This preprint open-label study (n=20) examines the effects of ketamine infusions (35mg/70kg, 6x) on biological ageing markers in individuals with depression (MDD) or PTSD. It finds reductions in epigenetic age as measured by OMICmAge, GrimAge V2, and PhenoAge biomarkers, as well as significant changes in Epigenetic Biomarker Proxies (EBPs) and surrogate protein markers following a 2-3 week treatment course. The study also reports expected decreases in depression and PTSD scores as measured by PHQ-9 and PCL-5.
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Berman, R. M., Cappiello, A., Anand, A. et al. · Biological Psychiatry (2000)
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 2-3 week treatment course of 0.5 mg/kg ketamine 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.
Major depressive disorder (MDD) and posttraumatic stress disorder (PTSD) are linked with poorer healthspan and elevated risk of age-related disease and premature mortality. Researchers have increasingly used biological ageing biomarkers — including telomere length, immune-cell composition, and DNA methylation (DNAm)-based “epigenetic clocks” — to quantify ageing-related changes that may underlie these adverse outcomes. First-generation clocks estimate chronological age, while second-generation measures such as GrimAge, OMICmAge, and DNAmPhenoAge capture clinical features tied to morbidity and mortality; DunedinPACE estimates the pace of ageing. Prior studies have reported accelerated epigenetic ageing in MDD and PTSD and associations between DNAm-derived proxies for proteins/metabolites and disease risk, but the effects of rapid-acting treatments on these molecular ageing markers are not well characterised. This pilot study, led by Dawson and colleagues, set out to examine whether a short course of ketamine infusions affects DNAm, immune-cell composition, and multiple epigenetic biomarkers of ageing in patients with treatment-resistant or persistent symptoms of MDD and/or PTSD. The investigators hypothesised that a six-infusion ketamine induction (0.5 mg/kg over 2–3 weeks) would reduce predicted biological age and alter DNAm surrogate markers for proteins, metabolites and clinical measures, alongside clinical symptom changes. The study aims to connect ketamine’s clinical effects with potential epigenetic mechanisms relevant to biological ageing.
Twenty participants with moderate-to-severe symptoms of MDD and/or PTSD despite prior antidepressant treatment were recruited through Wild Health, Inc. in Lexington, KY. Participants received six subanesthetic intravenous ketamine infusions (0.5 mg/kg) administered over 2–3 weeks. Peripheral blood was collected at baseline and approximately 10 days after completing the infusion series, yielding 40 samples in total; one participant discontinued treatment due to adverse effects. Clinical laboratory assays were performed at LabCorp and DNAm profiling was performed at TruDiagnostic Labs. Clinical measures included 60 laboratory tests (for 16 participants with complete paired lab data) and self-reported symptom scales: the PTSD Checklist for DSM-5 (PCL-5) and Patient Health Questionnaire-9 (PHQ-9). Wilcoxon signed-rank tests were used to compare pre- and post-treatment clinical measures (significance threshold p < 0.05). DNAm assessment used 500 ng DNA per sample, bisulfite conversion and hybridisation to the Illumina Infinium HumanMethylationEPIC BeadChip. Preprocessing employed the Minfi package with ssNoob normalisation, k-nearest neighbours imputation for missing CpGs and a 12-cell immune deconvolution approach to estimate cell-type proportions. Principal component (PC)-based versions of epigenetic clocks were computed where applicable to improve longitudinal reliability; clocks and measures computed included DNAmPhenoAge, PC PhenoAge, GrimAge (including GrimAge v2), OMICmAge, DNAm telomere length (DNAmTL), DunedinPACE, and SystemsAge across 11 physiological systems. Epigenetic age acceleration (EAA) was derived from regression residuals of epigenetic age on chronological age, with the first three control-probe PCs included to adjust for batch effects. For multiple-testing correction across related measures, the authors used a ‘‘number of independent degrees of freedom’’ approach based on principal components to derive PC-adjusted Bonferroni thresholds. An epigenome-wide association study (EWAS) compared CpG methylation pre- versus post-treatment using limma with empirical Bayes regression adjusted for sex, age, batch and three PCs; models also included estimated immune cell levels. Probes with unadjusted p < 0.001 were examined, and FDR was reported but not used as the primary filter. Enrichment analyses for differentially methylated loci (DMLs) in promoter/enhancer regions used rGREAT. Correlation analyses between changes in epigenetic markers and clinical/laboratory variables were performed to probe concordance between molecular ageing signals and non-epigenetic measures.
