This open-label trial (n=25) found that six weeks of low-dose oral ketamine treatment for PTSD was associated with small but significant decreases in both BDNF and VEGF-A blood biomarkers, which were positively correlated with each other, suggesting these two proteins may interact as part of the biological mechanism behind ketamine's effects on PTSD symptoms.
Papers cited by this study that are also in Blossom
Berman, R. M., Cappiello, A., Anand, A. et al. · Biological Psychiatry (2000)
Can, A. T., Hermens, D. F., Dutton, M. et al. · Translational Psychiatry (2021)
Rational
Ketamine has been investigated as a treatment alternative for PTSD for the last 20 years, yet there have been limited reports of biological changes or biomarker characterisation related to treatment.
Objectives
To address this significant gap, this study analysed blood samples from 25 participants with PTSD who took part in an open-label 6-week trial of low dose oral ketamine treatment.
Methods
Serum and plasma samples were quantified before and after ketamine treatment for brain-derived neurotrophic factor (BDNF), vascular endothelial growth factor A (VEGF-A), serotonin, FK506 binding protein 51 (FKBP51) and a panel of cytokines (interleukin (IL)-1β, IL-2, IL-4, IL-6, IL-12p70, IL-17 A and tumour necrosis factor alpha (TNFα)).
Results
Analysis of BDNF and VEGF-A levels from serum detected a significant positive correlation between the two biomarkers and a small but statistically significant decrease in both measures after ketamine treatment. This novel finding reinforces theories that ketamine’s effects may rely on a reciprocal interaction between BDNF and VEGF-A, offering potential insights into a biological mechanism underpinning PTSD symptom reduction. Additionally, the analysis of FKBP51 and serotonin revealed novel relationships between these biomarkers and clinical scales, before and after ketamine treatment. Finally, significant changes or relationships involving the immune cytokines were not detected, possibly because half the participants presented with low-grade inflammation while the other half did not.
Conclusions
This study represents a much-needed broad analysis of blood biomarkers before and after ketamine treatment for PTSD and reveals important biological changes and relationships related to this treatment.
Trauma exposure is common and roughly 10% of exposed people develop post-traumatic stress disorder (PTSD), characterised by re-experiencing, avoidance, negative affect and hyperarousal. Standard treatments include exposure-based psychotherapies and pharmacotherapy with selective serotonin reuptake inhibitors, but remission and recovery rates are limited. Ketamine, an N-methyl-D-aspartate (NMDA) receptor antagonist, has shown promise in treatment-resistant depression and PTSD, predominantly in intravenous (IV) studies. The authors note practical and cost advantages of low dose oral ketamine and indicate that their recent open-label oral ketamine trial for PTSD produced response rates comparable to IV studies, but emphasise that very little is known about the blood biomarker changes that accompany ketamine treatment in PTSD patients. L. and colleagues set out to begin filling this gap by analysing circulating levels of several candidate biomarkers before and after a six-week, low dose oral ketamine regimen in people with PTSD. The targets examined were brain-derived neurotrophic factor (BDNF), vascular endothelial growth factor A (VEGF-A), serotonin, FK506 binding protein 51 (FKBP51) and a panel of cytokines (IL-1β, IL-2, IL-4, IL-6, IL-12p70, IL-17A and TNFα). The stated aim was to identify biomarker changes across treatment and follow-up and to explore relationships between biomarker trajectories and clinical measures of PTSD and wellbeing.
Samples for this analysis were collected from the Oral Ketamine Trial on PTSD (OKTOP), with the full details and primary outcomes of the open-label clinical trial described in. Baseline biomarker data only has been previously reported in the context of other PTSD-related analyses in. The trial was approved by the Metro North Health Human Research Ethics Committee (HREC) (Project ID: 42836; HREC/18/QPCH/28), the University of the Sunshine Coast HREC (A181190) and prospectively registered with the Clinical Trials Registry of Australia and New Zealand (ANZCTR trial registration number: ACTRN12618001965291, registered 05 December 2018). Of note, study participants were allowed to remain on previously prescribed psychotropic medications during the trial, with ketamine treatment acting an augmentative treatment. To maximize the number of samples for analysis, trial participants diagnosed with PTSD via the Clinician Administered PTSD Scale for DSM-5 (CAPS-5) were included in this study if blood samples were available from the baseline assessment before ketamine treatment ("pretreatment") and at least one sample after ketamine treatment ("post-treatment" -~2 h after the sixth/final ketamine dose, "follow-up 1-week" -1-7 days after the sixth/final ketamine dose and/or "follow-up 1-month" -28-32 days after the sixth/final ketamine dose). This generated a study cohort of n = 25 participants (Table), consisting of n = 25 pre-treatment samples, n = 19 post-treatment samples, n = 24 follow-up 1-week samples and n = 23 follow-up 1-month samples. Additionally, n = 14 mid-treatment samples (~ 2 h after the third ketamine dose) were included in the biomarker mean comparison and change analysis. Response to ketamine treatment was defined in the trial as ≥ 50% reduction in PTSD Checklist for DSM-5 (PCL-5) score from pre-treatment, with these samples representing response rates of # responders/# non-responders/# missing samples or outcomes of 15/3/7 at post-treatment, 15/8/2 at follow-up 1-week and 12/11/2 at follow-up 1-month for all 25 participants, respectively.
