Genome-wide methylation profiles reveal quantitative views of human aging rates

Multiplexing and massive parallel sequencing of targeted DNA methylation to predict chronological age

Estimation of chronological age is particularly informative in forensic contexts. Assessment of DNA methylation status allows for the prediction of age, though the accuracy may vary across models. In this study, we started with a carefully designed discovery cohort with more elderly subjects than other age categories, to diminish the effect of epigenetic drifting. We applied multiplexing and massive parallel sequencing of targeted DNA methylation, which let us to construct a model comprising 25 CpG sites with substantially improved accuracy (MAE = 2.279, R = 0.920). This model is further validated by an independent cohort (MAE = 2.204, 82.7% success (±5 years)). Remarkably, in a multi-center test using trace blood samples from forensic caseworks, the correct predictions (±5 years) are 91.7%. The nature of our analytical pipeline can easily be scaled up with low cost. Taken together, we propose a new age-prediction model featuring accuracy, sensitivity, high-throughput, and low cost. This model can be readily applied in both classic and newly emergent forensic contexts that require age estimation.

Ambient outdoor heat and accelerated epigenetic aging among older adults in the US

Extreme heat is well-documented to adversely affect health and mortality, but its link to biological aging-a precursor of the morbidity and mortality process-remains unclear. This study examines the association between ambient outdoor heat and epigenetic aging in a nationally representative sample of US adults aged 56+ (N = 3686). The number of heat days in neighborhoods is calculated using the heat index, covering time windows from the day of blood collection to 6 years prior. Multilevel regression models are used to predict PCPhenoAge acceleration, PCGrimAge acceleration, and DunedinPACE. More heat days over short- and mid-term windows are associated with increased PCPhenoAge acceleration (e.g., Bprior7-dayCaution+heat: 1.07 years). Longer-term heat is associated with all clocks (e.g., Bprior1-yearExtremecaution+heat: 2.48 years for PCPhenoAge, Bprior1-yearExtremecaution+heat: 1.09 year for PCGrimAge, and Bprior6-yearExtremecaution+heat: 0.05 years for DunedinPACE). Subgroup analyses show no strong evidence for increased vulnerability by sociodemographic factors. These findings provide insights into the biological underpinnings linking heat to aging-related morbidity and mortality risks.

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.

Epigenetic entropy, social disparity, and health and lifespan in the Women's Health Initiative

The pace of aging varies between individuals and is marked by changes in DNA methylation (DNAm) including an increase in randomness or entropy. Here, we computed epigenetic scores of aging and entropy using DNAm datasets from the Women's Health Initiative (WHI). We investigated how different epigenetic aging metrics relate to demographic and health variables, and mortality risk. Income and education, two proxies of socioeconomics (SE), had consistent associations with epigenetic aging and entropy. Notably, stochastic increases in DNAm at sites targeted by the polycomb proteins were significantly related to both aging and SE. While higher income was associated with reduced age-related DNAm changes in White women, the protective effect of income was diminished in Black and Hispanic women, and on average, Black and Hispanic women had relatively more aged epigenomes. Faster pace of aging, as estimated by the DunedinPACE, predicted higher mortality risk, while the maintenance of methylation at enhancer regions was associated with improved survival. Our findings demonstrate close ties between social and economic factors and aspects of epigenetic aging, suggesting potential biological mechanisms through which societal disparities may contribute to differences in health outcomes and lifespan across demographic groups.

Job involvement in heavy manual or physical work associated with epigenetic age: A Mendelian randomization study

Engaging in heavy manual or physical work has been linked to various health outcomes, but its effect on physiological aging, as measured by epigenetic clocks, remains unclear. This study aims to analyze the causal relationship between heavy manual or physical work and epigenetic age acceleration using Mendelian randomization (MR). In this study, we explored the causal link between job involvement in heavy manual or physical work and epigenetic age acceleration measured by 4 different epigenetic clocks using 2-sample MR. Our analytical approach included inverse-variance weighting (IVW), MR-Egger, weighted median, and weighted mode methods. The primary analyses utilized IVW with random effects, supplemented by sensitivity and heterogeneity tests using both IVW and MR-Egger. MR-Egger was also applied for pleiotropy testing. Additionally, a leave-one-out analysis helped identify potentially impactful single-nucleotide polymorphisms. The analysis revealed a positive association between heavy manual or physical work and epigenetic clock acceleration. There statistically significant associations between heavy manual or physical work with a higher PhenoAge and HannumAge acceleration (β = 1.692, 95% CI [0.349 to 3.035], P = .013 for PhenoAge; β = 0.917, 95% CI [0.024 to 1.809], P = .044 for HannumAge, respectively). The heterogeneity test revealed that our IVW analysis exhibited minimal heterogeneity (P > .05), and the pleiotropy test findings confirmed the absence of pleiotropy within our IVW analysis (P > .05). Our study provides partial evidence for a causal effect between heavy manual or physical work and epigenetic age acceleration. Further experimental research is required to confirm these findings.

Identification of a novel hypermethylation marker, ZSCAN18, and construction of a diagnostic model in cervical cancer

PURPOSE

Cervical cancer (CC), a common female malignancy, has been linked to alterations in DNA methylation. This study employed an integrated "dry-wet lab" strategy combining bioinformatics, machine learning, and experimental validation to identify novel methylation biomarkers for CC.

METHODS

Methylome and transcriptome data from the TCGA and GEO cohorts (n=349 discovery, n=414 validation) were analyzed to identify differentially methylated CpGs. The top candidates were validated by pyrosequencing, methylation-specific PCR, and quantitative assays. Diagnostic models were developed, and functional studies were performed for the target markers.

