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Pre-treatment allostatic load and metabolic dysregulation predict SSRI response in major depressive disorder: a preliminary report

Published online by Cambridge University Press:  22 May 2020

Christina M. Hough*
Affiliation:
Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco (UCSF) School of Medicine, San Francisco, CA, USA Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
F. Saverio Bersani
Affiliation:
Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco (UCSF) School of Medicine, San Francisco, CA, USA Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
Synthia H. Mellon
Affiliation:
Department of OB/GYN and Reproductive Sciences, UCSF School of Medicine, San Francisco, CA, USA
Alexandra E. Morford
Affiliation:
Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco (UCSF) School of Medicine, San Francisco, CA, USA Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
Daniel Lindqvist
Affiliation:
Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco (UCSF) School of Medicine, San Francisco, CA, USA Department of Clinical Sciences, Section for Psychiatry, Lund University, Lund, Sweden
Victor I. Reus
Affiliation:
Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco (UCSF) School of Medicine, San Francisco, CA, USA
Elissa S. Epel
Affiliation:
Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco (UCSF) School of Medicine, San Francisco, CA, USA
Owen M. Wolkowitz
Affiliation:
Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco (UCSF) School of Medicine, San Francisco, CA, USA
*
Author for correspondence: Christina M. Hough, E-mail: [email protected]

Abstract

Background

Major depressive disorder (MDD) is associated with increased allostatic load (AL; a measure of physiological costs of repeated/chronic stress-responding) and metabolic dysregulation (MetD; a measure of metabolic health and precursor to many medical illnesses). Though AL and MetD are associated with poor somatic health outcomes, little is known regarding their relationship with antidepressant-treatment outcomes.

Methods

We determined pre-treatment AL and MetD in 67 healthy controls and 34 unmedicated, medically healthy MDD subjects. Following this, MDD subjects completed 8-weeks of open-label selective serotonin reuptake inhibitor (SSRI) antidepressant treatment and were categorized as ‘Responders’ (⩾50% improvement in depression severity ratings) or ‘Non-responders’ (<50% improvement). Logistic and linear regressions were performed to determine if pre-treatment AL or MetD scores predicted SSRI-response. Secondary analyses examined cross-sectional differences between MDD and control groups.

Results

Pre-treatment AL and MetD scores significantly predicted continuous antidepressant response (i.e. absolute decreases in depression severity ratings) (p = 0.012 and 0.014, respectively), as well as post-treatment status as a Responder or Non-responder (p = 0.022 and 0.040, respectively), such that higher pre-treatment AL and MetD were associated with poorer SSRI-treatment outcomes. Pre-treatment AL and MetD of Responders were similar to Controls, while those of Non-responders were significantly higher than both Responders (p = 0.025 and 0.033, respectively) and Controls (p = 0.039 and 0.001, respectively).

Conclusions

These preliminary findings suggest that indices of metabolic and hypothalamic-pituitary-adrenal-axis dysregulation are associated with poorer SSRI-treatment response. To our knowledge, this is the first study to demonstrate that these markers of medical disease risk also predict poorer antidepressant outcomes.

Type
Original Article
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press.

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