In addition to the CAPS-5conducted at pre-treatment to confirm study eligibility, the following clinical rating scales were completed by participants at pre-treatment, post-treatment and follow-up timepoints to assess study outcomes: (a) PCL-5, a 20-item self-report measure often used in conjunction with the CAPS-5 to monitor PTSD symptom change across patient treatment; (b) Depression, Anxiety, and Stress Scale (DASS-21), 21-item self-report measure assessing perceived depressive, anxiety, and stress symptoms; and (c) World Health Organization Well-Being Index (WHO-5), a 5-item self-report global rating scale measuring subjective wellbeing.
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This analysis used samples and data from the open-label Oral Ketamine Trial on PTSD (OKTOP). The clinical trial delivered low dose oral ketamine once weekly for six weeks in a titrating-up manner; participants were allowed to stay on previously prescribed psychotropic medications (ketamine acted as an augmentative treatment). Participants were eligible for the biomarker analysis if they had a DSM-5 PTSD diagnosis (confirmed with the Clinician-Administered PTSD Scale for DSM-5, CAPS-5) and available blood samples at baseline (pre-treatment) and at least one post-treatment timepoint. The cohort for this biomarker study comprised 25 participants, with sample counts of n = 25 pre-treatment, n = 19 post-treatment (~2 hours after the sixth/final dose), n = 24 at 1-week follow-up (1–7 days after final dose), n = 23 at 1-month follow-up (28–32 days after final dose) and n = 14 mid-treatment (~2 hours after the third dose) included for some analyses. Treatment response in the trial was defined as ≥ 50% reduction in PCL-5 score from pre-treatment. Clinical outcome measures collected at pre-treatment, post-treatment and follow-up timepoints included the PTSD Checklist for DSM-5 (PCL-5), the Depression, Anxiety and Stress Scale (DASS-21) and the WHO-5 Well-Being Index, in addition to CAPS-5 at baseline for eligibility. Non-fasting whole blood was drawn at midday/early afternoon into serum separator and EDTA tubes, processed to serum and plasma, aliquoted and stored at −80 °C; samples were processed promptly or kept at 4 °C for up to 3.5 hours before centrifugation. Biomarker quantification used multiplex ProcartaPlex/Luminex assays for BDNF and VEGF-A (assessed in both serum and plasma), high-sensitivity ProcartaPlex assays for the panel of cytokines (measured in plasma), and ELISAs for serotonin (serum) and FKBP51 (plasma). Limits of quantification and assay detectability were reported: cytokine detectability ranged roughly from 72% to 95% depending on the analyte, and FKBP51 was within detectable range in 66% of samples. Values below assay lower limits of quantification (LLOQ) were assigned half the LLOQ for statistical analyses. Statistical analyses were performed in R (v4.3.3). The Skillings–Mack test, a non-parametric repeated-measures test that accommodates missing data, was used to screen for biomarker changes across five timepoints. Linear mixed models (lme4) were used to assess change-from-baseline patterns, modelling participant as a random effect and timepoint as a fixed effect; change values were derived from log-transformed biomarker concentrations. Immune biomarker data were normalised and clustered to identify inflammatory subgroups; Spearman rank correlations were computed for biomarker–clinical relationships and p-values were adjusted for multiple comparisons with the Benjamini–Hochberg false discovery rate method.
