Comparison of inflammatory markers as moderators of depression outcomes: A CO-MED study

BACKGROUND

Prior work suggests some individual immunomarkers may be useful moderators of treatment response between antidepressant medications. The relative moderating effect of individual immunomarkers remains unclear. It is also unknown whether combinations of immunomarkers have a superior moderating effect compared to any individual immunomarker.

METHODS

Baseline immunomarker levels were assayed using multiplex from a subset of participants in the CO-MED trial (n = 143). Individual and combinations of 19 immunomarkers were modeled as moderators between the three treatment arms (escitalopram monotherapy, escitalopram-bupropion and venlafaxine-mirtazapine) across a variety of depression outcomes.

RESULTS

Only IL-13 demonstrated a consistent moderating effect across all depression outcome measures. High IL-13 (>20 pg/ml) was associated with higher remission rates to bupropion-escitalopram (67%) versus escitalopram (24%) whereas low IL-13 was associated higher remission rates to escitalopram (59%) versus bupropion-escitalopram (38%). A similar, but weaker moderating effect was observed with venlafaxine-mirtazapine versus escitalopram. The addition of multiple immunomarkers did not consistently improve predictive modeling.

LIMITATIONS

This is a secondary analysis of a single clinical trial with a relatively small sample size per treatment arm. The testing of specific individual and combinations of biomarkers was data-driven.

CONCLUSIONS

Among 19 immunomarkers, Il-13 was the best single moderator of treatment outcome. Combinations of immunomarkers were not meaningfully superior to Il-13.

Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography

The understanding and treatment of psychiatric disorders, which are known to be neurobiologically and clinically heterogeneous, could benefit from the data-driven identification of disease subtypes. Here, we report the identification of two clinically relevant subtypes of post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) on the basis of robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the default mode network. We identified the disease subtypes by analysing, via unsupervised and supervised machine learning, the power-envelope-based connectivity of signals reconstructed from high-density resting-state electroencephalography in four datasets of patients with PTSD and MDD, and show that the subtypes are transferable across independent datasets recorded under different conditions. The subtype whose functional connectivity differed most from those of healthy controls was less responsive to psychotherapy treatment for PTSD and failed to respond to an antidepressant medication for MDD. By contrast, both subtypes responded equally well to two different forms of repetitive transcranial magnetic stimulation therapy for MDD. Our data-driven approach may constitute a generalizable solution for connectome-based diagnosis.

Cortical Connectivity Moderators of Antidepressant vs Placebo Treatment Response in Major Depressive Disorder: Secondary Analysis of a Randomized Clinical Trial

IMPORTANCE

Despite the widespread awareness of functional magnetic resonance imaging findings suggesting a role for cortical connectivity networks in treatment selection for major depressive disorder, its clinical utility remains limited. Recent methodological advances have revealed functional magnetic resonance imaging-like connectivity networks using electroencephalography (EEG), a tool more easily implemented in clinical practice.

OBJECTIVE

To determine whether EEG connectivity could reveal neural moderators of antidepressant treatment.

DESIGN, SETTING, AND PARTICIPANTS

In this nonprespecified secondary analysis, data were analyzed from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinic Care study, a placebo-controlled, double-blinded randomized clinical trial. Recruitment began July 29, 2011, and was completed December 15, 2015. A random sample of 221 outpatients with depression aged 18 to 65 years who were not taking medication for depression was recruited and assessed at 4 clinical sites. Analysis was performed on an intent-to-treat basis. Statistical analysis was performed from November 16, 2018, to May 23, 2019.

INTERVENTIONS

Patients received either the selective serotonin reuptake inhibitor sertraline hydrochloride or placebo for 8 weeks.

MAIN OUTCOMES AND MEASURES

Electroencephalographic orthogonalized power envelope connectivity analyses were applied to resting-state EEG data. Intent-to-treat prediction linear mixed models were used to determine which pretreatment connectivity patterns were associated with response to sertraline vs placebo. The primary clinical outcome was the total score on the 17-item Hamilton Rating Scale for Depression, administered at each study visit.

RESULTS

Of the participants recruited, 9 withdrew after first dose owing to reported adverse effects, and 221 participants (150 women; mean [SD] age, 37.8 [12.7] years) underwent EEG recordings and had high-quality pretreatment EEG data. After correction for multiple comparisons, connectome-wide analyses revealed moderation by connections within and between widespread cortical regions-most prominently parietal-for both the antidepressant and placebo groups. Greater alpha-band and lower gamma-band connectivity predicted better placebo outcomes and worse antidepressant outcomes. Lower connectivity levels in these moderating connections were associated with higher levels of anhedonia. Connectivity features that moderate treatment response differentially by treatment group were distinct from connectivity features that change from baseline to 1 week into treatment. The group mean (SD) score on the 17-item Hamilton Rating Scale for Depression was 18.35 (4.58) at baseline and 26.14 (30.37) across all time points.

