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. Author manuscript; available in PMC: 2026 Jun 25.
Published in final edited form as: Neurobiol Aging. 2026 May 29;167:1–11. doi: 10.1016/j.neurobiolaging.2026.05.010

Post-Traumatic Stress Disorder Moderates the Association between BrainAge Acceleration and GrimAge Acceleration

Jenna Beakas a,b, Alexandra K Dwulit a,b,c, Ahmed Hussain a,b, Melanie E Garrett d, Allison Ashley-Koch d, Ashley N Clausen e, Nathaniel G Harnett f,g, Kerry J Ressler f,g, Sanne J H van Rooij h, Jennifer S Stevens h, Alicia K Smith h,p, Anthony S Zannas i,j, Sarah D Linnstaedt k,l,m,n, Tanja Jovanovic o, Karestan C Koenen p, Samuel A McLean n,q, Alyssa R Roeckner r; VA Mid-Atlantic MIRECC Workgroupb, E Kate Webb s, Rajendra A Morey a,b, Seyma Katrinli t
PMCID: PMC13293161  NIHMSID: NIHMS2183889  PMID: 42235098

Abstract

Individuals with post-traumatic stress disorder (PTSD) are at higher risk for age-related physical comorbidities, such as cardiovascular disorders, and exhibit accelerated epigenetic and brain aging. The present study examined PTSD as a moderator of the association between DNA methylation (DNAm)-based systemic aging (GrimAge, PhenoAge) and brain aging, indexed by MRI-based and DNAm-derived BrainAge estimates, in 174 lifetime PTSD cases and 138 trauma-exposed controls from four cohorts. Peripheral DNAm assayed with the MethylationEPIC BeadChip was used to calculate epigenetic age acceleration (EAA), using multiple clocks (GrimAge, PC GrimAge, PC PhenoAge), and DNAm-based BrainAge acceleration. Neuroimaging-derived BrainAge estimates were calculated with T1-weighted MRI scans that were processed using FreeSurfer v5.3 and run on BrainageR. Meta-analysis of the four cohorts examined the interactions between lifetime PTSD and EAA on BrainAge acceleration. EAA measures were intercorrelated (0.43 < rmeta < 0.84), but MRI-based BrainAge acceleration was not correlated with any EAA estimate (rmeta = 0.03, p = 0.56). Lifetime PTSD moderated the association between PC GrimAge acceleration and DNAm-based BrainAge acceleration (Standardized Mean Difference [SMD] = 0.84 [95% CI: −1.51, −0.17], p = 0.01), and the association between GrimAge acceleration and MRI-based BrainAge acceleration (SMD = −0.40 [95% CI: −0.74, −0.07], p = 0.02). Stratified analyses confirmed that the positive associations between GrimAge acceleration and BrainAge acceleration were attenuated or absent in PTSD cases relative to trauma-exposed controls. Trauma-exposed individuals represent a vulnerable population who may benefit from personalized approaches guided by multi-modal measures of biological aging to prevent and manage modifiable medical disease processes.

Keywords: PTSD, BrainAge, GrimAge, Epigenetics, Trauma, Interaction

1. Introduction

Post-traumatic stress disorder (PTSD) is a psychiatric condition characterized by a prolonged stress response to trauma, and it can be a risk factor for several age-related conditions, including neurodegenerative processes (Miller and Sadeh, 2014), cardiovascular and cardiometabolic disorders (Edmondson and Von Känel, 2017), and immune dysregulation (Núñez-Rios et al., 2022). Chronic stress involves functional reorganization of the brain that is not quickly reversible (Eggers, 2007); for example, repeated activation of the hypothalamic-pituitary-adrenal (HPA) axis that occurs when reexperiencing the trauma dysregulates glucocorticoid (GC) signaling (Miller and Sadeh, 2014). Dysregulated GC signaling increases systemic oxidative stress and inflammation, which is known to accelerate aging (Lohr et al., 2015; Miller and Sadeh, 2014). The oxidative stress and inflammation-promoting symptoms of PTSD may explain the association between PTSD and neurodegeneration, neuronal cell death, accelerated aging of the brain, and increased mortality (Edmondson and Von Känel, 2017; Lohr et al., 2015; Miller and Sadeh, 2014). Patients with PTSD are therefore a vulnerable population who may benefit from prediction of biological aging in order to tailor interventions and reduce the risk or impact of modifiable disease processes.

Epigenetic clocks leverage age-related DNA methylation (DNAm) variation at select cytosine-phosphate-guanine (CpG) dinucleotide sites (‘clock CpGs’) to estimate biological age, with epigenetic age acceleration (EAA) representing the extent to which biological aging deviates from chronological aging. First generation epigenetic clocks, HannumAge (Hannum et al., 2013) and HorvathAge (Horvath, 2013), were trained on chronological age, associate with all-cause mortality, but weakly predict disease outcomes. Second generation epigenetic clocks, PhenoAge (Levine et al., 2018) and GrimAge (Lu et al., 2019), were trained on current health measures predictive of morbidity and mortality. GrimAge predicts time-to-death in the context of aging-related lifestyle factors, such as smoking pack-years and metabolic biomarkers (Lu et al., 2019). PhenoAge was trained on blood-based clinical biomarkers of phenotypic age to predict mortality (Levine et al., 2018). GrimAge and PhenoAge are more predictive of all-cause mortality, age-related disorders, and adverse health outcomes and relate most consistently to indices of cognition and brain health compared to other epigenetic clocks (Hillary et al., 2021; Krivonosov et al., 2022; Lu et al., 2019; McCrory et al., 2021; O’Shea et al., 2023; Reed et al., 2022). To boost reliability among epigenetic clocks and reduce technical noise, Higgins-Chen et al. (2022) developed the principal component (PC) based versions of these epigenetic clocks, which outperform their non-PC based counterparts in aging phenotypes and mortality predictions (Higgins-Chen et al., 2022). While the above-mentioned epigenetic clocks each produce a single biological age estimate, they fail to account for within-person heterogeneity across different physiological systems. Systems Age clock addresses this gap by providing age estimates for 11 distinct physiological systems, including a DNAm-derived BrainAge estimate (Sehgal et al., 2025).

