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Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables.

ABSTRACT: Individuals with depression differ substantially in their response to treatment with antidepressants. Specific predictors explain only a small proportion of these differences. To meaningfully predict who will respond to which antidepressant, it may be necessary to combine multiple biomarkers and clinical variables. Using statistical learning on common genetic variants and clinical information in a training sample of 280 individuals randomly allocated to 12-week treatment with antidepressants escitalopram or nortriptyline, we derived models to predict remission with each antidepressant drug. We tested the reproducibility of each prediction in a validation set of 150 participants not used in model derivation. An elastic net logistic model based on eleven genetic and six clinical variables predicted remission with escitalopram in the validation dataset with area under the curve 0.77 (95%CI; 0.66-0.88; p?=?0.004), explaining approximately 30% of variance in who achieves remission. A model derived from 20 genetic variables predicted remission with nortriptyline in the validation dataset with an area under the curve 0.77 (95%CI; 0.65-0.90; p?

SUBMITTER: Iniesta R

PROVIDER: S-EPMC5882876 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables.

Iniesta Raquel R Hodgson Karen K Stahl Daniel D Malki Karim K Maier Wolfgang W Rietschel Marcella M Mors Ole O Hauser Joanna J Henigsberg Neven N Dernovsek Mojca Zvezdana MZ Souery Daniel D Dobson Richard R Aitchison Katherine J KJ Farmer Anne A McGuffin Peter P Lewis Cathryn M CM Uher Rudolf R

Scientific reports 20180403 1

Individuals with depression differ substantially in their response to treatment with antidepressants. Specific predictors explain only a small proportion of these differences. To meaningfully predict who will respond to which antidepressant, it may be necessary to combine multiple biomarkers and clinical variables. Using statistical learning on common genetic variants and clinical information in a training sample of 280 individuals randomly allocated to 12-week treatment with antidepressants esc ...[more]

PMID: 29615645

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Project description:BackgroundMajor depressive disorder (MDD) is often comorbid with metabolic diseases such as obesity, cardiovascular disease, and type 2 diabetes. A potential link between these disorders is adiponectin, an adipocyte-derived circulating hormone with insulin-sensitizing, anti-inflammatory, and neuroplasticity effects. Reductions in plasma levels of adiponectin have been reported in both humans with depression and in the chronic-defeat mouse model of depression. However, the predictive value of adiponectin for treatment response to depression has not been determined.MethodsWe investigated the potential predictive effect of baseline adiponectin levels in patients who provided plasma and were undergoing one of three pharmacological treatments (escitalopram monotherapy; escitalopram plus bupropion; and venlafaxine plus mirtazapine) in the Combining Medications to Enhance Depression Outcomes clinical trial (n=160). Specifically, we assessed whether adiponectin moderates-that is, differentially predicts-treatment response among the treatment arms. Improvements with treatment were assessed using change in the clinician-rated 30-item Inventory of Depressive Symptomatology (IDS-C) from baseline through week 12. Moderator effects were tested using separate pairwise repeated measures mixed-effects models with a treatment-arm-by-adiponectin interaction.ResultsBaseline adiponectin levels moderated treatment outcome between two combination therapies. Specifically, low adiponectin predicted better response to escitalopram plus bupropion compared to venlafaxine plus mirtazapine, whereas high adiponectin predicted better response to venlafaxine plus mirtazapine compared to escitalopram plus bupropion (F=4.84, p=0.03). Adiponectin levels did not correlate with baseline depression severity (r=-0.03, p=.59).ConclusionsAntidepressant selection for patients with MDD can be personalized using pre-treatment blood-based biomarkers, such as adiponectin, thereby improving treatment outcomes.

Project description:Background: Hippocampal atrophy has been consistently reported in major depressive disorder with more recent focus on subfields. However, literature on hippocampal volume changes after antidepressant treatment has been limited. The first-line treatments for depression include antidepressant medication (ADM) or cognitive-behavior therapy (CBT). To understand the differential effects of CBT and ADM on the hippocampus, we investigated the volume alterations of hippocampal subfields with treatment, outcome, and chronicity in treatment-naïve depression patients. Methods: Treatment-naïve depressed patients from the PReDICT study were included in this analysis. A total of 172 patients who completed 12 weeks of randomized treatment with CBT (n = 45) or ADM (n = 127) were included for hippocampal subfield volume analysis. Forty healthy controls were also included for the baseline comparison. Freesurfer 6.0 was used to segment 26 hippocampal substructures and bilateral whole hippocampus from baseline and week 12 structural MRI scans. A generalized linear model with covariates of age and gender was used for group statistical tests. A linear mixed model for the repeated measures with covariates of age and gender was used to examine volumetric changes over time and the contributing effects of treatment type, outcome, and illness chronicity. Results: Of the 172 patients, 85 achieved remission (63/127 ADM, 22/45 CBT). MDD patients showed smaller baseline volumes than healthy controls in CA1, CA3, CA4, parasubiculum, GC-ML-DG, Hippocampal Amygdala Transition Area (HATA), and fimbria. Over 12 weeks of treatment, further declines in the volumes of CA1, fimbria, subiculum, and HATA were observed regardless of treatment type or outcome. CBT remitters, but not ADM remitters, showed volume reduction in the right hippocampal tail. Unlike ADM remitters, ADM non-responders had a decline in volume in the bilateral hippocampal tails. Baseline volume of left presubiculum (regardless of treatment type) and right fimbria and HATA in CBT patients were correlated with a continuous measure of clinical improvement. Chronicity of depression had no effect on any measures of hippocampal subfield volumes. Conclusion: Two first-line antidepressant treatments, CBT and ADM, have different effects on hippocampal tail after 12 weeks. This finding suggests that remission achieved via ADM may protect against progressive hippocampal atrophy by altering neuronal plasticity or supporting neurogenesis. Studies with multimodal neuroimaging, including functional and structural analysis, are needed to assess further the impact of two different antidepressant treatments on hippocampal subfields.

Dataset Information

Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables.

Publications

Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables.

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