Biological markers of aging across the menopause transition: current evidence.

ABSTRACTTelomere length and DNA methylation (DNAm) clocks serve as markers of biological aging and have been linked to mortality risk. This study applies additive Bayesian networks (ABNs) to examine associations between DNAm clocks, telomere length, and mortality, with a focus on racial and sex differences in aging. Data from three US cohorts—NHANES (n = 2522), HRS (n = 1029), and HANDLS (n = 92–470)—were analyzed using correlation matrices, Cox models, ABNs, and generalized structural equation models (GSEM) with mortality from the National Death Index. Epigenetic clocks, particularly GrimAgeEAA, HannumAgeEAA, and DunedinPoAM (or DunedinPACE), were stronger mortality predictors than telomere length. ABNs highlighted key relationships, consistently linking age and GrimAgeEAA to mortality in NHANES and HRS. GSEM models derived from ABNs indicated an inverse association between female sex and GrimAgeEAA in NHANES (β = −0.500) and HRS (β = −0.563), suggesting slower biological aging in women, although GrimAge clock incorporates sex in its definition. GrimAgeEAA strongly predicted mortality (LnHR, β ± SE of +0.476 ± 0.0393 in NHANES and +0.511 ± 0.0775 in HRS). Non‐Hispanic Black adults exhibited accelerated aging via DunedinPoAM, partially mediating their higher mortality risk. Hispanic adults in NHANES had unique associations with PhenoAgeEAA (β = +0.197), a mortality predictor. DNAm clocks, particularly GrimAgeEAA, outperform telomere length in predicting mortality. Second‐generation epigenetic aging markers offer insights into demographic disparities in aging and mortality, with ABNs revealing complex interrelations among aging biomarkers, sex, race, and mortality risk.

Exploring the Unseen Effects of Epigenetic Age Acceleration in Low-Risk MDS: Associations with Unique Cytokine Profiles

Clonal hematopoiesis of indeterminate potential (CHIP) is a common precursor state for blood cancers that most frequently occurs due to mutations in the DNA‐methylation modifying enzymes DNMT3A or TET2. We used DNA‐methylation array and whole‐genome sequencing data from four cohorts together comprising 5522 persons to study the association between CHIP, epigenetic clocks, and health outcomes. CHIP was strongly associated with epigenetic age acceleration, defined as the residual after regressing epigenetic clock age on chronological age, in several clocks, ranging from 1.31 years (GrimAge, p < 8.6 × 10−7) to 3.08 years (EEAA, p < 3.7 × 10−18). Mutations in most CHIP genes except DNA‐damage response genes were associated with increases in several measures of age acceleration. CHIP carriers with mutations in multiple genes had the largest increases in age acceleration and decrease in estimated telomere length. Finally, we found that ~40% of CHIP carriers had acceleration >0 in both Hannum and GrimAge (referred to as AgeAccelHG+). This group was at high risk of all‐cause mortality (hazard ratio 2.90, p < 4.1 × 10−8) and coronary heart disease (CHD) (hazard ratio 3.24, p < 9.3 × 10−6) compared to those who were CHIP−/AgeAccelHG−. In contrast, the other ~60% of CHIP carriers who were AgeAccelHG− were not at increased risk of these outcomes. In summary, CHIP is strongly linked to age acceleration in multiple clocks, and the combination of CHIP and epigenetic aging may be used to identify a population at high risk for adverse outcomes and who may be a target for clinical interventions.