RESULTS

Eighteen differentially methylated CpGs were identified, with five top candidates (three in the ZSCAN18 promoter) showing diagnostic potential. ZSCAN18 promoter methylation levels and positivity rates were significantly greater in CC tissues than in normal tissues (p<0.05), reaching 77.8% (21/27) in ThinPrep cytology test (TCT) samples. The ridge regression diagnostic model achieved an AUC of 0.9421 in the validation cohort. Similarly, ZSCAN18 overexpression suppressed CC cell proliferation (p<0.05).

CONCLUSIONS

This study established a rapid, effective and systematic systemic research strategy to screen novel methylation markers for CC. ZSCAN18 promoter methylation correlates with cervical lesion severity, and the diagnostic model enhances the diagnostic ability. These findings highlight the dual role of ZSCAN18 as a diagnostic marker and potential therapeutic target.

Blood and Bone-Derived DNA Methylation Ages Predict Mortality After Geriatric Hip Fracture: A Pilot Study

BACKGROUND

The purpose of this study was to (1) perform the first analysis of bone-derived DNA methylation, (2) compare DNA methylation clocks derived from bone with those derived from whole blood, and (3) establish a relationship between DNA methylation age and 1-year mortality within the geriatric hip fracture population.

METHODS

Patients ≥65 years old who presented to a Level-I trauma center with a hip fracture were prospectively enrolled from 2020 to 2021. Preoperative whole blood and intraoperative bone samples were collected. Following DNA extraction, RRBS (reduced representation bisulfite sequencing) libraries for methylation clock analysis were prepared. Sequencing data were analyzed using computational algorithms previously described by Horvath et al. to build a regression model of methylation (biological) age for each tissue type. Student t tests were used to analyze differences (Δ) in methylation age versus chronological age. Correlation between blood and bone methylation ages was expressed using the Pearson R coefficient.

RESULTS

Blood and bone samples were collected from 47 patients. DNA extraction, sequencing, and methylation analysis were performed on 24 specimens from 12 subjects. Mean age at presentation was 85.4 ± 8.65 years. There was no difference in DNA extraction yield between the blood and bone samples (p = 0.935). The mean follow-up duration was 12.4 ± 4.3 months. The mortality cohort (4 patients, 33%) showed a mean ΔAgeBone of 18.33 ± 6.47 years and mean ΔAgeBlood of 16.93 ± 4.02 years. In comparison, the survival cohort showed a significantly lower mean ΔAgeBone and ΔAgeBlood (7.86 ± 6.7 and 7.31 ± 7.71 years; p = 0.026 and 0.039, respectively). Bone-derived methylation age was strongly correlated with blood-derived methylation age (R = 0.81; p = 0.0016).

CONCLUSIONS

Bone-derived DNA methylation clocks were found to be both feasible and strongly correlated with those derived from whole blood within a geriatric hip fracture population. Mortality was independently associated with the DNA methylation age, and that age was approximately 17 years greater than chronological age in the mortality cohort. The results of the present study suggest that prevention of advanced DNA methylation may play a key role in decreasing mortality following hip fracture.

LEVEL OF EVIDENCE

Prognostic Level I . See Instructions for Authors for a complete description of levels of evidence.

Molecular counting enables accurate and precise quantification of methylated ctDNA for tumor-naive cancer therapy response monitoring

Personalized cancer treatment can significantly extend survival and improve quality of life for many patients, but accurate and real-time therapy response monitoring remains challenging. To overcome logistical and technical challenges associated with therapy response monitoring via imaging scans or assays that track the variant allele fraction (VAF) of somatic mutations in circulating tumor DNA (ctDNA), we developed a tumor-naive liquid biopsy assay that leverages Quantitative Counting Template (QCT) technology to accurately and precisely quantify methylated ctDNA (Northstar Response™). The assay achieves < 10% coefficient of variation at 1% tumor fraction, which is 2 × lower than tumor-naive, targeted-panel approaches using VAF. The assay accurately distinguishes 0.25% absolute changes in contrived tumor fraction (AUC > 0.94) and performs well in 12 solid tumor types. Finally, in a small cohort of patients with lung, colorectal, or pancreatic cancer, the assay detected changes in ctDNA methylation that correlate with clinical outcomes. With its precise quantification of ctDNA methylation, Northstar Response is a novel tool for therapy response monitoring with the potential to inform clinical decision making for cancer treatment.

An Update on Neuroaging on Earth and in Spaceflight

Over 400 articles on the pathophysiology of brain aging, neuroaging, and neurodegeneration were reviewed, with a focus on epigenetic mechanisms and numerous non-coding RNAs. In particular, this review the accent is on microRNAs, the discovery of whose pivotal role in gene regulation was recognized by the 2024 Nobel Prize in Physiology or Medicine. Aging is not a gradual process that can be easily modeled and described. Instead, multiple temporal processes occur during aging, and they can lead to mosaic changes that are not uniform in pace. The rate of change depends on a combination of external and internal factors and can be boosted in accelerated aging. The rate can decrease in decelerated aging due to individual structural and functional reserves created by cognitive, physical training, or pharmacological interventions. Neuroaging can be caused by genetic changes, epigenetic modifications, oxidative stress, inflammation, lifestyle, and environmental factors, which are especially noticeable in space environments where adaptive changes can trigger aging-like processes. Numerous candidate molecular biomarkers specific to neuroaging need to be validated to develop diagnostics and countermeasures.

