GrimAge Outperforms Other Epigenetic Clocks in the Prediction of Age-Related Clinical Phenotypes and All-Cause Mortality

Resetting the aging clock through epigenetic reprogramming: Insights from natural products

Epigenetic modifications play a critical role in regulating gene expression under various physiological and pathological conditions. Epigenetic modifications reprogramming is a recognized hallmark of aging and a key component of the aging clock used to differentiate between chronological and biological age. The potential for prospective diagnosis and regulatory capabilities position epigenetic modifications as an emerging drug target to extend longevity and alleviate age-related organ dysfunctions. In the past few decades, numerous preclinical studies have demonstrated the therapeutic potential of natural products in various human diseases, including aging, with some advancing to clinical trials and clinical application. This review highlights the discovery and recent advancements in the aging clock, as well as the potential use of natural products as anti-aging therapeutics by correcting disordered epigenetic reprogramming. Specifically, the focus is on the imbalance of histone modifications, alterations in DNA methylation patterns, disrupted ATP-dependent chromatin remodeling, and changes in RNA modifications. By exploring these areas, new insights can be gained into aging prediction and anti-aging interventions.

A deep learning sex-specific body composition ageing biomarker using dual-energy X-ray absorptiometry scan

BACKGROUND

Chronic diseases are closely linked to alterations in body composition, yet there is a need for reliable biomarkers to assess disease risk and progression. This study aimed to develop and validate a biological age indicator based on body composition derived from dual-energy X-ray absorptiometry (DXA) scans, offering a novel approach to evaluating health status and predicting disease outcomes.

METHODS

A deep learning model was trained on a reference population from the UK Biobank to estimate body composition biological age (BCBA). The model's performance was assessed across various groups, including individuals with typical and atypical body composition, those with pre-existing diseases, and those who developed diseases after DXA imaging. Key metrics such as c-index were employed to examine BCBA's diagnostic and prognostic potential for type 2 diabetes, major adverse cardiovascular events (MACE), atherosclerotic cardiovascular disease (ASCVD), and hypertension.

RESULTS

Here we show that BCBA strongly correlates with chronic disease diagnoses and risk prediction. BCBA demonstrated significant associations with type 2 diabetes (odds ratio 1.08 for females and 1.04 for males, p < 0.0005), MACE (odds ratio 1.10 for females and 1.11 for males, p < 0.0005), ASCVD (odds ratio 1.07 for females and 1.10 for males, p < 0.0005), and hypertension (odds ratio 1.06 for females and 1.04 for males, p < 0.0005). It outperformed standard cardiovascular risk profiles in predicting MACE and ASCVD.

CONCLUSIONS

BCBA is a promising biomarker for assessing chronic disease risk and progression, with potential to improve clinical decision-making. Its integration into routine health assessments could aid early disease detection and personalised interventions.

The associations between biological markers of aging and appetite loss across adulthood: retrospective case-control data from the INSPIRE-T study

Appetite loss is a common clinical condition in older adulthood, but how this condition associates with biological aging remains unknown. The present study aims to examine the associations of biological aging markers with appetite loss in community-dwelling people aged 21 to 102 years. This retrospective case-control study used baseline data from the INSPIRE-T cohort in Toulouse, France. Each of the 49 cases with appetite loss was sex- and age-matched to two controls without appetite loss (n = 147; median age of 79 years, interquartile range: 19.5; 67% women). Appetite loss was assessed using a single yes-no question from the World Health Organization´s Integrated Care for Older People screening tool. Biomarkers (first- and second-generation DNA methylation-based epigenetic clocks [Horvath, Hannum, PhenoAge, and GrimAge], the inflammatory aging clock iAge, and Adenosine triphosphatase inhibitory factor 1-IF1) were derived from blood samples. Logistic regression analyzed the associations of these markers with appetite loss. In fully adjusted models, accelerated aging using GrimAge was the only biomarker associated with appetite loss (Odds Ratio = 1.21, 95% Confidence Interval: 1.03, 1.43). When stratified by age (≤ 65 years vs. > 65 years) and sex, this association remained significant only in individuals over 65 years and men. Future research is needed to explore the potential mechanisms involved, as well as how other biological drivers of aging (e.g., cell senescence, deregulated nutrient sensing) relate to appetite loss.

Genetic and environmental contributions to epigenetic aging across adolescence and young adulthood

BACKGROUND

Epigenetic aging estimators commonly track chronological and biological aging, quantifying its accumulation (i.e., epigenetic age acceleration) or speed (i.e., epigenetic aging pace). Their scores reflect a combination of inherent biological programming and the impact of environmental factors, which are suggested to vary at different life stages. The transition from adolescence to adulthood is an important period in this regard, marked by an increasing and, then, stabilizing epigenetic aging variance. Whether this pattern arises from environmental influences or genetic factors is still uncertain. This study delves into understanding the genetic and environmental contributions to variance in epigenetic aging across these developmental stages. Using twin modeling, we analyzed four estimators of epigenetic aging, namely Horvath Acceleration, PedBE Acceleration, GrimAge Acceleration, and DunedinPACE, based on saliva samples collected at two timepoints approximately 2.5 years apart from 976 twins of four birth cohorts (aged about 9.5, 15.5, 21.5, and 27.5 years at first and 12, 18, 24, and 30 years at second measurement occasion).

RESULTS

Half to two-thirds (50-68%) of the differences in epigenetic aging were due to unique environmental factors, indicating the role of life experiences and epigenetic drift, besides measurement error. The remaining variance was explained by genetic (Horvath Acceleration: 24%; GrimAge Acceleration: 32%; DunedinPACE: 47%) and shared environmental factors (Horvath Acceleration: 26%; PedBE Acceleration: 47%). The genetic and shared environmental factors represented the primary sources of stable differences in corresponding epigenetic aging estimators over 2.5 years. Age moderation analyses revealed that the variance due to individually unique environmental sources was smaller in younger than in older cohorts in epigenetic aging estimators trained on chronological age (Horvath Acceleration: 47-49%; PedBE Acceleration: 33-68%). The variance due to genetic contributions, in turn, potentially increased across age groups for epigenetic aging estimators trained in adult samples (Horvath Acceleration: 18-39%; GrimAge Acceleration: 24-43%; DunedinPACE: 42-57%).

CONCLUSIONS

Transition to adulthood is a period of the increasing variance in epigenetic aging. Both environmental and genetic factors contribute to this trend. The degree of environmental and genetic contributions can be partially explained by the design of epigenetic aging estimators.

DNA methylation age acceleration is associated with incident cognitive impairment in the health and retirement study

BackgroundDNA methylation clocks have emerged as promising biomarkers for cognitive impairment and dementia. Longitudinal studies exploring the association between DNA methylation clocks and cognitive decline have been constrained by limited sample sizes and a lack of diversity.ObjectiveOur study aimed to investigate associations between DNA methylation clocks and incident cognitive impairment using a larger and US nationally-representative sample from the Health and Retirement Study.MethodsWe measured DNA methylation age acceleration in 2016 by regressing the DNA methylation clocks, including GrimAge, against chronological age. Cognitive change over time was determined by Langa-Weir cognition status from 2016 to 2018. Multivariable logistic regression evaluated the association between DNA methylation age acceleration and cognitive change, adjusting for cell-type proportions, demographic, and health factors. We also applied inverse probability weighting to address potential selection bias from varying loss-to-follow-up rates.ResultsThe analytic sample (N = 2713) was 54% female, 8.4% Black/African American, 86% White, 7.5% Hispanic, and 68 years old at baseline. During the two years of follow-up, 12% experienced cognitive change and had higher baseline GrimAge (mean = 1.2 years) acceleration compared to those maintaining normal cognition (mean = -0.8 years). A one-year increase in GrimAge acceleration was associated with 1.05 times higher adjusted and survey-weighted odds of cognitive change during follow-up (95% CI: 1.01-1.10). This association was consistent after accounting for loss-to-follow-up (OR = 1.07, 95% CI: 1.04-1.11).ConclusionsOur study offers insights into DNA methylation age acceleration associated with cognitive change over time, suggesting avenues for improved prevention, diagnosis, and treatment.

