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Does the 5-HT1A rs6295 polymorphism influence the safety and efficacy of citalopram therapy in the oldest old?

Research output: Contribution to journalArticle

Author(s)

Greg Scutt, Andrew Overall, Railton Scott, Bhavik Patel, Lamia Hachoumi, Mark Yeoman, Juliet Wright

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Abstract

Major depressive disorder (MDD) in older people is a relatively common, yet hard to treat problem. In this study, we aimed to establish if a single nucleotide polymorphism in the 5-HT1A receptor gene (rs6295) determines antidepressant response in patients aged > 80 years (the oldest old) with MDD.

Nineteen patients aged at least 80 years with a new diagnosis of MDD were monitored for response to citalopram 20 mg daily over 4 weeks and genotyped for the rs6295 allele. Both a frequentist and Bayesian analysis was performed on the data. Bayesian analysis answered the clinically relevant question: ‘What is the probability that an older patient would enter remission after commencing selective serotonin reuptake inhibitor (SSRI) treatment, conditional on their rs6295 genotype?’

Individuals with a CC (cytosine–cytosine) genotype showed a significant improvement in their Geriatric Depression Score (p = 0.020) and cognition (p = 0.035) compared with other genotypes. From a Bayesian perspective, we updated reports of antidepressant efficacy in older people with our data and calculated that the 4-week relative risk of entering remission, given a CC genotype, is 1.9 [95% highest-density interval (HDI) 0.7–3.5], compared with 0.52 (95% HDI 0.1–1.0) for the CG (cytosine–guanine) genotype. The sample size of n = 19 is too small to draw any firm conclusions, however, the data suggest a trend indicative of a relationship between the rs6295 genotype and response to citalopram in older patients, which requires further investigation.

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Details

Original languageEnglish
Pages (from-to)355-366
Number of pages12
JournalTherapeutic Advances in Drug Safety
Volume9
Issue number7
Early online date23 Apr 2018
DOIs
Publication statusPublished - 1 Jul 2018

    Research areas

  • ageing, Bayesian analysis, depression, pharmacogenomics

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