Cerebrospinal fluid metabolomes of treatment-resistant depression subtypes and ketamine response: a pilot study.

Depression varies widely among individuals, making treatment selection and dosing difficult, especially for newer drugs like ketamine. In a pilot study of 29 patients with treatment-resistant depression, researchers analyzed over 300 metabolites in cerebrospinal fluid and used multivariate analysis to reduce the data to two main dimensions. The first dimension correlated age with levels of specific metabolites and depression and anxiety scores. The second dimension correlated autism scores, male gender, and cognitive fatigue with bipolar diagnosis, lithium use, and another metabolite disturbance. The findings suggest that complex, treatment-resistant depression can be mapped onto two pathophysiological domains, which may eventually guide personalized treatment choices.

Study at a glance

Abstract

Depression is a disorder with variable presentation. Selecting treatments and dose-finding is, therefore, challenging and time-consuming. In addition, novel antidepressants such as ketamine have sparse optimization evidence. Insights obtained from metabolomics may improve the management of patients. The objective of this study was to determine whether compounds in the cerebrospinal fluid (CSF) metabolome correlate with scores on questionnaires and response to medication. We performed a retrospective pilot study to evaluate phenotypic and metabolomic variability in patients with treatment-resistant depression using multivariate data compression algorithms. Twenty-nine patients with treatment-resistant depression provided fasting CSF samples. Over 300 metabolites were analyzed in these samples with liquid chromatography-mass spectrometry. Chart review provided basic demographic information, clinical status with self-reported questionnaires, and response to medication. Of the 300 metabolites analyzed, 151 were present in all CSF samples and used in the analyses. Hypothesis-free multivariate analysis compressed the resultant data set into two dimensions using Principal Component (PC) analysis, accounting for ~ 32% of the variance. PC1 accounted for 16.9% of the variance and strongly correlated with age in one direction and 5-methyltetrahydrofolate, homocarnosine, and depression and anxiety scores in the opposite direction. PC2 accounted for 15.4% of the variance, with one end strongly correlated with autism scores, male gender, and cognitive fatigue scores, and the other end with bipolar diagnosis, lithium use, and ethylmalonate disturbance. This small pilot study suggests that complex treatment-resistant depression can be mapped onto a 2-dimensional pathophysiological domain. The results may have implications for treatment selection for depression subtypes.