Methylation Biomarkers of Lung Cancer Risk: A Systematic Review and Meta-Analysis

Background: Lung cancer (LC) is the leading cause of cancer deaths worldwide among both men and women, and represents a major public health challenge. DNA methylation (DNAm) has shown potential in identifying individuals at higher risk of LC, but the overall evidence has not been systematically evaluated. This review and meta-analysis aims to evaluate and summarize existing research on the association between blood DNAm levels and LC risk. Methods: Searches were conducted in PubMed, Web of Science, and Scopus for studies published until February 2024, following PRISMA and MOOSE guidelines. Eleven studies met the eligibility criteria. Results: Using a random effects model, our pooled analysis showed a significant association between increased DNAm levels and LC risk (OR 1.24, 95% CI 1.10-1.39; I2 = 93.90%, p = 0.0001). Stratifying the results by study design showed a stronger association in two prospective cohort studies (OR 1.61; 95% CI 1.36-1.90; I2 = 14.42%, p = 0.32), while case-control studies showed a weaker association (OR 1.05; 95% CI 0.99-1.11; I2 = 70.57%, p = 0.0001). Sensitivity analyses indicated that omitting individual studies did not significantly alter the LC risk estimates. Conclusions: These findings suggest that higher blood DNAm levels are associated with an increased risk of LC, especially in long-term cohort studies. Further research is recommended to explore the potential of DNAm as a screening biomarker for LC and to clarify the role of other influencing factors.

Healthy Aging Metabolomic and Proteomic Signatures Across Multiple Physiological Compartments

The study of biomarkers in biofluids and tissues expanded our understanding of the biological processes that drive physiological and functional manifestations of aging. However, most of these studies were limited to examining one biological compartment, an approach that fails to recognize that aging pervasively affects the whole body. The simultaneous modeling of hundreds of metabolites and proteins across multiple compartments may provide a more detailed picture of healthy aging and point to differences between chronological and biological aging. Herein, we report proteomic analyses of plasma and urine collected in healthy men and women, age 22-92 years. Using these data, we developed a series of metabolomic and proteomic predictors of chronological age for plasma, urine, and skeletal muscle. We then defined a biological aging score, which measures the departure between an individual's predicted age and the expected predicted age for that individual based on the full cohort. We show that these predictors are significantly and independently related to clinical phenotypes important for aging, such as inflammation, iron deficiency anemia, muscle mass, and renal and hepatic functions. Despite a different set of selected biomarkers in each compartment, the different scores reflect a similar degree of deviation from healthy aging in single individuals, thus allowing identification of subjects with significant accelerated or decelerated biological aging.

Uncertainty quantification in epigenetic clocks via conformalized quantile regression

DNA methylation (DNAm) is a chemical modification of DNA that can be influenced by various factors, including age, the environment, and lifestyle. An epigenetic clock is a predictive tool that measures biological age based on DNAm levels. It can provide insights into an individual's biological age, which may differ from their chronological age. This difference, known as the epigenetic age acceleration, may reflect health status and the risk for age-related diseases. Moreover, epigenetic clocks are used in studies of aging to assess the effectiveness of anti-aging interventions and to understand the underlying mechanisms of aging and disease. Various epigenetic clocks have been developed using samples from different populations, tissues, and cell types, typically by training high-dimensional linear regression models with an elastic net penalty. While these models can predict mean biological age based on DNAm with high precision, there is a lack of uncertainty quantification which is important for interpreting the precision of age estimations and for clinical decision-making. To understand the distribution of a biological age clock beyond its mean, we propose a general pipeline for training epigenetic clocks, based on an integration of high-dimensional quantile regression and conformal prediction, to effectively reveal population heterogeneity and construct prediction intervals. Our approach produces adaptive prediction intervals not only achieving nominal coverage but also accounting for the inherent variability across individuals. By using the data collected from 728 blood samples in 11 DNAm datasets from children, we find that our quantile regression-based prediction intervals are narrower than those derived from conventional mean regression-based epigenetic clocks. This observation demonstrates an improved statistical efficiency over the existing pipeline for training epigenetic clocks. In addition, the resulting intervals have a synchronized varying pattern to age acceleration, effectively revealing cellular evolutionary heterogeneity in age patterns in different developmental stages during individual childhoods and adolescent cohort. Our findings suggest that conformalized high-dimensional quantile regression can produce valid prediction intervals and uncover underlying population heterogeneity. Although our methodology focuses on the distribution of measures of biological aging in children, it is applicable to a broader range of age groups to improve understanding of epigenetic age beyond the mean. This inference-based toolbox could provide valuable insights for future applications of epigenetic interventions for age-related diseases.

Scalable Screening of Ternary-Code DNA Methylation Dynamics Associated with Human Traits

Epigenome-wide association studies (EWAS) are transforming our understanding of the interplay between epigenetics and complex human traits and phenotypes. We introduce the Methylation Screening Array (MSA), a new iteration of the Infinium technology for scalable and quantitative screening of trait associations of nuanced ternary-code cytosine modifications in larger, more inclusive, and stratified human populations. MSA integrates EWAS, single-cell, and cell-type-resolved methylome profiles, covering diverse human traits and diseases. Our first MSA applications yield multiple biological insights: we revealed a previously unappreciated role of 5-hydroxymethylcytosine (5hmC) in trait associations and epigenetic clocks. We demonstrated that 5hmCs complement 5-methylcytosines (5mCs) in defining tissues and cells' epigenetic identities. In-depth analyses highlighted the cell type context of EWAS and GWAS hits. Using this platform, we conducted a comprehensive human 5hmC aging EWAS, discovering tissue-invariant and tissue-specific aging dynamics, including distinct tissue-specific rates of mitotic hyper- and hypomethylation rates. These findings chart a landscape of the complex interplay of the two forms of cytosine modifications in diverse human tissues and their roles in health and disease.