BackgroundAtopic dermatitis is a chronic inflammatory skin disease resulting from the complex interplay of genetic and environmental factors, meriting exploration using temporally-dynamic biomarkers. DNA methylation-based algorithms have been trained to accurately estimate biological age, and deviation of predicted age from true age (epigenetic age acceleration) has been implicated in several inflammatory diseases, including asthma. ObjectiveTo determine the role of epigenetic and biological aging, telomere length, and epigenetically-inferred abundance of seven inflammatory biomarkers in atopic dermatitis. MethodsWe performed DNA methylation-based analyses in a pediatric atopic dermatitis cohort (n=24, mean age 2.56±0.28y) and age-matched healthy subjects (n=24, mean age 2.09±0.15y) derived from blood, using five validated algorithms that assess epigenetic (Horvath, Skin&Blood) and biological age (PhenoAge, GrimAge), telomere length (TelomereLength), and inflammatory biomarker levels. ResultsEpigenetic and biological age, but not telomere length, were accelerated in atopic dermatitis patients for four algorithms: Horvath (+0.88 years; 95%CI 0.33-1.4; p=2.3x10-3), Skin&Blood (+0.95 years; 95%CI 0.67-1.2; p=1.8x10-8), PhenoAge (+8.2 years; 95%CI 3.4-13.0; p=1.3x10-3), and GrimAge (+1.8 years 95%CI 0.22-3.3; p=0.026). Moreover, patients had increased levels of beta-2-microglobulin (+47,584.4 ng/ml; p=0.029), plasminogen activation inhibitor 1 (+3,432.9 ng/ml; p=1.1x10-5) and cystatin C (+31,691 ng/ml; p=4.0x10-5), while levels of tissue inhibitor metalloproteinase 1 (-370.7 ng/ml; p=7.5x10-4) were decreased versus healthy subjects. ConclusionDNA methylation changes associated with epigenetic and biological aging, and inflammatory proteins appear early in life in pediatric atopic dermatitis and may be relevant clinical biomarkers of pathophysiology.

Study question Can a simplified and validated peripheral epigenetic clock, that test the methylation pattern of CpG sites at five genes, predict in vitro fertilization (IVF) success? Summary answer Epigenetic clocks may serve as reliable predictors of IVF success, particularly in women aged 31–35, and they could enhance the accuracy of multiparametric prediction models. What is known already In IVF, finding reliable success predictors is challenging. In recent years, the concept of fertility as a ‘sixth vital sign’ or as a ‘proxy for overall health’ has gained traction, suggesting that fertility may reflect a broader set of factors, including genetic predisposition, environmental exposures, and lifestyle influences. Within this context, epigenetic mechanisms are emerging as promising biomarkers of biological age, offering more accuracy than chronological age alone. Epigenetic clocks, which measure biological age through DNA methylation, could show potential as IVF success predictors, though their role in reproductive outcomes requires further research. Study design, size, duration The project was a single-center, observational prospective study involving 379 women who underwent IVF between 03-2022 and 06-2023. The primary outcome was to assess the predictive accuracy of the epigenetic clock on live birth (LB) rate through multivariate and subgroup analyses. We investigated whether women with a LB after IVF had a younger epigenetic age or exhibited epigenetic deceleration compared to those without, and whether this difference provided additional predictive value beyond ovarian reserve markers. Participants/materials, setting, methods Women of reproductive age who underwent IVF treatment were recruited without age restrictions. Exclusion criteria included severe male factor infertility and any systemic diseases that could affect pregnancy outcomes. A whole blood sample in EDTA was collected prior to ovarian stimulation protocols. The “Zbiec-Piekarska2” epigenetic clock model was applied, analyzing the methylation status of five key genes associated with biological aging (C1orf132, ELOVL2, KLF14, FHL2, TRIM59) from DNA extracted from peripheral leukocytes. Main results and the role of chance Among 379 women, those with a LB (n = 204) were younger, had better ovarian reserve markers, and retrieved more oocytes, compared to those without a LB (n = 175). Women with LB were epigenetically younger (36 ± 5 vs. 39 ± 5 years, p &amp;lt; 0.001), and epigenetic age showed moderate predictive power (AUC 0.663, 95%CI: 0.599-0.726). After adjusting for ovarian reserve parameters (follicular stimulating hormon-FSH, antral follicular count-AFC) both epigenetic age and epigenetic age acceleration differed significantly: the adjusted odds-ratio (adjOR) for LB per year of age was 0.90 (95%CI: 0.86-0.95, p &amp;lt; 0.001) and 0.92 (95%CI: 0.87-0.98, p = 0.01) respectively, suggesting that IVF success was more likely in epigenetically younger women, beyond their ovarian reserve. This difference was lost in subgroup analysis based on infertility cause. When comparing epigenetic age performance with other parameters across chronological age groups, epigenetic age and epigenetic age acceleration were the most accurate predictors in women aged 31–35, with AUCs of 0.689 (95% CI: 0.564-0.814) and 0.695 (95% CI: 0.575-0.816), respectively. Multiparametric models combining epigenetic age with ovarian reserve markers slightly improved predictive accuracy: AUC was of 0.692 (95% CI: 0.637-0.747) with AFC, and of 0.693 (95% CI: 0.635-0.750) with AMH. Limitations, reasons for caution The subgroup analyses based on infertility diagnosis may have been underpowered due to small sample sizes. Larger studies are needed, particularly for idiopathic infertility, where epigenetic disruption may play a role. Additionally, the use of different epigenetic clocks, especially fertility-specific models, could enhance performance in the reproductive field. Wider implications of the findings Our findings highlight the potential of epigenetic clocks as IVF success predictors, particularly in specific age-groups and within multiparametric models. They contribute to the growing evidence of the key role of epigenetic mechanisms in reproductive aging. Further research could integrate epigenetic clocks into fertility assessments, enabling personalized approaches and tailored counseling. Trial registration number No

