Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Predicting antidepressant treatment outcome based on socioeconomic status and citalopram dose

The Pharmacogenomics Journal volume 19, pages 538–546 (2019)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Trivedi MH, Rush AJ, Wisniewski SR, Nierenberg AA, Warden D, Ritz L, et al. Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice. Am J Psychiatry. 2006;163:28–40.

    Article  Google Scholar 

  2. Simon GE, Perlis RH. Personalized medicine for depression: can we match patients with treatments? Am J Psychiatry. 2010;167:1445–55.

    Article  Google Scholar 

  3. Labermaier C, Masana M, Muller MB. Biomarkers predicting antidepressant treatment response: how can we advance the field? Dis Markers. 2013;35:23–31.

    Article  Google Scholar 

  4. Simon GE, Von Korff M, Rutter CM, Peterson DA. Treatment process and outcomes for managed care patients receiving new antidepressant prescriptions from psychiatrists and primary care physicians. Arch Gen Psychiatry. 2001;58:395–401.

    Article  CAS  Google Scholar 

  5. Smith K. Trillion-dollar brain drain. Nature. 2011;478:15.

    Article  CAS  Google Scholar 

  6. Branchi I. The double edged sword of neural plasticity: increasing serotonin levels leads to both greater vulnerability to depression and improved capacity to recover. Psychoneuroendocrinology. 2011;36:339–51.

    Article  CAS  Google Scholar 

  7. Licinio AW, Wong ML, Licinio J. Biological and behavioural antidepressant treatment responses with the selective serotonin reuptake inhibitor fluoxetine can be determined by the environment. Mol Psychiatry. 2017;22:484.

    Article  CAS  Google Scholar 

  8. Alboni S, Poggini S, Garofalo S, Milior G, El Hajj H, Lecours C, et al. Fluoxetine treatment affects the inflammatory response and microglial function according to the quality of the living environment. Brain Behav Immun. 2016;58:261–71.

  9. Alboni S, van Dijk RM, Poggini S, Milior G, Perrotta M, Drenth T, et al. Fluoxetine effects on molecular, cellular and behavioral endophenotypes of depression are driven by the living environment. Mol Psychiatry. 2017;22:552–61.

    Article  CAS  Google Scholar 

  10. Branchi I, Santarelli S, Capoccia S, Poggini S, D’Andrea I, Cirulli F, et al. Antidepressant treatment outcome depends on the quality of the living environment: a pre-clinical investigation in mice. PLoS ONE. 2013;8:e62226.

    Article  CAS  Google Scholar 

  11. Chiarotti F, Viglione A, Giuliani A, Branchi I. Citalopram amplifies the influence of living conditions on mood in depressed patients enrolled in the STAR*D study. Transl Psychiatry. 2017;7:e1066.

    Article  CAS  Google Scholar 

  12. Mastronardi C, Paz-Filho GJ, Valdez E, Maestre-Mesa J, Licinio J, Wong ML. Long-term body weight outcomes of antidepressant–environment interactions. Mol Psychiatry. 2011;16:265–72.

    Article  CAS  Google Scholar 

  13. Wong ML, Licinio J. Research and treatment approaches to depression. Nat Rev Neurosci. 2001;2:343–51.

    Article  CAS  Google Scholar 

  14. Reilly TJ, MacGillivray SA, Reid IC, Cameron IM. Psychometric properties of the 16-item Quick Inventory of Depressive Symptomatology: a systematic review and meta-analysis. J Psychiatr Res. 2015;60:132–40.

    Article  Google Scholar 

  15. Rush AJ, Bernstein IH, Trivedi MH, Carmody TJ, Wisniewski S, Mundt JC, et al. An evaluation of the Quick Inventory of Depressive Symptomatology and the Hamilton Rating Scale for Depression: a sequenced treatment alternatives to relieve depression trial report. Biol Psychiatry. 2006;59:493–501.

    Article  Google Scholar 

  16. Trivedi MH, Rush AJ, Ibrahim HM, Carmody TJ, Biggs MM, Suppes T, et al. The Inventory of Depressive Symptomatology, Clinician Rating (IDS-C) and Self-Report (IDS-SR), and the Quick Inventory of Depressive Symptomatology, Clinician Rating (QIDS-C) and Self-Report (QIDS-SR) in public sector patients with mood disorders: a psychometric evaluation. Psychol Med. 2004;34:73–82.

    Article  CAS  Google Scholar 

  17. Warden D, Rush AJ, Trivedi MH, Fava M, Wisniewski SR. The STAR*D Project results: a comprehensive review of findings. Curr Psychiatry Rep. 2007;9:449–59.

    Article  Google Scholar 

  18. Adler NE, Ostrove JM. Socioeconomic status and health: what we know and what we don’t. Ann N Y Acad Sci. 1999;896:3–15.

    Article  CAS  Google Scholar 

  19. Cohen A, Gilman SE, Houck PR, Szanto K, Reynolds CF 3rd. Socioeconomic status and anxiety as predictors of antidepressant treatment response and suicidal ideation in older adults. Soc Psychiatry Psychiatr Epidemiol. 2009;44:272–7.

    Article  Google Scholar 

  20. Iosifescu DV, Clementi-Craven N, Fraguas R, Papakostas GI, Petersen T, Alpert JE, et al. Cardiovascular risk factors may moderate pharmacological treatment effects in major depressive disorder. Psychosom Med. 2005;67:703–6.

