Chloe P O'Connell1, Andrea N Goldstein-Piekarski1, Charles B Nemeroff1, Alan F Schatzberg1, Charles Debattista1, Tania Carrillo-Roa1, Elisabeth B Binder1, Boadie W Dunlop1, W Edward Craighead1, Helen S Mayberg1, Leanne M Williams1. 1. From the School of Medicine and the Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif.; the Sierra-Pacific Mental Illness Research, Education, and Clinical Center, VA Palo Alto Health Care System, Palo Alto, Calif.; the Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami; the Department of Translational Research in Psychiatry, Max Planck Institute for Psychiatry, Munich; the Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta; and the Department of Psychology, Emory University, Atlanta.
Abstract
OBJECTIVE: Genetic variation within the hypothalamic-pituitary-adrenal (HPA) axis has been linked to risk for depression and antidepressant response. However, these associations have yet to produce clinical gains that inform treatment decisions. The authors investigated whether variation within HPA axis genes predicts antidepressant outcomes within two large clinical trials. METHOD: The test sample comprised 636 patients from the International Study to Predict Optimized Treatment in Depression (iSPOT-D) who completed baseline and 8-week follow-up visits and for whom complete genotyping data were available. The authors tested the relationship between genotype at 16 candidate HPA axis single-nucleotide polymorphisms (SNPs) and treatment outcomes for three commonly used antidepressants (escitalopram, sertraline, and extended-release venlafaxine), using multivariable linear and logistic regression with Bonferroni correction. Response and remission were defined using the Hamilton Depression Rating Scale. Findings were then validated using the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study of outcome predictors in treatment-naive patients with major depression. RESULTS: The authors found that the rs28365143 variant within the corticotropin-releasing hormone binding protein (CRHBP) gene predicted antidepressant outcomes for remission, response, and symptom change. Patients homozygous for the G allele of rs28365143 had greater remission rates, response rates, and symptom reductions. These effects were specific to drug class. Patients homozygous for the G allele responded significantly better to the selective serotonin reuptake inhibitors escitalopram and sertraline than did A allele carriers. In contrast, rs28365143 genotype was not associated with treatment outcomes for the serotonin norepinephrine reuptake inhibitor venlafaxine. When patients were stratified by race, the overall effect of genotype on treatment response remained. In the validation sample, the GG genotype was again associated with favorable antidepressant outcomes, with comparable effect sizes. CONCLUSIONS: These findings suggest that a specific CRHBP SNP, rs28365143, may have a role in predicting which patients will improve with antidepressants and which type of antidepressant may be most effective. The results add to the foundational knowledge needed to advance a precision approach to personalized antidepressant choices.
RCT Entities:
OBJECTIVE: Genetic variation within the hypothalamic-pituitary-adrenal (HPA) axis has been linked to risk for depression and antidepressant response. However, these associations have yet to produce clinical gains that inform treatment decisions. The authors investigated whether variation within HPA axis genes predicts antidepressant outcomes within two large clinical trials. METHOD: The test sample comprised 636 patients from the International Study to Predict Optimized Treatment in Depression (iSPOT-D) who completed baseline and 8-week follow-up visits and for whom complete genotyping data were available. The authors tested the relationship between genotype at 16 candidate HPA axis single-nucleotide polymorphisms (SNPs) and treatment outcomes for three commonly used antidepressants (escitalopram, sertraline, and extended-release venlafaxine), using multivariable linear and logistic regression with Bonferroni correction. Response and remission were defined using the Hamilton Depression Rating Scale. Findings were then validated using the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study of outcome predictors in treatment-naive patients with major depression. RESULTS: The authors found that the rs28365143 variant within the corticotropin-releasing hormone binding protein (CRHBP) gene predicted antidepressant outcomes for remission, response, and symptom change. Patients homozygous for the G allele of rs28365143 had greater remission rates, response rates, and symptom reductions. These effects were specific to drug class. Patients homozygous for the G allele responded significantly better to the selective serotonin reuptake inhibitors escitalopram and sertraline than did A allele carriers. In contrast, rs28365143 genotype was not associated with treatment outcomes for the serotoninnorepinephrine reuptake inhibitor venlafaxine. When patients were stratified by race, the overall effect of genotype on treatment response remained. In the validation sample, the GG genotype was again associated with favorable antidepressant outcomes, with comparable effect sizes. CONCLUSIONS: These findings suggest that a specific CRHBP SNP, rs28365143, may have a role in predicting which patients will improve with antidepressants and which type of antidepressant may be most effective. The results add to the foundational knowledge needed to advance a precision approach to personalized antidepressant choices.
Authors: Andrew D Johnson; Robert E Handsaker; Sara L Pulit; Marcia M Nizzari; Christopher J O'Donnell; Paul I W de Bakker Journal: Bioinformatics Date: 2008-10-30 Impact factor: 6.937
Authors: T O Harris; S Borsanyi; S Messari; K Stanford; S E Cleary; H M Shiers; G W Brown; J Herbert Journal: Br J Psychiatry Date: 2000-12 Impact factor: 9.319
Authors: R O'Hara; C M Schröder; R Mahadevan; A F Schatzberg; S Lindley; S Fox; M Weiner; H C Kraemer; A Noda; X Lin; H L Gray; J F Hallmayer Journal: Mol Psychiatry Date: 2007-03-13 Impact factor: 15.992
Authors: Zane Zeier; Linda L Carpenter; Ned H Kalin; Carolyn I Rodriguez; William M McDonald; Alik S Widge; Charles B Nemeroff Journal: Am J Psychiatry Date: 2018-04-25 Impact factor: 18.112
Authors: Dallece E Curley; Ashley E Webb; Douglas J Sheffler; Carolina L Haass-Koffler Journal: Front Behav Neurosci Date: 2021-11-29 Impact factor: 3.558
Authors: Duan Liu; Yongxian Zhuang; Lingxin Zhang; Huanyao Gao; Drew Neavin; Tania Carrillo-Roa; Yani Wang; Jia Yu; Sisi Qin; Daniel C Kim; Erica Liu; Thanh Thanh Le Nguyen; Joanna M Biernacka; Rima Kaddurah-Daouk; Boadie W Dunlop; W Edward Craighead; Helen S Mayberg; Elisabeth B Binder; Mark A Frye; Liewei Wang; Richard M Weinshilboum Journal: Mol Psychiatry Date: 2020-11-23 Impact factor: 13.437