Previous studies examining epigenetic age differences across groups of individuals who are the same chronological age suggest that stress accelerates biological aging (Horvath and Raj, 2018; Poganik et al., 2023). EAA has previously been implicated in neurocognitive decline, as well as cardiovascular disease, cancer, metabolic disorders, and diabetes (Perna et al., 2016; Pottinger et al., 2021; Salameh et al., 2020). Cross-sectional and longitudinal evidence support that individuals with PTSD exhibit significantly higher EAA compared to trauma-exposed controls (Bourassa et al., 2025; Katrinli et al., 2020; Smith et al., 2024; Thurston et al., 2025; Wang et al., 2022; Wolf et al., 2018a; Yang et al., 2021; Zannas et al., 2023; Zhao et al., 2025), and suggest decreases in CD28 cell surface marker counts on CD8+ T cells as a potential driver of accelerated GrimAge in those with PTSD (Yang et al., 2021). Prospectively, GrimAge acceleration predicted future PTSD diagnosis and severity (Smith et al., 2024; Zannas et al., 2023), and PTSD onset was associated with increases in PC PhenoAge and PC GrimAge acceleration (Bourassa et al., 2025). While, to the best of our knowledge, no studies have tested for associations between DNAm-derived BrainAge from Systems Age clock and PTSD, Systems Age is predictive of mortality, and the derived BrainAge score most strongly associated with cognitive function (Sehgal et al., 2025).

In addition to measuring whole-body and brain aging from DNAm, changes in how fast the brain ages can also be predicted from neuroimaging and are a potential target for understanding how certain disease processes influence aging. Brain aging trajectories can be predicted from voxel-wise structural neuroimaging data with a validated machine-learning pipeline, BrainageR (Cole et al., 2018). BrainAge acceleration (i.e., the deviation of predicted BrainAge from chronological age) has been previously examined as a neural biomarker for aging in myriad psychiatric disorders that may be associated with accelerated aging. After adjusting for antidepressant use, patients with depression and anxiety exhibited accelerated brain aging, with an average acceleration of over 2.5 years in depression and anxiety when compared to controls (Han et al., 2021; Koutsouleris et al., 2014). A large study of patients with PTSD (n=882) and trauma-exposed controls (n=1,347) found interacting effects of age, sex, and PTSD on brain aging (Clausen et al., 2022). Younger males with PTSD exhibited more advanced brain age than older males with PTSD (Clausen et al., 2022). Controls exhibited a similar pattern, but the relationship was not as strong (Clausen et al., 2022). Females with PTSD had more advanced brain age than female controls (Clausen et al., 2022). Thus, the neurologic effects of PTSD may vary across the lifespan and by sex, creating a critical window at a younger age for brain aging with PTSD (Clausen et al., 2022).

Previous studies indicate associations between EAA and markers of brain aging in PTSD, suggesting these aging processes might interact in trauma-exposed populations. For example, in first generation clocks, advanced DNAm Hannum age was associated with reduced microstructural integrity in the genu of the corpus callosum and negatively associated with performance on working memory tasks via fractional anisotropy in the genu; however, these associations with neuroimaging parameters were not found in the Horvath DNAm age (Wolf et al., 2016). Utilizing second generation clocks, accelerated GrimAge has been linked with reduced amygdala volume—particularly in subregions like the cortical-amygdaloid transition and accessory basal nuclei—shortly after trauma exposure (Zannas et al., 2023). Similarly, higher GrimAge acceleration has been associated with reduced cortical thickness in brain areas involved in emotion processing and threat response, such as the orbitofrontal cortex (OFC) and posterior cingulate cortex (PCC) (Katrinli et al., 2023, 2020). DNAm-derived BrainAge from Systems Age clock has been associated with cranial volume decline in Alzheimer’s disease patients (Sehgal et al., 2025), suggesting the clock’s potential utilization in measuring neuroanatomical changes in psychiatric disorders, such as PTSD.

Although these findings suggest a relationship between epigenetic and brain aging following trauma, it remains unclear whether PTSD enhances this convergence (i.e., parallel acceleration of both measures) or disrupts it by disproportionately accelerating one measure relative to the other. Given PTSD’s known associations with distinct physiological mechanisms—such as HPA axis dysregulation, inflammation, and oxidative stress—that could differentially influence brain versus systemic aging, examining PTSD as a moderator is critical for clarifying how trauma-related psychopathology shapes biological aging. Thus, our goal was to investigate whether PTSD moderates the association between DNAm-based systemic aging (measured by GrimAge, PC GrimAge, and PC PhenoAge) and brain aging (both DNAm- and neuroimaging-derived). We hypothesized that PTSD would moderate this relationship, reflecting differential aging processes in the systemic vs. brain-specific biomarkers.

2. Methods and Materials

2.1. Participants

Data on 312 participants with trauma exposure were compiled from four independent cohorts: VA Mid-Atlantic Mental Illness, Education, and Clinical Center (MIRECC) African American (AA) Ancestry (N = 66), MIRECC European American (EA) Ancestry (N = 62), Grady Trauma Project (GTP, N = 57), and Advancing Understanding of RecOvery afteR traumA (AURORA, N = 127). All participants provided written informed consent, reported age and sex, completed a PTSD assessment, provided a blood DNA sample by venipuncture, and completed a 3D T1-weighted brain scan with magnetic resonance imaging (MRI). Participants (n = 57) with greater than one year time gap between the blood draw/PTSD assessment and brain scan were removed from analyses (n = 23 MIRECC AA, n = 34 MIRECC EA).

Inclusion and exclusion criteria varied across cohorts, as described in Table S1. MIRECC participants served in the US military on or after September 11, 2001 and were recruited from VA hospitals in the Southeastern United States (Brancu et al., 2017). Veterans meeting current diagnostic criteria for a substance use disorder, neurological disorder, active psychosis, and moderate-to-severe head injuries were excluded. GTP recruited African American adult females from waiting rooms of primary care or obstetrical-gynecological clinics of Grady Memorial Hospital in Atlanta, Georgia (Gillespie et al., 2009). AURORA is a large multi-ancestry cohort study (n < 3000) that includes adults who presented to the emergency department within 72 hours after exposure to psychological trauma, and MRI scans and blood samples were collected six months post-trauma (McLean et al., 2020). Control participants included patients who were trauma-exposed, but did not have a history of PTSD. Demographic and clinical characteristics are described in Table 1 (full sample) and Supplementary Tables S2 – S5 (cohort-level). The studies were approved by the Institutional Review Boards of the Department of Veteran Affairs, Duke University Health System, Emory University School of Medicine, Grady Health Systems Research Oversight Committee, and all other institutions participating in the AURORA study (McLean et al., 2020).