Exposome-wide association study of environmental chemical exposures and epigenetic aging in the national health and nutrition examination survey

Epigenetic clocks can serve as pivotal biomarkers linking environmental exposures with biological aging. However, research on the influence of environmental exposures on epigenetic aging has largely been limited to a small number of chemicals and specific populations. We harnessed data from the National Health and Nutrition Examination Survey 1999-2000 and 2001-2002 cycles to examine exposome-wide associations between environmental exposures and epigenetic aging. A total of 8 epigenetic aging biomarkers were obtained from whole blood in 2,346 participants ranging from 50-84 years of age. A total of 64 environmental exposures including phthalates, metals, pesticides, dioxins, and polychlorinated biphenyls (PCBs) were measured in blood and urine. Associations between log2-transformed/standardized exposure measures and epigenetic age acceleration (EAA) were assessed using survey-weighted generalized linear regression. A 1 standard deviation (SD) increase in log2 serum cadmium levels was associated with higher GrimAge acceleration (beta = 1.23 years, p = 3.63e-06), higher GrimAge2 acceleration (beta = 1.27 years, p = 1.62e-05), and higher DunedinPoAm (beta = 0.02, p = 2.34e-05). A 1 SD increase in log2 serum cotinine levels was associated with higher GrimAge2 acceleration (beta = 1.40 years, p = 6.53e-04) and higher DunedinPoAm (beta = 0.03, p = 6.31e-04). Associations between cadmium and EAA across several clocks persisted in sensitivity models adjusted for serum cotinine levels, and other associations involving lead, dioxins, and PCBs were identified. Several environmental exposures are associated with epigenetic aging in a nationally representative US adult population, with particularly strong associations related to cadmium and cotinine across several epigenetic clocks.

Epigenetics-Based Age Acceleration Associated with 2,3,7,8 TCDD Exposure in Older Americans

2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) is highly toxic with potential impacts on aging. While previous studies have linked TCDD exposure to reduced telomere length and altered sperm DNA methylation (DNAm) age, its relationship with epigenetic aging remains unclear. This study investigated the association between serum TCDD levels and epigenetic clocks derived from DNAm in whole blood in older adults. Using data from the 1999-2002 National Health and Nutrition Examination Survey, we analyzed 589 participants aged 50 to 79 years with available blood TCDD and DNA methylation measures. Blood TCDD levels were measured by high-resolution gas chromatography/isotope-dilution high-resolution mass spectrometry. The six DNAm-based epigenetic clocks included Horvath Age, Hannum Age, SkinBlood Age, Pheno Age, Grim Age, and Grim Age2. Multivariable regression analysis showed significant associations between TCDD levels and Horvath Age, Hannum Age, Pheno Age, Grim Age, and Grim Age2. However, when using lipid-adjusted TCDD levels, significant associations remained only for PhenoAge (β = 0.73; SE, 0.31; p = 0.0258) and Grim Age2 (β = 0.44; SE, 0.21; p = 0.0472). The strongest non-linear trends were observed for PhenoAge, Grim Age, and Grim Age2, suggesting a threshold-dependent impact of TCDD on DNAm aging processes. Our findings suggest that TCDD exposure is associated with accelerated epigenetic aging, particularly in mortality-related clocks, with a dose-dependent and non-linear pattern.

The role of protective genetic variants in modulating epigenetic aging

Several progeroid syndromes' causative mutations have been linked to epigenetic age acceleration as measured via several epigenetic clocks. At the same time, several protective variants have also been discovered that can reduce the risk of developing certain age-related disorders. However, the impact of these protective variants on epigenetic aging has not been well elucidated. Our research, which involved screening over 14,669 healthy individuals enrolled in the Qatar BioBank (QBB) and sequenced by the Qatar Genome Project (QGP), identified individuals carrying protective variants against age-related disorders, including Alzheimer's disease (AD), type 2 diabetes (T2D), and atherosclerosis. In this study, we measured methylation levels in blood DNA using the EPIC v2 arrays. In addition, epigenetic age was calculated using various epigenetic clocks. Our analysis revealed that the APOE*E2 protective variant reduces the rate of GrimAge epigenetic aging when compared to individuals with the APOE4 AD risk allele. Furthermore, our differential DNA methylation analysis discovered the association of the PCSK9 protective variant with specific biological processes related to immune function and the cardiovascular system. In conclusion, APOE*E2 protective variants have a positive impact on epigenetic aging, while PCSK9 protective variants have a significant effect on DNA methylation signatures. Further studies are needed to better understand the underlying mechanisms by which protective variants influence epigenetic aging, particularly the role of APOE*E2 protective variants in biological aging. Furthermore, additional research is required to fully uncover the processes that might enable specific targeted therapies to mimic the effects of beneficial mutations, such as LOF variants in PCSK9, in reducing the risk of geriatric disorders.