Sample and assay performance: The analysed cohort included 25 participants with varying completeness of longitudinal samples (see Methods). Assay detectability varied by analyte: BDNF, VEGF-A and serotonin were within detectable ranges for all tested samples, whereas cytokine detection rates ranged from ~72% to 95% across targets and FKBP51 was detectable in 66% of samples. Values below LLOQ were imputed as half-LLOQ. Overall repeated-measures screening (Skillings–Mack) identified that plasma BDNF, serum VEGF-A, IL-2, IL-4 and IL-6 showed significant changes at some point during the trial. However, substantial inter-individual variability was evident across most biomarkers. Change-from-baseline mixed-model analyses focused on serum measures (BDNF and VEGF-A) because plasma results for these targets were not significant in more detailed analyses. Serum VEGF-A showed statistically significant decreases from baseline at multiple timepoints: mid-treatment (decrease 0.062 log10 pg/ml, p = 0.030), post-treatment (decrease 0.052 log10 pg/ml, p = 0.043), follow-up 1-week (decrease 0.065 log10 pg/ml, p = 0.007) and follow-up 1-month (decrease 0.050 log10 pg/ml, p = 0.038). Serum BDNF exhibited small but significant decreases from baseline at mid-treatment (decrease 0.216 log10 pg/ml, p = 0.010), follow-up 1-week (decrease 0.157 log10 pg/ml, p = 0.026) and follow-up 1-month (decrease 0.142 log10 pg/ml, p = 0.046). BDNF, VEGF-A and serotonin levels from plasma did not show significant change over treatment in the more detailed analyses. Cytokines and inflammatory clustering: Normalisation and clustering of immune biomarkers identified two stable inflammatory subgroups across timepoints: a “high” inflammatory group (n = 12) and a “low” inflammatory group (n = 13). The two groups differed significantly across most cytokines (IL-1β, IL-2, IL-4, IL-12p70, IL-17A and TNFα at all four major timepoints; IL-6 at pre-treatment and follow-up 1-week). Ketamine treatment did not appear to produce consistent changes in circulating cytokine levels overall, and inflammatory group membership did not correlate significantly with participant demographics, PTSD severity, comorbid depression or perceived stress, nor with treatment response in this cohort. Biomarker–clinical correlations: Several correlations between biomarkers and clinical scales were reported. At pre-treatment, serum BDNF correlated negatively with CAPS-5 scores (Spearman r = −0.487, p = 0.014; adjusted p = 0.080) and FKBP51 correlated positively with PCL-5 (r = 0.497, p = 0.016; adjusted p = 0.072); these adjusted p-values did not meet conventional significance thresholds after multiple-testing correction. Serum VEGF-A at post-treatment correlated negatively with PCL-5 (r = −0.509, p = 0.011; adjusted p = 0.039), a result that survived adjustment. Serotonin levels were positively correlated with WHO-5 wellbeing scores after ketamine treatment (post-treatment r = 0.632, p = 0.005; adjusted p = 0.022), with weaker and non-significant adjusted correlations at later follow-ups. BDNF and VEGF-A serum levels were positively correlated with each other across timepoints (examples include pre-treatment r = 0.415, p = 0.039, adjusted p = 0.149; post-treatment r = 0.691, p = 0.001, adjusted p = 0.006). The extracted text reports that serotonin and FKBP51 were negatively correlated at pre- and post-treatment, but the numerical statistics presented in the extraction appear internally inconsistent, so the exact strength and direction of those correlations are not clearly reported in the provided text. FKBP51 levels were consistently higher in the high-inflammatory group, with differences reaching statistical significance at pre-treatment and follow-up 1-month. Clinical response counts: The study reports responder/non-responder counts for the 25 participants by timepoint using the ≥ 50% PCL-5 reduction definition: post-treatment 15 responders / 3 non-responders / 7 missing, follow-up 1-week 15/8/2, and follow-up 1-month 12/11/2.
L. and colleagues present what they describe as the first broad blood biomarker analysis in people with PTSD undergoing ketamine treatment. The main reported biological findings were small but statistically detectable decreases in serum BDNF and serum VEGF-A after low dose oral ketamine, and a robust correlation between BDNF and VEGF-A across timepoints. The authors interpret these changes in the context of prior literature that links BDNF and VEGF-A to neurotrophic and neuroplastic processes implicated in antidepressant response and note preclinical evidence that ketamine’s sustained antidepressant actions require interactions between BDNF and VEGF-A. They suggest that the observed decreases in serum BDNF and VEGF-A after symptom improvement may reflect a normalising process in PTSD, where baseline BDNF is often elevated relative to other psychiatric disorders. The investigators also report distinct inflammatory subgroups within their PTSD cohort (roughly half with low-level systemic inflammation and half without), and they found that circulating cytokine concentrations did not change consistently across ketamine treatment and follow-up. FKBP51 emerged as associated with inflammatory group status and showed positive correlations with PTSD symptom measures at baseline. Serotonin levels were positively associated with self-reported wellbeing after treatment, and the authors note a novel reported relationship between circulating serotonin and FKBP51 that was present during active PTSD and immediately after treatment but not at later follow-up. The authors position these biomarker–clinical relationships as biologically plausible and worthy of further investigation. Key limitations are acknowledged: small sample size, incomplete sample sets for some participants, multiple comparisons that reduced statistical power after correction, the open-label (non-randomised, unblinded) trial design that may bias self-reported outcomes, and the allowance for concurrent psychotropic medications that could confound biomarker changes. The authors stress that several correlations lost significance after p-value correction and that many findings require replication in larger, independent cohorts with more comprehensive and sensitive biomarker panels. They also highlight the value of longitudinal, within-subject analyses given the substantial interpersonal variability observed in biomarker levels. Overall, the authors conclude that their results add preliminary evidence of serum BDNF and VEGF-A decreases after ketamine treatment in PTSD and identify several biomarker–clinical relationships (including serotonin, FKBP51 and inflammatory subgroup differences) that merit further study, while emphasising that definitive conclusions await larger, controlled investigations.