Table 1.

Demographic and Clinical Characteristics

Variable Full Sample (N = 312) Controls (N = 138) Lifetime PTSD (N = 174) Comparison between groups
Weighted Mean (Pooled SD) p-value
Age 38.91 (11.72) 39.48 (11.12) 38.46 (12.16) 0.44
BrainAge (MRI) 36.80 (11.00) 37.00 (11.36) 36.64 (11.10) 0.78
BrainAge (MRI) acceleration −1.20 (6.50) −1.36 (11.36) −1.06 (6.20) 0.69
BrainAge (DNAm) 74.34 (18.98) 74.66 (17.97) 74.10 (18.27) 0.52
BrainAge (DNAm) acceleration −0.75 (12.32) 0.11 (11.62) −1.43 (12.86) 0.28
GrimAge 55.20 (14.63) 54.27 (14.03) 55.93 (15.07) 0.32
GrimAge acceleration −1.04 (4.24) −1.05 (4.24) −0.88 (4.55) 0.44
PC GrimAge 54.64 (9.58) 55.04 (9.09) 54.34 (9.94) 0.52
PC GrimAge acceleration −0.61 (3.13) −0.74 (2.93) −0.51 (3.29) 0.52
PC PhenoAge 44.43 (10.85) 44.10 (10.31) 44.66 (11.28) 0.65
PC PhenoAge acceleration −0.16 (5.08) −0.24 (5.00) −0.09 (5.16) 0.79
% with (n)
Females 56% (174) 49% (68) 61% (106) 0.052
Males 44% (138) 51% (70) 39% (68)
Non-Hispanic Black 60% (186) 62% (85) 58% (101) 0.91
Non-Hispanic White 32% (101) 30% (42) 34% (59)
Hispanic 7% (21) 7% (9) 7% (12)
Other 1% (4) 1% (2) 1% (2)
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Abbreviations: PTSD, posttraumatic stress disorder; SD, standard deviation. The lifetime PTSD group includes individuals with current PTSD or a history of PTSD. P-values are from T-tests for continuous variables and chi-square tests for categorical variables. T-tests were run with equal variances assumed. Significant differences are indicated with an asterisk (*).

2.2. Clinical assessment

PTSD symptom severity and diagnostic status were assessed within the MIRECC and GTP cohorts with the Structured Clinical Interview for DSM-5 (SCID-5) (First, 2015), Clinician-Administered PTSD Scale for DSM-IV (CAPS-4) (Pfohl et al., 1997) or DSM-V (CAPS-5) (Weathers et al., 2017), and Mini-international Neuropsychiatric Interview (MINI) (Sheehan et al., 1998). Current PTSD symptom severity and diagnostic status within the AURORA cohort were evaluated six months post-trauma with the PTSD Checklist for DSM-V Civilian (PCL-5) (Blevins et al., n.d.) and a previously reported score threshold of ⩾ 31 (Blevins et al., n.d.; Bovin et al., 2016; Kessler et al., 2021). Lifetime PTSD symptom severity and diagnostic status was separately assessed, relative to participants lifetime exposure to traumatic events prior to the index trauma, first with the abbreviated 6-item PTSD Checklist - Civilian Version (PCL-C) (Lang and Stein, 2005) followed by the full PCL-5 for AURORA participants who indicated at least “some” for one or more of the items on the PCL-C. AURORA participants were diagnosed with lifetime PTSD if their PCL-5 score was ⩾ 31 for at least one month prior to the index trauma or if they met criteria for current PTSD.

We focused on lifetime PTSD rather than current PTSD because we anticipated that prolonged or chronic symptomatology would exert a greater cumulative biological impact, thus increasing the likelihood of observing measurable changes in aging biomarkers. Current PTSD diagnoses may reflect relatively recent symptom onset, which might not yet have had sufficient time to manifest in detectable acceleration of aging markers. Additionally, reporting on lifetime PTSD addressed temporality differences (< 1 year) between the PTSD assessment and the epigenetic and brain age measures (i.e., blood draw and MRI scan). See Supplementary Tables S6 and S7 for cohort-level PTSD assessment details.

2.3. Magnetic resonance imaging

Neuroimaging data was collected on 3-Tesla MRI systems. Each site provided and analyzed high-resolution T1-weighted structural brain MRI scans with optimized tissue contrast. Preprocessing and quality assurance for all raw T1-weighted images followed standardized ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) protocols for image analysis harmonization across sites. Images were examined for gross pathology, image quality, and quality of automated segmentation (http://enigma.ini.usc.edu/protocols/imaging-protocols/). FreeSurfer version 5.3 (MIRECC, GTP) and 6.0.1 (AURORA) were selected for preprocessing following already established preprocessing procedures for a pre-trained BrainAge algorithm as well as to maintain consistency in segmentation results across sites. Site-specific scanning protocols and parameters are provided in the Supplementary Methods (section 1.4) and Supplementary Table S8.

2.4. BrainAge calculation

Three pre-trained machine learning pipelines that estimate BrainAge relative to chronological age were compared by Clausen et al. (2022) (Clausen et al., 2022). BrainageR provided the strongest relationship between chronological and predicted age (R = 0.72) and lowest error (MAE = 5.68) and was therefore utilized in the present analysis (Clausen et al., 2022). BrainageR is a voxel-wise pipeline that segments T1-weighted scans into gray and white matter to apply a Gaussian process regression model to predict chronological age (Clausen et al., 2022; Cole et al., 2018). BrainAge acceleration (the residuals obtained from regressing predicted BrainAge on chronological age) was calculated to determine positive (older appearing brains) or negative (younger appearing brains) brain aging.

2.5. Epigenetic tests or assays: DNA methylation

DNAm quality control (QC) was performed separately for each dataset (GTP, MIRECC EA, MIRECC AA, AURORA). GTP QC process was previously described in Katrinli et al. (2020) (Katrinli et al., 2020). DNA was extracted from whole blood and interrogated using MethylationEPIC BeadChip (Illumina) and Human-Methylation450 BeadChip (Illumina). Raw methylation beta values were determined via GenomeStudio. Samples with probe detection call rates < 90% and average intensity values of either < 50% of experiment-wide sample mean or < 2000 arbitrary units (AU) were removed using R package CpGassoc (Barfield et al., 2012). Probes that had detection p-values > 0.01 were set as missing. CpG sites that cross hybridized between autosomes and sex chromosomes were removed (McCartney et al., 2016).