Adverse childhood experiences (ACEs), potentially traumatic experiences occurring before the age of 18 years, are associated with epigenetic aging later in life and may be transmitted across generations. To test evidence of the transmission of biological embedding of life experience across generations by analyzing maternal ACEs and epigenetic clocks measured in mothers during pregnancy and in their children at birth. For this cross-sectional study, data from the Accessible Resource for Integrated Epigenomic Studies (ARIES) substudy of the Avon Longitudinal Study of Parents and Children (ALSPAC) were analyzed. The ALSPAC study recruited 14 541 women who gave birth in the Avon Health District in the UK between April 1, 1991, and December 31, 1992. The ARIES substudy comprised 1018 mother-offspring dyads based on the availability of DNA samples profiled in 2014. Epigenetic age was estimated using DNA methylation-based epigenetic clocks (including Horvath, Hannum, GrimAge, PhenoAge, and DunedinPACE) in mothers during pregnancy and the Knight and Bohlin cord blood epigenetic clocks in newborns. Analyses were performed between October 1, 2022, and November 30, 2023. A composite measure of maternal ACEs was the primary exposure in both maternal and offspring models; as a secondary analysis, individual ACEs were measured separately. The Edinburgh Postnatal Depression Scale (EPDS) was used to investigate depression during pregnancy as an exposure. Changes in epigenetic age acceleration (EAA) were investigated as the primary outcome in maternal models during pregnancy. Changes in epigenetic gestational age acceleration (GAA) were the primary outcome in offspring analyses. Linear regression analyses were used to determine the association between maternal ACEs and both outcomes. This study included 883 mother-child dyads. The mean (SD) maternal age at delivery was 29.8 (4.3) years. Pregnant women with higher ACE scores exhibited higher GrimAge EAA (β, 0.22 [95% CI, 0.12 to 0.33] years; P < .001). Maternal ACEs were not associated with GAA in newborns using P < .05 as a cutoff to determine statistical significance. Depression was associated with higher GrimAge EAA (β, 0.06 [95% CI, 0.02 to 0.10] years; P = .01) in mothers during pregnancy, but not in newborns, and did not mediate the association between ACEs and EAA. The findings of this study suggest that maternal ACEs may be associated with epigenetic aging later in life, including during pregnancy, supporting a role for maternal ACEs in offspring development and health later in life.

