Effects of ketamine on metabolic parameters in depressive disorders: A systematic review
Bibliographic record
Abstract
BACKGROUND: Persons with Major Depressive Disorder (MDD), notably treatment-resistant depression (TRD), are differentially affected by type 2 diabetes mellitus and associated morbidity. Ketamine is highly efficacious in the treatment of adults living with MDD, notably TRD. Herein, we sought to determine the effect of ketamine on metabolic parameters in animal stress paradigms and human studies. METHODS: We performed a comprehensive search on PubMed, OVID, and Scopus databases for primary research articles from inception to May 5, 2024. Study screening and data extraction were performed by two reviewers (S.W. and G.H.L.). Both preclinical and clinical studies were included in this review. RESULTS: Results from the preclinical studies indicate that in experimental diabetic conditions, ketamine does not disrupt glucose-insulin homeostasis. Within adults with MDD, ketamine is associated with GLUT3 transporter upregulation and differentially affects metabolomic signatures. In adults with TRD, ketamine induces increased brain glucose uptake in the prefrontal cortex. Available evidence suggests that ketamine does not adversely affect metabolic parameters. LIMITATIONS: There are a paucity of clinical studies evaluating the effects of ketamine on glucose-insulin homeostasis in adults with MDD. CONCLUSIONS: Our results indicate that ketamine is not associated with significant and/or persistent disruptions in metabolic parameters. Available evidence indicates that ketamine does not adversely affect glucose-insulin homeostasis. These results underscore ketamine's efficacy and safety as an antidepressant treatment that is not associated with metabolic disturbances commonly reported with current augmentation therapies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".