Pubertal timing as a predictor of epigenetic aging and mortality risk in young adulthood

Early pubertal timing is associated with adverse health in adulthood. These effects may be mediated by DNA methylation changes associated with accelerated cellular aging and mortality risk, but few studies tested associations between pubertal timing and epigenetic markers in adulthood. Additionally, pubertal timing effects often vary by sex and are understudied in diverse youth. Thus, this longitudinal study examined links between pubertal timing and later epigenetic aging and mortality risk together with sex differences in predominantly Black youth. Participants included 350 individuals (58% female, 42% male; 80% Black, 19% non-Hispanic White). Perceived pubertal timing relative to peers and self-reported phenotypic pubertal timing based on age-adjusted Tanner scores were assessed during early adolescence (Mage = 13) whereas epigenetic aging (GrimAge, DunedinPace of Aging Calculated from the Epigenome, and PhenoAge) and mortality risk were measured during young adulthood (Mage = 27). After adjusting for covariates (smoking, body mass index, family income, early-life stress, race/ethnicity, sex, parenthood), early pubertal timing (both perceived and phenotypic) predicted higher epigenetic mortality risk, and early phenotypic pubertal timing predicted accelerated DunedinPace of Aging Calculated from the Epigenome. Both perceived and phenotypic early pubertal timing were correlated with accelerated GrimAge. Off-time phenotypic pubertal timing (i.e., early and late) was associated with accelerated PhenoAge in males only whereas perceived off-time pubertal timing was unexpectedly linked with lower PhenoAge acceleration. These findings extend prior research by linking two dimensions of early pubertal timing with epigenetic mortality risk and accelerated aging in racially diverse young adults and showing nonlinear effects on PhenoAge acceleration that differ across pubertal timing measures and show some sex differences. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Assessing Metabolic Ageing via DNA Methylation Surrogate Markers: A Multicohort Study in Britain, Ireland and the USA

Metabolomics and epigenomics have been used to develop 'ageing clocks' that assess biological age and identify 'accelerated ageing'. While metabolites are subject to short-term variation, DNA methylation (DNAm) may capture longer-term metabolic changes. We aimed to develop a hybrid DNAm-metabolic clock using DNAm as metabolite surrogates ('DNAm-metabolites') for age prediction. Within the UK Airwave cohort (n = 820), we developed DNAm metabolites by regressing 594 metabolites on DNAm and selected 177 DNAm metabolites and 193 metabolites to construct 'DNAm-metabolic' and 'metabolic' clocks. We evaluated clocks in their age prediction and association with noncommunicable disease risk factors. We additionally validated the DNAm-metabolic clock for the prediction of age and health outcomes in The Irish Longitudinal Study of Ageing (TILDA, n = 488) and the Health and Retirement Study (HRS, n = 4018). Around 70% of DNAm metabolites showed significant metabolite correlations (Pearson's r: > 0.30, p < 10-4) in the Airwave test set and overall stronger age associations than metabolites. The DNAm-metabolic clock was enriched for metabolic traits and was associated (p < 0.05) with male sex, heavy drinking, anxiety, depression and trauma. In TILDA and HRS, the DNAm-metabolic clock predicted age (r = 0.73 and 0.69), disability and gait speed (p < 0.05). In HRS, it additionally predicted time to death, diabetes, cardiovascular disease, frailty and grip strength. DNAm metabolite surrogates may facilitate metabolic studies using only DNAm data. Clocks built from DNAm metabolites provided a novel approach to assess metabolic ageing, potentially enabling early detection of metabolic-related diseases for personalised medicine.

Three generations of epigenetic clocks in mediating the adverse effect of smoking on metabolic health

AIMS

Metabolic syndrome (MetS) is a composite disorder that includes abdominal obesity, impaired glucose levels, high blood pressure, and dyslipidemia. Smoking can alter epigenetic profiles and is a critical modifiable risk factor for MetS. We aim to explore the epigenetic age acceleration (EAA) that can mainly deliver smoking influences on metabolic health.

METHODS

We conducted a mediation analysis of 2,474 individuals with data in the Taiwan Biobank. Current and former smoking and the respective pack-years were included as four exposure factors. Seven markers of DNA methylation (DNAm) covering three generations of epigenetic clocks were included as mediators. Seven metabolic outcomes included MetS status (yes vs. no) and six related traits.

RESULTS

GrimEAA and DunedinPACE mediated the associations of the four smoking factors with MetS, fasting glucose, triglyceride, and high-density lipoprotein cholesterol levels (false discovery rate < 0.05). GrimEAA and DunedinPACE respectively mediated 48.2% and 24.2% of current smoking's effect on MetS and 60.9% and 26.1% of current smoking pack-year's effect on MetS risk. The DNAm plasminogen activator inhibitor 1 level mediated the adverse effects of current smoking status and pack-years on all seven metabolic outcomes.

CONCLUSION

The GrimEAA-mediated proportions were approximately two times greater than the DunedinPACE-mediated proportions.

Stability of Aging- and Cognition-Related Methylation Profile Scores Across Two Waves in Children and Adolescents

DNA-methylation profile scores (MPSs) index biology relevant for lifelong physical and cognitive health, but information on their longitudinal stability in childhood is lacking. Using two waves of data collected from 2014 to 2022 (Mlag between waves = 2.41 years) from N = 407 participants (Mage = 12.05 years, 51% female, 60% White), test-retest correlations were estimated for four salivary MPSs related to aging (PhenoAgeAccel, GrimAgeAccel, DunedinPACE), and cognitive function (Epigenetic-g). MPSs varied in longitudinal stability (test-retest rs = 0.38 to 0.76). MPSs did not differ in children exposed to the COVID-19 pandemic, but race-ethnic and sex differences were apparent. Further research is necessary to understand which environmental perturbations impact DNA-methylation trajectories and when children are most sensitive to those impacts.

Epigenetic age acceleration is related to cognitive decline in the elderly: Results of the Austrian Stroke Prevention Study

AIM

Epigenetic clocks, quantifying biological age through DNA methylation (DNAmAge), have emerged as potential indicators of brain aging. As the variety of DNAmAge algorithms grows, consensus on their efficacy in predicting age-related changes is lacking. This study aimed to explore the intricate relationship between diverse DNAmAge algorithms and structural and cognitive markers of brain aging.

METHODS

Within a cohort of 796 elderly patients (mean age, 65.8 ± 7.9 years), we scrutinized 11 DNAmAge algorithms, including Horvath, Hannum, Zhang's clocks, PhenoAge, GrimAge, DunedinPACE, and principal component (PC)-based PCHorvath, PCHannum, PCPhenoAge, and PCGrimAge. We evaluated their association with baseline cognition and cognitive decline, assessed through follow-up evaluations at three (T1) and six (T2) years postbaseline. Additionally, we examined their relationship with structural magnetic resonance imaging markers of brain aging, including white matter.

RESULTS

Zhang's clock was the best predictor of decline in memory (β = -0.04) and global cognition (β = -0.03), whereas PCGrimAge was the best predictor of speed decline (β = -0.17). The DNAmAge algorithms were the second-best predictors in explaining cognitive variability after education in memory and global cognition (R2 partial = 1.66% to 2.82%) and the best predictors for speed decline (R2 partial = 2.13%). PC-trained DNAmAge algorithms outperformed their respective original version.

CONCLUSION

DNAmAge algorithms are strong and independent predictors of cognitive decline in the normal elderly population and explain additional variability in cognitive decline beyond that accounted for by conventional risk factors.

Associations between five indicators of epigenetic age acceleration and all-cause and cause-specific mortality among US adults aged 50 years and older

BACKGROUND

Although DNA methylation age estimators (DNAmAges) are reliable tools for predicting aging, their effectiveness in predicting mortality risk has not been fully validated. This study compared the predictive utility of five different DNAmAges (HorvathAge, HannumAge, PhenoAgeAge, GrimAge and GrimAge2) for all-cause and cause-specific mortality among adults aged ≥ 50 years.

METHODS

We screened 1966 participants adults aged ≥ 50 from the National Health and Nutrition Examination Survey (1999-2002) and linked them to the National Death Index to obtain cause and status of death. We used weighted Cox proportional hazards models to examine the associations between epigenetic age acceleration (EAA) measured by different DNAmAges and all-cause and cause-specific mortality in the general population, adjusting for various covariates including age, smoking status and chronic diseases. We used restricted cubic splines to explore nonlinear associations. Finally, stratified analyses were performed to assess the relationship between DNA age estimators and stratification variables.

RESULTS

The multivariable adjustment model showed that EAA measured by HorvathAge (AAHorvathAge), HannumAge (AAHannumAge), PhenoAge (AAPhenoAge), GrimAge (AAGrimAge) and GrimAge2 (AAGrimAge) were significantly associated with the risk of death, among which AAGrimAge and AAGrimAge2 had stronger statistical correlation and the correlation pattern was positively correlated. Specifically, each 5-year increase in AAGrimAge was associated with a 44% increased risk of all-cause death, a 33% increased risk of cardiovascular death and a 54% increased risk of non-cardiovascular death. And each 5-year increase in AAGrimAge2 was associated with a 40% increased risk of all-cause death, a 33% increased risk of cardiovascular death and a 47% increased risk of non-cardiovascular death. In contrast, AAHorvathAge, AAHannumAge and AAPhenoAge showed a J-shaped correlation with the risk of all-cause mortality and non-cardiovascular mortality, with the inflection points of all-cause mortality and non-cardiovascular mortality occurring at AAHorvathAge of 2.29 and 2.8, AAHannumAge of 3.07 and 2.97, and AAPhenoAge of - 7.65 and 7.04, respectively. No interaction was found between DNAmAges and stratification variables.

CONCLUSIONS

AAGrimAge and AAGrimAge2 outperformed AAHorvathAge, AAHannumAge and AAPhenoAge in predicting mortality risk, and the association pattern was positive.

Epigenetic associations with kidney disease in individuals of African ancestry with APOL1 high-risk genotypes and HIV

BACKGROUND

Apolipoprotein L1 (APOL1) high-risk variants are major determinants of chronic kidney disease (CKD) in people of African ancestry. Previous studies have identified epigenetic changes in relation to kidney function and CKD, but not in individuals with APOL1 high-risk genotypes. We conducted an epigenome-wide analysis of CKD and estimated glomerular filtration rate (eGFR) in in people of African ancestry and APOL1 high-risk genotypes with HIV.