Participant characteristics: the cohort comprised 20 individuals, 75% female (n=15) and 25% male (n=5). Diagnostic composition was 65% with comorbid MDD and PTSD (n=13), 10% MDD only (n=2), and 25% PTSD only (n=5). Mean chronological ages reported were 41.78 for one subgroup and 35.62 for another (extracted text presents both values but does not clearly map them to groups). Paired baseline and post-treatment blood samples were available for all 20 participants; 16 participants had paired clinical laboratory panels. Clinical outcomes: symptom scores on both self-report scales fell markedly after ketamine. PCL-5 scores declined (Wilcoxon p = 0.00000381) with a median change of 33 points, and PHQ-9 scores declined (p = 0.0000949) with a median change of 11 points. No individual laboratory test among the 16 participants with paired labs reached statistical significance after treatment; a few variables approached significance (absolute monocytes p = 0.0723; LDL-C p = 0.0743; LDL size p = 0.0829; monocytes p = 0.0857). Epigenetic ageing: several DNAm-based ageing metrics showed reductions after ketamine. PhenoAge decreased (p = 0.024), GrimAge v2 decreased (p = 0.021), and OMICmAge decreased (p = 0.0094). After adjustment using the PC-based variance approach and PC-adjusted Bonferroni correction, only OMICmAge remained statistically significant (PC-adjusted Bonferroni p = 0.047). Most other clocks trended downward but did not reach significance; SystemsAge Inflammation approached significance (unadjusted p = 0.076). DunedinPACE and DNAmTL did not show significant changes in the reported results. Immune-cell composition: estimation of 12 immune-cell subsets via DNAm deconvolution revealed a significant reduction in CD4 memory T cells after ketamine (unadjusted p = 0.038). Neutrophils showed a trend toward increase (unadjusted p = 0.053). Other cell subsets (CD4 naïve, CD8 naïve, CD8 memory, B naïve, B memory, basophils, regulatory T cells, eosinophils, monocytes and natural killer cells) did not change notably between timepoints. Correlations between molecular and clinical measures: correlation analyses identified several associations between epigenetic age measures and clinical lab values. The paper reports moderate negative correlations between OMICmAge and HbA1c (reported as r² = -0.58, p < 0.001) and red blood cell distribution width (r² = -0.52, p < 0.001), and a positive correlation with serum folate (r² = 0.62, p < 0.001). OMICmAge and GrimAge v2 were reported to correlate negatively with absolute lymphocyte count (reported r² ≈ -0.48 to -0.49, p < 0.001). PhenoAge showed distinct patterns, correlating positively with creatinine, total protein, WBC, globulin and albumin and negatively with eGFR and BUN/creatinine ratio. Importantly, changes in PCL-5 and PHQ-9 did not correlate with OMICmAge; GrimAge v2 had a weak correlation with PHQ-9 but not PCL-5, and PhenoAge had negative correlations with both symptom scales. Epigenome-wide results and enrichment: the EWAS identified 1,144 CpG sites meeting an unadjusted p < 0.001 threshold, of which 764 were hypermethylated and 380 hypomethylated post-treatment. One CpG (cg03703650 on chromosome 12, in the promoter of the advillin gene) reached FDR significance (FDR = 0.0025) and was hypomethylated after treatment. Enrichment analysis of hypermethylated CpGs implicated processes related to the cell cycle, DNA replication and immune-related functions such as regulation of T cell differentiation; hypomethylated sites were enriched for circadian sleep regulation, growth plate cartilage morphogenesis, cervix development, hindbrain tangential cell migration and indoleamine 2,3-dioxygenase activity. Epigenetic biomarker proxies: out of 396 Epigenetic Biomarker Proxies (EBPs), 17 showed significant pre–post differences under the PC-adjusted Bonferroni threshold. Similarly, 13 of 116 Marioni Episcore protein estimates reached PC-adjusted significance (examples included MMP1, NRTK3, SHBG, Afamin, Contactin 4, Galectin 4, RARRES2 and TNFRSF17). However, these markers did not survive conventional false-discovery rate (FDR) correction, with reported FDR values ranging from 0.197 to 0.585.
