Trial reportThe pharmacogenomics journal2019
Predicting antidepressant treatment outcome based on socioeconomic status and citalopram dose.
Trial report in The pharmacogenomics journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- A network-based analysis anticipates time to recovery from major depression revealing a plasticity by context interplay.Translational psychiatry · 2025Trial
- Permissive and instructive causality: integrating biological and environmental factors in mental health.Psychological medicine · 2026Review
- Socioeconomic Status Is Associated With Reward Processing, Interleukin 1β, Striatal Connectivity, and Antidepressant Outcomes in Individuals With Major Depressive Disorder: A CAN-BIND-1 Report.Biological psychiatry global open science · 2026Article
- Anticipating depression trajectories by measuring plasticity and change through symptom network dynamics.European psychiatry : the journal of the Association of European Psychiatrists · 2025Article
- More and Less Fear in Serotonin Transporter Knockout Mice.Genes, brain, and behavior · 2025Article
- Biomarkers of stress resilience: A review.Neuroscience applied · 2024Article
- Heterogeneity of depression across the socioeconomic spectrum.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- A paradigm shift in translational psychiatry through rodent neuroethology.Molecular psychiatry · 2023Review
- Using Machine Learning to Predict Antidepressant Treatment Outcome From Electronic Health Records.Psychiatric research and clinical practice · 2023Article
- Combinatorial approaches for treating neuropsychiatric social impairment.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Review
- Time moderates the interplay between 5-HTTLPR and stress on depression risk: gene x environment interaction as a dynamic process.Translational psychiatry · 2022Article
- The interaction between Environmental Enrichment and fluoxetine in inhibiting sucrose-seeking renewal in mice depend on social living condition.Psychopharmacology · 2022Article
- What Can Reinforcement Learning Models of Dopamine and Serotonin Tell Us about the Action of Antidepressants?Computational psychiatry (Cambridge, Mass.) · 2022Review
- Antidepressant-like effect of a selenopropargylic benzamide in mice: involvement of the serotonergic system.Psychopharmacology · 2020Article
- Do people living in disadvantaged circumstances receive different mental health treatments than those from less disadvantaged backgrounds?BMC public health · 2020Article
- Predictors of remission after repetitive transcranial magnetic stimulation for the treatment of major depressive disorder: An analysis from the randomised non-inferiority THREE-D trial.EClinicalMedicine · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Selective serotonin reuptake inhibitors (SSRIs), the most prescribed antidepressant drugs, have incomplete efficacy and no clear mechanism of action. In addition, no reliable methods to identify patients who will benefit from treatment is available. In this study, we show that citalopram, a commonly used SSRI, produces a dose-dependent amplification of the influence of the environment on mood, making the severity of symptoms dependent on the level of socioeconomic status (SES). As a consequence, based on SES, we were able to predict which patients would show remission following 12 weeks of treatment in the high, but not the low dose group. Our findings support a novel mechanism of action for SSRIs, which calls for a permissive rather than an instructive role of these drugs, and indicate that treatment outcome can be predicted based on SES and dose. Finally, our findings suggest that the patient's social and economic conditions should be considered in setting up personalized strategies aimed at enhancing SSRI efficacy.
Indexed as
Identifiers
30723316What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.