METHODS

DNA methylation profiles from peripheral blood mononuclear cells of 119 individuals with APOL1 high-risk genotypes (mean age 48 years, 49% female, median CD4 count 515 cells/mm3, 90% HIV-1 RNA <200 copies/mL, 23% with CKD) were obtained by Illumina MethylationEPIC BeadChip. Differential methylation analysis of CKD considered technical and biological covariates. We also assessed associations with eGFR. Replication was pursued in three independent multi-ancestry cohorts with and without HIV.

RESULTS

DNA methylation levels at 14 regions were associated with CKD. The strongest signals were located in SCARB1, DNAJC5B and C4orf50. Seven of the 14 signals also associated with eGFR, and most showed evidence for a genetic basis. Four signals (in SCARB1, FRMD4A, CSRNP1 and RAB38) replicated in other cohorts, and 11 previously reported epigenetic signals for kidney function or CKD replicated in our cohort. We found no significant DNA methylation signals in, or near, the APOL1 promoter region.

CONCLUSIONS

We report several novel as well as previously reported epigenetic associations with CKD and eGFR in individuals with HIV having APOL1 high-risk genotypes. Further investigation of pathways linking DNA methylation to APOL1 nephropathies is warranted.

LinAge2: providing actionable insights and benchmarking with epigenetic clocks

Biological aging is marked by a decline in resilience at the cellular and systemic levels, driving an exponential increase in mortality risk. Here, we evaluate several clinical and epigenetic clocks for their ability to predict mortality, demonstrating that clocks trained on survival and functional aging outperform those trained on chronological age. We present an enhanced clinical clock that predicts mortality more accurately and provides actionable insights for guiding personalized interventions. These findings highlight the potential of mortality-predicting clocks to inform clinical decision-making and promote strategies for healthy longevity.

Association of blood cadmium levels with epigenetic age acceleration in U.S. adults aged > 50 years

Objectives

DNA methylation (DNAm) is a sensitive biomarker of aging-related processes, and novel epigenetic-based "clocks" can estimate accelerated biological aging. Cadmium (Cd) can alter cellular processes that promote aging and disrupt global methylation patterns. However, few studies have investigated the association between blood Cd and accelerated aging. We aimed to investigate the association between blood Cd and four DNAm-based epigenetic age accelerations in individuals over 50 in the United States, using data from the National Health and Nutrition Examination Survey (NHANES).

Methods

DNAm-epigenetic biomarkers and blood Cd data from the NHANES database (1999-2002) were retrieved for this study. We evaluated four epigenetic ages: HorvathAge, HannumAge, PhenoAge, and GrimAge. Age acceleration was calculated by extracting the residuals from the regression of chronological age on each epigenetic age measure. We used weighted linear regression models and subgroup analyses to investigate the associations between blood Cd levels and these age accelerations, adjusting for potential confounding factors.

Results

Higher blood Cd levels (≥0.5 μg/dl) were significantly associated with increased age acceleration for PhenoAge (β = 1.37, P = 0.017) and GrimAge (β = 1.31, P = 0.003) in adjusted models. A significant association was also observed for HannumAge (β = 0.94, P = 0.016), although this association was not significant for continuous Cd levels (P = 0.111). No significant associations were found for HorvathAge. Subgroup analyses indicated consistent associations across demographic and lifestyle subgroups, with no significant interactions.

Conclusions

In this study, after adjusting for confounders, blood Cd levels were positively associated with PhenoAge acceleration and GrimAge acceleration in people over 50 in the United States. These results may be useful in proposing interventions in environmental exposures to slow the aging process and prevent age-related diseases.

Cross-sectional and longitudinal association of seven DNAm-based predictors with metabolic syndrome and type 2 diabetes

BACKGROUND

To date, various epigenetic clocks have been constructed to estimate biological age, most commonly using DNA methylation (DNAm). These include "first-generation" clocks such as DNAmAgeHorvath and "second-generation" clocks such as DNAmPhenoAge and DNAmGrimAge. The divergence of one's predicted DNAm age from chronological age, termed DNAmAge acceleration (AA), has been linked to mortality and various aging-related conditions, albeit with varying findings. In metabolic syndrome (MetS) and type 2 diabetes (T2D), it remains inconclusive which DNAm-based predictor(s) is/are closely related to these two metabolic conditions. Therefore, we examined the cross-sectional associations between seven DNAm-based predictors and prevalent metabolic conditions in participants with methylation data from the KORA study. We also analyzed the longitudinal association with time-to-incident T2D and the relative prognostic value compared to clinical predictors from the Framingham 8-year T2D risk function in predicting incident disease over eight years.

RESULTS

GrimAA and PhenoAA difference demonstrated consistently significant associations in the cross-sectional and longitudinal analyses. GrimAA difference reported a larger effect: with prevalent MetS at F4 (odds ratio = 1.09, 95% confidence interval = [1.06-1.13], p = 2.04E-08), with prevalent T2D at F4 (odds ratio = 1.09 [1.04-1.13], p = 1.38E-04) and with time-to-incident T2D (hazards ratio = 1.05 [1.01-1.10], p = 0.02) for each year increase in GrimAA difference. Mortality risk score was significantly associated with both prevalent metabolic conditions but not in the longitudinal analysis. The inclusion of DNAm-based predictor in the model with Framingham clinical predictors improved discriminative ability, albeit not significantly. Notably, the DNAm-based predictor, when fitted separately, showed a discriminative ability comparable to that of the model with clinical predictors. Overall, no clear pattern of significant associations was identified in the epigenetic measures from the "first-generation" clocks.

CONCLUSIONS

GrimAA, PhenoAA difference and mortality risk score, derived from the "second-generation" clocks, demonstrated significant associations with both MetS and T2D. These DNAm-based predictors may be useful biomarkers for risk stratification and disease prognosis in our study sample of European ancestry. Further research is warranted to investigate the generalizability of our findings across different ancestries and to examine the underlying shared biological mechanisms.

Lack of a Causal Association between DNA Methylation GrimAge Acceleration and Brain Tumor Incidence: A Two-Sample Mendelian Randomization Study

Objective

To investigate the potential causal relationship between DNA methylation GrimAge acceleration (GAA) and brain tumor incidence using a two-sample Mendelian randomization (MR) approach.

Methods

We leveraged publicly available genome-wide association study (GWAS) summary data for GAA (34,467 participants) and brain tumor incidence (491,542 participants). Twenty-six single nucleotide polymorphisms (SNPs) served as instrumental variables for GAA. Inverse variance weighted (IVW) was the primary method, complemented by MR-Egger, weighted median, simple mode, and weighted mode. Sensitivity analyses tested heterogeneity and pleiotropy.

Results

The IVW analysis indicated no significant causal effect of GAA on brain tumor risk (β = -0.006, p = 0.908). Other MR methods concurred. Sensitivity checks, including heterogeneity and MR-Egger intercept tests, supported these null findings.

Conclusion

Our results do not support a causal association between GrimAge acceleration and brain tumor incidence. Accelerated epigenetic aging, as measured by GAA, may not be a direct driver of brain tumor risk. Further investigations should explore other epigenetic or genetic factors implicated in brain tumor etiology.

Longitudinal associations of epigenetic aging with cognitive aging in Hispanic/Latino adults from the Hispanic Community Health Study/Study of Latinos

Due to the paucity of longitudinal DNA methylation data (DNAm), especially among Hispanic/Latino adults, the association between changes in epigenetic clocks over time and cognitive aging phenotypes has not been investigated. This longitudinal study included 2671 Hispanic/Latino adults (57 years; 66% women) with blood DNAm data and neurocognitive function assessed at two visits approximately 7 years apart. We evaluated the associations of 5 epigenetic clocks and their between-visit change with multiple measures of cognitive aging that included a global cognitive function score at each visit, between-visit change in global cognitive function score, MCI diagnosis, and presence of significant cognitive decline at visit 2 (V2). There were significant associations between greater acceleration for all clocks and lower global cognitive function at each visit. The strongest associations were observed for GrimAge and DunedinPACE. Similar results were observed for domain-specific cognitive function at each visit and MCI diagnosis at V2. There was a significant association of decline in global cognitive function with increase in age acceleration between the two visits for PhenoAge and GrimAge. Between-visit increase in age acceleration for these two clocks was also associated with a greater risk of MCI diagnosis and presence of significant cognitive decline at V2. Epigenetic aging is associated with lower global and domain-specific cognitive function, greater cognitive decline, and greater risk of MCI in Hispanic/Latino adults. Longitudinal assessment of change in age acceleration for second-generation clocks, GrimAge and PhenoAge may provide additional value in predicting cognitive aging beyond a single time point assessment.