MIRECC AA and EA QC were performed as previously described in Kimbrel et al. (2023) (Kimbrel et al., 2023). DNA was extracted from whole blood and samples with sufficient DNA yield and quality were submitted for analysis on either the Infinium HumanMethylation450 Beadchip or the Infinium MethylationEPIC Beadchip. Internal replicates were included and checked for consistency using single nucleotide polymorphisms (SNPs) that were incorporated on each array. Sample and probe QC was done using minfi (Aryee et al., 2014) and ChAMP (Morris et al., 2014) R packages. Samples were excluded if average fluorescence intensity signal was below 2,000 arbitrary units or < 50% of mean intensity of all samples, > 10% of the probes were not detectable (detection value of p < 0.001), presence of a sex mismatch, or if the sample was deemed an outlier on principal component analysis plots. Probe QC and data normalization was done within each batch using the wateRmelon R package (Pidsley et al., 2013). Probes not detected (p > 0.001) in > 10% of samples and those hybridizing to multiple locations in the genome were removed. Raw beta values were normalized using the dasen approach (Pidsley et al., 2013) and adjustments for batch and chip were done using ComBat in the sva R package (Leek et al., 2012).

AURORA QC is described in Zannas et al. (2023) (Zannas et al., 2023). DNA was extracted from blood and isolated using chemagen magnetic bead technology via Chemagic 360 instrumentation (PerkinElmer, Waltham, MA, USA). The DNA purity and concentration were determined using UV/Vis on a Lunatic reader (Unchained Labs, Pleasanton, CA, USA), and bisulfite conversion of the isolated DNA was done using EZ-96 DNA Methylation Kits (Zymo Research, Irvine, CA, USA). DNAm was quantified using the Infinium Human MethylationEPIC BeadChip (Illumina Inc., San Diego, CA, USA). To account for possible technical batch effects, DNA samples from different outcomes were randomized across beadchips. Quality control was done using the CHAMP R package (Tian et al., 2017). Methylation data were cleaned by removing probes with low detection (p > 0.1) or with beadcount < 3 in at least 5% of samples; previously identified cross-reactive and polymorphic probes; probes containing SNPs that overlap with CpG or single base extension sites, or when the CpG probe was located near short insertions or deletions; and on probes located on X and Y chromosomes. The data were visually inspected and any remaining batch effects were removed using ComBat (Johnson et al., 2007).

In all cohorts, QC-processed DNAm beta values were used to calculate GrimAge, PC GrimAge, PC PhenoAge, and BrainAge (from Systems Age) clocks. GrimAge was calculated using the two methods: 1) the original method described by Lu and colleagues (Lu et al., 2019) and 2) the PC-based approach (Higgins-Chen et al., 2022). PhenoAge (Levine et al., 2018) was estimated with the PC-based approach (Higgins-Chen et al., 2022). BrainAge was calculated using the Systems Age clock following the methods described in Sehgal and colleagues (2025) (Sehgal et al., 2025). EAA was calculated as the residual between DNAm age estimates and chronological age.

2.6. Statistical Analyses

Within each cohort, linear regression models tested the associations between EAA, BrainAge acceleration (both MRI- and DNAm-based), and lifetime PTSD. To test whether PTSD moderates the association between EAA and BrainAge acceleration, an interaction term (lifetime PTSD × EAA) was included. All models were adjusted for sex (if applicable) and the time gap between the blood draw/PTSD assessment and brain scan (only applicable for MIRECC studies). Post-hoc sensitivity analysis explored the possible confounding effects of blood cell composition by including DNAm derived blood cell proportions (i.e., CD8T, CD4T, NK, B cell, and monocyte cell proportions) as covariates. Cohort-level regression coefficients (β) and standard errors (SE) were combined using inverse-variance weighted (IVW) meta-analysis via the metagen function in the R package meta (Balduzzi et al., 2019). Fixed-effect models were used as the primary analytic approach, given the small number of studies (k = 4) and the a priori expectation that the cohorts were estimating a common underlying effect. Between-study heterogeneity was assessed using Cochran’s Q statistic, with p < 0.05 indicating significant heterogeneity. When significant heterogeneity was detected, random-effects models were conducted as sensitivity analyses using restricted maximum likelihood (REML) estimation of the between-study variance (τ2). To account for multiple testing across the four epigenetic clock predictors within each outcome, Bonferroni correction was applied.

3. Results

3.1. Correlations between DNAm-based and MRI-based age and age acceleration estimates

Chronological age was strongly correlated with all epigenetic age and brain age estimates (0.50 < rmeta < 0.95, pmeta < 0.001). All epigenetic age estimates showed intercorrelations (0.60 < rmeta < 0.96, pmeta < 0.001, Figure 1A). Notably, the weakest correlation was between MRI-based BrainAge and DNAm-based BrainAge estimates (rmeta = 0.42, pmeta < 0.001, Figure 1A).

Figure 1. Correlations among epigenetic, brain, and chronological age metrics and their acceleration measures.

Figure 1.

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Heatmap of pairwise Pearson correlations among (A) Chronological age, GrimAge, PC GrimAge, PhenoAge, BrainAge (DNAm), and BrainAge (MRI) estimates. (B) GrimAge acceleration, PC GrimAge acceleration, PhenoAge acceleration, BrainAge (DNAm) acceleration, and BrainAge (MRI) acceleration estimates. Age acceleration measures represent residuals from regressing age estimates on chronological age. Cells display Pearson correlation coefficients (r) with associated p-values. Color intensity reflects the magnitude and direction of correlations (blue = positive, red = negative). Diagonal elements represent self-correlations (r = 1).

The EAA estimates also showed strong intercorrelations (0.43 < rmeta < 0.84, pmeta < 0.001, Figure 1B). However, MRI-based BrainAge acceleration was not correlated with any of the EAA estimates, including DNAm-based BrainAge acceleration (rmeta = 0.03, pmeta = 0.56, Figure 1B), indicating that epigenetic and neuroimaging-based measures of accelerated aging may capture distinct biological processes.