Investigating genetic links between biological aging and adverse pregnancy outcomes

Observational studies suggest a link between biological aging and adverse pregnancy outcomes (APOs), but causal relationships remain unclear. This study aimed to investigate the relationship between genetically predicted biological aging traits and APOs. Genetic summary statistics from the genome-wide association study (GWAS) of the IEU open GWAS, FinnGen, and meta-analysis were analyzed using Mendelian randomization (MR) to infer causality. Biological aging indicators included facial aging, frailty index, and epigenetic aging markers. APOs included gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HTP), preterm birth (PTB), and pregnancy loss (PL). The primary MR analyses utilized the inverse variance weighted method, followed by sensitivity analyses. Reverse MR and multivariable MR were employed to explore reverse causality and potential mediating effects. We found that the frailty index was positively associated with GDM (OR = 2.00, 95% CI 1.44-2.77, P = 3.41E - 5), HTP (OR = 2.09, 95% CI 1.33-3.29, P = 0.001), and PL (OR = 1.22, 95% CI 1.03-1.46, P = 0.023) risks. Inverse MR showed that susceptibility to HTP (β = 0.05, 95% CI 0.03-0.07, P = 4.43E - 6) and PL (β = 0.06, 95% CI 0.01-0.11, P = 0.011) was positively correlated with the frailty index, while PTB was positively correlated with PhenoAge (β = 0.24, 95% CI 0.02-0.46, P = 0.035). Our findings suggest a genetic association between the frailty index and susceptibility to GDM, HTP, and PL. Closer monitoring of biological aging indicators during pregnancy may be necessary to prevent APOs.

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.

Biological Age Acceleration and Colonic Polyps in Persons under Age 50

Epigenetic clocks can quantify DNA methylation by measuring the methylation levels at specific sites in the genome, which correlate with biological age (BA). Accelerated aging, where BA exceeds chronologic age, has been studied in relation to cancer development, but its utility in cancer prevention remains unclear. Accelerated aging holds promise as a tool to explain the increase in early-onset colorectal cancer (EOCRC). We investigate the association of accelerated aging and the presence of preneoplastic polyps (PNP) in the colon, defined as tubular adenomas and sessile serrated adenomas. In this study of persons under age 50 undergoing colonoscopy, we used peripheral blood samples to determine BA and age acceleration metrics. Age acceleration was determined by interrogating DNA methylation at specific CpG sites across the genome, which has been shown to correlate with age. We then conducted logistic regression analyses to evaluate the association between age acceleration and PNPs. In total, 51 patient samples were evaluated. We found that that the odds of harboring a PNP are 16% higher with 1 year of accelerated aging, as measured by GrimAge. However, the strongest risk factor for PNPs remained male sex. This represents one of the first studies to explore accelerated aging and PNP in patients under the age of 50. A risk-stratified approach to EOCRC screening would minimize unnecessary colonoscopies and minimize healthcare burden while addressing the increase in EOCRC. Our findings suggest that BA calculations with peripheral blood collections could be an important component of such a risk model. Prevention Relevance: Understanding the association of accelerated aging and colorectal PNPs presents an opportunity to develop a risk-stratified approach to colorectal cancer screening in young persons.

A biological age based on common clinical markers predicts health trajectory and mortality risk in dogs

Predicting aging trajectories through biomarkers of biological aging can guide interventions that optimize healthy lifespan in humans and companion animals. Differences in physiology, genetics, nutrition, and lifestyle limit the generalization of such biomarkers and may therefore require species-specific algorithms. Here, we compared correlations of standard clinical blood parameters with survival probability in humans with those of the two most common mammalian companion animals, cats and dogs, and highlighted universal and species-specific relationships. Based on this comparative analysis, we generated and validated an algorithm that predicts biological age in canines using a longitudinal dataset with health records, blood count, and clinical chemistry from 829 dogs spanning over 12 years. Positive deviations of biological from chronological age (AgeDev) measured by this composite score significantly correlated with a decreased survival probability (hazard ratio = 1.75 per 1 year of AgeDev, p = 3.7e - 06). Importantly, in nearly half of the dogs whose biological age was accelerated by more than 1 year, none or only a single individual marker scored outside its respective reference range, suggesting practical applications for the detection of unfavorable health trajectories. Analyzing samples from a unique 14-year life-long diet restriction study, we show that restricted caloric intake lowers biological age, an effect that can be quantified at midlife years before a difference in survival is observed. Thus, a biological age clock based on clinical blood tests predicts the health trajectories of dogs for use in research and veterinary practice.

Methylation-based forensic age estimation in blood, buccal cells, saliva and semen: A comparison of two technologies

Forensic age estimation of stain donors through DNA methylation has been intensively studied in recent years. To date, there are many published age estimation tools which are based on technologies including pyrosequencing, minisequencing, or MPS. With the implementation of such tools into routine forensic casework in many laboratories worldwide, there is a need for thorough evaluation and performance comparison. In this study, we tested published age estimation tools that are based on either minisequencing or MPS on four body fluids (blood, saliva, buccal cells and semen). All samples were analyzed with both technologies and the age estimates were compared. Biological replicates were taken from ten (blood, saliva, buccal cells) or 12 individuals (semen) to assess the reproducibility of each tool. Our study demonstrates high accuracy in estimating chronological age for various body fluids using both technologies, except for semen. The mean absolute errors (MAEs) ranged from three to five years for blood, saliva and buccal cells, while semen exhibited a higher MAE of seven to eight years. Despite the overall good performance for blood, saliva, and buccal cells, significant discrepancies were observed for some individuals both between the two technologies or when compared to their chronological age. Conclusively, we demonstrated that forensic age estimation tools based on two different technologies are similarly accurate for blood, saliva and buccal cells, while the semen tools need some adjustments before implementation into forensic casework. Our results could be helpful in the decision-making process for laboratories seeking to newly establish an age estimation workflow.