Dawson and colleagues interpret their findings as preliminary evidence that a six-infusion ketamine induction can reduce self-reported PTSD and depressive symptoms and produce measurable changes in DNAm-based ageing, notably a decline in OMICmAge. They emphasise OMICmAge’s construction — integrating multiple protein, metabolite and clinical DNAm surrogates across biological systems — as a potential reason it detected a treatment-associated signal where some other clocks did not. The authors note that prior animal and human studies have reported ketamine-associated changes in metabolites and lipids (for example ribitol, uridine, triglycerides and creatinine) that overlap with components used to build OMICmAge, which could plausibly explain that clock’s sensitivity to ketamine-induced changes. The investigators also discuss altered EBPs and Marioni protein proxies observed after treatment and relate these to existing literature linking proline, BUN, carnitines, adenosine, MMPs, coagulation factor VII, SHBG and TNFRSF17 to depression or treatment response. They suggest that EWAS findings — enrichment of hypermethylated sites in pathways regulating T cell differentiation and hypomethylated sites in circadian-related processes — align with mechanistic hypotheses for ketamine’s antidepressant and anti-inflammatory actions, including suppression of pathogenic Th17-mediated neuroinflammation and potential chronotherapeutic effects on sleep and circadian synchrony. The single FDR-significant CpG (advillin promoter) is highlighted as biologically interesting given advillin’s role in neurite outgrowth and expression in habenular regions linked to depression, although the authors acknowledge that no direct prior association with MDD or PTSD is established. Regarding immune findings, the decrease in CD4 memory T cells is framed as consistent with a reduction in biological age and multimorbidity risk, given prior associations between higher CD4 memory proportions and older biological age. Correlations between epigenetic age measures and clinical labs (for example lymphocyte counts, HbA1c, folate) are presented as further evidence of interplay between metabolic, immune and epigenetic signals, though the authors note that symptom improvements (PCL-5, PHQ-9) did not correlate strongly with the epigenetic-age changes. Key limitations the authors acknowledge include the small sample size (n=20), absence of a control group, use of a fixed ketamine dose rather than clinical titration, and reliance on self-report for symptom measures that could be biased. They characterise the study as exploratory and call for replication in larger, controlled cohorts to validate the epigenetic-age effects and to dissect causal relationships between ketamine, immune/metabolic changes and epigenetic ageing markers. The authors conclude that their results add to evidence that ketamine can affect molecular biomarkers tied to ageing and point to candidate pathways for further mechanistic study.
Major depressive disorder (MDD) is a debilitating condition characterized by changes in mood and affect persisting for at least two weeks. Posttraumatic stress disorder (PTSD) occurs in patients who have undergone or seen a traumatic event, and presents as re-experiencing traumatic events, and intense and disturbing thoughts or feelings in afflicted subjects. Studies have linked MDD with increased risk for functional impairments and premature mortality, as well as a variety of diseases, including cardiovascular, cerebrovascular, and metabolic disorders. Similar to MDD, PTSD is generally associated with reduced healthspan, lower quality of life, and higher risk of early death. In recent years, there has been growing interest in utilizing various aging-related biomarkers, such as telomere length, immune cell proportions, and DNA methylation (DNAm) levelsto quantify biological changes that occur when individuals age to help explain adverse health outcomes. Epigenetic clocks are commonly utilized biomarkers of aging based on DNAm. First-generation epigenetic clocks predict chronological age. Second-generation biomarkers of aging, such as the GrimAge, OMICmAge, and DNAmPhenoAge, instead measure clinical features associated with aging. OMICmAge, in particular, has shown strong associations with mortality and various chronic diseases, including depression. Meanwhile, SystemsAge markers capture aging in 11 distinct physiological systems: blood, brain, heart, hormone, immune, inflammatory, kidney, liver, lung, metabolic, and musculoskeletal. Finally, the third-generation biomarker of aging, DunedinPACE, predicts the rate of aging, rather than approximating biological age. DNA methylation is not limited to aging clocks; it can also be used to predict metabolites, clinical measures, and proteins, often demonstrating stronger associations with clinical outcomes and disease risk than their non-DNA methylation counterparts. Previous research has used these biomarkers to investigate the impact of MDD and PTSD on biological aging. MDD has been discovered to be linked to multiple biomarkers of DNAm age acceleration, as well as advanced DNAm age in blood. Epigenome-wide association studies have also demonstrated the