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Meta-analysis of serotonin transporter gene promoter polymorphism (5-HTTLPR) association with antidepressant efficacy
Abstract
In the last decade the serotonin transporter gene promoter polymorphism (5-HTTLPR) was likely the most studied genetic variant as predictor of antidepressant response. Nevertheless results are not consistent across studies and previous meta-analysis, since various factors seem to modulate its effect on antidepressant response. With the aim of clarifying this issue, we systematically reviewed literature, selecting 33 studies for an exploratory analysis without any a priori hypothesis. Then we analyzed separately 19 studies performed on Caucasians and 11 on Asians. We tested two phenotypes--remission and response rates--and three genotype comparisons--ll versus ls/ss, ss versus ll/ls and ll versus ss - using the Cochrane review manager. Evaluations were performed separately for SSRIs and mixed/other drugs. Possible clinical modulators were investigated. In the exploratory analysis, we found an association between l allele and l/l genotype and remission. When the analysis was split for ethnic group, in Caucasians we found an association between l allele and both response (OR = 1.58, C.I. 1.16-2.16, p = 0.004), and remission (OR = 1.53, C.I. 1.14-2.04, p = 0.004) in the SSRI group. Only a marginal association between l allele and remission (OR = 1.41, C.I. 1.02-1.95, p = 0.04) survived pooling together mixed antidepressant treatments. In Asians, a small effect of 5-HTTLPR on remission for mixed antidepressants was detected (OR = 2.10, C.I. 1.15-3.84, p = 0.02). Gender, age and age at onset modulated the association in Caucasians. Gender, age and depression severity at baseline modulated the association in Asians. In conclusion, in Caucasians 5-HTTLPR may be a predictor of antidepressant response and remission, while in Asians it does not appear to play a major role.
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“Pharmacogenetics studies have shown an association between certain polymorphisms in SLC6A4 and the response to antidepressants in patients with mood disorders.”
From Epigenetic mechanisms of major depression: Targeting neuronal plasticity (2018) · cites this paper as [131]
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Metadata
- Authors
- Porcelli, S., Fabbri, C., & Serretti, A.
- Affiliations
- University of Bologna (IT)
- Corresponding author
- Serretti, Alessandro
- Venue
- European Neuropsychopharmacology
- Publisher
- Elsevier BV
- Published
- 2012-04
- Volume
- 22
- Issue
- 4
- Pages
- 239-258
- ISSN
- 0924-977X (ISSN-L)
- OpenAlex
- W2035090322
- Cited by
- 331
- References
- 93
- Citations by year
- 2026: 4 · 2025: 11 · 2024: 8 · 2023: 18 · 2022: 18 · 2021: 24 · 2020: 19 · 2019: 20 · 2018: 31 · 2017: 27 · 2016: 42 · 2015: 33 · 2014: 27 · 2013: 36 · 2012: 13
- FWCI
- 2.32
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- European Neuropsychopharmacology (publishedVersion)
- PubMed (publishedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Health Sciences › Medicine › Pharmacology (medical) › Treatment of Major Depression
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- 5-HTTLPR, serotonin transporter gene, promoter polymorphism, antidepressant response, antidepressant remission, genetic predictors of treatment response, selective serotonin reuptake inhibitors, ethnic differences, Caucasians, Asians, age of onset, depression severity, sex differences
- MeSH terms
- Age Factors, Alleles, Antidepressive Agents, Depression, Female, Genotype, Humans, Male, Polymorphism, Genetic, Promoter Regions, Genetic, Remission Induction, Sex Factors, White People, Asian People, Serotonin Plasma Membrane Transport Proteins
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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