The immunosenescence clock: A new method for evaluating biological age and predicting mortality risk

Precisely assessing an individual's immune age is critical for developing targeted aging interventions. Although traditional methods for evaluating biological age, such as the use of cellular senescence markers and physiological indicators, have been widely applied, these methods inherently struggle to capture the full complexity of biological aging. We propose the concept of an 'immunosenescence clock' that evaluates immune system changes on the basis of changes in immune cell abundance and omics data (including transcriptome and proteome data), providing a complementary indicator for understanding age-related physiological transformations. Rather than claiming to definitively measure biological age, this approach can be divided into a biological age prediction clock and a mortality prediction clock. The main function of the biological age prediction clock is to reflect the physiological state through the transcriptome data of peripheral blood mononuclear cells (PBMCs), whereas the mortality prediction clock emphasizes the ability to identify people at high risk of mortality and disease. We hereby present nearly all of the immunosenescence clocks developed to date, as well as their functional differences. Critically, we explicitly acknowledge that no single diagnostic test can exhaustively capture the intricate changes associated with biological aging. Furthermore, as these biological functions are based on the acceleration or delay of immunosenescence, we also summarize the factors that accelerate immunosenescence and the methods for delaying it. A deep understanding of the regulatory mechanisms of immunosenescence can help establish more accurate immune-age models, providing support for personalized longevity interventions and improving quality of life in old age.

Hallmarks of aging: A user's guide for comparative biologists

Since the first description of a set of characteristics of aging as so-called hallmarks or pillars in 2013/2014, these characteristics have served as guideposts for the research in aging biology. They have been examined in a range of contexts, across tissues, in response to disease conditions or environmental factors, and served as a benchmark for various anti-aging interventions. While the hallmarks of aging were intended to capture generalizable characteristics of aging, they are derived mostly from studies of rodents and humans. Comparative studies of aging including species from across the animal tree of life have great promise to reveal new insights into the mechanistic foundations of aging, as there is a great diversity in lifespan and age-associated physiological changes. However, it is unclear how well the defined hallmarks of aging apply across diverse species. Here, we review each of the twelve hallmarks of aging defined by Lopez-Otin in 2023 with respect to the availability of data from diverse species. We evaluate the current methods used to assess these hallmarks for their potential to be adapted for comparative studies. Not unexpectedly, we find that the data supporting the described hallmarks of aging are restricted mostly to humans and a few model systems and that no data are available for many animal clades. Similarly, not all hallmarks can be easily assessed in diverse species. However, for at least half of the hallmarks, there are methods available today that can be employed to fill this gap in knowledge, suggesting that these studies can be prioritized while methods are developed for comparative study of the remaining hallmarks.

Biological aging of different blood cell types

Biological age (BA) captures detrimental age-related changes. The best-known and most-used BA indicators include DNA methylation-based epigenetic clocks and telomere length (TL). The most common biological sample material for epidemiological aging studies, whole blood, is composed of different cell types. We aimed to compare differences in BAs between blood cell types and assessed the BA indicators' cell type-specific associations with chronological age (CA). An analysis of DNA methylation-based BA indicators, including TL, methylation level at cg16867657 in ELOVL2, as well as the Hannum, Horvath, DNAmPhenoAge, and DunedinPACE epigenetic clocks, was performed on 428 biological samples of 12 blood cell types. BA values were different in the majority of the pairwise comparisons between cell types, as well as in comparison to whole blood (p < 0.05). DNAmPhenoAge showed the largest cell type differences, up to 44.5 years and DNA methylation-based TL showed the lowest differences. T cells generally had the "youngest" BA values, with differences across subsets, whereas monocytes had the "oldest" values. All BA indicators, except DunedinPACE, strongly correlated with CA within a cell type. Some differences such as DNAmPhenoAge-difference between naïve CD4 + T cells and monocytes were constant regardless of the blood donor's CA (range 20-80 years), while for DunedinPACE they were not. In conclusion, DNA methylation-based indicators of BA exhibit cell type-specific characteristics. Our results have implications for understanding the molecular mechanisms underlying epigenetic clocks and underscore the importance of considering cell composition when utilizing them as indicators for the success of aging interventions.

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.

Genetic and epigenetic bases of long-term adverse effects of childhood cancer therapy

Over the past decade, genome-scale molecular profiling of large childhood cancer survivorship cohorts has led to unprecedented advances in our understanding of the genetic and epigenetic bases of therapy-related adverse health outcomes in this vulnerable population. To facilitate the integration of knowledge generated from these studies into formulating next-generation precision care for survivors of childhood cancer, we summarize key findings of genetic and epigenetic association studies of long-term therapy-related adverse effects including subsequent neoplasms and cardiomyopathies among others. We also discuss therapy-related genotoxicities including clonal haematopoiesis and DNA methylation, which may underlie accelerated molecular ageing. Finally, we highlight enhanced risk prediction models for survivors of childhood cancer that incorporate both genetic factors and treatment exposures, aiming to achieve enhanced accuracy in predicting risks for this population. These new insights will hopefully inspire future studies that harness both expanding omics resources and evolving data science methodology to accelerate the translation of precision medicine for survivors of childhood cancer.

Application of DNA Methylation-Based Age Estimation to Construct an Age Structure of Humpback Whales in a Newly Emerged Wintering Ground Around Hachijojima Island, Tokyo Metropolis, Japan

Using a noninvasive DNA methylation (DNAm)-based age estimation method, we investigate the age structure of humpback whales that newly emerged around Hachijojima Island, Tokyo Metropolis, Japan to uncover the role of this area for this species. We measured DNAm frequencies at three age-related genes (GRIA2, CDKN2A, and TET2) from 26 biopsy skin samples of 21 unique humpback whales (15 males and 6 females) randomly collected in the winters of 2018-2021 and estimated their ages using the known age estimation model for humpback whales. The estimated ages of the 21 individuals were 2.95-30.40 years old with a mean of 12.02, and the resulting age structure in 5-year increments was roughly normally distributed with a peak at 10.00-14.99 class, suggesting the dominance of young adult males in this water. The observations (young males, male aggressive behavior for mating, whale song, and mother-calf pair) indicated that newly emerged humpback whales appeared to utilize Hachijojima Island as their new wintering ground, expanding the northern limit of the wintering area in the western North Pacific from previously known.