3.2. Associations between epigenetic age acceleration, DNAm-based BrainAge acceleration, and lifetime PTSD

PTSD diagnosis was not significantly associated with any of the EAA estimates in main effect analyses (Supplementary Table S9). However, PTSD significantly moderated the association between PC GrimAge acceleration and DNAm-based BrainAge acceleration (Table 2, Figure 2A, Supplementary Figure S1). Specifically, the positive association between PC GrimAge acceleration and BrainAge acceleration observed in trauma-exposed controls was attenuated in individuals with lifetime PTSD. Full meta-analysis outputs for each clock and cohort-level summary statistics are provided in Supplementary Tables S10 – S13. Sensitivity analyses adjusting for blood cell composition attenuated these associations (Supplementary Tables S14 – S17).

Table 2.

Interaction Between PTSD and Epigenetic Age Acceleration in Relation to BrainAge Acceleration

BrainAge (DNAm) acceleration as the outcome
Age Acceleration × PTSD interaction SMD [95% CI] z p-value Cochran's Q
GrimAge acceleration −0.28 [−0.83; 0.26] −1.02 0.31 2.39
PC GrimAge acceleration −0.84 [−1.51; −0.17] −2.47 0.01 † 3.16
PC PhenoAge acceleration −0.13 [−0.53; 0.27] −0.63 0.53 2.80
BrainAge (MRI) acceleration −0.09 [−0.47; 0.30] −0.44 0.66 4.50
BrainAge (MRI) acceleration as the outcome
EAA × PTSD interaction SMD [95% CI] z p-value Cochran's Q
GrimAge acceleration −0.40 [−0.74; −0.07] −2.37 0.02 9.10*
PC GrimAge acceleration −0.46 [−0.93; 0.01] −1.94 0.05 6.01
PC PhenoAge acceleration −0.20 [−0.49; 0.08] −1.43 0.15 3.24
BrainAge (DNAm) acceleration −0.04 [−0.15; 0.08] −0.65 0.51 3.44
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Results from inverse-variance weighted meta-analysis of PTSD × EAA interactions predicting BrainAge acceleration. Bold p-values indicate p < 0.05. SMD = standardized mean difference; Q = Cochran’s Q statistic for heterogeneity;

*

indicates p < 0.05 in Cochran’s Q statistic for heterogeneity.

†

indicates significance after a Bonferroni correction for four tests.

Figure 2. Forest plots of PTSD × epigenetic age acceleration interaction effects on brain age acceleration.

Figure 2.

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(A) DNAm-based brain age acceleration predicted by PC GrimAge acceleration × PTSD. (B) MRI-based brain age acceleration predicted by GrimAge acceleration × PTSD. (C) MRI-based brain age acceleration predicted by PC GrimAge acceleration × PTSD. (D) MRI-based brain age acceleration predicted by GrimAge acceleration × PTSD, adjusted for estimated cell composition. Forest plots display standardized mean differences (SMDs) and 95% confidence intervals for the interaction between PTSD and epigenetic age acceleration (EAA) predicting brain age acceleration across models. Positive SMDs indicate stronger positive associations between EAA and brain age acceleration in individuals with PTSD, whereas negative values indicate weaker or inverse associations. Error bars represent the 95% confidence interval, and the center of the error bars represent effect sizes in each cohort. Filled circles indicate a significant association (p < 0.05). The vertical line indicates an effect size of 0.

Stratified analyses confirmed this moderation pattern (Table 3). Among trauma-exposed controls, PC GrimAge acceleration showed a strong positive association with DNAm-based BrainAge acceleration, suggesting that individuals with accelerated mortality-related aging also showed accelerated epigenetic brain aging. While this association remained significant among individuals with lifetime PTSD, it was notably weaker, with a 32% reduction in the strength of association.

Table 3.

Association Between Epigenetic Age Acceleration and BrainAge Acceleration in Lifetime PTSD Cases and Trauma-Exposed Controls

BrainAge (DNAm) acceleration as the outcome – PTSD Cases
Age Acceleration SMD [95% CI] z p-value Cochran's Q
GrimAge acceleration 1.18 [0.87; 1.49] 7.37 1.67E-13 † 2.06
PC GrimAge acceleration 1.71 [1.29; 2.13] 8.00 1.33E-15 4.30
PC PhenoAge acceleration 1.56 [1.29; 1.84] 11.20 4.26E-29 7.33
BrainAge (MRI) acceleration 0.05 [−0.23; 0.32] 0.33 0.74 1.61
BrainAge (DNAm) acceleration as the outcome – Trauma-exposed Controls
Age Acceleration SMD [95% CI] z p-value Cochran's Q
GrimAge acceleration 1.42 [0.98; 1.86] 6.29 3.16E-10 † 0.66
PC GrimAge acceleration 2.52 [2.02; 3.03] 9.76 1.76E-22 † 2.30
PC PhenoAge acceleration 1.59 [1.32; 1.86] 11.51 1.21E-30 † 2.05
BrainAge (MRI) acceleration 0.17 [−0.09; 0.43] 1.29 0.20 6.33
BrainAge (MRI) acceleration as the outcome – PTSD Cases
EAA SMD [95% CI] z p-value Cochran's Q
GrimAge acceleration −0.12 [−0.31; 0.08] −1.18 0.24 1.11
PC GrimAge acceleration −0.18 [−0.46; 0.10] −1.26 0.21 1.31
PC PhenoAge acceleration 0.03 [−0.14; 0.20] 0.32 0.75 2.08
BrainAge (DNAm) acceleration 0.00 [−0.07; 0.07] 0.04 0.97 1.37
BrainAge (MRI) acceleration as the outcome – Trauma-exposed Controls
EAA SMD [95% CI] z p-value Cochran's Q
GrimAge acceleration 0.35 [0.07; 0.63] 2.43 0.01 † 12.03*
PC GrimAge acceleration 0.31 [−0.08; 0.70] 1.55 0.12 9.24*
PC PhenoAge acceleration 0.26 [0.04; 0.48] 2.28 0.02 7.56
BrainAge (DNAm) acceleration 0.04 [−0.05; 0.14] 0.84 0.40 7.74
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Results from inverse-variance weighted meta-analysis of the association between epigenetic age acceleration (EAA) and BrainAge acceleration, stratified by PTSD status. Bold p-values indicate p < 0.05. SMD = standardized mean difference; Q = Cochran’s Q statistic for heterogeneity;

*

indicates p < 0.05 in Cochran’s Q statistic for heterogeneity.

†

indicates significance after a Bonferroni correction for four tests.

