therapies: available evidence to inform clinical practice and research. J Subst Abuse Treat 28, 321–329. , , , , & . (
- 1995) Differential distribution of exogenous BDNF, NGF, and NT-3 in the brain corresponds to the relative abundance and distribution of high-affinity and low-affinity neurotrophin receptors. J Comp Neurol 357, 296–317. , , , , , & . (
- 2006) Association between the DRD2 A1 allele and response to methadone and buprenorphine maintenance treatments. Am J Med Genet B Neuropsychiatr Genet 141, 323–331. , & . (
- 2005) Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21, 263–265. , , & . (
- 2005) Significant association of BDNF haplotypes in European-American male smokers but not in European-American female or African-American smokers. Am J Med Genet B Neuropsychiatr Genet 139, 73–80. , , , , , , & . (
- 1997) Psychiatric and substance use comorbidity among treatment-seeking opioid abusers. Arch Gen Psychiatry 54, 71–80. , , , & . (
- 2001) The relationship of psychiatric comorbidity to treatment outcomes in methadone maintained patients. Drug Alcohol Depend 61, 271–280. , , , & . (
- 1993) A psychobiological model of temperament and character. Arch Gen Psychiatry 50, 975–990. , & . (
- 1994) The Temperament and Character Inventory (TCI): a guide to its development and use. Center for Psychobiology of Personality, St Louis, MI. , , & . (
- 2006) ABCB1 and cytochrome P450 genotypes and phenotypes: influence on methadone plasma levels and response to treatment. Clin Pharmacol Ther 80, 668–681. , , , , , , & . (
- 2003) Neurotrophins and their receptors: a convergence point for many signalling pathways. Nat Rev Neurosci 4, 299–309. . (
- 2005) Brain-derived neurotrophic factor (Val66Met) genetic polymorphism is associated with substance abuse in males. Brain Res Mol Brain Res 140, 86–90. , , , , & . (
- 2005) Regional expression of brain derived neurotrophic factor (BDNF) is correlated with dynamic patterns of promoter methylation in the developing mouse forebrain. Brain Res Mol Brain Res 140, 1–9. & . (
- 2006) The molecular genetic architecture of human personality: beyond self-report questionnaires. Mol Psychiatry 11, 427–445. . (
- 2003) The BDNF val66met polymorphism affects activity-dependent secretion of BDNF and human memory and hippocampal function. Cell 112, 257–269. , , , , , , , , , , & . (
- 2002) The structure of haplotype blocks in the human genome. Science 296, 2225–2229. , , , , , , , , , , , , , , , , & . (
- GAO. (1990) Methadone maintenance: some treatment programs are not effective; greater Federal oversights needed. United States General Accounting Office, Washington, DC (GAO/HRD-90–104).
- 2005) Serotonin transporter promoter polymorphism genotype is associated with temperament, personality traits and illegal drugs use among adolescents. J Neural Transm 112, 1397–1410. , , , , , , , , , & . (
- 2002) Estudio de fiabilidad y validez de la versión española de la entrevista clínica Addiction Severity Index (ASI). In Iraurgi, I., Gonzalez, F. (eds), Instrumentos de evaluación en drogodependencias. Aula Medica Ediciones, Madrid, pp. 271–308. , , , , & . (
- 2007) Dynamic BDNF activity in nucleus accumbens with cocaine use increases self-administration and relapse. Nat Neurosci 10, 1029–1037. , , , , & . (
- 2007a) Brain-derived neurotrophic factor Val66Met and psychiatric disorders: meta-analysis of case-control studies confirm association to substance-related disorders, eating disorders, and schizophrenia. Biol Psychiatry 61, 911–922. , , , , & . (
- 2007b) A brain-derived neurotrophic factor (BDNF) haplotype is associated with antidepressant treatment outcome in mood disorders. Pharmacogenomics J [Epub ahead of print]. , , , , , , , , & . (
- 2001) Psychometric properties of the Temperament and Character Inventory (TCI) questionnaire in a Spanish psychiatric population. Acta Psychiatr Scand 103, 143–147. , , , , , & . (
- 2006) Diagnosis of comorbid psychiatric disorders in substance users assessed with the Psychiatric Research Interview for Substance and Mental Disorders for DSM-IV. Am J Psychiatry 163, 689–696. , , , , & . (
- 2002) Evaluating the state dependency of the Temperament and Character Inventory dimensions in patients with major depression: a methodological contribution. J Affect Disord 69, 31–38. , , , , , , , & . (
