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Objectives: The purpose of this study is to use independent datasets to externally validate the three symptom clusters of unipolar depression identified by Chekroud, to evaluate personalized treatment trajectories and outcomes based on these symptom clusters, and to verify predictors. Methods: The Quick Inventory of Depressive Symptomatology-Self Report (QIDS-SR16)11 QIDS-SR16: Quick Inventory of Depressive Symptomatology-Self Report. and Hamilton Rating Scale for Depression (Ham-D)22 Ham-D: Hamilton Rating Scale for Depression. data from two placebo controlled, double-blind clinical trials (Dual Therapy and Duloxetine) were used for external validation. Machine learning methods were applied to replicate the three symptom clusters and to produce treatment trajectories. Penalized logistic regressions were conducted to identify top baseline variables that best predicted treatment outcomes. Results: The variables Chekroud identified as comprising sleep, atypical and core emotional clusters are replicated. Treatment trajectories demonstrate that dual treatment (escitalopram and bupropion) performed best across all symptom clusters but did not outperform escitalopram monotherapy over time. For each symptom cluster, there were differences in treatment efficacy among antidepressants. Conclusion: By using different treatment trajectories based on a patient's symptom cluster profile, clinicians could potentially select best fit antidepressants to achieve the biggest benefit. Our results showed that total baseline QIDS, Ham-D score, anxiety disorder diagnosis and course of depressive illness were the best baseline predictors. Results could enhance personalized depression treatment plans and help to improve outcomes. Clinical trials registration: NCT00519428, NCT00360724.

BackgroundTo assess whether patients with Persistent Depressive Disorder (PDD) have abnormal levels of N-acetyl-aspartate (NAA) and whether those levels normalize following treatment with the antidepressant medication duloxetine. Furthermore, we conducted post hoc analyses of other important brain metabolites to understand better the cellular and physiological determinants for changes in NAA levels.MethodsWe acquired proton (1H) magnetic resonance spectroscopic imaging (MRSI) data on a 3 Tesla (3T), GE Magnetic Resonance Imaging (MRI) scanner in 41 patients (39.9±10.4 years, 22 males) with PDD at two time points: before the start and at the end of a 10-week, placebo-controlled, double-blind, randomized controlled trial (RCT) of the antidepressant medication duloxetine. Patients were randomized such that 21 patients received the active medication and 20 patients received placebo during the 10 week period of the trial. In addition, we acquire 1H MRSI data once in 29 healthy controls (37.7±11.2 years, 17 males).FindingsPatients had significantly higher baseline concentrations of NAA across white matter (WM) pathways and subcortical gray matter, and in direct proportion to the severity of depressive symptoms. NAA concentrations declined in duloxetine-treated patients over the duration of the trial in the direction toward healthy values, whereas concentrations increased in placebo-treated patients, deviating even further away from healthy values. Changes in NAA concentration did not mediate medication effects on reducing symptom severity, however; instead, changes in symptom severity partially mediated the effects of medication on NAA concentration, especially in the caudate and putamen.InterpretationThese findings, taken together, suggest that PDD is not a direct consequence of elevated NAA concentrations, but that a more fundamental pathophysiological process likely causes PDD and determines the severity of its symptoms. The findings also suggest that although duloxetine normalized NAA concentrations in patients, it did so by modulating the severity of depressive symptoms. Medication presumably reduced depressive symptoms through other, as yet unidentified, brain processes.Trial registrationClinicalTrials.gov NCT00360724.