Predictors of Response to Ketamine in Treatment Resistant Major Depressive Disorder and Bipolar Disorder
Carola Rong, Caroline Park, Joshua D. Rosenblat, Mehala Subramaniapillai, Hannah Zuckerman, Dominika Fus, Yena Lee, Zihang Pan, Elisa Brietzke, Rodrigo B. Mansur, Danielle S. Cha, Leanna M.W. Lui, Roger S McIntyre
International Journal of Environmental Research and Public Health April 17, 2018 DOI: 10.3390/ijerph15040771 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Narrative review Peer reviewed |
|---|---|
| Population | Patients with treatment-resistant depressive symptoms as part of major depressive disorder or bipolar disorder |
| Intervention | Ketamine |
| Topics | Addiction Anxiety Depression Ketamine Esketamine |
| Keywords | Bipolar disorder Neurochemistry Antidepressant Clinical psychology Mood Oncology Neurology |
| Citations | 116 |
| Key findings | High body mass index and a positive family history of alcohol use disorder were the most replicated pretreatment predictors of response to ketamine in treatment-resistant depression. |
Abstract
Objectives: Extant evidence indicates that ketamine exerts rapid antidepressant effects in treatment-resistant depressive (TRD) symptoms as a part of major depressive disorder (MDD) and bipolar disorder (BD). The identification of depressed sub-populations that are more likely to benefit from ketamine treatment remains a priority. In keeping with this view, the present narrative review aims to identify the pretreatment predictors of response to ketamine in TRD as part of MDD and BD.
Method: .
Results: Multiple baseline pretreatment predictors of response were identified, including clinical (i.e., Body Mass Index (BMI), history of suicide, family history of alcohol use disorder), peripheral biochemistry (i.e., adiponectin levels, vitamin B12 levels), polysomnography (abnormalities in delta sleep ratio), neurochemistry (i.e., glutamine/glutamate ratio), neuroimaging (i.e., anterior cingulate cortex activity), genetic variation (i.e., Val66Met BDNF allele), and cognitive functioning (i.e., processing speed). High BMI and a positive family history of alcohol use disorder were the most replicated predictors.
Conclusions: A pheno-biotype of depression more, or less likely, to benefit with ketamine treatment is far from complete. Notwithstanding, metabolic-inflammatory alterations are emerging as possible pretreatment response predictors of depressive symptom improvement, most notably being cognitive impairment. Sophisticated data-driven computational methods that are iterative and agnostic are more likely to provide actionable baseline pretreatment predictive information.