BackgroundEmerging epidemiological evidence shows myasthenia gravis (MG) is associated with age-related biological processes, but its mechanism of causality remains unexplained. This bidirectional Mendelian randomization (MR) study aimed to clarify the causal relationships between quantifiable biomarkers of aging and MG.MethodsWe extracted genetic instrumental variables for three aging biomarkers: telomere length, epigenetic clocks, and mitochondrial DNA copy number (mtDNA-CN) and MG from the public GWAS databases. The main causal effect estimates were obtained by the inverse variance weighted method, and supplementary sensitivity analysis was used to evaluate potential heterogeneity and pleiotropy effects.ResultsOverall, genetically predicted HannumAge and mtDNA-CN were associated with MG (OR = 0.909, 95% CI 0.834–0.991, P = 0.030; OR = 1.592, 95% CI 1.025–2.473, P = 0.039), though these associations did not survive false discovery rate (FDR) correction. Subgroup analyses showed a negative causal effect of HannumAge on early-onset MG (EOMG) (OR = 0.775, 95% CI 0.667–0.901, P = 0.001, PFDR =0.005), and a potential positive association of mtDNA-CN with late-onset MG (LOMG) (OR = 1.756, 95% CI 1.030–2.995, P = 0.039, PFDR =0.193). Reverse MR identified that EOMG causally increased epigenetic clocks (PhenoAge: OR = 1.056, 95% CI 1.004–1.111, P = 0.036; GrimAge: OR = 1.098, 95% CI 1.055–1.143, P < 0.001; HannumAge: OR = 1.100, 95% CI 1.058–1.144, P < 0.001), with the GrimAge and HannumAge association remaining significant after FDR correction. No evidence supported causal associations of MG/LOMG with aging biomarkers.ConclusionOur findings demonstrate a bidirectional causality between EOMG and epigenetic aging clocks, which indicates that there is a self-reinforcing pathophysiological cycle. The epigenetic age acceleration is both a driver and a result of the progression of EOMG. The correlation between mtDNA-CN and LOMG suggests that there may be a potential compensatory adaptation mechanism to combat chronic oxidative stress in age-related autoimmunity. These results highlight the complex and subtype-dependent contributions of biological aging to the autoimmune-mediated pathology of MG, and provide key mechanistic insights into the subtype-specific aging in MG.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13148-026-02083-3.

Introduction: Epigenetic clocks help to establish a pace of biological aging. Many studies employ biologic aging scores but typically assume consistent aging pace as individuals age. We examined demographic differences in biologic aging over time in an older population. Methods: DNA methylation data are from the Age Gene/Environment Susceptibility-Reykjavik Study (AGES-RS) cohort (n=2,081 participants; 57.6% female; baseline mean [SD] age 75.5 [4.8]). The participants were examined in 2002-2006 (AGES-1) and after approximately 5-year follow-up (AGES-2). We calculated the AGES-1 and AGES-2 Epigenetic Age Acceleration (EAA) separately using the DunedinPACE clock. EAA &gt;1 implies fast biological aging, EAA &lt;1 implies slow aging, and EAA~1 suggests similar biological and chronological aging pace. Individuals were categorized as having slow (&lt;1 SD of the mean), stable (within one SD), or fast (&gt;1 SD) EAA. We investigated age (&lt;76 vs. ≥76yrs) and sex differences in biological aging, as well as changes in biological aging pace. Results: Mean EAA (SD) at AGES-1 was 1.10 (0.11) and 1.13 (0.12) at AGES-2, with a strong correlation (r=0.83). While mean (SD) chronological age was comparable between women (75.4, [4.9]) and men (75.7 [4.7]) in AGES-1, women had lower EAA (1.07 [0.11]) than men (1.12 [0.10]; p &lt; 2.2e-16). At AGES-1, people &lt;76yrs had EAA 1.09 (0.11), while those aged ≥76yrs had 1.11 (0.11), p = 0.007. At AGES-1, 68.7% were stable, 15.1% slow, and 16.1% fast agers. At AGES-2, 74.6% maintained their pace, with 25.4% (n=528) changing categories. Of the 528, almost one-quarter (23.7%) went from fast to slow biological aging. Stratifying by sex, among the 314 (26.2%) women who switched categories, most (76.4%) transitioned from slow to fast, while 23.6% changed from fast to slow. Among males, 214 (24.2%) experienced EAA shifts, with 23.8% from fast to slow. When considering age groups, 297 individuals (24.3%) aged &lt; 76yrs had shifts compared to 231 (26.8%) aged ≥76yrs. Notably, the shift from slow to fast was 75.8% among those &lt; 76yrs compared to 77.1% in those ≥76yrs. Conclusion: In this older population cohort (&gt;67 years), approximately a quarter (25.4%) displayed shifts in biological aging due to methylation signals. More women had EAA shifts, but men often showed a greater biological aging. Older individuals (≥76yrs) also shifted from slow to fast aging. These findings highlight the dynamic nature of biological aging and its susceptibility to environmental influences. Further research is needed to investigate what factors are associated with these changes over time and whether the changes have clinical significance.

