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. 2022 Jul 21;17(7):e0271793.
doi: 10.1371/journal.pone.0271793. eCollection 2022.

A comprehensive approach to predicting weight gain and therapy response in psychopharmacologically treated major depressed patients: A cohort study protocol

Maria S Simon  1 , Barbara B Barton  1 , Catherine Glocker  1 , Richard Musil  1
Affiliations

A comprehensive approach to predicting weight gain and therapy response in psychopharmacologically treated major depressed patients: A cohort study protocol

Maria S Simon et al. PLoS One. .

Abstract

Background: A subgroup of patients with Major Depressive Disorder shows signs of low-grade inflammation and metabolic abberances, while antidepressants can induce weight gain and subsequent metabolic disorders, and lacking antidepressant response is associated with inflammation.

Objectives: A comprehensive investigation of patient phenotypes and their predictive capability for weight gain and treatment response after psychotropic treatment will be performed. The following factors will be analyzed: inflammatory and metabolic markers, gut microbiome composition, lifestyle indicators (eating behavior, physical activity, chronotype, patient characteristics (childhood adversity among others), and polygenic risk scores.

Methods: Psychiatric inpatients with at least moderate Major Depressive Disorder will be enrolled in a prospective, observational, naturalistic, monocentric study using stratified sampling. Ethical approval was obtained. Primary outcomes at 4 weeks will be percent weight change and symptom score change on the Montgomery Asberg Depression Rating Scale. Both outcomes will also be binarized into clinically relevant outcomes at 5% weight gain and 50% symptom score reduction. Predictors for weight gain and treatment response will be tested using multiple hierachical regression for continuous outcomes, and multiple binary logistic regression for binarized outcomes. Psychotropic premedication, current medication, eating behavior, baseline BMI, age, and sex will be included as covariates. Further, a comprehensive analysis will be carried out using machine learning. Polygenic risk scores will be added in a second step to estimate the additional variance explained by genetic markers. Sample size calculation yielded a total amount of N = 171 subjects.

Discussion: Patient and physician expectancies regarding the primary outcomes and non-random sampling may affect internal validity and external validity, respectively. Through the prospective and naturalistic design, results will gain relevance to clinical practice. Examining the predictive value of patient profiles for weight gain and treatment response during pharmacotherapy will allow for targeted adjustments before and concomitantly to the start of treatment.

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Conflict of interest statement

I have read the journal’s policy and the authors of this manuscript have the following competing interests: RM has received reimbursement by Janssen-Cilag, Emalex Biosciences, Boehringer-Ingelheim, and Oryzon for carrying out studies, and has received speaker’s honoraria from Otsuka over the past five years, all outside the submitted work. No other potential or actual financial or non-financial competing interests apply. MSS, BB, and CG have declared that no competing interests exist. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

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Fig 1. Study flow.
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