Taiwan population-based epigenetic clocks and their application to long-term air pollution exposure

Most epigenetic clocks have been developed in populations of European or Hispanic descent; therefore, population-specific models are needed for Asian cohorts to enhance predictive accuracy and generalizability. This study aims to develop epigenetic clocks in a Taiwanese cohort and examine the association between long-term air pollution exposure and epigenetic age acceleration (EAA). The Taiwan Biobank (TWB) has been recruiting community-based adults aged 30-70 years since 2012, enrolling 173,806 participants by the end of 2022. Among them, 2,469 participants were selected for serum DNA methylation (DNAm) analysis. Epigenetic ages were estimated using penalized elastic net regression, with residuals defined as TWB-based epigenetic age acceleration (TWBEAA) and healthy-subset-based acceleration (TWBhEAA). Additionally, four previously established EAAs were obtained using Horvath's online DNA Methylation Age Calculator: DNAmEAA, DNAmSBEAA, PhenoEAA, and GrimEAA. Air pollution exposure levels at participants' residential townships were estimated from pre-1 day to pre-1 year using a kriging-based spatial interpolation method. Associations were assessed using multiple linear regression models, with robustness verified through Bayesian Kernel Machine Regression (BKMR). The TWBAge (325 CpG sites) and TWBhAge (179 CpG sites) prediction models demonstrated high accuracy (R2 = 0.95) in predicting chronological age. In the single-pollutant model, pre-1 year PM2.5 exposure was significantly associated with TWBhEAA (β = 0.67 [0.14-1.19], year) and DNAmEAA (β = 0.93 [0.03-1.83], year), while O3 exposure showed a positive association with DNAmSBEAA (β = 0.53 [0.29-0.77], year) and a negative association with GrimEAA (β = -0.44 [-0.70 to -0.17], year). BKMR analysis confirmed these findings. This study is among the first attempts to develop epigenetic clocks tailored for Asian population, providing evidence of air pollution's role in accelerating biological aging. Our findings highlight PM2.5 and O3 exposure as major contributors to EAA, emphasizing the need for air pollution mitigation strategies to promote healthier aging.

Somatic mutation as an explanation for epigenetic aging

DNA methylation marks have recently been used to build models known as epigenetic clocks, which predict calendar age. As methylation of cytosine promotes C-to-T mutations, we hypothesized that the methylation changes observed with age should reflect the accrual of somatic mutations, and the two should yield analogous aging estimates. In an analysis of multimodal data from 9,331 human individuals, we found that CpG mutations indeed coincide with changes in methylation, not only at the mutated site but with pervasive remodeling of the methylome out to ±10 kilobases. This one-to-many mapping allows mutation-based predictions of age that agree with epigenetic clocks, including which individuals are aging more rapidly or slowly than expected. Moreover, genomic loci where mutations accumulate with age also tend to have methylation patterns that are especially predictive of age. These results suggest a close coupling between the accumulation of sporadic somatic mutations and the widespread changes in methylation observed over the course of life.

Lead and cadmium exposure was associated with faster epigenetic aging in a representative sample of adults aged 50 and older in the United States

BACKGROUND

Lead and cadmium are among the most prevalent environmental toxicants and are highly detrimental to human health. While prior studies link heavy metal exposure to reduced telomere length and increased DNA methylation age, their relationship with epigenetic age acceleration (EAA) remains understudied. This study investigates whether exposure to lead and cadmium accelerates biological aging.

METHODS

This cross-sectional study analyzed data from 2201 participants aged 50 or older from the 1999-2002 NHANES. Blood lead and cadmium levels were measured using simultaneous multi-element atomic absorption spectrometry. Eight DNA-methylation-based epigenetic clocks were included in the analysis: Hannum Age, Horvath pan-tissue Age, PhenoAge, GrimAge, GrimAge version 2, Skin Blood Age, epiTOC, and DNAmTL. EAA for each individual was calculated as the residuals from the regression of estimated epigenetic age on chronological age.

RESULTS

Of the 2201 American older adults, the mean (SE, standard error) chronological age was 65.75 (0.21), which was closest to the mean GrimAge (65.99; SE = 0.19). After adjusting for demographics, lifestyle factors, comorbidities, and cell type composition, multivariate linear regression analyses revealed associations of blood lead and cadmium levels with significantly higher Hannum Age, Grim Age, Grim Age2, Skin Blood Age (associated with lead only), as well as Phenotypic Age and DNAmTL (associated with cadmium only). Quartile-based analyses of blood lead and cadmium levels according to quartiles revealed consistent and strong associations between greater exposure to lead or cadmium (e.g., the fourth quartile of the metals) and EAA. Among lifestyle factors, smoking had a pronounced impact on accelerated aging, especially in the Grim Age and Grim Age2.

CONCLUSIONS

We found that exposure to lead and cadmium was associated with accelerated epigenetic age. These findings suggest the potential role of lead and cadmium in EAA and propose the integration of environmental factors to refine epigenetic age prediction.

Adolescent empathy and epigenetic aging in adulthood: Substance use as a mediator

Prosocial behavior during adolescence has been associated with better physical health, including slower epigenetic aging. However, little is known about the specific role of empathy in epigenetic aging and the mechanisms explaining this relationship. One such mechanism may be substance use, which is predicted by low empathy and contributes to accelerated epigenetic aging. Thus, the present study examined whether empathy during early adolescence predicts epigenetic aging in young adulthood and whether substance use in late adolescence and young adulthood mediates this effect. Participants included 343 individuals (58% female, 81% Black, 19% White) who were interviewed at mean ages of 13, 17, and 27 years. Participants self-reported their empathy at Time 1 and their alcohol, tobacco, and cannabis use at Times 2 and 3. At Time 3, epigenetic aging was assessed from salivary DNA using the GrimAge, DunedinPACE, and PhenoAge clocks. A regression analysis demonstrated that higher empathy in early adolescence uniquely predicted lower epigenetic aging on the GrimAge clock in young adulthood even after adjusting for environmental and sociodemographic risk factors. Mediation models revealed that the link between empathy and lower epigenetic aging on all three clocks was mediated by lower tobacco use. These results suggest that higher empathy during early adolescence may contribute to better health throughout the lifespan due to lower tobacco use and slower epigenetic aging. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Quantification of Epigenetic Aging in Public Health

Estimators of biological age hold promise for use in preventive medicine, for early detection of chronic conditions, and for monitoring the effectiveness of interventions aimed at improving population health. Among the promising biomarkers in this field are DNA methylation-based biomarkers, commonly referred to as epigenetic clocks. This review provides a survey of these clocks, with an emphasis on second-generation clocks that predict human morbidity and mortality. It explores the validity of epigenetic clocks when considering factors such as race, sex differences, lifestyle, and environmental influences. Furthermore, the review addresses the current challenges and limitations in this research area.

The Association of Physical Function and Physical Performance With DNA Methylation Clocks in Oldest-Old Living in Singapore-The SG90 Cohort

Deoxyribonucleic acid (DNA) methylation (DNAm) clocks estimate biological age according to DNA methylation. This study investigated the associations between measures of physical function and physical performance and 10 DNAm clocks in the oldest-old in Singapore. The SG90 cohort included a subset of community-dwelling oldest-old from the Singapore Chinese Health Study (SCHS) and Singapore Longitudinal Ageing Study (SLAS). Physical function and performance were assessed using Basic Activities of Daily Living (BADL), Instrumental Activities of Daily Living (IADL), World Health Organization Disability Assessment Schedule (WHODAS), Short Physical Performance Battery (SPPB), Timed Up and Go (TUG), handgrip strength, normal gait speed, SPPB fast gait speed (FGS), and. DNAm age from peripheral blood mononuclear cells (PBMC) was measured using 18 DNAm clocks, including first generation clocks (PCHorvath1, PCHorvath2, PCHannum, AltumAge, ENCen100+, ENCEN40+, IntrinClock, RetroAgev1 and RetroAgev2) second and third generation clocks (PCPhenoAge, PCGrimAge, GrimAge2, ZhangMRscore, DNAmFitAge and DunedinPACE) and causality-enriched clocks (YingCausAge, YingAdaptAge, YingDamAge). Linear regression was used to analyze associations. The 433 oldest-old individuals had a median age of 88.6 years [87.5; 90.4] and were predominantly Chinese (95.6%) and female (60.3%). Better performance in IADL, WHODAS, SPPB, SPPB FGS and balance were associated with lower GrimAge2 after adjustment for age, sex, and smoking status (pAdj < .05). GrimAge2 outperformed other DNAm clocks after adjustment for DNAm smoking-pack-years and DNAm-based cell compositions. Better physical function and physical performance were associated with lower DNAm age deviation and pace of aging. Longitudinal and intervention studies are needed to explore biological mechanisms underlying these observed associations.

Sleep traits causally affect epigenetic age acceleration: a Mendelian randomization study

Sleep disorders (SDs) are a common issue in the elderly. Epigenetic clocks based on DNA methylation (DNAm) are now considered highly accurate predictors of the aging process and are associated with age-related diseases. This study aimed to investigate the causal relationship between sleep traits and the epigenetic clock using Mendelian randomization (MR) analysis. The genome-wide association study (GWAS) statistics for epigenetic clocks (HannumAge, intrinsic epigenetic age acceleration [IEAA], PhenoAge, and GrimAge) and sleep traits were obtained from the UK Biobank (UKB), 23andMe and Finngen. Moreover, crucial instrumental variables (IVs) were evaluated. Inverse variance weighted (IVW), MR-Egger, weighted median (WM), weighted mode, and simple mode methods were employed to assess the causal relationship between them. Multiple analyses were performed for quality control evaluation. Our study showed that self-reported insomnia may speed up the aging process by GrimAge clock, while GrimAge acceleration could faintly reduce self-reported insomnia. Epigenetic clocks mainly influence sleep traits by PhenoAge and GrimAge with weak effects. This may indicate that early interventions of SDs could be a breaking point for aging and age-related diseases. Further studies are required to elucidate the potential mechanisms involved.