involvement of differentially methylated genes in MDD in a variety of age-related biological processes, including metabolism and inflammatory response. Additionally, individuals with MDD have been found to have higher transcriptomic age compared to healthy controls. Likewise, PTSD is positively associated with epigenome-wide DNAm markers of increased cellular age, potentially mediated by single nucleotide polymorphisms (SNPs) in the Klotho longevity gene. It is also reportedly linked with an enhanced pace of epigenetic aging over time, based on measurements from the Horvath and GrimAge clocks. Studies have also found advanced DNAm age in the motor cortex of individuals with PTSD (28), as well as higher RNA age predictions compared to controls. These commonalities between MDD, PTSD, and biological aging bring to light the potential utility of antidepressants and other PTSD medication in protecting against accelerated biological aging. In particular, ketamine, an N-methyl-D-aspartate (NMDA) receptor antagonist, has been found to induce marked improvement in depressive patients following intravenous administration, causing a drastic reduction in depressive symptoms after only three days of treatment. Further studies have evaluated the effectiveness of ketamine and its enantiomers in monotherapy and adjunctive therapy in treating MDD and treatment-resistant depression (TRD). Research has also explained the prospective benefit of using ketamine on patients with PTSD, and its potential utility in reversing the synaptic abnormalities induced by PTSD. In light of these, there is reason to believe that ketamine, as an antidepressant, can potentially reduce biological aging. Literature has demonstrated the ability of psychiatric medications, including antidepressants, to decelerate aging and extend lifespan in model organisms. There is also evidence that antidepressant use is linked to lower predicted brain age, and lessens mortality and disease risk in humans. Taken together, these lines of evidence suggest that ketamine can contribute to reducing epigenetic aging in patients diagnosed with MDD and PTSD, without exacerbating already-increased biological aging in these diseases. Nevertheless, there remains a dearth of information on the impact of ketamine on biological aging, as measured by molecular biomarkers and epigenetic aging biomarkers. Therefore, this study aims to comprehensively investigate the effect of a rapidly-acting agent, ketamine, on epigenetic aging in a cohort of patients diagnosed with MDD and/or PTSD whose symptoms did not remit with standard psychiatric treatment. The study will look into the individuals' baseline DNA methylation levels, immune cell composition, and epigenetic age, and measure these parameters after ketamine administration. We hypothesize that a ketamine induction series will significantly reduce the patients' epigenetic age predictions, immune cell subsets, and proxies of metabolites, clinical measures, and proteins after the treatment course. This pilot study shall also provide insight on the biological processes and molecular functions that mediate the effects of ketamine in depression, PTSD, and epigenetic aging.
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Singh, J. B., Fedgchin, M., Daly, E. J. et al. · American Journal of Psychiatry (2016)
Leal, G. C., Bandeira, I. D., Correia-Melo, F. S. et al. · European Archives of Psychiatry and Clinical Neuroscience (2020)
Feder, A., Costi, S., Rutter, S. B. et al. · American Journal of Psychiatry (2021)
To assess ketamine's effect on biological age in patients with MDD and PTSD, 20 participants were recruited by Wild Health, Inc., Lexington, KY. All had moderate to severe symptoms despite prior antidepressant treatment. They underwent six subanesthetic ketamine infusions (0.5 mg/kg) over 2-3 weeks. Blood samples were collected at baseline and 10 days post-treatment, with 40 samples in total. One participant did not complete all infusions due to adverse effects. Samples were processed at LabCorp for clinical tests and TruDiagnostic Labs for DNA methylation analysis.
The following panels were taken for the clinical screening laboratory blood tests: Urine Drug Screen and Urine Pregnancy Test (females), Complete blood count (CBC), Comprehensive metabolic panel-14 (CMP-14), Folate (Serum), Free T3, Free T4, Free Testosterone, Vitamin B12, TSH (Thyroid Stimulating Hormone), C-Reactive Protein-Cardiac, Vitamin D-25-Hydroxy, Apolipoprotein B, Hemoglobin A1c, Homocysteine, NMR LipoProfile+Lipids (Advanced Lipid Panel), Cortisol, and Insulin using clinically validated assays for baseline assessments; obtained by venipuncture for whole blood sampling at a LabCorp facility of the participant's choosing. Only 16 patients were able to take a baseline and post-treatment measurement for the laboratory blood tests. To look at the differences between the participants' non-epigenetic measures, including clinical data from 60 laboratory tests and self-reported scores based on the PTSD Checklist for DSM-5 (PCL-5) and Patient Health Questionnaire-9 (PHQ-9) for MDD, we conducted a Wilcoxon signed-rank test for each set of scores in R. A p-value < 0.05 was used to denote significance.