Non-fasting whole blood was collected at pre-treatment (week 0), mid-treatment (week 3), post-treatment (week 6), follow-up 1-week (week 7) and follow-up 1-month (week 10) timepoints at midday/early afternoon by a certified phlebotomist using serum separator tubes (SST) and K2EDTA (plasma) collection tubes. Samples were held at room temp for 30 min (to allow for serum clotting), before being centrifuged at 2465 x g for 15 min at 4 °C to generate serum and plasma, which was aliquoted and stored at -80 °C. If samples could not be processed immediately after clotting, tubes were transferred to 4 °C for a maximum of 3.5 h before processing continued.
Before analysis, stored serum and plasma was thawed on ice and centrifuged at 10,000 x g for 10 min at 4 °C to remove any particulates before testing. BDNF and VEGF-A were quantified from both serum and plasma while serotonin was quantified from serum only and FKBP51 and all cytokines were quantified from plasma only. Standard ProcartaPlex simplex assays (ThermoFisher Scientific, Australia) were combined for the detection of BDNF (lower limit of quantification (LLOQ) of 2.03 pg/ml) and VEGF-A (LLOQ of 4.88 pg/ml) while high-sensitivity assays were used to quantify IL-1β (LLOQ of 0.28 pg/ml), IL-2 (LLOQ of 0.88 pg/ml), IL-4 (LLOQ of 1.29 pg/ml), IL-6 (LLOQ of 1.29 pg/ml), IL-12p70 (LLOQ of 0.77 pg/ml), IL-17 A (LLOQ of 0.30 pg/ml) and TNFα (LLOQ of 0.62 pg/ml). Assays were conducted according to the manufacturer's instruction, read on a Luminex 200 instrument (ThermoFisher) and processed using the ProcartaPlex 9 (40%) * Mean ± standard deviation (range) or count (percentage), as appropriate. CAPS-5 Clinician-Administered PTSD Scale for DSM-5, DV domestic violence, SSRI Selective serotonin reuptake inhibitors, SNRI Serotonin and norepinephrine reuptake inhibitors by subtracting log transformed pre-treatment levels from log transformed subsequent timepoint levels (similar to. The cytokine heatmap was generated with heatmap.2 (in gplots)after scaling samples within each target (subtract target mean and divide by the target standard deviation). Comparisons of biomarker levels by immune group were calculated using the Wilcoxon rank sum test (also known as the Mann-Whitney U test), with p-values adjusted for multiple comparisons using Benjamini-Hochberg (BH) false discovery rate method. Spearman rank correlations were calculated using rcorr (in Hmisc) (Harrell Jr 2025), visualised using corrplot) and p-values adjusted using the BH false discovery rate method.