Study question Do young women with idiopathic early ovarian ageing have changes in telomere length and epigenetic age indicating accelerated biological aging? Summary answer The telomere length and epigenetic age were comparable to those in young women with normal ovarian ageing. What is known already Increased risk of several health events usually considered to be age-related such as cardiovascular disease, osteoporosis, over-all morbidity and mortality have been associated with premature and early menopause when compared to the risk in women with normal menopausal age suggesting an accelerated general ageing process associated to early ovarian ageing. It is unclear whether the onset of this process may start before menopause. Study design, size, duration A prospective cohort study. Young women (≤ 37 years) having ART at two Danish Public fertility clinics during the period 2016 to 2018 were divided into two groups dependent on their ovarian reserve status: early ovarian ageing (EOA) (N = 55) and normal ovarian ageing (NOA)( N = 52). Number of oocytes harvested in first and subsequent cycles was used as a marker of ovarian reserve. Blood samples was drawn at time of oocyte retrieval to assess biological age. Participants/materials, setting, methods EOA was defined as ≥ 2 IVF cycles with ≤ 5 harvested oocytes despite sufficient stimulation with FSH and NOA as ≥ 8 oocytes harvested in minimum 1 cycle. Known causes influencing the ovarian reserve (endometriosis, ovarian surgery, etc.) was reason for exclusion. Relative telomere length (qPCR) and epigenetic age acceleration (DNA methylation levels) were measured in white blood cells as markers of accelerated biological ageing. Main results and the role of chance Relative telomere length was comparable with a mean of 0.46 (± sd 0.12) in the EOA group and 0.47 (0.14) in the normal ovarian ageing group (p = 0.64). The difference of predicted mean epigenetic age and mean chronological age (i.e. epigenetic age acceleration) was, insignificantly, 0.5 years older in the EOA group when compared to the NOA group( (–1.02 years (2.62) and –1.57 years (2.56), respectively, p = 0.27)), but this difference disappeared when adjusting for chronological age. Limitations, reasons for caution Discrete changes in epigenetic age acceleration may not have been captured as the study only had power to detect an age acceleration of ≥ 2 years. Wider implications of the findings: By analysis of biomarkers for ageing in whole blood, we did not find any indications of a premenopausal accelerated aging in young women with idiopathic EOA. Further investigations in a similar cohort of premenopausal women is needed to fully elucidate the potential relationship between premenopausal accelerated biological ageing and EOA. Trial registration number The study was approved by the Danish Data protection Agency (nr 1–16–02–320–14) and the Regional committee on health research ethics of Central Region Denmark (jr.no 1–10–72–142–14).