Peripheral whole blood samples were obtained using the lancet and capillary method, and preserved through mixing with lysis buffer. 500 ng of DNA was extracted and subjected to bisulfite conversion using the EZ DNA Methylation Kit from Zymo Research, following the manufacturer's protocols. The converted samples were then randomly allocated to designated wells on the Infinium HumanMethylationEPIC BeadChip, and then subsequently amplified, hybridized onto the array, and stained. Samples were then washed and imaged using the Illumina iScan SQ instrument to capture raw image intensities. DNAm data was pre-processed using the Minfi package in R. No outlier samples were identified using ENmixand methylation values were normalized using the ssNoob method. We utilized the k-nearest neighbors algorithm to impute missing CpG values and finally adopted a 12-cell immune deconvolution method to estimate cell type proportions.
Using DNA methylation, we estimated the biological age of the participants by using the following epigenetic biomarkers of aging: DNAmPhenoAge (13), GrimAge, GrimAge v2, and OMICmAge second-generation biomarkers, DNAmTL, which estimates telomere length, the third-generation DunedinPACE, and SystemsAge. We used a custom R script available via GitHub () to compute the principal component-based epigenetic clocks for the DNAmPhenoAge, GrimAge, GrimAge v2, and telomere length. For non-principal component-based DNAmPhenoAge epigenetic metrics, we used the methyAge function in the ENmix package in R. DunedinPACE was calculated using the PACEProjector function from the DunedinPACE package from GitHub (). We calculated the SystemsAge using the authors' script, which will be incorporated into the methylCIPHER package upon publication (). We computed the epigenetic age acceleration (EAA) of these metrics through fitting a regression model between the participants' chronological age and different epigenetic age measures. To control for potential batch effects, we also included in the regression model the first 3 principal components (PCs) calculated from control probes, following the methodology prescribed by Joyce et al. Wilcoxon signed-rank tests were performed using EAA values between timepoints at a significance level of p < 0.05. Due to the number of tests conducted for the statistical analysis of clinical test epigenetic clocks, EBP, and Marioni Episcore markers, a multi-test correction was conducted using the "number of independent degrees of freedom" approach previously described. Briefly, principal components of each measure were calculated using prcomp() in R, and the minimum number of PC's which achieve at least 95% cumulative variance was used to adjust for Bonferroni correction. We then conducted a correlation analysis between the biomarkers of aging and all non-epigenetic measures to investigate if the changes in lab values and questionnaire responses are concordant with changes in the predictions of biological age. Finally, we compared baseline and post-treatment levels of DNA methylation surrogate markers for multiple proteins, metabolites, and clinical variables.
We used the limma package to perform an epigenome-wide association study (EWAS), comparing genome-wide CpG methylation between pre-and post-treatment. Empirical Bayes regression models were adjusted for sex, age, batch, and three principal components. Moderated t-tests were used to assess for significance, and probes with an unadjusted p-value < 0.001 were considered significantly differentially methylated. While the false-discovery rate (FDR) was calculated, it was not used as a filter. LogFC values indicated hypermethylation (logFC > 0) or hypomethylation (logFC < 0) post-treatment. Enrichment analysis using rGREAT identified gene ontology (GO) terms associated with differentially methylated loci (DMLs) in promoter and enhancer regions.
This study analyzed clinical and epigenetic data from patients diagnosed with MDD and/or PTSD (n=20) before and after administration of ketamine infusions over the time-course. Of the 20 participants, 65% (n=13) had a comorbid diagnosis of MDD and PTSD, 10% (n=2) were diagnosed only with MDD, and 25% (n=5) had a diagnosis of only PTSD (Supplementary Figure). Mean baseline scores in the PCL-5 and PHQ-9 are also described. Blood samples were taken to evaluate DNA methylation levels among the patients to calculate their epigenetic age, the immune subsets, and the DNA methylation levels pre-and post-ketamine treatment. Subjects were made up of 75% (n=15) female and 25% (n=5) male participants, with a mean chronological age of 41.78 and 35.62, respectively. Tabledetails the demographic information of patients recruited in this study.
We utilized a Wilcoxon signed-rank test to compare the participant's scores (N=20) before and after receiving the ketamine treatment course in the PCL-5 and PHQ-9. Results showed a significant decline in both PCL-5 (p=0.00000381) and PHQ-9 (p=0.0000949) scores after the participants received treatment with ketamine, with a median difference of 33 and 11 points, respectively. On the other hand, none of the laboratory tests (N=16) exhibited a significant difference following ketamine administration (Supplementary Table). Notably, a few clinical variables approached significance, such as absolute monocytes (p=0.0723), LDL-C (p=0.0743), LDL size (p=0.0829), and monocytes (p=0.0857).
