Proteomic patterns associated with ketamine response in major depressive disorders.
Nan Zhou, Xiaolei Shi, Runhua Wang, Chengyu Wang, Xiaofeng Lan, Guanxi Liu, Weicheng Li, Yanling Zhou, Yuping Ning
Cell Biology and Toxicology January 10, 2025 DOI: 10.1007/s10565-024-09981-3 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational cohort Peer reviewed |
|---|---|
| Sample size | 30 |
| Population | Patients with major depressive disorder |
| Intervention | Ketamine |
| Duration | Two weeks of treatment |
| Topics | Depression Ketamine Esketamine |
| Keywords | Biomarkers Immune response Plasma proteomics Depression treatment Ketamine therapy Personalized medicine Mental health research |
| Citations | 1 |
| Key findings | Three pre-treatment plasma proteins strongly predicted ketamine response, and immune-related pathways were activated in association with its antidepressive effect. |
Abstract
Major depressive disorder (MDD) is characterized by persistent feelings of sadness and loss of interest. Ketamine has been widely used to treat MDD owing to its rapid effect in relieving depressive symptoms. Importantly, not all patients respond to ketamine treatment. Identifying sub-populations who will benefit from ketamine, as well as those who may not, prior to treatment initiation, would significantly advance precision medicine in patients with MDD. Here, we used mass spectrometry-based plasma proteomics to analyze matched pre- and post-ketamine treatment samples from a cohort of 30 MDD patients whose treatment outcomes and demographic and clinical characteristics were considered. Ketamine responders and non-responders were identified according to their individual outcomes after two weeks of treatment. We analyzed proteomic alterations in post-treatment samples from responders and non-responders and identified a collection of six proteins pivotal to the antidepressive effect of ketamine. Subsequent co-regulation analysis revealed that pathways related to immune response were involved in ketamine response. By comparing the proteomic profiles of samples from the same individuals at the pre- and post-treatment time points, dynamic proteomic rearrangements induced by ketamine revealed that immune-related processes were activated in association with its antidepressive effect. Furthermore, receiver operating characteristic curve analysis of pre-treatment samples revealed three proteins with strong predictive performance in determining the response of patients to ketamine before receiving treatment. These findings provide valuable knowledge about ketamine response, which will ultimately lead to more personalized and effective treatments for patients. The study was registered in the Chinese Clinical Trials Registry (ChiCTR-OOC-17012239) on May 26, 2017.
Comparable studies
Other observational and cohort studies on ketamine for depression, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| Concomitant BDNF and sleep slow wave changes indicate ketamine-induced plasticity in major depressive disorder Patients with treatment-resistant major depressive disorder | 2012 | Observational cohort | n = 30 |
| Altered peripheral immune profiles in treatment-resistant depression: response to ketamine and prediction of treatment outcome Healthy controls and actively depressed patients with treatment-resistant depression... | 2017 | Observational cohort | n = 59 |
| Clinical Predictors of Ketamine Response in Treatment-Resistant Major Depression Treatment-resistant inpatients with DSM-IV-TR-diagnosed major depressive disorder or... | 2014 | Post hoc analysis of pooled data from four studies | n = 108 |
| An investigation of amino-acid neurotransmitters as potential predictors of clinical improvement to ketamine in depression Drug-free patients with major depressive disorder | 2011 | Observational cohort | n = 14 |
| Efficacy of ketamine therapy in the treatment of depression Drug-free/naïve men with severe depression, no history of psychotic disorder, head... | 2019 | Observational cohort | n = 25 |