Adverse Childhood Experiences and Accelerated Epigenetic Aging in the Hispanic Community Health Study/Study of Latinos: Nativity as an Effect Modifier

BACKGROUND

Whether adverse childhood experiences (ACEs) are associated with accelerated epigenetic aging over time among the Hispanic/Latino population remains unknown. This study examined the longitudinal association between ACEs and epigenetic age acceleration (EAA), as well as potential effect modifiers, among a sample of Hispanic/Latino adults.

METHODS

We analyzed 960 Hispanic/Latino adults with DNA methylation (DNAm) profile data from two visits (approximately six years apart) sampled from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). We used PhenoAge, GrimAge, and DunedinPace, a biomarker for the pace of biological aging, to calculate epigenetic aging deviations. Linear mixed models were fit to estimate the association between ACEs and EAA measured by each epigenetic aging measure, adjusting for sex, age, and parental highest education level. Sex and nativity were also assessed as potential effect modifiers.

RESULTS

A one-unit increase in ACE score was associated with a 0.16-year (95 %CI: 0.06, 0.26, p = 0.002) higher GrimAge acceleration (AgeAccelGrim) at Visit 1. Among US-born individuals, a one-unit increase in ACE score was associated with a 0.35-year (95 %CI: 0.12, 0.58, p = 0.003) higher AgeAccelGrim and 0.01-biological year/calendar year (95 %CI: 0.01, 0.02, p = 0.0003) higher DunedinPACE at Visit 1, but statistically significantly weaker associations were found among foreign/US-territory born individuals (p for interaction=0.039 in AgeAccelGrim and 0.001 in DuendinPACE). No association was found between ACEs and the rate of change in EAA between two visits.

CONCLUSION

ACEs are associated with a higher EAA over time among Hispanic/Latino adults at a constant rate. Hispanic/Latino born in the US are more susceptible to the increased EAA related to ACEs compared with those born in a foreign country or US territory.

Association of Cytomegalovirus Serostatus with ELOVL2 Methylation: Implications for Lipid Metabolism, Inflammation, DNA Damage, and Repair Capacity in the MARK-AGE Study Population

Cytomegalovirus (CMV) infection has been linked to accelerated biological aging, potentially increasing the risk of cardiovascular disease. DNA methylation of the gene Elongation Of Very Long Chain Fatty Acids-Like 2 (ELOVL2) is a molecular biomarker for aging, and its gene product is involved in polyunsaturated fatty acid synthesis, which impacts immune and inflammatory responses. This study, conducted in the MARK-AGE population, aimed to investigate the relationship between CMV infection and ELOVL2 methylation in adults aged 35-75, as well as the influence of CMV IgG levels on lipid metabolism, inflammation, DNA damage, and DNA repair. Our data revealed a higher prevalence of ischemic heart disease, atrial fibrillation, hypertension, and diabetes in CMV-positive individuals. CMV IgG levels were positively associated with ELOVL2 methylation at specific CpG sites and with increased expression of DNA methyltransferase-1 (DNMT1). CMV IgG was linked to lipid imbalances, such as increased BMI, VLDL-cholesterol, triglycerides, and HDL1-cholesterol. Additionally, ELOVL2 methylation was associated with systemic inflammation markers, lipid parameters and altered T-cell subsets. A negative correlation was observed between CMV IgG levels and both baseline DNA integrity and repair capacity. These results suggest that CMV infection might promote cardiovascular disease through ELOVL2 hypermethylation, lipid dysregulation, inflammation, and DNA damage.

How epigenetics impacts stroke risk and outcomes through DNA methylation: A systematic review

The impact of DNA methylation (DNAm) on epigenetics has gained prominence in recent years due to its potential influence on ischemic stroke (IS) and treatment outcomes. DNAm is reversible and a better understanding of its role in IS could help identify novel therapeutic targets. The aim of this systematic review was to compile the available data on DNAm in the risk and prognosis of IS and to explore its therapeutic potential. The review process followed the PRISMA criteria. We searched the Pubmed and Cochrane databases to identify studies that used hypothesis free methodological approaches. Of the 459 studies identified, 34 met the inclusion criteria. The studies were categorized as follows: risk of IS; outcomes; and DNAm age. Most studies used genotyping array technology rather than whole-genome sequencing. DNAm testing was mainly based on blood samples. Most studies involved European cohorts. Most of the studies were performed at a single-center with recruitment at the time of stroke. In a few studies, health status was determined longitudinally. This systematic review shows that IS patients are biologically older than expected and present characteristic DNAm patterns related to stroke risk and outcomes. These patterns could be used to develop new treatments with epidrugs.

DNA methylation biomarkers of intellectual/developmental disability across the lifespan

Epigenetic mechanisms, including DNA methylation, act at the interface of genes and environment by allowing a static genome to respond and adapt to a dynamic environment during the lifespan of an individual. Genome-wide DNA methylation analyses on a wide range of human biospecimens are beginning to identify epigenetic biomarkers that can predict risk of intellectual/developmental disabilities (IDD). DNA methylation-based epigenetic signatures are becoming clinically useful in categorizing benign from pathogenic genetic variants following exome sequencing. While DNA methylation marks differ by tissue source, recent studies have shown that accessible perinatal tissues, such as placenta, cord blood, newborn blood spots, and cell free DNA may serve as accessible surrogate tissues for testing epigenetic biomarkers relevant to understanding genetic, environmental, and gene by environment interactions on the developing brain. These DNA methylation signatures may also provide important information about the biological pathways that become dysregulated prior to disease progression that could be used to develop early pharmacological interventions. Future applications could involve preventative screenings using DNA methylation biomarkers during pregnancy or the newborn period for IDDs and other neurodevelopmental disorders. DNA methylation biomarkers in adolescence and adulthood are also likely to be clinically useful for tracking biological aging or co-occurring health conditions that develop across the lifespan. In conclusion, DNA methylation biomarkers are expected to become more common in clinical diagnoses of IDD, to improve understanding of complex IDD etiologies, to improve endpoints for clinical trials, and to monitor potential health concerns for individuals with IDD as they age.

Associations between lifestyle factors, physiological conditions, and epigenetic age acceleration in an Asian population

Epigenetic clocks use DNA methylation (DNAm) levels to predict an individual's biological age. However, relationships between lifestyle/biomarkers and epigenetic age acceleration (EAA) in Asian populations remain unknown. We here explored associations between lifestyle factors, physiological conditions, and epigenetic markers, including HannumEAA, IEAA, PhenoEAA, GrimEAA, DunedinPACE, DNAm-based smoking pack-years (DNAmPACKYRS), and DNAm plasminogen activator inhibitor 1 level (DNAmPAI1). A total of 2474 Taiwan Biobank (TWB) individuals aged between 30 and 70 provided physical health examinations, lifestyle questionnaire surveys, and blood and urine samples. Partial correlation analysis (while adjusting for chronological age, smoking, and drinking status) demonstrated that 29 factors were significantly correlated with at least one epigenetic marker (Pearson's correlation coefficient |r|> 0.15). Subsequently, by exploring the model with the smallest Akaike information criterion (AIC), we identified the best model for each epigenetic marker. As a DNAm-based marker demonstrated to predict healthspan and lifespan with greater accuracy, GrimEAA was also found to be better explained by lifestyle factors and physiological conditions. Totally 15 factors explained 44.7% variability in GrimEAA, including sex, body mass index (BMI), waist-hip ratio (WHR), smoking, hemoglobin A1c (HbA1c), high-density lipoprotein cholesterol (HDL-C), creatinine, uric acid, gamma-glutamyl transferase (GGT), hemoglobin, and five cell-type proportions. In summary, smoking, elevated HbA1c, BMI, WHR, GGT, and uric acid were associated with more than one kind of EAA. At the same time, higher HDL-C and hemoglobin were related to epigenetic age deceleration (EAD). These findings offer valuable insights into biological aging.