Average quantities for each biomarker tested are reported in Tableby timepoint. BDNF (from plasma), VEGF-A (from serum), IL-2, IL-4 and IL-6 were found to have had a significantly change in their mean biomarker quantity at some point throughout the trial (Table). However, because of the nature of the Skillings-Mack repeated measure test with missing data test, post-hoc analyses on individual timepoint differences was not possible. Despite some biomarkers showing significant group-levels changes at some point throughout the oral ketamine trial, significant variation was observed within all biomarkers, as evident by the large standard deviations in Table. To address this individual variability and investigate changes further, Analysis App (ThermoFisher). An overall study linearity of dilution of 0.97 ± 0.02 and percent recovery of 100.8 ± 13.1% of Luminex standards was achieved. Enzyme-linked immunosorbent assays (ELISAs) were used to quantify serotonin (Fast Track kit, Immusmol, France; LLOQ of 15 ng/ml) and FKBP51 (Human kit, Invitrogen, ThermoFisher, Australia; LLOQ of 0.41 ng/ml) according to the manufacturer's instructions and processed using GainData ( h t t p s : / / w w w . a r i g o b i o . c o m / e l i s a -a n a l y s i s). All samples from an individual were tested on the same assay plate to reduce within-subject variability. BDNF, VEGF-A and serotonin levels were within assay detectable ranges for all samples tested while detectable cytokine levels ranged from 95% (IL-1β and IL-12p70), 90% (IL-4 and IL-6), 80% (IL-2) and 72% (IL-17 A and TNFα) of samples tested. FKBP51 levels were within the assay detectable range for 66% of samples. Samples with targets below their assay's LLOQ were assigned a value of half their LLOQ for statistical analysis.
All analyses were performed using R Statistical Software (v4.3.3) (R Core Team 2024). The Skillings-Mack test (Skillings and Mack 1981) was used to compare biomarker repeated measures across five timepoints in an incomplete block design. This nonparametric ANOVA-type test generalizes the Friedman test and accommodates missing data. Analysis was conducted in R using the Skillings.Mack package. Changes in biomarker levels compared to baseline were assessed by linear mixed model analysis using lme4 packageand lmerTest package) and visualised using ggplot2. Models used change values for each biomarker, which were calculated significant decreases in VEGF-A levels from pre-treatment were detected at mid-treatment (decrease of 0.062 log10 pg/ ml, p = 0.030), post-treatment (decrease of 0.052 log10 pg/ml, p = 0.043), follow-up 1-week (decrease of 0.065 log10 pg/ml, p = 0.007) and follow-up 1-month (decrease of 0.050 log10 pg/ ml, p = 0.038) (Fig.). BDNF, VEGF-A and serotonin levels from plasma did not show any significant change over the course of ketamine treatment (Online Resource 2). Given the lack of significant change in BDNF and VEGF-A from plasma in this more detailed analysis, the remaining analyses in this study focused on BDNF and VEGF-A levels from serum only. Additionally, there was no significant difference in any biomarker pattern throughout the trial by trauma type (childhood verses other trauma) (Online Resource 1).
To understand the immune status of the study group better and investigate cytokine measurements in more detail, immune biomarkers were normalised and clustered for all additional data transformation and analysis was performed with the biomarkers BDNF, VEGF-A, and serotonin (where quantifiable results were obtained from all the samples tested (see methods)). Each participant's BDNF, VEGF-A and serotonin quantities were log-transformed and subtracted from their pre-treatment values, thereby setting each participant's pre-treatment value to "zero" and converting subsequent timepoint values to the change in biomarker level from pre-treatment. Linear mixed model analysis was then used to assess the pattern of change. Models considered change in biomarker quantity and study timepoints as fixed effects and individual participants as random effects. The age and sex of the study participants were tested as additional fixed effects and found not to be significant for these biomarkers in this dataset, so were omitted from the final models. From this change analysis, BDNF and VEGF-A levels from serum were found to show small but significant decreases after ketamine treatment (Fig.). Small BDNF decreases from pretreatment were detected at mid-treatment (decrease of 0.216 log10 pg/ml, p = 0.010), follow-up 1-week (decrease of 0.157 log10 pg/ml, p = 0.026) and follow-up 1-month (decrease of 0.142 log10 pg/ml, p = 0.046) (Fig.). Concurrently, Examination of the inflammatory clusters revealed two important findings. The first was that ketamine did not appear to significantly alter circulating cytokine levels during or after treatment. The levels of each cytokine remained relatively consistent between timepoints (Online Resource 3) and the distinct separation between the inflammatory groups was maintained throughout the treatment course (Fig.). There was no significant correlation or difference detected between a participant's inflammatory group status and their personal demographics (sex, age, PTSD severity, presence of co-morbid depression or perceived stress at the time of blood collection) or their treatment response in the trial, leaving the reason for the two distinct inflammatory subgroups unknown. study participants and timepoints (Fig.). This clustering revealed two distinct inflammatory groups within the study cohort: a "high" inflammatory group (n = 12), with average cytokine levels above the group average (green colours in Fig.) and a "low" inflammatory group (n = 13), with average cytokine levels below the group average (red colours in Fig.). Between these inflammatory groups, there was a statistically significant difference between IL-1β, IL-2, IL-4, IL-12p70, IL-17 A and TNFα levels at all four major study timepoints (pre-treatment, post-treatment, follow-up 1-week and follow-up 1-month), with IL-6 having significant differences at pre-treatment and follow-up 1-week (Online Resource 3).). Green star denotes group mean. Wilcoxon rank sum test was used to determine statistical differences, with p-values adjusted for multiple comparisons using BH false discovery rate. ns = not significant (p > 0.05) emerged. In relation to PTSD symptoms, only BDNF levels from serum correlated negatively with CAPS-5 scores at pre-treatment (r s (23)=-0.487, p = 0.014, p-adjusted = 0.080) (Fig.), while FKBP51 levels correlated positively with PCL-5 at pre-treatment (r s (21) = 0.497, p = 0.016, p-adjusted = 0.072) and VEGF-A levels correlated negatively with PCL-5 at post-treatment (r s (16)=-0.509, p = 0.011, p-adjusted = 0.039) (Fig.and, respectively). Next, serotonin levels were consistently positively correlated with WHO-5 scores after ketamine treatment (post-treatment: r s (16) = 0.632, p = 0.005, p-adjusted = 0.022; follow-up 1-week: r s (20) = 0.425, p = 0.48, p-adjusted = 0.111; followup 1-month: r s (19) = 0.440, p = 0.046, p-adjusted = 0.138) (Fig., Online Resource 4).
