Abstract
Metabolomics is a developing and promising tool for exploring molecular pathways underlying symptoms of depression and predicting depression recovery. The AbsoluteIDQ™ p180 kit was used to investigate whether plasma metabolites (sphingomyelins, lysophosphatidylcholines, phosphatidylcholines, and acylcarnitines) from a subset of participants in the Combining Medications to Enhance Depression Outcomes (CO-MED) trial could act as predictors or biologic correlates of depression recovery. Participants in this trial were assigned to one of three pharmacological treatment arms: escitalopram monotherapy, bupropion-escitalopram combination, or venlafaxine-mirtazapine combination. Plasma was collected at baseline in 159 participants and again 12 weeks later at study exit in 83 of these participants. Metabolite concentrations were measured and combined with clinical and sociodemographic variables using the hierarchical lasso to simultaneously model whether specific metabolites are particularly informative of depressive recovery. Increased baseline concentrations of phosphatidylcholine C38:1 showed poorer outcome based on change in the Quick Inventory of Depressive Symptoms (QIDS). In contrast, an increased ratio of hydroxylated sphingomyelins relative to non-hydroxylated sphingomyelins at baseline and a change from baseline to exit suggested a better reduction of symptoms as measured by QIDS score. All metabolite-based models performed superior to models only using clinical and sociodemographic variables, suggesting that metabolomics may be a valuable tool for predicting antidepressant outcomes.
Highlights
It has become increasingly clear that depression is heterogeneous in its pathophysiology and treatment outcomes
We generated metabolite-free models to determine the relative magnitude of effect of the nonmetabolite variables and to validate whether this modeling approach would agree with other literature on these non-metabolite features (Fig. 1)
Higher baseline Quick Inventory of Depressive Symptoms (QIDS), and statin use were predictive of larger changes in QIDS
Summary
It has become increasingly clear that depression is heterogeneous in its pathophysiology and treatment outcomes. The development and validation of genetic, proteomic, and/or metabolomic methodologies may be essential in identifying the pathophysiology of disease expression as well as precision medicine for depression. Metabolomics has recently emerged as a valuable field of inquiry in psychiatry because unlike genomics, it captures the dynamic nature of the disease, and unlike proteomics, it measures the final products of complex interactions among numerous proteins, signaling cascades, and cellular environments.[1,2]. Several groups have studied metabolomic differences in depressed populations relative to healthy controls[3,4,5]. The collective knowledge of metabolomic differences between depressed patients and healthy controls remains difficult to interpret from these studies because of several limitations: (1) most depressed participants were taking medications, and the impact of the various drugs are undefined; (2) several aspects of
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