This study explores whether inflammatory biomarkers act as moderators of clinical response to omega-3 (n-3) fatty acids in subjects with Major Depressive Disorder (MDD). 155 subjects with DSM-IV MDD, a baseline 17-item Hamilton Depression Rating Scale (HAM-D-17) score ≥ 15 and baseline biomarker data (IL-1ra, IL-6, hs-CRP, leptin, adiponectin), were randomized between 05/18/06 and 06/30/11, to 8 weeks of double-blind treatment with eicosapentaenoic acid (EPA)-enriched n-3 1060 mg/day, docosahexaenoic acid (DHA)-enriched n-3 900 mg/day, or placebo. Outcomes were determined using mixed model repeated measures (MMRM) analysis for “high” and “low” inflammation groups based on individual and combined biomarkers. Results are presented in terms of standardized treatment effect size (ES) for change in HAM-D-17 from baseline to treatment week 8. While overall treatment group differences were negligible (ES=−0.13 to +0.04), subjects with any “high” inflammation improved more on EPA than placebo (ES=−0.39) or DHA (ES=−0.60) and less on DHA than placebo (ES=+0.21); furthermore, EPA-placebo separation increased with increasing numbers of markers of high inflammation. Subjects randomized to EPA with “high” IL-1ra or hs-CRP or low adiponectin (“high” inflammation) had medium ES decreases in HAM-D-17 scores versus subjects “low” on these biomarkers. Subjects with “high” hs-CRP, IL-6 or leptin were less placebo-responsive than subjects with low levels of these biomarkers (medium to large ES differences). Employing multiple markers of inflammation facilitated identification of a more homogeneous cohort of subjects with MDD responding to EPA versus placebo in our cohort. Studies are needed to replicate and extend this proof of concept work.

Objective To inform the first-line treatment choice between cognitive behavior therapy (CBT) or an antidepressant medication for treatment-naïve adults with major depressive disorder by defining a neuroimaging biomarker that differentially identifies the outcomes of remission and treatment failure to these interventions. Method Functional magnetic resonance imaging resting state functional connectivity analyses using a bilateral subcallosal cingulate cortex (SCC) seed was applied to 122 patients from the Prediction of Remission to Individual and Combined Treatments (PReDICT) study who completed 12 weeks of randomized treatment with CBT or antidepressant medication. Of the 122, 58 achieved remission (Hamilton Depression Rating Scale, HDRS) ≤7 at weeks 10 and 12); 24 were treatment failures (HDRS <30% decrease from baseline). A 2×2 ANOVA using voxel-wise subsampling permutation tests compared the interaction of treatment and outcome. ROC curves constructed using brain connectivity measures were used to determine possible classification rates for differential treatment outcomes. Results The resting state functional connectivity of three regions with the SCC was differentially associated with outcomes of remission and treatment failure to CBT and antidepressant medication, and survived application of the subsample permutation tests: left anterior ventrolateral/insula prefrontal cortex, dorsal midbrain, and left ventromedial prefrontal cortex. Using the summed SCC functional connectivity scores for these three regions, we demonstrated overall classification rates of 72–78% for remission and 75–89% for treatment failure. Positive summed functional connectivity was associated with remission with CBT and treatment failure with medication, whereas negative summed functional connectivity scores were associated with remission to medication and treatment failure with CBT. Conclusions Imaging-based depression subtypes defined using resting state functional connectivity differentially identified an individual’s probability of remission or treatment failure with first-line treatment options for major depression. This biomarker should be explored in future research through prospective testing and as a component of multivariate treatment prediction models.