The present study represents the first broad analysis of blood biomarkers to explore biological changes and relationships associated with ketamine treatment for PTSD. By investigating these mechanisms, our work takes an important step towards addressing the significant gap in understanding of how ketamine facilitates PTSD symptom improvement. We have identified key, albeit small, changes in BDNF and VEGF-A levels following treatment, along with significant associations between blood biomarkers and PTSD clinical measures. BDNF has been extensively investigated across a range of mental health conditions, including PTSD. It assumes a crucial role in neuronal survival, growth and plasticity and, as such, is essential for learning and memory development. Previous studies by our team (as well as many others) have reported relationships between circulating BDNF and PTSD. These relationships include correlations between BDNF levels and PTSD symptom severity, as well as differences in BDNF levels between cohorts with and without PTSD. Interestingly, while meta-analysis of the literature has found BDNF levels lower in cohorts with major depressive disorder (MDD), bipolar disorder, schizophrenia, panic disorder or obsessive-compulsive disorder, the same meta-analysis reported that BDNF levels are higher in cohorts with PTSD. This observation suggests that PTSD may have distinct biological characteristics compared to the other psychiatric conditions, even though depression (as characterised by MDD) is often co-occurring. Moreover, it may be that BDNF might be considered a "goldilocks protein", where levels that are either too low or too high contribute to the emergence of symptoms. Since elevated BDNF levels are often observed The results for FKBP51, on the other hand, revealed a significant association between FKBP51 levels and inflammatory group status (Fig.). At the four study timepoints where FKBP51 was tested, average FKBP51 levels were consistently higher in the "high" inflammatory group, with this difference reaching statistical significance at the pre-treatment and follow-up 1-month timepoints (Fig., Online Resource 3). No difference by inflammatory group was detected for BDNF, VEGF-A or serotonin levels from serum for any timepoint in the study (Online Resource 3).
At each of the four major study timepoints, correlations were examined between blood biomarkers and study clinical scales (PCL-5, DASS-21 and WHO-5) to identify potential temporal relationships between these measures (Fig., Online Resource 4). Notably, strong relationships were observed among immune cytokines (IL-1β, IL-2, IL-4, IL-6, IL-12p70, IL-17 A and TNFα) and clinical scales (PCL-5, DASS-21 subscales and WHO-5) across study timepoints (Fig.). Immune biomarker comparisons revealed only IL-6 diverged from the overall pattern of strong positive correlation between the cytokines at all the study timepoints. In terms of clinical scales, PCL-5 scores strongly correlated with the other scales at all the timepoints (including CAPS-5 scores at pre-treatment (r s (23) = 0.593, p = 0.002, p-adjusted = 0.026)). DASS-21 subscales for anxiety, depression and stress and WHO-5 showed strengthening correlation as participants completed the ketamine treatment and continued to follow-up assessments (Fig.). Examining the non-immune blood biomarkers, BDNF and VEGF-A levels from serum appeared to only correlate to each other, with positive correlations detected at all timepoints throughout the study (Fig.; pre-treatment: r s (23) = 0.415, p = 0.039, p-adjusted = 0.149; post-treatment: r s (17) = 0.691, p = 0.001, p-adjusted = 0.006; follow-up 1-week: r s (22) = 0.478, p = 0.018, p-adjusted = 0.054; followup 1-month: r s (21) = 0.472, p = 0.023, p-adjusted = 0.075)). Serotonin levels were negatively correlated with FKBP51 at pre-and post-treatment (pre-treatment: r s (23) = 0.415, p = 0.039, p-adjusted = 0.149; post-treatment: r s (17) = 0.691, p = 0.001, p-adjusted = 0.006) and a subset of cytokines (IL-4, IL-12p70, IL-17 A and TNFα) at follow-up 1-month (Fig.). Finally, in addition to a relationship with serotonin, FKBP51 also exhibited sporadic correlations with cytokines IL-1β, IL-2, IL-4 and IL-6 at various timepoints across the trial (Fig., Online Resource 4). Examining the relationships between blood biomarkers and clinical scales, several noteworthy correlations The findings from the present study show a correlation between VEGF-A and BDNF serum levels (Fig.) and a parallel VEGF-A serum level decrease after ketamine treatment (Fig.), supporting existing evidence that highlight the interdependence of BDNF and VEGF-A in mediating ketamine's therapeutic effects. VEGF-A is best known for its roles in angiogenesis and blood vessel permeability but, like BDNF, it also has recognised neurotropic activity. Both BDNF and VEGF-A have been linked genetically to antidepressant treatment responses in people) and rodent studies have identified that