Examination of main effects revealed that both predictors independently contributed to the model. PC GrimAge acceleration showed a robust positive association with DNAm-based BrainAge acceleration across the full sample (Supplementary Table S11). Additionally, PTSD diagnosis was associated with lower DNAm-based BrainAge acceleration, suggesting a complex relationship whereby PTSD cases showed relatively slower epigenetic brain aging when accounting for mortality-related EAA.

3.3. Associations between epigenetic age acceleration, MRI-based BrainAge acceleration, and lifetime PTSD

Similar to the DNAm findings, PTSD diagnosis was not directly associated with MRI-based BrainAge acceleration (Supplementary Table S9). However, PTSD significantly moderated the relationship between GrimAge acceleration and MRI-based BrainAge acceleration (Figure 2B, Supplementary Figure S2), with some evidence for a similar pattern with PC GrimAge acceleration (Table 2, Figure 2C, Supplementary Figure S3). These results indicate that PTSD disrupted the associations between mortality-related epigenetic aging and structural brain aging. However, the findings regarding GrimAge acceleration should be interpreted with caution due to significant heterogeneity between cohorts and the substantial attenuation of the GrimAge acceleration × PTSD interaction in random-effects models (SMD = −0.47 [95% CI: −1.11, 0.18], pmeta = 0.16). Full meta-analysis outputs for each clock and cohort-level summary statistics and sensitivity analyses are provided in Supplementary Tables S18 – S25. Notably, the GrimAge acceleration × PTSD interaction effect remained significant in the sensitivity analysis that adjusted for blood cell composition (SMD = −0.36 [95% CI: −0.70; −0.02], pmeta = 0.04), with substantial reductions in heterogeneity between cohorts (Q = 6.36, p = 0.10, Figure 1D, Supplementary Table S22).

Stratified analyses revealed that GrimAge acceleration was positively associated with MRI-based BrainAge acceleration among trauma-exposed controls, but not in PTSD cases, indicating a complete decoupling of epigenetic-based mortality risk signals from structural brain aging in those with a history of PTSD.

4. Discussion

The current study explored the degree to which PTSD influences the coupling between epigenetic and neuroanatomical markers of aging, using multiple epigenetic clocks (GrimAge, PC GrimAge, and PC PhenoAge) and two complementary brain aging measures (one derived from blood-based DNAm [Systems Age clock] and the other from structural MRI [BrainageR]). Lifetime PTSD diagnosis was not associated with any of the epigenetic or neuroimaging-based age acceleration estimates. Our findings indicated a significant interaction of lifetime PTSD diagnosis and mortality-related EAA in predicting BrainAge acceleration. Specifically, PTSD moderated the association between PC GrimAge acceleration and DNAm-based BrainAge acceleration, and between GrimAge acceleration and MRI-based BrainAge acceleration. In both cases, the positive association of EAA with BrainAge acceleration in trauma-exposed controls was attenuated or absent among individuals with lifetime PTSD. Notably, PC PhenoAge acceleration and DNAm-based BrainAge acceleration did not show significant interactions with PTSD, suggesting specificity of the disruption to mortality-related epigenetic clocks.

4.1. Biological Age Comparison

Various studies have systematically compared biological aging measures in community samples (Belsky et al., 2018; Jansen et al., 2021; Kim et al., 2017; Murabito et al., 2018), and results suggest minimal overlap between biological clocks derived from different physiological markers and data types (Hägg et al., 2019). Consistent with this, we observed that while EAA estimates were strongly intercorrelated (0.43 < rmeta < 0.84), MRI-based BrainAge acceleration was not correlated with any EAA measure, including the DNAm-based BrainAge acceleration (rmeta = 0.03, p = 0.56). The lack of correlation between DNAm-based and MRI-derived brain age acceleration underscores that these measures capture fundamentally different aspects of brain aging: one reflecting peripheral epigenetic signals related to brain health (Sehgal et al., 2025), the other reflecting structural neuroanatomical changes (Cole et al., 2018). Brain aging and/or epigenetic aging are associated with trauma (Bourassa et al., 2024b; Bourassa and Sbarra, 2024; Miller and Sadeh, 2014; Zannas et al., 2023), PTSD (Bourassa et al., 2024b; Clausen et al., 2022; Lohr et al., 2015; Wolf et al., 2018a), cognitive functioning (Zheng et al., 2022), chronic disease (Bourassa et al., 2024a), and mortality (Bourassa et al., 2024a), among many other disease states. Similarly, brain phenotypes are associated with GrimAge in healthy and cognitively impaired populations (Whitman et al., 2024). However, to the best of our knowledge, no studies have tested associations between BrainAge and epigenetic age acceleration in the context of PTSD. The present study addressed this gap using multiple epigenetic clocks and two complementary brain aging measures. Our results demonstrate that the relationship between epigenetic and brain aging biomarkers is not fixed, but context-dependent, modulated both by PTSD status and by the modality through which brain aging is assessed. This has practical implications: in non-clinical trauma-exposed populations, epigenetic and brain aging measures appear to index a shared underlying process, supporting their combined use in mortality and disease-risk prediction. In PTSD, however, the partial or complete uncoupling of these measures suggests that reliance on a single biological aging biomarker may underestimate or mischaracterize aging trajectories. There is further need for multi-modal comparisons in clinical populations, particularly for stress-based psychiatric disorders like PTSD with elevated mortality and morbidity risk.

4.2. Sequelae or Vulnerability

PTSD attenuated the associations between PC GrimAge acceleration and DNAm-based BrainAge acceleration, as well as GrimAge acceleration and MRI-based BrainAge acceleration, suggesting a disruption in the typical relationship between mortality and brain-related biological aging markers. These findings support previous work linking the experience of trauma to accelerated epigenetic (Bourassa et al., 2024b; Miller and Sadeh, 2014; Wolf et al., 2018b) and brain aging (Clausen et al., 2022). This disruption could reflect differential impacts of PTSD-related stress, compared to trauma exposure without PTSD development, on neuroanatomical versus systemic aging. Several explanations for this differential effect are possible: (1) accelerated brain aging may be a sequela of acute and chronic phase exposure to traumatic stress, or (2) more rapid brain aging may represent a vulnerability to developing PTSD in the setting of trauma exposure, or (3) both #1 and #2 are acting in concert.