Given that MDD and PTSD are associated with increased morbidity and mortality, we investigated ketamine's effect on epigenetic biomarkers of aging most related to mortality risk. These included PhenoAge, PC PhenoAge, PC GrimAge, GrimAge v2, and OMICmAge; the DunedinPACE; and Systems Ages for blood, brain, heart, hormone, inflammation, immune, kidney, liver, lung, metabolic, musculoskeletal, and overall SystemsAge. We also investigated DNAmTL, a telomere length proxy, as telomere length has been reported to be reduced in MDD and PTSD. Where applicable, we utilized the principal component versions of the biomarkers, which have high test-retest reliability, which is especially important for longitudinal studies. Results revealed a significant reduction in epigenetic age between timepoints, PhenoAge (p=0.024), GrimAge V2 (p=0.021), and OMICmAge (p=0.0094) (Figure). However, after adjusting by principal component variance explaining clocks, only OMICmAge retained significance (PC-adjusted Bonferonni p-value = 0.047). Other epigenetic markers did not demonstrate any significant changes; however, most epigenetic biomarkers exhibited a downward trend in epigenetic age after Ketamine treatment (Supplementary Table). Interestingly, Systems Age Inflammation approached significance, with an unadjusted p-value of 0.076.
We quantified 12 different immune cell subsets between timepoints using EpiDISH (2023), and found a significant reduction in CD4T memory cells (unadjusted p=0.038) after ketamine treatment (Figure). There was also a trend towards increased neutrophils (unadjusted p=0.053). Meanwhile, CD4T Naïve, CD8T Naïve, CD8T Memory, B Naïve, B Memory, Basophils, regulatory T-cells, Eosinophils, Monocytes, and Natural Killer cells did not demonstrate any notable difference after ketamine treatment (Supplementary Table).
To investigate whether there is a relationship between the changes in clinical variables and biological age, we conducted a correlation analysis between all these markers (Supplementary Figure). OMICmAge showed a moderate negative correlation with HbA1c (r² = -0.58, p < 0.001) and red blood cell distribution width (r² = -0.52, p < 0.001), and a positive correlation with serum folate levels (r² = 0.62, p < 0.001), which negatively correlated with PhenoAge (r² = -0.72, p < 0.001) and GrimAge V2 (r² = -0.17, p < 0.05). Both OMICmAge (r² = -0.48, p < 0.001) and GrimAge V2 (r² = -0.49, p < 0.001) showed moderate negative correlations with absolute lymphocyte count, while PhenoAge did not (r² = 0.13, p > 0.05). GrimAge V2 also had a negative correlation with lymphocyte count (r² = -0.61, p < 0.001) and a positive correlation with neutrophils (r² = 0.64, p < 0.001). PhenoAge differed by showing positive correlations with creatinine (r² = 0.52, p < 0.001), total protein (r² = 0.65, p < 0.001), WBC (r² = 0.5, p < 0.001), globulin (r² = 0.59, p < 0.001), and albumin (r² = 0.5, p < 0.001), which were weakly or negatively correlated with the other biomarkers. PhenoAge also had negative correlations with eGFR (r² = -0.58, p < 0.001) and BUN/Creatinine Ratio (r² = -0.5, p < 0.001), whereas these were positively correlated with GrimAge V2 (eGFR: r² = 0.43, p < 0.001; BUN/Creatinine Ratio: r² = 0.17, p > 0.05) and OMICmAge (eGFR: r² = 0.41, p < 0.001; BUN/Creatinine Ratio: r² = 0.33, p < 0.001). Notably, changes in PCL-5 and PHQ-9 scores did not correlate with OMICmAge, despite significant decreases in all three measures after ketamine treatment. GrimAge V2 did not significantly correlate with PCL-5 but had a weak correlation with PHQ-9 (r² = 0.04, p < 0.01). PhenoAge exhibited a negative correlation with both PCL-5 (r² = -0.39, p < 0.001) and PHQ-9 (r² = -0.16, p < 0.05).
We further assessed the impact of Ketamine usage upon DNA methylation surrogate markers for multiple proteins, metabolites, and clinical variables. Results revealed 17 significantly different EBPs pre-and post-treatment (Table) out of 396 EBPs based on an PC-adjusted Bonferroni p-value < 0.05. Analysis of the Marioni Episcore protein estimates identified 13 Episcore protein markers out of 116 showed a significant (PC-adjusted Bonferroni p-value < 0.05) difference pre-and post-ketamine treatment after comparison with the Wilcox test: MMP.1 (p=0.002), NRTK3 (p=0.009), SHBG (p=0.021), Afamin (p=0.037), Contactin 4 (p=0.027), Galectin 4 (p=0.027), RARRES2 (p=0.027), and TNFRSF17 (p=0.04). However, similar to the EBPs, these markers did not reach the threshold for significance after multiple correction adjustment (False Discovery Rate, FDR), with their FDR ranging from 0.197 to 0.585 (Table).