the sustained antidepressant action of ketamine requires a reciprocal interdependence of both BDNF and VEGF-A on each other and the brain. Rodent models of depression have found that ketamine rapidly increases BDNF and VEGF-A levels in the medial prefrontal cortex, which increases the number and function of synaptic spines, and in the hippocampus, which enhances neurogenesis and contributes to the antidepressant effects observed. How these processes differ in PTSD (where our previous work has also detected elevated levels of VEGF-A in a PTSD cohort verses controls)) remains to be elucidated. Interestingly, at the posttreatment timepoint in this study, BDNF from serum did not significantly correlate with any clinical scales, but VEGF-A levels from serum significantly negatively correlated with PCL-5 and DASS depression and stress subscales and significantly positively correlated with BDNF at the same time (Fig.). Although small study sample sizes and individual variability in biomarkers can influence the detection of statistically significant relationships, the consistent associations observed between BDNF, VEGF-A and the clinical scales before and after treatment reinforce the notion that a biologically meaningful mechanism may be at play and that the present findings warrant further investigation. The other group of significant biomarkers commonly investigated in relation to mental health (and PTSD) are the immune cytokines. A dysregulation of the immune system, commonly reported as a systemic, low-grade inflammation, has been observed in individuals with PTSD. In the present study, half of our trial participants appeared to have low-level inflammation, while the other half did not (Fig.). The reason for this subgrouping was not apparent from the demographic information collected and did not appear to impact treatment response with ketamine. However, the variability in immune status within our PTSD cohort may help explain why other studies do not always detect significant immune differences related to PTSD status. In the present study, there did not appear to be a consistent detectable change in these circulating cytokine levels throughout the in PTSD, the small but significant decrease detected in our cohort from serum following ketamine treatment (Fig.) may suggests a potential biological mechanism through which ketamine contributes to PTSD symptom improvement. This supports the notion that BDNF increases in reaction to stress-induced neuronal damage but decreases when the stressor (i.e., PTSD symptoms) is mitigated. Interestingly, reducing elevated levels of BDNF may be a biomarker of general PTSD symptom improvement and not limited to just ketamine treatment.recently reported the outcome of a 3-week cognitive processing therapy (CPT) treatment for military veterans with PTSD, showing that while BDNF levels at post-treatment study timepoints negatively correlated with PTSD symptoms, the change in each individual's BDNF level from pretreatment to post-treatment showed an overall significant decrease, similar to the findings in this study (Fig.). Similarly to our present study (Table),) also reported a wide variation in pre-and post-treatment BDNF levels among individual. This suggests that there may not be a universal "optimal" BDNF level, but rather that each individual has a unique baseline influenced by genetic and/ or biological factors. Disruptions to this balance may only become apparent though broad comparisons between PTSD and control cohorts (as seen in previous literatureor through longitudinal individual BDNF analysis (as observed in this study and. The relationship between BDNF and PTSD also involves more dimensions than just a general amount in circulation. Our previous work has determined that the form of BDNF being measured (proBDNF verses mature BDNF verses total BDNF) is important in detecting correlations with PTSD symptom severity, and that serum but not plasma levels differentiated PTSD from non-PTSD cohorts). The difference between plasma BDNF levels (representing only freely circulating BDNF) and serum BDNF levels (which reflect both circulating plasma levels and platelet-stored BDNF reserves) are significant and have led to the suggestion that plasma BDNF levels may reflect cerebral levels more closely while serum BDNF associations suggest that platelet biology may have an underappreciated role in mental health. Finally, it is important to acknowledge that BDNF in all its forms has important links to many mental health and neurological conditions, and while it is unlikely that any BDNF measurement alone will be distinct enough to serve as an independent biomarker for PTSD, investigations into the complex relationship between BDNF and PTSD will continue to advance our understanding of how this important neurotrophin is involved in mental health. There were limitations to this study that need to be acknowledged and considered when interpreting the results. The