Early resilience and epigenetic ageing: Results from the prospective Young Finns Study with a 31-year follow-up

Evidence is accumulating on the connection of early adversities and harsh family environment with epigenetic ageing. We investigated whether early psychosocial resilience is associated with epigenetic ageing in adulthood. We used the population-based Young Finns data (n = 1593). Early psychosocial resilience was assessed in 1980-1989 across five broad domains: (1) index of psychological strength (self-esteem at home/in general/at school, perceived possibilities to influence at home, internal life control), (2) index of social satisfaction (perceived support from family/friends and life satisfaction), (3) index of leisure time activities (hobbies and physical fitness), (4) index of responsible health behaviors (infrequent smoking or alcohol consumption), and (5) index of school career (school grades and adaptation). Epigenetic ages were calculated for blood samples from 2011, and the analyses were performed with variables describing age deviation (AgeDevHannum, AgeDevHorvath, AgeDevPheno, AgeDevGrim) and DunedinPACE. Covariates included early family environment, polygenic risk scores for schizophrenia and major depression, adulthood education, and adulthood health behaviors. All of the early resilience indexes were associated with lower levels of epigenetic ageing in adulthood, most consistently with AgeDevGrim and DunedinPACE. The associations of psychological strength and social satisfaction, in particular, seemed to be non-linear. In a smaller subsample (n = 289), high early resilience was related to lower AgeDevGrim over a 25-year follow-up in those who had high "baseline" levels of AgeDevGrim. In conclusion, early resilience seems to associate with lower level of epigenetic ageing in adulthood. Our results tentatively suggest that early resilience may increase "equality in epigenetic ageing" in a general population.

Smoke's Enduring Legacy: Bridging Early-Life Smoking Exposures and Later-Life Epigenetic Age Acceleration

Current literature states that early-life exposure to smoking produces adverse health outcomes in later life, primarily as a result of subsequent engagements with firsthand smoking. The implications of prior research are that smoking cessation can reduce health risk in later life to levels comparable to the risk of those who have never smoked. However, recent evidence suggests that smoking exposure during childhood can have independent and permanent negative effects on health-in particular, on epigenetic aging. This investigation examines whether the effect of early-life firsthand smoking on epigenetic aging is more consistent with (1) a sensitive periods model, which is characterized by independent effects due to early firsthand exposures; or (2) a cumulative risks model, which is typified by persistent smoking. The findings support both models. Smoking during childhood can have long-lasting effects on epigenetic aging, regardless of subsequent engagements. Our evidence suggests that adult cessation can be effective but that the epigenetic age acceleration in later life is largely due to early firsthand smoking itself.

Precise and interpretable neural networks reveal epigenetic signatures of aging across youth in health and disease

Introduction

DNA methylation (DNAm) age clocks are powerful tools for measuring biological age, providing insights into aging risks and outcomes beyond chronological age. While traditional models are effective, their interpretability is limited by their dependence on small and potentially stochastic sets of CpG sites. Here, we propose that the reliability of DNAm age clocks should stem from their capacity to detect comprehensive and targeted aging signatures.

Methods

We compiled publicly available DNAm whole-blood samples (n = 17,726) comprising the entire human lifespan (0-112 years). We used a pre-trained network-coherent autoencoder (NCAE) to compress DNAm data into embeddings, with which we trained interpretable neural network epigenetic clocks. We then retrieved their age-specific epigenetic signatures of aging and examined their functional enrichments in age-associated biological processes.

Results

We introduce NCAE-CombClock, a novel highly precise (R2 = 0.978, mean absolute error = 1.96 years) deep neural network age clock integrating data-driven DNAm embeddings and established CpG age markers. Additionally, we developed a suite of interpretable NCAE-Age neural network classifiers tailored for adolescence and young adulthood. These clocks can accurately classify individuals at critical developmental ages in youth (AUROC = 0.953, 0.972, and 0.927, for 15, 18, and 21 years) and capture fine-grained, single-year DNAm signatures of aging that are enriched in biological processes associated with anatomic and neuronal development, immunoregulation, and metabolism. We showcased the practical applicability of this approach by identifying candidate mechanisms underlying the altered pace of aging observed in pediatric Crohn's disease.

Discussion

In this study, we present a deep neural network epigenetic clock, named NCAE-CombClock, that improves age prediction accuracy in large datasets, and a suite of explainable neural network clocks for robust age classification across youth. Our models offer broad applications in personalized medicine and aging research, providing a valuable resource for interpreting aging trajectories in health and disease.

The Genetic and Epigenetic Arms of Human Ageing and Longevity

This proposed review aims to shed light on the major genetic and epigenetic contributions to the ageing process and longevity of individuals. In this context, we summarize the state of knowledge on the most important longevity and ageing genetic variants, and their interactions with the environment, in achieving a healthy lifespan. We also explore the contribution of lifestyle and the influence of non-heritable environmental factors on ageing (i.e., epigenetics). Accordingly, we discuss the role of inflammageing as one of the major targets to overcome morbidity and mortality in older people for the maintenance of healthy ageing. This more integrated view of longevity will display not only the underlying mechanisms at play but also invites the reader to rethink both our ageing process and our attitudes toward age.

Proteomics-based aging clocks in midlife and late-life and risk of dementia

Background

Biological age can be quantified by composite proteomic scores, called aging clocks. We investigated whether biological age acceleration (a discrepancy between chronological and biological age) in midlife and late-life is associated with cognitive function and risk of dementia.

Methods

We used two population-based cohort studies: Atherosclerosis Risk in Communities (ARIC) Study and Multi-Ethnic Study of Atherosclerosis (MESA). Proteomics-based aging clocks (PACs) were created in ARIC at midlife (mean age: 58 years, n=11,758) and late-life (mean age: 77 years, n=4,934) using elastic net regression models in two-thirds of dementia-free participants and validated in the remaining one-third of participants. Age acceleration (AA) was calculated as residuals after regressing PACs on chronological age. We validated the midlife PAC in the MESA cohort (mean age: 62 years, n=5,829). We used multivariable linear and Cox proportional hazards regression to assess the association of AA with cognitive function and dementia incidence, respectively.

Results

In ARIC, every five years AA was associated with lower global cognitive function: difference: -0.11, 95% confidence interval (CI): -0.16, -0.06) using midlife AA and difference: -0.17, CI: -0.23, -0.12 using late-life AA. Consistently, midlife AA was associated with higher risk of dementia (hazard ratio [HR]: 1.20 [CI: 1.04, 1.36]) and more prominently when using late-life AA (HR: 2.14 [CI:1.67, 2.73]). Similar findings were observed in the MESA study: every five years AA was associated with lower global cognitive function (difference: -0.08 [CI: -0.14, -0.03]) and higher risk of dementia (HR:1.23 [CI: 1.04, 1.46]).

Conclusion

Accelerated biological age - as defined by the plasma proteome - is associated with lower cognitive function and predicts a higher risk of dementia in midlife and more prominently in late-life.

Insights to aging prediction with AI based epigenetic clocks

Over the past century, human lifespan has increased remarkably, yet the inevitability of aging persists. The disparity between biological age, which reflects pathological deterioration and disease, and chronological age, indicative of normal aging, has driven prior research focused on identifying mechanisms that could inform interventions to reverse excessive age-related deterioration and reduce morbidity and mortality. DNA methylation has emerged as an important predictor of age, leading to the development of epigenetic clocks that quantify the extent of pathological deterioration beyond what is typically expected for a given age. Machine learning technologies offer promising avenues to enhance our understanding of the biological mechanisms governing aging by further elucidating the gap between biological and chronological ages. This perspective article examines current algorithmic approaches to epigenetic clocks, explores the use of machine learning for age estimation from DNA methylation, and discusses how refining the interpretation of ML methods and tailoring their inferences for specific patient populations and cell types can amplify the utility of these technologies in age prediction. By harnessing insights from machine learning, we are well-positioned to effectively adapt, customize and personalize interventions aimed at aging.

The prospective association of cellular markers of biological aging with menopause in the Coronary Artery Risk Development in Young Adults Study

OBJECTIVE

Evidence from cross-sectional studies mainly among postmenopausal women suggests that biological aging is associated with reproductive senescence. We evaluated the prospective association of cellular markers of biological aging measured during the premenopausal period, and changes in these markers, with age at menopause.

METHODS

We studied 583 premenopausal women (39% Black) from the Coronary Artery Risk Development in Young Adults Study who had data on biological aging markers in 2000-2001 and reached menopause by 2020-2021. Linear regression models were used to evaluate the association of telomere length, mitochondrial DNA copy number, intrinsic or extrinsic epigenetic age acceleration, and PhenoAge or GrimAge acceleration with age at menopause.

RESULTS

The mean age at baseline was 41.2 ± 3.3 years, with the mean age at menopause being 49.1 (median, 50) years. About one in five women had surgical menopause. In chronological age-adjusted models, only baseline GrimAge acceleration was associated with age at menopause; women whose epigenetic age was older than their chronological age reached menopause at 0.12 years (~6 weeks) earlier compared with women with equal epigenetic and chronological age ( β = -0.123; 95% CI, -0.224 to -0.022; P = 0.018). However, this association was not statistically significant after adjustment for sociodemographic, behavior/lifestyle, and metabolic factors. Similar results were observed when changes in these biological aging markers were evaluated. The same associations were observed in analyses limited to women who reached natural menopause.

CONCLUSIONS

Sociodemographic, behavior/lifestyle, and metabolic factors remain comparable, if not more robust predictors of the age at menopause compared with cellular measures of biological age.

Depression and Accelerated Aging: The Eveningness Chronotype and Low Adherence to the Mediterranean Diet Are Associated with Depressive Symptoms in Older Subjects

BACKGROUND AND OBJECTIVES

Depression often results in premature aging, which increases the risk of other chronic diseases, but very few studies have analyzed the association between epigenetic biomarkers of aging and depressive symptoms. Similarly, limited research has examined the joint effects of adherence to the Mediterranean diet (MedDiet) and chronotype on depressive symptoms, accounting for sex differences. Therefore, these are the objectives of our investigation in a Mediterranean population at high cardiovascular risk.