Associations between Epigenetic Age Acceleration and Psychoneurological Symptoms in Sickle Cell Disease

Accelerated aging is a hallmark of Down syndrome (DS), with adults experiencing early‐onset Alzheimer's disease and premature aging of the skin, hair, and immune and endocrine systems. Accelerated epigenetic aging has been found in the blood and brain tissue of adults with DS but when premature aging in DS begins remains unknown. We investigated whether accelerated aging in DS is already detectable in blood at birth. We assessed the association between age acceleration and DS using five epigenetic clocks in 346 newborns with DS and 567 newborns without DS using Illumina MethylationEPIC DNA methylation array data. We compared two epigenetic aging clocks (DNAmSkinBloodClock and pan‐tissue DNAmAge) and three epigenetic gestational age clocks (Haftorn, Knight, and Bohlin) between DS and non‐DS newborns using linear regression adjusting for observed age, sex, batch, deconvoluted blood cell proportions, and genetic ancestry. Targeted sequencing of GATA1 was performed in a subset of 184 newborns with DS to identify somatic mutations associated with transient abnormal myelopoiesis. DS was significantly associated with increased DNAmSkinBloodClock (effect estimate = 0.2442, p < 0.0001), with an epigenetic age acceleration of 244 days in newborns with DS after adjusting for potential confounding factors (95% confidence interval: 196–292 days). We also found evidence of epigenetic age acceleration associated with somatic GATA1 mutations among newborns with DS (p = 0.015). DS was not associated with epigenetic gestational age acceleration. We demonstrate that accelerated epigenetic aging in the blood of DS patients begins prenatally, with implications for the pathophysiology of immunosenescence and other aging‐related traits in DS.

BackgroundDNA methylation (DNAm) has unique properties which makes it a potential biomarker for lifestyle-related exposures. Epigenetic clocks, particularly DNAm-based biological age predictors [epigenetic age (EA)], represent an exciting new area of clinical research and deviations of EA from chronological age [epigenetic age acceleration (EAA)] have been linked to overall health, age-related diseases, and environmental exposures. ObjectivesThis observational study investigates the relationships between biological aging and various dietary factors within the LifeLines-DEEP Cohort. These factors include diet quality, processed food consumption, dietary glycemic load, and intake of vitamins involved in maintaining the epigenetic homeostasis (vitamins B-9, B-12, B-6, B-2, and C). MethodsDietary records collected using food-frequency questionnaires were used to estimate diet quality [LifeLines Diet Score (LLDS)], measure the intake of unprocessed/ultraprocessed food according to the NOVA food classification system, and the adequacy of the dietary intake of vitamins B-9, B-12, B-2, B-6, and C. EA using Horvath, Hannum, Levine, and Horvath2 epigenetic clock models and DNAm-predicted telomere length (DNAm-TL) were calculated from DNAm data in 760 subjects. Associations between dietary factors and EAA were tested, adjusting for sex, energy intake, and body composition. ResultsLLDS was associated with EAA (EAA_Horvath: β: −0.148; P = 1 × 10−4; EAA_Hannum: β: −0.148; P = 9 × 10−5; EAA_Levine: β: −0.174; P = 1 × 10−5; and EAA_Horvath2: β: −0.176; P = 4 × 10−6) and DNAm-TL (β: 0.116; P = 0.003). Particularly, EAA was associated with dietary glycemic load (EAA_Horvath: β: 0.476; P = 9 × 10−10; EAA_Hannum: β: 0.565; P = 1 × 10−13; EAA_Levine: β: 0.469; P = 5 × 10−9; EAA_Horvath2: β: 0.569; P = 1 × 10−13; and DNAmTL adjusted for age: β: −0.340; P = 2 × 10−5) and different measures of food processing (NOVA classes 1 and 4). Positive EAA was also associated with inadequate intake of vitamin B-12 (EAA_Horvath: β: −0.167; P = 0.002; EAA_Hannum: β: −0.144; P = 0.007; and EAA_Horvath2: β: −0.126; P = 0.019) and C (EAA_Hannum: β: −0.136; P = 0.010 and EAA_Horvath2: β: −0.151; P = 0.005). ConclusionsOur findings corroborate the hypothesis that nutrition plays a pivotal role in influencing epigenetic homeostasis, especially DNAm, thereby contributing to individual health trajectories and the pace of aging.