We conducted an epigenome-wide association study (EWAS) to examine DNA methylation changes after ketamine treatment, adjusting regression models for individuals, 12 immune cell levels, and principal components. To control for multiple comparisons, we used FDR and selected a model with a lambda value of 1.14, indicating no overfitting. We identified 1,144 CpG sites (p < 0.001), with 764 hypermethylated and 380 hypomethylated after treatment (Figure). Only one CpG site at Chromosome 12 (cg03703650) was significantly hypomethylated (FDR = 0.0025), located in the promoter of the advillin gene, involved in actin bundling and neurite outgrowth. Using the 1,144 CpG sites meeting the uncorrected p-value < 0.001, we performed two enrichment analyses, one for hypermethylated and another for hypomethylated sites. Among hypermethylated CpG sites, we identified an enrichment in processes regulating the nuclear cycle and DNA replication biological processes related to the immune system, such as regulation of T cell differentiation and mast cells, and molecular functions linked mainly to 1-phosphatidylinositol regulator activity. On the other hand, hypomethylated CpG sites were enriched in the regulation of circadian sleep, the growth plate cartilage morphogenesis, cervix development, and hindbrain tangential cell migration. We also found that these sites are mainly related to indoleamine 2,3-dioxygenase activity. The full list of associated gene ontology terms can be found in Supplementary Table.
This study found that administering ketamine, a rapid-acting antidepressant and off-label drug for PTSD, led to significant reductions in self-reported PTSD and MDD symptoms measured using PCL-5 and PHQ-9, as well as decreases in biological age according to the OMICmAge clock, but not among any other clock assessed. OMICmAge's significant decline highlights its unique biomarker proxies' ability to detect associations with depression, PTSD, and potentially other brain diseases. Created by integrating 16 protein, 14 metabolite, and 10 clinical epigenetic biomarkers across 8 biological systems, OMICmAge is particularly robust in identifying factors central to biological aging. In fact, previous investigation showed OMICmAge having the highest odds and hazard ratios for depression compared to other aging biomarkers. Ketamine administration has also been linked to changes in the metabolites and proteins used in OMICmAge, such as ribitol, uridine, triglycerides, and creatinine. Animal studies in rodents found a change in ribitol and other key elements in energy metabolism, such as phosphate and propanoic acid, post-ketamine treatment. Creatine and phosphocreatine have also been observed to be downregulated in the hippocampus of both rats and mice after one dose of ketamine. There also seems to be a reduction in uridine and glutamine as opposed to an increase in urea concentrations in ketamine-treated rats. Similarly, human studies have discovered an increase in triglycerides, cholesteryl esters, and several phosphatidylcholines in the human plasma of both healthy patients and those with treatment-resistant depression. This may explain OMICmAge's effectiveness in capturing epigenetic aging changes in patients with MDD and/or PTSD. We observed variation in some EBPs after ketamine administration. Prior research has linked some of these EBPs, such as proline, blood urea nitrogen (BUN), carnitines, and adenosine, to depression. For instance, high proline levels may worsen depressive symptoms via the microbiota-gut-brain axis; BUN and carnitines in combination have shown protective effects against depression (66); and BUN independently has shown protective effects against depression. Carnitines were found to decrease in depressive patients and subjects with a history of trauma, and carnitine supplementation has been shown to improve symptoms. Adenosine, associated with both depression and ketamine treatment, plays a role in ketamine's anti-inflammatory and antidepressant effects by regulating glutamate neurotransmission. In mice models of depression, ketamine upregulated the adenosine 1 receptor, leading to antidepressant and anti-anxiety effects. It also increased AMPAR activation, which inhibited glutamate release, suggesting that ketamine's antidepressant effects may involve the regulation of glutamate neurotransmission. Previous studies have linked significant Marioni protein markers, such as Matrix metalloproteinases (MMPs) 1 and 2, blood coagulation factor VII, sex hormone binding globulin (SHBG), and TNFRSF17, to depression risk and prognosis. MMPs regulate inflammation and cytokine processing, with elevated MMP2 levels in cerebrospinal fluid associated with depression and schizophrenia, proportional to symptom severity. However, some studies report decreased MMP2 expression in depression. Coagulation factor VII has also been linked to increased depression riskand suicidal behavior, especially in aging populations. Meanwhile, SHBG has been linked to cognitive impairment in patients with comorbid schizophrenia and depression, with elevated SHBG levels associated