METHODS

We analyzed 465 older subjects (aged 55-75) with metabolic syndrome and assessed depressive symptoms by the Beck Depression Inventory (BDI-II). MedDiet adherence was measured with the 17-item MedDiet score, and chronotype with the Morningness-Eveningness Questionnaire (MEQ). Blood DNA methylation was analyzed, and epigenomic biomarkers of age acceleration were determined. We focused on the Dunedin Pace of Aging Computed from the Epigenome (DunedinPACE). We fitted multivariable models with interaction terms.

RESULTS

Prevalence of depression was statistically higher in women (p < 0.001). MedDiet adherence was strongly and inversely associated with depressive symptoms in the whole population (p < 0.01), while the MEQ score was inversely associated (p < 0.05). In the joint analysis, both MedDiet adherence and chronotype remained statistically associated with the BDI-II score (p < 0.05), showing additive effects. No interaction effects were observed. In women, a higher score in depressive symptoms was significantly associated with faster age acceleration (measured with the DunedinPACE biomarker). This association remained significant even after adjustment for MedDiet adherence and chronotype.

CONCLUSIONS

In older subjects with metabolic syndrome, the eveningness chronotype was associated with greater depressive symptoms, but a higher adherence to the MedDiet could potentially counteract the chronotype risk with additive effects. Women showed stronger associations, and importantly, we reported for the first time in this population that depressive symptoms were associated with accelerated aging.

Epigenetic Clocks: Beyond Biological Age, Using the Past to Predict the Present and Future

Predicting health trajectories and accurately measuring aging processes across the human lifespan remain profound scientific challenges. Assessing the effectiveness and impact of interventions targeting aging is even more elusive, largely due to the intricate, multidimensional nature of aging-a process that defies simple quantification. Traditional biomarkers offer only partial perspectives, capturing limited aspects of the aging landscape. Yet, over the past decade, groundbreaking advancements have emerged. Epigenetic clocks, derived from DNA methylation patterns, have established themselves as powerful aging biomarkers, capable of estimating biological age and assessing aging rates across diverse tissues with remarkable precision. These clocks provide predictive insights into mortality and age-related disease risks, effectively distinguishing biological age from chronological age and illuminating enduring questions in gerontology. Despite significant progress in epigenetic clock development, substantial challenges remain, underscoring the need for continued investigation to fully unlock their potential in the science of aging.

Epigenetic Mechanisms in Aging: Extrinsic Factors and Gut Microbiome

BACKGROUND/OBJECTIVES

Aging is a natural physiological process involving biological and genetic pathways. Growing evidence suggests that alterations in the epigenome during aging result in transcriptional changes, which play a significant role in the onset of age-related diseases, including cancer, cardiovascular disease, diabetes, and neurodegenerative disorders. For this reason, the epigenetic alterations in aging and age-related diseases have been reviewed, and the major extrinsic factors influencing these epigenetic alterations have been identified. In addition, the role of the gut microbiome and its metabolites as epigenetic modifiers has been addressed.

RESULTS

Long-term exposure to extrinsic factors such as air pollution, diet, drug use, environmental chemicals, microbial infections, physical activity, radiation, and stress provoke epigenetic changes in the host through several endocrine and immune pathways, potentially accelerating the aging process. Diverse studies have reported that the gut microbiome plays a critical role in regulating brain cell functions through DNA methylation and histone modifications. The interaction between genes and the gut microbiome serves as a source of adaptive variation, contributing to phenotypic plasticity. However, the molecular mechanisms and signaling pathways driving this process are still not fully understood.

CONCLUSIONS

Extrinsic factors are potential inducers of epigenetic alterations, which may have important implications for longevity. The gut microbiome serves as an epigenetic effector influencing host gene expression through histone and DNA modifications, while bidirectional interactions with the host and the underexplored roles of microbial metabolites and non-bacterial microorganisms such as fungi and viruses highlight the need for further research.

Development of a novel transcriptomic measure of aging: Transcriptomic Mortality-risk Age (TraMA)

Increasingly, research suggests that aging is a coordinated multi-system decline in functioning that occurs at multiple biological levels. We developed and validated a transcriptomic (RNA-based) aging measure we call Transcriptomic Mortality-risk Age (TraMA) using RNA-seq data from the 2016 Health and Retirement Study using elastic net Cox regression analyses to predict 4-year mortality hazard. In a holdout test sample, TraMA was associated with earlier mortality, more chronic conditions, poorer cognitive functioning, and more limitations in activities of daily living. TraMA was also externally validated in the Long Life Family Study and several publicly available datasets. Results suggest that TraMA is a robust, portable RNAseq-based aging measure that is comparable, but independent from past biological aging measures (e.g., GrimAge). TraMA is likely to be of particular value to researchers interested in understanding the biological processes underlying health and aging, and for social, psychological, epidemiological, and demographic studies of health and aging.

Assessing the utility of epigenetic clocks for health prediction in South Korean

Epigenetic clocks have been developed to track both chronological age and biological age, which is defined by physiological biomarkers and the risk of adverse health outcomes. Epigenetic age acceleration (EAA) has been found to predict various diseases, aging-related factors, and mortality. However, epigenetic clocks have predominantly been developed with individuals of European or Hispanic ancestry, and their association with health outcomes and environmental factors has not been sufficiently assessed in East Asian populations. Here, we investigated nine epigenetic clocks: five trained on chronological age (first-generation) and four on biological age (second-generation), using DNA methylation data from blood samples of South Koreans. EAAs of second-generation epigenetic clocks reflected the risk of chronic diseases (type 2 diabetes and hypertension), levels of health-related blood markers (alanine aminotransferase, aspartate aminotransferase, high density lipoprotein, triglyceride, and high sensitivity C-reactive protein), and lung functions (percentage of predicted FEV1 and percentage of predicted FVC), while EAAs of first generation clocks did not. Using follow-up data, we also found that EAAs of second-generation clocks were associated with the time to onset risks of chronic diseases. Health behavior factors (drinking, smoking, exercise, body mass index, and waist-hip ratio), socioeconomic status (income level and educational attainment), and psychosocial status were associated with EAAs of second-generation clocks, while only smoking status was associated with EAAs of first-generation clocks. We conducted validation analyses in an independent South Korean cohort and replicated the association of EAAs with health outcomes and environmental factors. Age acceleration of epigenetic clocks is influenced by various environmental factors and can serve as an effective predictor of health in South Korea.

Socioeconomic-Status-Based Disrespect, Discrimination, Exclusion, and Shaming: A Potential Source of Health Inequalities?

Observing an association between socioeconomic status (SES) and health reliably leads to the question, "What are the pathways involved?" Despite enormous investment in research on the characteristics, behaviors, and traits of people disadvantaged with respect to health inequalities, the issue remains unresolved. We turn our attention to actions of more advantaged groups by asking people to self-report their exposure to disrespect, discrimination, exclusion, and shaming (DDES) from people above them in the SES hierarchy. We developed measures of these phenomena and administered them to a cross-sectional U.S. national probability sample (N = 1,209). Consistent with the possibility that DDES represents a pathway linking SES and health, the SES→health coefficient dropped substantially when DDES variables were controlled: 112.9% for anxiety, 43.8% for self-reported health, and 49.4% for cardiovascular-related conditions. These results illustrate a need for a relational approach emphasizing the actions of more advantaged groups in shaping health inequities.

The role of patient-specific variables in protein corona formation and therapeutic efficacy in nanomedicine

Despite their potential, the adoption of nanotechnology in therapeutics remains limited, with only around eighty nanomedicines approved in the past 30 years. This disparity is partly due to the "one-size-fits-all" approach in medical design, which often overlooks patient-specific variables such as biological sex, genetic ancestry, disease state, environment, and age that influence nanoparticle behavior. Nanoparticles (NPs) must be transported through systemic, microenvironmental, and cellular barriers that vary across heterogeneous patient populations. Key patient-dependent properties impacting NP delivery include blood flow rates, body fat distribution, reproductive organ vascularization, hormone and protein levels, immune responses, and chromosomal differences. Understanding these variables is crucial for developing effective, patient-specific nanotechnologies. The formation of a protein corona around NPs upon exposure to biological fluids significantly alters NP properties, affecting biodistribution, pharmacokinetics, cytotoxicity, and organ targeting. The dynamics of the protein corona, such as time-dependent composition and formation of soft and hard coronas, depend on NP characteristics and patient-specific serum components. This review highlights the importance of understanding protein corona formation across different patient backgrounds and its implications for NP design, including sex, ancestry, age, environment, and disease state. By exploring these variables, we aim to advance the development of personalized nanomedicine, improving therapeutic efficacy and patient outcomes.

