Epigenetic aging and DNA methylation biomarker changes following ketamine treatment in patients with MDD and PTSD: a pilot study
Bibliographic record
Abstract
Major depressive disorder (MDD) and posttraumatic stress disorder (PTSD) are debilitating psychiatric conditions associated with poor health outcomes similarly observed in non-pathological aging. Ketamine is a dissociative anesthetic and NMDA receptor antagonist with demonstrated rapid reduction in symptoms associated with Treatment Resistant Depression (TRD) and PTSD. Ketamine's effects on biological aging have not been extensively studied among patients with moderate to severe symptoms of depression and/or trauma. To address this gap, this study looked at the changes in non-epigenetic measures, DNA methylation levels, immune cell composition, and biological age based on various epigenetic biomarkers of aging, of 20 participants at baseline and after completion of a course of six ketamine 0.5 mg/kg infusions in individuals with MDD or PTSD. As expected, depression and PTSD scores decreased in participants following ketamine infusion treatments as measured by the PHQ-9 and PCL-5. We observed a reduction in epigenetic age in the OMICmAge, GrimAge V2, and PhenoAge biomarkers. In order to better understand the changes in epigenetic age, we also looked at the underlying levels of various Epigenetic Biomarker Proxies (EBPs) and surrogate protein markers and found significant changes following ketamine treatment. The results are consistent with existing literature on ketamine's effects on different biomarkers. These results underline the ability of GrimAge V2, PhenoAge, and OMICmAge in particular, to capture signals associated with key clinical biomarkers, and add to the growing body of literature on ketamine's epigenetic mechanisms and their effect on biological aging. Clinical Trial Code. NCT05294835.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".