Major depressive disorder (MDD) and bipolar disorder (BD) lack robust biomarkers useful for screening purposes in a clinical setting. A systematic review of the literature was conducted on metabolomic studies of patients with MDD or BD through the use of analytical platforms such as in vivo brain imaging, mass spectrometry, and nuclear magnetic resonance. Our search identified a total of 7,590 articles, of which 266 articles remained for full-text revision. Overall, 249 metabolites were found to be dysregulated with 122 of these metabolites being reported in two or more of the studies included. A list of biomarkers for MDD and BD established from metabolites found to be abnormal, along with the number of studies supporting each metabolite and a comparison of which biological fluids they were reported in, is provided. Metabolic pathways that may be important in the pathophysiology of MDD and BD were identified and predominantly center on glutamatergic metabolism, energy metabolism, and neurotransmission.Using online drug registries, we also illustrate how metabolomics can facilitate the discovery of novel candidate drug targets. K E Y W O R D Sbiochemical, biomarker panel, mood disorders, omics

Major depressive disorder (MDD) is a heterogeneous disorder. Our hypothesis is that neurological symptoms correlate with the severity of MDD symptoms. One hundred eighty-four outpatients with MDD completed a self-report questionnaire on past and present medical history. Patients were divided into three roughly equal depression severity levels based on scores from the APA Severity Measure for Depression—Adult (n = 66, 58, 60, for low, medium, high severity, respectively). We saw a significant and gradual increase in the frequency of “muscular paralysis” (1.5–5.2–16.7%) and “balance problems” (21.2–36.2–46.6%) from low to medium to high severity groups. We repeated the analysis using only the two most extreme severity categories: low severity (66 samples) vs. high severity (60 samples). High severity patients were also found to experience more “angina” symptoms than low severity patients (27.3 vs. 50%). The three significant clinical variables identified were introduced into a binary logistic regression model as the independent variables with high or low severity as the dependent variable. Both “muscular paralysis” and “balance problems” were significantly associated with increased severity of depression (odds ratio of 13.5 and 2.9, respectively), while “angina” was associated with an increase in severity with an odds ratio of 2.0, albeit not significantly. We show that neurological exam or clinical history could be useful biomarkers for depression severity. Our findings, if replicated, could lead to a simple clinical scale administered regularly for monitoring patients with MDD.

Psychosis is a symptomatic endpoint with many causes, complicating its pathophysiological characterization and treatment. Our study applies unsupervised clustering techniques to analyze metabolomic data, acquired using 2 different tandem mass spectrometry (MS-MS) methods, from an unselected group of 120 patients with psychosis. We performed an independent analysis of each of the 2 datasets generated, by both hierarchical clustering and k-means. This led to the identification of biochemically distinct groups of patients while reducing the potential biases from any single clustering method or datatype. Using our newly developed robust clustering method, which is based on patients consistently grouped together through different methods and datasets, a total of 20 clusters were ascertained and 78 patients (or 65% of the original cohort) were placed into these robust clusters. Medication exposure was not associated with cluster formation in our study. We highlighted metabolites that constitute nodes (cluster-specific metabolites) vs hubs (metabolites in a central, shared, pathway) for psychosis. For example, 4 recurring metabolites (spermine, C0, C2, and PC.aa.C38.6) were discovered to be significant in at least 8 clusters, which were identified by at least 3 different clustering approaches. Given these metabolites were affected across multiple biochemically different patient subgroups, they are expected to be important in the overall pathophysiology of psychosis. We demonstrate how knowledge about such hubs can lead to novel antipsychotic medications. Such pathways, and thus drug targets, would not have been possible to identify without patient stratification, as they are not shared by all patients, due to the heterogeneity of psychosis.