BackgroundChronological age is a prominent risk factor for many types of cancers including colorectal cancer (CRC). Yet, the risk of CRC varies substantially between individuals, even within the same age group, which may reflect heterogeneity in biological tissue aging between people. Epigenetic clocks based on DNA methylation are a useful measure of the biological aging process with the potential to serve as a biomarker of an individual’s susceptibility to age-related diseases such as CRC.MethodsWe conducted a genome-wide DNA methylation study on samples of normal colon mucosa (N = 334). Subjects were assigned to three cancer risk groups (low, medium, and high) based on their personal adenoma or cancer history. Using previously established epigenetic clocks (Hannum, Horvath, PhenoAge, and EpiTOC), we estimated the biological age of each sample and assessed for epigenetic age acceleration in the samples by regressing the estimated biological age on the individual’s chronological age. We compared the epigenetic age acceleration between different risk groups using a multivariate linear regression model with the adjustment for gender and cell-type fractions for each epigenetic clock. An epigenome-wide association study (EWAS) was performed to identify differential methylation changes associated with CRC risk.ResultsEach epigenetic clock was significantly correlated with the chronological age of the subjects, and the Horvath clock exhibited the strongest correlation in all risk groups (r > 0.8, p < 1 × 10−30). The PhenoAge clock (p = 0.0012) revealed epigenetic age deceleration in the high-risk group compared to the low-risk group.ConclusionsAmong the four DNA methylation-based measures of biological age, the Horvath clock is the most accurate for estimating the chronological age of individuals. Individuals with a high risk for CRC have epigenetic age deceleration in their normal colons measured by the PhenoAge clock, which may reflect a dysfunctional epigenetic aging process.

Epigenetic aging biomarkers and occupational exposure to benzene, trichloroethylene and formaldehyde

BackgroundAging has been reported as a major risk factor for severe symptoms and higher mortality rates in COVID-19 patients. Molecular hallmarks such as epigenetic alterations and telomere attenuation reflect the biological process of aging. Epigenetic clocks have been shown to be valuable tools for measuring biological age in various tissues and samples. As such, these epigenetic clocks can determine accelerated biological aging and time-to-mortality across various tissues. Previous reports have shown accelerated biological aging and telomere attrition acceleration following SARS-CoV-2 infection. However, the effect of accelerated epigenetic aging on outcome (death/recovery) in COVID-19 patients with acute respiratory distress syndrome (ARDS) has not been well investigated.ResultsIn this study, we measured DNA methylation age and telomere attrition in 87 severe COVID-19 cases with ARDS under mechanical ventilation. Furthermore, we compared dynamic changes in epigenetic aging across multiple time points until recovery or death. Epigenetic age was measured using the Horvath, Hannum, DNAm skin and blood, GrimAge, and PhenoAge clocks, whereas telomere length was calculated using the surrogate marker DNAmTL. Our analysis revealed significant accelerated epigenetic aging but no telomere attrition acceleration in severe COVID-19 cases. In addition, we observed epigenetic age deceleration at inclusion versus end of follow-up in recovered but not in deceased COVID-19 cases using certain clocks. When comparing dynamic changes in epigenetic age acceleration (EAA), we detected higher EAA using both the Horvath and PhenoAge clocks in deceased versus recovered patients. The DNAmTL measurements revealed telomere attrition acceleration in deceased COVID-19 patients between inclusion and end of follow-up and a significant change in dynamic telomere attrition acceleration when comparing patients who recovered versus those who died.ConclusionsEAA and telomere attrition acceleration were associated with treatment outcomes in hospitalized COVID-19 patients with ARDS. A better understanding of the long-term effects of EAA in COVID-19 patients and how they might contribute to long COVID symptoms in recovered individuals is urgently needed.