Biomaterial Database

Effects of crack cocaine addiction and stress-related genes on peripheral BDNF levels. 2017

Diego L Rovaris, and Jaqueline B Schuch, and Rodrigo Grassi-Oliveira, and Breno Sanvicente-Vieira, and Bruna S da Silva, and Consuelo Walss-Bass, and Diana Müller, and Anderson R Stolf, and Lisia von Diemen, and Keila M M Ceresér, and Thiago G Pianca, and Claudia M Szobot, and Felix H P Kessler, and Tatiana Roman, and Claiton H D Bau

Department of Genetics, Instituto de Biociências, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.

This study examined the effects of glucocorticoid receptor (NR3C1), corticotropin-releasing hormone receptor 1 (CRHR1), and brain-derived neurotrophic factor (BDNF) genes on susceptibility to crack cocaine addiction and BDNF levels. Crack addicted patients who sought treatment (n = 280) and non-addicted individuals (n = 241) were assessed. Three SNPs in NR3C1 (rs6198, rs41423247, and rs10052957), three in CRHR1 (rs12944712, rs110402, and rs878886), and one in BDNF (rs6265) were genotyped. No significant effect was seen in the case-control analyses. Crack cocaine addicted patients showed significantly lower serum BDNF levels. Significant effects were observed for NR3C1 rs41423247 and rs10052957. These effects were restricted to non-addicted individuals and they were supported by significant gene-by-disease status interactions. For CRHR1, all SNPs were associated with BDNF levels. Although there were significant effects only in the analysis restricted to non-addicted individuals, the lack of significant results in the gene-by-disease status interaction analyses suggest a general effect on BDNF levels. The haplotype analyses presented the same effect seen in the single marker analyses. This study suggests that SNPs in the NR3C1 and CRHR1 genes may influence BDNF levels, but this effect is blunted in the context of crack cocaine addiction. Therefore, our data may be interpreted in light of several studies showing pronounced effects of crack cocaine on BDNF levels. Since peripheral BDNF is a biomarker for several psychiatric phenotypes, our results may be useful in interpreting previous associations between stress-related SNPs, drug addiction, and depression.

UI	MeSH Term	Description	Entries
D008297	Male		Males
D008875	Middle Aged	An adult aged 45 - 64 years.	Middle Age
D011965	Receptors, Glucocorticoid	Cytoplasmic proteins that specifically bind glucocorticoids and mediate their cellular effects. The glucocorticoid receptor-glucocorticoid complex acts in the nucleus to induce transcription of DNA. Glucocorticoids were named for their actions on blood glucose concentration, but they have equally important effects on protein and fat metabolism. Cortisol is the most important example.	Corticoid Type II Receptor,Glucocorticoid Receptors,Glucocorticoids Receptor,Corticoid II Receptor,Corticoid Type II Receptors,Glucocorticoid Receptor,Receptors, Corticoid II,Receptors, Corticoid Type II,Receptors, Glucocorticoids,Corticoid II Receptors,Glucocorticoids Receptors,Receptor, Corticoid II,Receptor, Glucocorticoid,Receptor, Glucocorticoids
D002648	Child	A person 6 to 12 years of age. An individual 2 to 5 years old is CHILD, PRESCHOOL.	Children
D005260	Female		Females
D005838	Genotype	The genetic constitution of the individual, comprising the ALLELES present at each GENETIC LOCUS.	Genogroup,Genogroups,Genotypes
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000293	Adolescent	A person 13 to 18 years of age.	Adolescence,Youth,Adolescents,Adolescents, Female,Adolescents, Male,Teenagers,Teens,Adolescent, Female,Adolescent, Male,Female Adolescent,Female Adolescents,Male Adolescent,Male Adolescents,Teen,Teenager,Youths
D000328	Adult	A person having attained full growth or maturity. Adults are of 19 through 44 years of age. For a person between 19 and 24 years of age, YOUNG ADULT is available.	Adults
D016015	Logistic Models	Statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable. A common application is in epidemiology for estimating an individual's risk (probability of a disease) as a function of a given risk factor.	Logistic Regression,Logit Models,Models, Logistic,Logistic Model,Logistic Regressions,Logit Model,Model, Logistic,Model, Logit,Models, Logit,Regression, Logistic,Regressions, Logistic