4.3. Sequelae of PTSD

There is ample evidence to support the first scenario of sequelae. The pathophysiology of PTSD accelerates aging through interacting immune, inflammatory, and oxidative stress pathways (Miller et al., 2018; Katrinli et al., 2022; Yang et al., 2025). Ample evidence supports immune-related or inflammatory etiology for PTSD (Cohen et al., 2011; Eraly et al., 2014; Pervanidou et al., 2007), PTSD as a precursor to inflammation (Solomon et al., 2017; Toft et al., 2018), or a bidirectional relationship between PTSD and inflammation (Muniz Carvalho et al., 2021; Sumner et al., 2018). There are multiple proposed mechanisms connecting PTSD and the immune system (Katrinli et al., 2022; Yang et al., 2025). Alterations in peripheral immune markers (elevated inflammatory markers and decreased levels of anti-inflammatory markers) create a positive feedback loop to promote inflammation through CRP-mediated complement system activation (Eraly et al., 2014; Hori and Kim, 2019; Passos et al., 2015; Yang and Jiang, 2020; Yuan et al., 2019). Stress exposure triggers secretion of the corticotrophin-releasing hormone (CRH) and activates the HPA axis, contributing downstream to glucocorticoid resistance, increased sympathetic and decreased parasympathetic nervous system activity (Herman et al., 2012; Hori and Kim, 2019; Michopoulos et al., 2017; Sternberg, 2006; Tracey, 2009). Pro-inflammatory immune alterations also induce changes in neurotransmitter signaling that increases the risk of PTSD-associated fear and anxiety (Michopoulos et al., 2017).

The relevance of immune mechanisms to the present findings is underscored by the clock specificity of our results. GrimAge incorporates DNAm surrogates for several plasma proteins with immune and inflammatory functions, including PAI-1 (plasminogen activator inhibitor-1) and TIMP-1 (tissue inhibitor of metalloproteinases 1) (Lu et al., 2019), both of which have been independently linked to PTSD and inflammatory processes (Wolf et al., 2018a; Yang et al., 2021). The selective moderation by GrimAge clocks, rather than PhenoAge, is consistent with the hypothesis that PTSD disrupts systemic–brain aging coupling specifically through inflammatory and metabolic pathways that are captured by GrimAge’s composite biomarker architecture.

The neuroendocrine stress response has downstream consequences for the brain that may not be fully captured by peripheral epigenetic measures. Peripheral inflammation penetrates the blood brain barrier (BBB) and affects brain function through cytokine activity, with effects concentrated in brain regions implicated in PTSD symptomology, including the amygdala, hippocampus, medial prefrontal cortex, anterior cingulate cortex, and insula (Felger, 2018; Katrinli et al., 2022; Miller et al., 2013). Within these regions, microglia shift from homeostatic functions to producing pro- and anti-inflammatory cytokines that modulate the brain’s stress response (Katrinli et al., 2022). Concurrently, oxidative stress, arising from an imbalance between reactive oxygen species and antioxidant defenses, contributes to cell degeneration, blood-brain barrier disruptions, and altered brain morphology (Miller and Sadeh, 2014; Schiavone et al., 2013; Uttara et al., 2009), with chronic inflammation and oxidative damage operating synergistically through feed-forward signaling cascades (Miller et al., 2018). Critically, these brain-specific consequences of PTSD-related stress (e.g., microglial activation, region-specific neurodegeneration, and BBB compromise) may diverge from the systemic inflammatory and metabolic processes indexed by blood-based epigenetic clocks.

Distinct biological pathways of aging in the periphery versus the brain are difficult to discern since levels of inflammatory markers in peripheral blood do not accurately reflect central nervous system neuroimmune levels, such as C-reactive protein. Only levels of inflammatory markers IL-6 and TNFα are significantly correlated between cerebro-spinal fluid (CSF) and peripheral circulation (Gigase et al., 2023). Notably, bidirectional interactions between peripheral and central inflammatory processes occur in the gastrointestinal tract via cross-talk between the gut microbiota and the local immune environment. Environmental stressors disturb the homeostasis between microbiota and gut that signal the brain through multiple routes of the gut–brain axis, which elicit stress responses and perturbations of brain function (Dhabhar, 2024; Holzer et al., 2017). The poor correlation between peripheral and central inflammatory markers is consistent with our finding that epigenetic clocks derived from blood and MRI-based brain aging measures capture distinct aspects of the aging process, and may partly explain why PTSD differentially disrupts the coupling between these systems. Future research that builds on evidence of cellular degradation in PTSD through OXS, INF, and other mechanisms, ought to compare degradation rates between the periphery and central nervous system, and study its effects on aging and lifespan to characterize differential brain aging versus epigenetic aging in PTSD.

4.4. Vulnerability to PTSD

Although the present study did not detect accelerated brain or epigenetic aging in PTSD cases relative to controls, the observed decoupling of epigenetic and structural brain aging raises the question of whether pre-existing neuroanatomical features may confer vulnerability to PTSD. Various factors modify vulnerability and resilience to psychiatric illness and physical conditions, such as epigenetic mechanisms that influence cellular function (DNA methylation, histone modifications, non-coding RNA), childhood environment, and premorbid brain morphometry (Dudley et al., 2011). Hippocampal volume in discordant twin studies was not the outcome of trauma, but rather a risk factor for PTSD development and chronicity (Apfel et al., 2011; Gilbertson et al., 2002; Kremen et al., 2013; van Rooij et al., 2015; Wignall et al., 2004). The ENIGMA-PTSD working group found smaller hippocampal volume in PTSD (Logue et al., 2018) and increased post-traumatic stress (PTS) symptomatology over 24 years (Franz et al., 2020). These results suggest a positive feedback loop of smaller hippocampal volume and PTSD chronicity, whereby smaller hippocampal volume serves as (1) a risk factor for PTSD, (2) an outcome of PTSD, and (3) a mediator of PTS symptom intensity (Brewin et al., 2010; Fraser et al., 2015; Rutter, 2012). A small pre-existing hippocampus, whether inherited or a result of the environment, may be associated with deficits in contextual processing and cognition that negatively impact stress responses (Acheson et al., 2012). If such structural vulnerabilities also contribute to the neuroanatomical brain aging (captured by BrainageR), they could partly explain why PTSD disrupts the epigenetic–brain aging coupling observed in controls, as the structural brain aging signal in PTSD cases may reflect premorbid neuroanatomical variation rather than concurrent systemic aging processes. However, this interpretation remains speculative and would require longitudinal designs to test directly.