- 2005) Association study between brain-derived neurotrophic factor gene polymorphisms and methamphetamine abusers in Japan. Am J Med Genet B Neuropsychiatr Genet 132, 70–73. , , , , , , , , , , , , & . (
- 2005) BDNF variation and mood disorders: a novel functional promoter polymorphism and Val66Met are associated with anxiety but have opposing effects. Neuropsychopharmacology 30, 1353–1361. , , , , , , , , & . (
- 2006) Adolescent and young adult heroin patients: drug use and success in methadone maintenance treatment. J Addict Dis 25, 15–25. , , , , & . (
- 2004) Personality characteristics of women before and after recovery from an eating disorder. Psychol Med 34, 1407–1418. , , , , , , , , , , , , , , , , , & . (
- 2005) Genetic influences on impulsivity, risk taking, stress responsivity and vulnerability to drug abuse and addiction. Nat Neurosci 8, 1450–1457. , , & . (
- 2003) Estimation and tests of haplotype-environment interaction when linkage phase is ambiguous. Hum Hered 55, 56–65. , , , , , & . (
- 2004) BDNF serum concentrations in healthy volunteers are associated with depression-related personality traits. Neuropsychopharmacology 29, 795–798. , & . (
- 2005) Association of a functional BDNF polymorphism and anxiety-related personality traits. Psychopharmacology (Berl) 180, 95–99. , , , , , , & . (
- 2000) The D(2) dopamine receptor A(1) allele and opioid dependence: association with heroin use and response to methadone treatment. Am J Med Genet 96, 592–598. , , , , , , & . (
- 2005) Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity 95, 221–227. & . (
- 2005) Human brain derived neurotrophic factor (BDNF) genes, splicing patterns, and assessments of associations with substance abuse and Parkinson’s disease. Am J Med Genet B Neuropsychiatr Genet 134, 93–103. , , , , , , , , & . (
- 2006) Rodent BDNF genes, novel promoters, novel splice variants, and regulation by cocaine. Brain Res 1067, 1–12. , , , , & . (
- 1991) Human and rat brain-derived neurotrophic factor and neurotrophin-3: gene structures, distributions, and chromosomal localizations. Genomics 10, 558–568. , , , , , , , & . (
- 2006) The effect of temperament and character on response to selective serotonin reuptake inhibitors in panic disorder. Acta Psychiatr Scand 114, 203–210. , , , & . (
- 2004) Association study of brain-derived neurotrophic factor gene polymorphism and alcoholism. Alcohol Clin Exp Res 28, 1609–1612. , , , , , & . (
- 1980) An improved diagnostic evaluation instrument for substance abuse patients. The Addiction Severity Index. J Nerv Ment Dis 168, 26–33. , , & . (
- 2005) Neuronal activity regulates the developmental expression and subcellular localization of cortical BDNF mRNA isoforms in vivo. Mol Cell Neurosci 28, 556–570. , , , , & . (
- 2007) Dissecting the human BDNF locus: bidirectional transcription, complex splicing, and multiple promoters. Genomics 90, 397–406. , , , & . (
- 2005) Prophylactic lithium response and polymorphism of the brain-derived neurotrophic factor gene. Pharmacopsychiatry 38, 166–170. , , , , , , & . (
- 1994) Dopaminergic neurons in rat ventral midbrain express brain-derived neurotrophic factor and neurotrophin-3 mRNAs. J Comp Neurol 342, 321–334. , , , , & . (
- 1995) Neurotrophins and neuronal plasticity. Science 270, 593–598. . (
- 2004) Diagnosing comorbid psychiatric disorders in substance abusers: validity of the Spanish versions of the Psychiatric Research Interview for Substance and Mental Disorders and the Structured Clinical Interview for DSM-IV. Am J Psychiatry 161, 1231–1237. , , , & . (
- 1999) Role of maintenance treatment in opioid dependence. Lancet 353, 221–226. , & . (
- 2007) The effect of baseline cocaine use on treatment outcomes for heroin dependence over 24 months: findings from the Australian Treatment Outcome Study. J Subst Abuse Treat 33, 287–293. , , & . (
We would like to thank the patients for taking part in the study and the CAS Barceloneta nursing team and Laura Diaz for their valuable help with collecting the data. We would also like to thank Marta Ribasés for accurate BDNF SNP selection and Antonio Verdejo for helpful comments. We thank Marta Pulido, MD, for editing the manuscript and editorial assistance. Financial support was received from TV3 Marató (01/810), FIS G03/005, G03/184, PI06/0940 and CIBERESP (Spanish Ministry of Health).