small sample size and incomplete sample sets for some participants limit the generalisability of the study findings. This should be considered, especially when certain changes or relationships were not detected (i.e. cytokine results and differences by trauma type in Online Resource 1). When relationships were detected, the small study numbers in combination with multiple comparisons caused several interesting correlations to lose statistical significance after p-value correction. To give the reader as much objective information as possible, both uncorrected and corrected p-vales were reported and relationships with supporting evidence in the literature were discussed. However, all the relationships detected in this study need replication in independent, larger cohorts. The samples for this analysis were also derived from an open-label (non-randomised, unblinded) clinical trial using oral ketamine for PTSD treatment and this may have introduced bias in the self-reported clinical scales. Furthermore, study participants were allowed to continue their existing medications during the ketamine treatment trial, thus the possibility of combined or synergic effects of ketamine with other medications cannot be ruled out. Despite these limitations, many statistically significant changes and relationships were still detectable within the study cohort, even after correcting for multiple comparisons, and findings were generally consistent or logical in the context of published literature. In conclusion, this study reports the first broad analysis of blood biomarker in participants with PTSD before and after ketamine treatment. Significant decreases in BDNF and VEGF-A levels from serum were detected following treatment with ketamine, consistent with established knowledge that BDNF is typically elevated during PTSD and suggesting that BNDF may be a "goldilocks protein" that requires levels to be "just right" for the optimal mental health of individuals. This study adds new and significant information to our understanding of serotonin and FKBP51 in PTSD and symptom improvement. Finally, this study highlights the significant interpersonal variability in blood biomarker levels and reinforces the power of individual participant longitudinal analysis to understand important biological changes during illness and treatment. treatment and follow-up, but additional investigations with larger cohort sizes and broader, more sensitive panels are needed before any definitive statements can be made about immune biomarkers and changes related to ketamine treatment in PTSD. Finally, despite their recognised roles in wellbeing and stress responses, research on serotonin and FKBP51 in the context of PTSD remains limited. Serotonin dysfunction has been linked to the pathophysiology of traumarelated symptoms associated with PTSD, while abnormal increases in FKBP51 complexed with its glucocorticoid receptor appear to lead to glucocorticoid resistance, hyperarousal of the stress-response system, and symptoms of PTSD. Previous research in mice has detected increased gene expression of FKBP51 in serotonergic neurons from the dorsal raphe after stress, while human genotypephenotype analysis has found FKBP51 single nucleotide polymorphisms influenced SSRI treatment outcomes in MDD. Work byhas also shown that childhood trauma (which represents almost half of the index PTSD trauma type for participants in this study) was associated with DNA methylation in specific FKBP5 functional glucocorticoid response elements, leading to global gene expression changes in the immune system. These genetic childhood-induced trauma changes within FKBP5 resulted in higher risks of trauma-associated psychiatric, immune and metabolic disorders in exposed adults, which may help explain the association between the inflammatory groups and FKBP51 levels detected in this study (Fig.). To our knowledge, this is the first study to report a significant relationship between circulating serotonin and FKBP51 levels, finding these biomarkers were negatively correlated during PTSD and immediately after treatment (Fig.and). Notably, the correlation did not persist at the follow-up timepoints, prompting questions about how PTSD symptomology and its improvement may impact this relationship. Related to this was the fact that serotonin levels positively correlated with WHO-5 wellbeing scores, but only after ketamine treatment (Fig.and). This corroborates other research that has reported whole blood serotonin levels can relate to wellbeing (de Vries et al. 2022), but that relationship was only seen as positively related to positive affect but not related to negative affect). As such, the serotonin-wellbeing relationship was only detected once participants started feeling better. Alternatively, FKBP51 levels positively correlate with PCL-5 and DASS-21 stress scores at pre-treatment, suggesting its levels relate more strongly to the stress response linked to active PTSD.