Suboptimal dietary patterns are associated with accelerated biological aging in young adulthood: A study with twins

BACKGROUND & AIMS

Suboptimal diets increase morbidity and mortality risk. Epigenetic clocks are algorithms that can assess health and lifespan, even at a young age, before clinical manifestations of diseases. We investigated the association between dietary patterns and biological aging in young adult twins.

METHODS

The data were drawn from the population-based FinnTwin12 study and consisted of twins aged 21-25 years (n = 826). Food and beverage intakes were assessed using a food frequency questionnaire. Biological aging was estimated using the epigenetic clocks GrimAge and DunedinPACE. Latent class analysis was used to identify dietary patterns. The association between dietary patterns and biological aging was assessed using linear regression modeling at the individual level, followed by within-twin pair analyses to account for genetic liabilities and shared familial confounders.

RESULTS

Six dietary patterns were identified: 1) High fast food, low fruits and vegetables (F&V), 2) Plant-based, 3) Health-conscious, 4) Western with infrequent fish, 5) Western with regular fish, and 6) Balanced average. At the individual level, GrimAge acceleration was slower in the Plant-based, Health-conscious, and Balanced-average patterns compared to the High fast food, low F&V, and faster in the Western with infrequent fish pattern compared to the Balanced average, regardless of sex, nonalcoholic energy intake, smoking, and alcohol consumption. After further adjustment for BMI and sports participation, the strengths of the associations modestly decreased; however, the difference between the Balanced-average and High fast food, low F&V patterns remained significant. The pace of aging (DunedinPACE) was slower in the Plant-based pattern compared to the High fast food, low F&V and the Western with infrequent fish patterns after adjustment for sex, nonalcoholic energy intake, smoking, and alcohol. The effect sizes were attenuated and reached a non-significant level when BMI and sports participation were added to the model. Most of the associations were replicated in the within-pair analyses among all twin pairs and among dizygotic twin pairs, but the effect sizes tended to be smaller among monozygotic twin pairs. This suggests that genetics, but not a shared environment, may partially explain the observed associations between diet and biological aging.

CONCLUSION

Diets high in fast food, processed red meat, and sugar-sweetened beverages and low in fruits and vegetables are associated with accelerated biological aging in young adulthood. The clustering effect of lifestyle factors and genetic confounders should be considered when interpreting the findings.

A SuperLearner-based pipeline for the development of DNA methylation-derived predictors of phenotypic traits

BACKGROUND

DNA methylation (DNAm) provides a window to characterize the impacts of environmental exposures and the biological aging process. Epigenetic clocks are often trained on DNAm using penalized regression of CpG sites, but recent evidence suggests potential benefits of training epigenetic predictors on principal components.

METHODOLOGY/FINDINGS

We developed a pipeline to simultaneously train three epigenetic predictors; a traditional CpG Clock, a PCA Clock, and a SuperLearner PCA Clock (SL PCA). We gathered publicly available DNAm datasets to generate i) a novel childhood epigenetic clock, ii) a reconstructed Hannum adult blood clock, and iii) as a proof of concept, a predictor of polybrominated biphenyl exposure using the three developmental methodologies. We used correlation coefficients and median absolute error to assess fit between predicted and observed measures, as well as agreement between duplicates. The SL PCA clocks improved fit with observed phenotypes relative to the PCA clocks or CpG clocks across several datasets. We found evidence for higher agreement between duplicate samples run on alternate DNAm arrays when using SL PCA clocks relative to traditional methods. Analyses examining associations between relevant exposures and epigenetic age acceleration (EAA) produced more precise effect estimates when using predictions derived from SL PCA clocks.

CONCLUSIONS

We introduce a novel method for the development of DNAm-based predictors that combines the improved reliability conferred by training on principal components with advanced ensemble-based machine learning. Coupling SuperLearner with PCA in the predictor development process may be especially relevant for studies with longitudinal designs utilizing multiple array types, as well as for the development of predictors of more complex phenotypic traits.

Stay social, stay young: a bioanthropological outlook on the processes linking sociality and ageing

In modern human societies, social interactions and pro-social behaviours are associated with better individual and collective health, reduced mortality, and increased longevity. Conversely, social isolation is a predictor of shorter lifespan. The biological processes through which sociality affects the ageing process, as well as healthspan and lifespan, are still poorly understood. Unveiling the physiological, neurological, genomic, epigenomic, and evolutionary mechanisms underlying the association between sociality and longevity may open new perspectives to understand how lifespan is determined in a broader socio/evolutionary outlook. Here we summarize evidence showing how social dynamics can shape the evolution of life history traits through physiological and genetic processes directly or indirectly related to ageing and lifespan. We start by reviewing theories of ageing that incorporate social interactions into their model. Then, we address the link between sociality and lifespan from two separate points of view: (i) considering evidences from comparative evolutionary biology and bioanthropology that demonstrates how sociality contributes to natural variation in lifespan over the course of human evolution and among different human groups in both pre-industrial and post-industrial society, and (ii) discussing the main physiological, neurological, genetic, and epigenetic molecular processes at the interface between sociality and ageing. We highlight that the exposure to chronic social stressors deregulates neurophysiological and immunological pathways and promotes accelerated ageing and thereby reducing lifespan. In conclusion, we describe how sociality and social dynamics are intimately embedded in human biology, influencing healthy ageing and lifespan, and we highlight the need to foster interdisciplinary approaches including social sciences, biological anthropology, human ecology, physiology, and genetics.

