Biomedical subjects

Eric Vermetten

Publications and source records attributed to Eric Vermetten.

5 recordsLinked to original sources

PubMed2026

Efficacy and Safety of Psychedelic Microdosing on Psychological Outcomes in Healthy Adults: A Systematic Review and Meta-analysis.

BACKGROUND: Psychedelic microdosing has gained increasing popularity for enhancing mood and cognition, yet its effects on psychological outcomes in healthy adults remain unclear. We aim to evaluate the efficacy and safety of psychedelic microdosing on psychological outcomes in healthy/non-clinical adult population. METHODS: This review was registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD420251035294). We searched Embase, MEDLINE, and PsycINFO from inception to February 2026 for original studies in healthy adults using sub-hallucinogenic psychedelic doses on separate days. We included randomized studies, nonrandomized prospective studies, cross-sectional studies, and observational longitudinal designs and stratified meta-analyses by design. Random-effects models were used; safety in randomized controlled trials (RCTs) was pooled as risk differences (RDs). Risk of bias was assessed using the Joanna Briggs Institute (JBI) critical appraisal tools appropriate for each study design. RESULTS: 24 studies (3,681 participants) met inclusion criteria, 6 contributed to meta-analyses. RCTs of psilocybin and LSD microdosing showed subjective and neurophysiological effects but minimal impact on cognition, creativity, or sustained mood. Psilocybin altered EEG and speech with little behavioral change, while LSD caused transient mood and minor physiological effects without lasting cognitive or personality benefits. Observational studies indicated mood and personality improvements, likely influenced by expectancy or lifestyle factors. Meta-analyses included two parallel RCTs (3 comparisons; n = 117) and three non-RCT studies (n = 1,013). Adverse event analyses included two RCTs (4 comparisons; n = 109). RCTs showed no clear evidence of immediate symptom reduction: depressive symptoms (SMD = -0.19; 95% CI -0.56, 0.19; I2 = 0%), anxiety (SMD = -0.20; 95% CI -1.11, 0.71; I2 = 82.3%),and stress (SMD = 0.02; 95% CI -0.39, 0.43; I2 = 0%). Non-RCT within-arm estimates were imprecise and heterogeneous for depressive symptoms (SMCC -0.33; 95% CI -0.75, 0.08) and anxiety (SMCC -0.29; 95% CI-0.84, 0.26). Exploratory pooling across all designs suggested decreases in depressive symptoms and stress, while anxiety remained uncertain. Adverse event risks were similar between treatment and control. CONCLUSIONS: Microdosing does not show consistent immediate benefits for depressive, anxiety, or stress symptoms in healthy adults, and evidence from RCTs remains inconclusive. Although improvements were observed in some studies, these effects were not significantly different from placebo. Larger, well-designed RCTs are needed to clarify the efficacy and safety of psychedelic microdosing beyond placebo.

Humans↗

PubMed2024

Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts.

BACKGROUND: Incorporating genomic data into risk prediction has become an increasingly popular approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not. METHODS: Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts. RESULTS: The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p=0.006), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD. CONCLUSION: The inclusion of exposure variables adds to the predictive power of MRS. Classification-based MRS may be useful in predicting risk of future PTSD in populations with anticipated trauma exposure. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting PTSD and, relatedly, improve their performance in independent cohorts.

Humans↗

PubMed2024

Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts.

BACKGROUND: Incorporating genomic data into risk prediction has become an increasingly useful approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not. METHODS: Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts. RESULTS: The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p-0.003), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD. CONCLUSION: Results, especially those from the eMRS, reinforce earlier findings that methylation and trauma are interconnected and can be leveraged to increase the correct classification of those with vs. without PTSD. Moreover, our models can potentially be a valuable tool in predicting the future risk of developing PTSD. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting the condition and, relatedly, improve their performance in independent cohorts.

DNA methylation↗

PubMed2022

Epigenome-wide meta-analysis of PTSD symptom severity in three military cohorts implicates DNA methylation changes in genes involved in immune system and oxidative stress.

Epigenetic factors modify the effects of environmental factors on biological outcomes. Identification of epigenetic changes that associate with PTSD is therefore a crucial step in deciphering mechanisms of risk and resilience. In this study, our goal is to identify epigenetic signatures associated with PTSD symptom severity (PTSS) and changes in PTSS over time, using whole blood DNA methylation (DNAm) data (MethylationEPIC BeadChip) of military personnel prior to and following combat deployment. A total of 429 subjects (858 samples across 2 time points) from three male military cohorts were included in the analyses. We conducted two different meta-analyses to answer two different scientific questions: one to identify a DNAm profile of PTSS using a random effects model including both time points for each subject, and the other to identify a DNAm profile of change in PTSS conditioned on pre-deployment DNAm. Four CpGs near four genes (F2R, CNPY2, BAIAP2L1, and TBXAS1) and 88 differentially methylated regions (DMRs) were associated with PTSS. Change in PTSS after deployment was associated with 15 DMRs, of those 2 DMRs near OTUD5 and ELF4 were also associated with PTSS. Notably, three PTSS-associated CpGs near F2R, BAIAP2L1 and TBXAS1 also showed nominal evidence of association with change in PTSS. This study, which identifies PTSD-associated changes in genes involved in oxidative stress and immune system, provides novel evidence that epigenetic differences are associated with PTSS.

Adaptor Proteins, Signal Transducing↗

PubMed2020

Epigenome-wide meta-analysis of PTSD across 10 military and civilian cohorts identifies methylation changes in AHRR.

Epigenetic differences may help to distinguish between PTSD cases and trauma-exposed controls. Here, we describe the results of the largest DNA methylation meta-analysis of PTSD to date. Ten cohorts, military and civilian, contribute blood-derived DNA methylation data from 1,896 PTSD cases and trauma-exposed controls. Four CpG sites within the aryl-hydrocarbon receptor repressor (AHRR) associate with PTSD after adjustment for multiple comparisons, with lower DNA methylation in PTSD cases relative to controls. Although AHRR methylation is known to associate with smoking, the AHRR association with PTSD is most pronounced in non-smokers, suggesting the result was independent of smoking status. Evaluation of metabolomics data reveals that AHRR methylation associated with kynurenine levels, which are lower among subjects with PTSD. This study supports epigenetic differences in those with PTSD and suggests a role for decreased kynurenine as a contributor to immune dysregulation in PTSD.

Basic Helix-Loop-Helix Proteins↗