    Article  Google Scholar 

  21. Chekroud AM, Gueorguieva R, Krumholz HM, Trivedi MH, Krystal JH, McCarthy G. Reevaluating the efficacy and predictability of antidepressant treatments: a symptom clustering approach. JAMA Psychiatry. 2017;74:370–8.

    Article  Google Scholar 

  22. Li Y, Aggen S, Shi S, Gao J, Li Y, Tao M, et al. The structure of the symptoms of major depression: exploratory and confirmatory factor analysis in depressed Han Chinese women. Psychol Med. 2014;44:1391–401.

    Article  CAS  Google Scholar 

  23. Romera I, Delgado-Cohen H, Perez T, Caballero L, Gilaberte I. Factor analysis of the Zung self-rating depression scale in a large sample of patients with major depressive disorder in primary care. BMC Psychiatry. 2008;8:4.

    Article  Google Scholar 

  24. Shafer AB. Meta-analysis of the factor structures of four depression questionnaires: Beck, CES-D, Hamilton, and Zung. J Clin Psychol. 2006;62:123–46.

    Article  Google Scholar 

  25. Widaman KF, Ferrer E, Conger RD. Factorial invariance within longitudinal structural equation models: measuring the same construct across time. Child Dev Perspect. 2010;4:10–8.

    Article  Google Scholar 

  26. Buysse DJ, Hall M, Begley A, Cherry CR, Houck PR, Land S, et al. Sleep and treatment response in depression: new findings using power spectral analysis. Psychiatry Res. 2001;103:51–67.

    Article  CAS  Google Scholar 

  27. Staedt J, Hunerjager H, Ruther E, Stoppe G. Sleep cluster arousal analysis and treatment response to heterocyclic antidepressants in patients with major depression. J Affect Disord. 1998;49:221–7.

    Article  CAS  Google Scholar 

  28. Bruder GE, Sedoruk JP, Stewart JW, McGrath PJ, Quitkin FM, Tenke CE. Electroencephalographic alpha measures predict therapeutic response to a selective serotonin reuptake inhibitor antidepressant: pre- and post-treatment findings. Biol Psychiatry. 2008;63:1171–7.

    Article  CAS  Google Scholar 

  29. Fredman SJ, Fava M, Kienke AS, White CN, Nierenberg AA, Rosenbaum JF. Partial response, nonresponse, and relapse with selective serotonin reuptake inhibitors in major depression: a survey of current “next-step” practices. J Clin Psychiatry. 2000;61:403–8.

    Article  CAS  Google Scholar 

  30. Wiles N, Thomas L, Abel A, Ridgway N, Turner N, Campbell J, et al. Cognitive behavioural therapy as an adjunct to pharmacotherapy for primary care based patients with treatment resistant depression: results of the CoBalT randomised controlled trial. Lancet. 2012;381:375–84.

  31. Cuijpers P, Sijbrandij M, Koole SL, Andersson G, Beekman AT, Reynolds CF 3rd. Adding psychotherapy to antidepressant medication in depression and anxiety disorders: a meta-analysis. World Psychiatry. 2014;13:56–67.

    Article  Google Scholar 

  32. Gilbody S, Bower P, Fletcher J, Richards D, Sutton AJ. Collaborative care for depression: a cumulative meta-analysis and review of longer-term outcomes. Arch Intern Med. 2006;166:2314–21.

    Article  Google Scholar 

  33. Woltmann E, Grogan-Kaylor A, Perron B, Georges H, Kilbourne AM, Bauer MS. Comparative effectiveness of collaborative chronic care models for mental health conditions across primary, specialty, and behavioral health care settings: systematic review and meta-analysis. Am J Psychiatry. 2012;169:790–804.

    Article  Google Scholar 

Download references

Acknowledgements

Francesca Cirulli, PhD, and Vittoria Carolina Malpassuti, MSc, (Istituto Superiore di Sanità) helped in data analysis and interpretation, Jennifer Sienna provided critical reading, and Nadia Francia and Stella Falsini (Istituto Superiore di Sanità) gave editorial support.

Funding

The research was supported by the grant from the Italian Ministry of Health Ricerca Finalizzata RF-2011-02349921 to IB. STAR*D dataset was provided by the National Institutes of Health, USA to IB.

Author information

Author notes

  1. These authors contributed equally: Aurelia Viglione, Flavia Chiarotti

Authors and Affiliations

  1. Center for Behavioral Sciences and Mental Health, Istituto Superiore di Sanità, Rome, Italy

    Aurelia Viglione, Flavia Chiarotti, Silvia Poggini & Igor Branchi

  2. BIO@SNS lab, Scuola Normale Superiore di Pisa, Pisa, Italy

    Aurelia Viglione

  3. PhD Program in Behavioral Neuroscience, Sapienza University of Rome, Rome, Italy

    Silvia Poggini

  4. Department of Environment and Health, Istituto Superiore di Sanità, Rome, Italy

    Alessandro Giuliani

Authors
  1. Aurelia Viglione
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Flavia Chiarotti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Silvia Poggini
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alessandro Giuliani
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Igor Branchi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Igor Branchi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Viglione, A., Chiarotti, F., Poggini, S. et al. Predicting antidepressant treatment outcome based on socioeconomic status and citalopram dose. Pharmacogenomics J 19, 538–546 (2019). https://doi.org/10.1038/s41397-019-0080-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41397-019-0080-6

This article is cited by

Search

Advanced search

Quick links