Causal associations and shared genetic etiology of neurodegenerative diseases with epigenetic aging and human longevity

The causative mechanisms underlying the genetic relationships of neurodegenerative diseases with epigenetic aging and human longevity remain obscure. We aimed to detect causal associations and shared genetic etiology of neurodegenerative diseases with epigenetic aging and human longevity. We obtained large-scale genome-wide association study summary statistics data for four measures of epigenetic age (GrimAge, PhenoAge, IEAA, and HannumAge) (N = 34,710), multivariate longevity (healthspan, lifespan, and exceptional longevity) (N = 1,349,462), and for multiple neurodegenerative diseases (N = 6618-482,730), including Lewy body dementia, Alzheimer's disease (AD), Parkinson's disease, amyotrophic lateral sclerosis, and multiple sclerosis. Main analyses were conducted using multiplicative random effects inverse-variance weighted Mendelian randomization (MR), and conditional/conjunctional false discovery rate (cond/conjFDR) approach. Shared genomic loci were functionally characterized to gain biological understanding. Evidence showed that AD patients had 0.309 year less in exceptional longevity (IVW beta = -0.309, 95% CI: -0.38 to -0.24, p = 1.51E-19). We also observed suggestively significant causal evidence between AD and GrimAge age acceleration (IVW beta = -0.10, 95% CI: -0.188 to -0.013, p = 0.02). Following the discovery of polygenic overlap, we identified rs78143120 as shared genomic locus between AD and GrimAge age acceleration, and rs12691088 between AD and exceptional longevity. Among these loci, rs78143120 was novel for AD. In conclusion, we observed that only AD had causal effects on epigenetic aging and human longevity, while other neurodegenerative diseases did not. The genetic overlap between them, with mixed effect directions, suggested complex shared genetic etiology and molecular mechanisms.

Associations between Sleep and Physical Activity Behavior Clusters and Epigenetic Age Acceleration in Mexican Adolescents

INTRODUCTION

Epigenetic aging, a marker of biological aging measured by DNA methylation, may be affected by behaviors, including sleep and physical activity. However, investigations of physical activity and sleep with epigenetic aging among pediatric populations are scant and have not accounted for correlated behaviors.

METHODS

The study population included 472 Mexico City adolescents (52% female). Blood collection and 7-d wrist actigraphy (Actigraph GTX-BT) occurred during a follow-up visit when participants were 14.5 (2.09) yr. Leukocyte DNA methylation was measured with the Infinium MethylationEPIC array after bisulfite conversion, and nine epigenetic clocks were calculated. Sleep versus wake time was identified through a pruned dynamic programing algorithm, and physical activity was processed with Chandler cutoffs. Kmeans clustering was used to select actigraphy-assessed physical activity and sleep behavior clusters. Linear regression analyses were used to evaluate adjusted associations between the clusters and epigenetic aging.

RESULTS

There were three unique clusters: "Short sleep/high sedentary behavior," "Adequate sleep duration and late sleep timing/low moderate or vigorous physical activity (MVPA)," and "Adequate sleep duration/high MVPA." Compared with the "Adequate duration/high MVPA," adolescents with "Adequate duration and late sleep timing/low MVPA" had more accelerated aging for the GrimAge clock ( β = 0.63; 95% confidence interval, 0.07-1.19). In pubertal-stratified analyses, more mature adolescents in the "Adequate sleep duration and late sleep timing/low MVPA group" had accelerated epigenetic aging. In contrast, females in the "Short sleep/high sedentary" group had decelerated epigenetic aging for the Wu pediatric clock.

CONCLUSIONS

Associations between behavior clusters and epigenetic aging varied by pubertal status and sex. Contrary results in the Wu clock suggest the need for future research on pediatric-specific clocks.

The Long Shadow of Early Poverty: Poverty at Birth, Epigenetic Changes at Age 15, And Youth Outcomes at Age 22

Background

Early life socioeconomic conditions and race/ethnicity are critical determinants of long-term health and behavioral outcomes. Epigenetic changes, particularly those measured by the GrimAge biomarker, may mediate the impact of these early adversities on later life outcomes. This study investigates the relationships between race/ethnicity, poverty at birth, epigenetic aging at age 15, and subsequent self-rated health, school discipline, depression, and school dropout at age 22. We explored sex differences in these paths.

Methods

Data were drawn from the Fragile Families and Child Wellbeing Study (FFCWS), which included 733 youth with comprehensive follow-up data up to age 22. Structural Equation Modeling (SEM) was employed to assess the pathways from race/ethnicity and poverty at birth to epigenetic aging (GrimAge) at age 15, and subsequently to self-rated health and school discipline at age 22. The model controlled for potential confounders including sex, family structure, and parental education.

Results

Race/ethnicity and poverty at birth were significantly associated with higher GrimAge scores at age 15 (p < 0.05). Higher GrimAge scores were predictive of poorer self-rated health (β = -0.08, p < 0.05) and increased instances of school discipline (β = 0.13, p < 0.01) at age 22. The indirect effects of race/ethnicity and poverty at birth on self-rated health and school discipline through GrimAge were also significant (p < 0.05), suggesting that epigenetic aging partially mediates these relationships. Sex differences were also observed. Poverty at birth predicted faster epigenetic aging at age 15 for males not females. We also observed that faster epigenetic aging at age 15 was predictive of school discipline of male not female participants at age 22. In contrast, faster epigenetic aging at age 15 was predictive of self-rated health (SRH) of female not male participants at age 22.

Conclusions

This study provides evidence that with some sex differences, race/ethnicity and poverty at birth contribute to accelerated epigenetic aging (GrimAge) by age 15, which in turn predicts poorer self-rated health and increased school discipline issues by age 22. These findings emphasize the importance of early interventions targeting social determinants to mitigate long-term health and behavioral disparities. Addressing these early life conditions is crucial for improving health equity and outcomes in young adulthood.

Accelerated epigenetic aging and prospective morbidity and mortality among U.S. veterans

Epigenetic measures of aging derived from DNA methylation are promising biomarkers associated with prospective morbidity and mortality, but require validation in real-world medical settings. Using data from 2,216 post-9/11 veterans, we examined whether accelerated DunedinPACE aging scores were associated with chronic disease morbidity, predicted healthcare costs, and mortality assessed over an average of 13.1 years of follow up in VA electronic health records. Veterans with faster DunedinPACE aging scores developed more chronic disease and showed larger increases in predicted healthcare costs over the subsequent 5, 10, and 15 years. Faster aging was associated with incident myocardial infarction, stroke, diabetes, cancer, liver disease, and renal disease, as well greater risk of mortality due to all-causes and chronic disease. These findings provide evidence that accelerated epigenetic aging is associated with worsening prospective health across multiple chronic diseases and organ systems assessed using electronic health records from an integrated healthcare system.

DNA Methylation Age Mediates Effect of Metabolic Profile on Cardiovascular and General Aging

BACKGROUND

Alterations in lipid metabolism and DNA methylation are 2 hallmarks of aging. Connecting metabolomic, epigenomic, and aging outcomes help unravel the complex mechanisms underlying aging. We aimed to assess whether DNA methylation clocks mediate the association of circulating metabolites with incident atherosclerotic cardiovascular disease (ASCVD) and frailty.

METHODS

The China Kadoorie Biobank is a prospective cohort study with a baseline survey from 2004 to 2008 and a follow-up period until December 31, 2018. We used the Infinium Methylation EPIC BeadChip to measure the methylation levels of 988 participants' baseline blood leukocyte DNA. Metabolite profiles, including lipoprotein particles, lipid constituents, and various circulating metabolites, were measured using quantitative nuclear magnetic resonance. The pace of DNA methylation age acceleration (AA) was calculated using 5 widely used epigenetic clocks (the first generation: Horvath, Hannum, and Li; the second generation: Grim and Pheno). Incident ASCVD was ascertained through linkage with local death and disease registries and national health insurance databases, supplemented by active follow-up. The frailty index was constructed using medical conditions, symptoms, signs, and physical measurements collected at baseline.

RESULTS

A total of 508 incident cases of ASCVD were documented during a median follow-up of 9.5 years. The first generation of epigenetic clocks was associated with the risk of ASCVD (P<0.05). For each SD increment in LiAA, HorvathAA, and HannumAA, the corresponding hazard ratios for ASCVD risk were 1.16 (1.05-1.28), 1.10 (1.00-1.22), and 1.17 (1.04-1.31), respectively. Only LiAA mediated the association of various metabolites (lipids, fatty acids, histidine, and inflammatory biomarkers) with ASCVD, with the mediating proportion reaching up to 15% for the diameter of low-density lipoprotein (P=1.2×10-2). Regarding general aging, a 1-SD increase in GrimAA was associated with an average increase of 0.10 in the frailty index (P=2.0×10-3), and a 33% and 63% increased risk of prefrailty and frailty at baseline (P=1.5×10-2 and 5.8×10-2), respectively; this association was not observed with other clocks. GrimAA mediated the effect of various lipids, fatty acids, glucose, lactate, and inflammatory biomarkers on the frailty index, with the mediating proportion reaching up to 22% for triglycerides in very small-sized very low-density lipoprotein (P=6.0×10-3).

CONCLUSIONS

These findings suggest that epigenomic mechanisms may play a role in the associations between circulating metabolites and the aging process. Different mechanisms underlie the first and second generations of DNA methylation age in cardiovascular and general aging.