4.5. Limitations

Caution is warranted when interpreting the present findings that were derived from cross-sectional data. While there was a relatively balanced distribution of values for all demographic and clinical features across sites and diagnostic groups (lifetime PTSD cases and controls), the modest sample size limited statistical power to detect small effects. Significant between-cohort heterogeneity for the GrimAge acceleration × PTSD interaction predicting MRI-based BrainAge acceleration limits the generalizability of this specific finding, although heterogeneity was substantially reduced in sensitivity analyses adjusting for blood cell composition. The PC GrimAge × PTSD interaction predicting DNAm-based BrainAge showed no significant heterogeneity, providing more consistent evidence across cohorts. Additionally, the biological clocks have limitations. BrainageR was trained on chronological age in a sample of healthy individuals (n = 3,377) from seven cohorts that were not necessarily representative of the present sample. By contrast, GrimAge clocks were trained on time-to-death. While the different training methods of the biological clocks may be a limitation, they are all predictive of mortality and age-related disorders (Cole et al., 2018; Higgins-Chen et al., 2022; Lu et al., 2019; Sehgal et al., 2025), and therefore seem appropriate for age acceleration predictions in PTSD. Our study was conducted in lifetime PTSD, so our findings may not generalize to current PTSD. Furthermore, participants with lifetime PTSD may have had vastly different symptom burdens over the lifetime, potentially introducing heterogeneity within the patient population. We were unable to adjust for age of onset or duration of PTSD symptoms, as not all PTSD assessments collected these data. Future studies with harmonized PTSD assessments that include onset timing and chronicity measures would help address this limitation.

4.6. Conclusion and Future Directions

PTSD disrupts the coupling between mortality-related epigenetic aging and brain aging processes. Aging remains a multifaceted and complex process that is differentially influenced by PTSD across different biological markers sourced from different organ systems. The specificity of these effects to GrimAge-related, but not PhenoAge-related, clocks points to inflammatory and metabolic pathways as key mediators of this disruption. The use of PCA-based clocks, the Systems Age DNAm-based BrainAge estimate, and the examination of both epigenetic and structural brain aging outcomes extends prior work by demonstrating that the PTSD-related decoupling is not an artifact of a single clock or measurement modality, but a reproducible phenomenon across complementary biological aging metrics. Large, longitudinal clinical neuroimaging and epigenetic studies are needed to further characterize accelerated aging using biological clocks derived from varied algorithms, training data, biological and brain markers, and training labels (e.g. chronological age, functional age, time-to-death).

Supplementary Material

1
2

Highlights.

  • DNAm- and MRI-based BrainAge acceleration capture distinct aging processes

  • PTSD moderates GrimAge and BrainAge acceleration coupling across four cohorts, PTSD disrup

Acknowledgements

Veteran research was supported by the VA-Mid-Atlantic Mental Illness Education and Clinical Center and National Institute of Mental Health [R01 MH111671]. The Grady Trauma Project was supported by the National Institutes of Mental Health [R01 MH071537, R21 MH098212]. Support was also received from Emory and Grady Memorial Hospital General Clinical Research Center [UL1TR002378], NIH National Center for Research Resources [M01RR00039], National Center for Advancing Translational Sciences of the NIH [UL1TR000454]. The AURORA project was supported by the NIMH [U01MH110925], the US Army MRMC, One Mind, and The Mayday Fund. This work was also supported by a NARSAD Young Investigator and Foundation of Hope for Research and Treatment of Mental Illness grants (ASZ).

Disclosures

Dr. Koenen’s research has been supported by the Robert Wood Johnson Foundation, the Kaiser Family Foundation, the Harvard Center on the Developing Child, Stanley Center for Psychiatric Research at the Broad Institute of MIT and Harvard, the National Institutes of Health, One Mind, The Anonymous Foundation, and Cohen Veterans Bioscience. She has been a paid consultant for Baker Hostetler, Discovery Vitality, and the Department of Justice. She has been a paid external reviewer for the Chan Zuckerberg Foundation, the University of Cape Town, and Capita Ireland. She has had paid speaking engagements in the last three years with the American Psychological Association, European Central Bank. Sigmund Freud University-Milan, Cambridge Health Alliance, and Coverys. She receives royalties from Guilford Press and Oxford University Press.

Dr. McLean served as a consultant for Walter Reed Army Institute for Research and for Arbor Medical Innovations.

Dr. Ressler has performed scientific consultation for Bioxcel, Bionomics, Acer, and Jazz Pharm; serves on Scientific Advisory Boards for Sage, Boehringer Ingelheim, Senseye, and the Brain Research Foundation, and has received sponsored research support from Alto Neuroscience.

The remaining authors declare no competing interests.

Footnotes

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CRediT Author Contribution Statements:

JB: Formal Analysis, Data Curation, Project administration, Writing - Original Draft. AKD: Writing - Original Draft. AH: Data Curation, Project administration, Writing - Review & Editing. MEG: Formal Analysis, Data Curation, Writing - Review & Editing. AAK: Resources, Writing - Review & Editing. ANC: Formal Analysis, Data Curation, Writing - Review & Editing. NGH: Writing - Review & Editing. KJR: Resources, Writing - Review & Editing. SJHR: Investigation, Writing - Review & Editing. JSS: Investigation, Writing - Review & Editing. AKS: Resources, Conceptualization, Writing - Review & Editing. ASZ: Investigation, Writing - Review & Editing. SDL: Formal Analysis, Data Curation. TJ: Resources, Writing - Review & Editing. KCK: Resources, Writing - Review & Editing. SAM: Resources, Writing - Review & Editing. ARR: Formal Analysis, Writing - Review & Editing. EKW: Formal Analysis, Data Curation, Writing - Review & Editing. RAM: Resources, Conceptualization, Supervision, Writing - Review & Editing. SK: Conceptualization, Methodology, Supervision, Formal Analysis, Writing - Original Draft.

Cohort-level summary statistics are available within Supplementary Data. Individual-level data from the cohorts or cohort-level summary statistics will be made available upon request with the agreement of the cohort PIs. The raw DNA methylation data for the GTP cohort is available in the Gene Expression Omnibus database with the accession code GSE132203.

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Supplementary Materials

1
NIHMS2183889-supplement-1.docx (3.6MB, docx)
2
NIHMS2183889-supplement-2.xlsx (39KB, xlsx)

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