with higher depression risk in women, particularly post-menopause (78),74). Finally, TNFRSF17 has been found to be upregulated in women with postpartum depression, although no link has been established with MDD or PTSD. Together, these findings show a clear link between MDD and several EBPs and Marioni protein markers, highlighting their ability to capture disease-specific changes in MDD and PTSD. Additionally, this study revealed pathways related to hypomethylated and hypermethylated CpG sites post-ketamine treatment, offering insights into ketamine's antidepressant and PTSD effects. Notably, regulation of T cell differentiation was enriched in hypermethylated CpG sites, aligning with studies demonstrating the involvement of ketamine in the inhibition of the differentiation and reactivation pathogenic Th17 cells, which is upregulated in patients with MDD. Ketamine's suppression of Th17-mediated neuroinflammation via IL-16/STAT3 inhibition supports its role in reducing CNS inflammation, a key mechanism behind its antidepressant and PTSD effects. Additionally, hypomethylation of CpG sites linked to circadian regulation suggests ketamine may improve sleep duration and quality. Sleep disorders are a known symptom and risk factor for PTSD, and research has shown an association between MDD and PTSD and poor sleep quality, resulting in abnormal mood, and aberrant expression of circadian clock genes in depressive patients (88). Ketamine's rapid antidepressant effects may also involve resynchronizing the central clock to light cycles, causing decreased waking, and increased sleep, REM sleep, and slow-wave activity. Indeed, previous studies suggest ketamine has a chronotherapeutic effect, improving mood by shifting patients away from an "evening" body clock, further emphasizing the role of epigenetic changes in ketamine's antidepressant action and its impact on epigenetic age. Through our methylation analysis, we also discovered one CpG site with an adjusted p-value of 0.0025 associated with the advillin gene, which has been linked to axon regeneration and reduced neuropathic pain. Advillin is expressed in the habenula, which plays a role in depression and the sustained antidepressant effects of ketamine. However, studies have yet to establish a link between this gene and MDD or PTSD, and thus, further investigation is needed to better understand the role of the methylation of this specific location in the context of these diseases. This pilot study identified a significant decrease in CD4T memory cells, which typically increase with age as CD4T naïve cells decline. CD4T memory cells are also positively associated with biological age and multimorbidity, implying that lower biological age correlates with reduced CD4 memory T-cells. Reduction in CD4 memory T-cells after ketamine treatment may suggest its potential to lower biological age and improve outcomes in depression and PTSD. Notably, only SystemsAge Inflammation neared significance among epigenetic aging markers, highlighting the immune system's role in reducing epigenetic age post-treatment. The moderate negative correlation between lymphocyte count and OMICmAge and GrimAge V2 supports this link. Further investigation is needed to understand the correlations between OMICmAge, GrimAge V2, PhenoAge, and clinical variables. Although no significant changes in lab results were observed from baseline to post-treatment, some variables approached significance. Only PCL-5 and PHQ-9 scores significantly declined following ketamine treatment, yet they did not correlate with epigenetic aging. Correlations between epigenetic aging and other clinical variables were weak, suggesting independent changes that warrant further study. The small sample size (n=20) may have limited the detection of significant changes in aging biomarkers. Additionally, we used a fixed ketamine dose, while clinical practice often involves titration. The absence of a control group also limits validation, especially for self-reported PCL-5 and PHQ-9 scores, which can be biased. This pilot study was exploratory and requires validation in larger cohorts. In summary, this study discovered that a 2-3 week treatment course of six ketamine infusions reduced PTSD and MDD scores, evaluated using the PCL-5 and PHQ-9. Ketamine also reduced biological age in study participants, particularly as indicated by PhenoAge, GrimAge, and OMICmAge. Our findings on altered epigenetic biomarker proxies and Marioni protein markers support their association with depression and trauma disorders, offering insights into ketamine's clinical and epigenetic mechanisms. Additionally, we observed a decrease in CD4T memory cells, suggesting a link between ketamine and immune cell subsets, and how these may mediate a reduction in biological age. While our study supports ketamine's role in alleviating depressive and PTSD symptoms and its potential mechanisms involving the sleep/wake cycle and neuroinflammation, further research is needed to clarify these epigenetic alterations and their contribution to ketamine's antidepressant effects and its impact on biological age.