CONCLUSIONS AND RELEVANCE

These findings establish the utility of EEG-based network functional connectivity analyses for differentiating between responses to an antidepressant vs placebo. A role emerged for parietal cortical regions in predicting placebo outcome. From a treatment perspective, capitalizing on the therapeutic components leading to placebo response differentially from antidepressant response should provide an alternative direction toward establishing a placebo signature in clinical trials, thereby enhancing the signal detection in randomized clinical trials.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT01407094.

An electroencephalographic signature predicts antidepressant response in major depression

Antidepressants are widely prescribed, but their efficacy relative to placebo is modest, in part because the clinical diagnosis of major depression encompasses biologically heterogeneous conditions. Here, we sought to identify a neurobiological signature of response to antidepressant treatment as compared to placebo. We designed a latent-space machine-learning algorithm tailored for resting-state electroencephalography (EEG) and applied it to data from the largest imaging-coupled, placebo-controlled antidepressant study (n = 309). Symptom improvement was robustly predicted in a manner both specific for the antidepressant sertraline (versus placebo) and generalizable across different study sites and EEG equipment. This sertraline-predictive EEG signature generalized to two depression samples, wherein it reflected general antidepressant medication responsivity and related differentially to a repetitive transcranial magnetic stimulation treatment outcome. Furthermore, we found that the sertraline resting-state EEG signature indexed prefrontal neural responsivity, as measured by concurrent transcranial magnetic stimulation and EEG. Our findings advance the neurobiological understanding of antidepressant treatment through an EEG-tailored computational model and provide a clinical avenue for personalized treatment of depression.

Brain regulation of emotional conflict predicts antidepressant treatment response for depression

The efficacy of antidepressant treatment for depression is controversial due to the only modest superiority demonstrated over placebo. However, neurobiological heterogeneity within depression may limit overall antidepressant efficacy. We sought to identify a neurobiological phenotype responsive to antidepressant treatment by testing pretreatment brain activation during response to, and regulation of, emotional conflict as a moderator of the clinical benefit of the antidepressant sertraline versus placebo. Using neuroimaging data from a large randomized controlled trial, we found widespread moderation of clinical benefits by brain activity during regulation of emotional conflict, in which greater downregulation of conflict-responsive regions predicted better sertraline outcomes. Treatment-predictive machine learning using brain metrics outperformed a model trained on clinical and demographic variables. Our findings demonstrate that antidepressant response is predicted by brain activity underlying a key self-regulatory emotional capacity. Leveraging brain-based measures in psychiatry will forge a path toward better treatment personalization, refined mechanistic insights and improved outcomes.

Sex differences in the association of baseline c-reactive protein (CRP) and acute-phase treatment outcomes in major depressive disorder: Findings from the EMBARC study

Peripheral inflammation is associated with poor response to antidepressant treatments. However, whether sex differentially affects this association remains unknown. Participants of Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) with baseline plasma samples were included in this study (n = 220; male n = 75, female n = 145). Depression severity [Hamilton Rating Scale for Depression 17-item (HAMD-17)] was measured at baseline and weeks- 1, 2, 3, 4, 6, and 8. Plasma c-reactive protein (CRP) was measured with commercially-available ELISA kits at baseline, week-1, and week-8. Sex difference in prediction of baseline-to-week-8 HAMD-17 change by baseline CRP was tested with sex-by-baseline-CRP-by-time interaction in mixed model analysis. Additionally, changes in CRP from baseline-to-week-8 CRP and its association with HAMD-17 changes over that period were also evaluated. Covariates included body mass index, site, smoking status, and age. There was a significant sex difference in association of baseline-to-week-8 HAMD-17 reduction with baseline CRP (p = 0.033). Higher baseline CRP was associated with lower baseline-to-week-8 HAMD-17 reduction in females (p < 0.0001) but not in males (p = 0.632). Additionally, CRP was significantly reduced (p = 0.041, effect size = 0.254) from baseline-to-week-8, but there were no sex differences in this reduction (p = 0.249). Baseline-to-week-8 changes in HAMD-17 and CRP were not significantly associated either overall (p = 0.348) or based on sex (p = 0.370). In a large study of depressed outpatients, we replicated previous findings that elevated baseline CRP levels are associated with worse antidepressant treatment outcomes. However, this effect was limited only to females. These findings emphasize the importance of studying sex differences in biological mechanisms linking inflammation and depression.