The maternal X chromosome affects cognition and brain ageing in female mice

Female mammalian cells have two X chromosomes, one of maternal origin and one of paternal origin. During development, one X chromosome randomly becomes inactivated1-4. This renders either the maternal X (Xm) chromosome or the paternal X (Xp) chromosome inactive, causing X mosaicism that varies between female individuals, with some showing considerable or complete skew of the X chromosome that remains active5-7. Parent-of-X origin can modify epigenetics through DNA methylation8,9 and possibly gene expression; thus, mosaicism could buffer dysregulated processes in ageing and disease. However, whether X skew or its mosaicism alters functions in female individuals is largely unknown. Here we tested whether skew towards an active Xm chromosome influences the brain and body-and then delineated unique features of Xm neurons and Xp neurons. An active Xm chromosome impaired cognition in female mice throughout the lifespan and led to worsened cognition with age. Cognitive deficits were accompanied by Xm-mediated acceleration of biological or epigenetic ageing of the hippocampus, a key centre for learning and memory, in female mice. Several genes were imprinted on the Xm chromosome of hippocampal neurons, suggesting silenced cognitive loci. CRISPR-mediated activation of Xm-imprinted genes improved cognition in ageing female mice. Thus, the Xm chromosome impaired cognition, accelerated brain ageing and silenced genes that contribute to cognition in ageing. Understanding how Xm impairs brain function could lead to an improved understanding of heterogeneity in cognitive health in female individuals and to X-chromosome-derived pathways that protect against cognitive deficits and brain ageing.

War Exposure and DNA Methylation in Syrian Refugee Children and Adolescents

Importance

Exposure to war is associated with poor mental health outcomes. Adverse and traumatic experiences can lead to long-lasting DNA methylation changes, potentially mediating the link between adversity and mental health. To date, limited studies have investigated the impact of war on DNA methylation in children or adolescents, hampering our understanding of the biological impact of war exposure.

Objective

To identify salivary DNA methylation differences associated with war exposure in refugee children and adolescents.

Design, Setting, and Participants

This cohort study included Syrian refugee children and adolescents, and their primary caregiver were recruited from tented settlements in Lebanon. Data collection was carried out in 2 waves, 1 year apart, from October 2017 to January 2018 and October 2018 to January 2019. Children and their caregiver were interviewed, and children provided saliva samples for DNA extraction. Data analysis was conducted in 2022, 2023, and 2024.

Exposure

War exposure assessed by interviewing children and their caregiver using the War Events Questionnaire.

Main Outcomes and Measures

Salivary DNA methylation levels were assayed with the Infinium MethylationEPIC BeadChip (Illumina). Epigenetic aging acceleration was estimated using a set of preexisting epigenetic aging clocks. A literature search was conducted to identify previously reported DNA methylation correlates of childhood trauma.

Results

The study population included 1507 children and adolescents (mean [SD] age, 11.3 [2.4] years; age range, 6-19 years; 793 female [52.6%]). A total of 1449 children provided saliva samples for DNA extraction in year 1, and 872 children provided samples in year 2. Children who reported war events had a number of differentially methylated sites and regions. Enrichment analyses indicated an enrichment of gene sets associated with transmembrane transport, neurotransmission, and intracellular movement in genes that exhibited differential methylation. Sex-stratified analyses found a number of sex-specific DNA methylation differences associated with war exposure. Only 2 of 258 (0.8%) previously reported trauma-associated DNA methylation sites were associated with war exposure (B = -0.004; 95% CI, -0.005 to -0.003; Bonferroni P = .04 and B = -0.005; 95% CI, -0.006 to -0.004; Bonferroni P = .03). Any war exposure or bombardment was nominally associated with decreased epigenetic age using the Horvath multitissue clock (B = -0.39; 95% CI, -0.63 to -0.14; P = .007 and B = -0.42; 95% CI, -0.73 to -0.11; P = .002).

Conclusions and Relevance

In this cohort of Syrian refugee children and adolescents, war exposure was associated with a small number of distinct differences in salivary DNA methylation.

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.

Post-traumatic stress disorder: evolving conceptualization and evidence, and future research directions

The understanding of responses to traumatic events has been greatly influenced by the introduction of the diagnosis of post-traumatic stress disorder (PTSD). In this paper we review the initial versions of the diagnostic criteria for this condition and the associated epidemiological findings, including sociocultural differences. We consider evidence for post-traumatic reactions occurring in multiple contexts not previously defined as traumatic, and the implications that these observations have for the diagnosis. More recent developments such as the DSM-5 dissociative subtype and the ICD-11 diagnosis of complex PTSD are reviewed, adding to evidence that there are several distinct PTSD phenotypes. We describe the psychological foundations of PTSD, involving disturbances to memory as well as to identity. A broader focus on identity may be able to accommodate group and communal influences on the experience of trauma and PTSD, as well as the impact of resource loss. We then summarize current evidence concerning the biological foundations of PTSD, with a particular focus on genetic and neuroimaging studies. Whereas progress in prevention has been disappointing, there is now an extensive evidence supporting the efficacy of a variety of psychological treatments for established PTSD, including trauma-focused interventions - such as trauma-focused cognitive behavior therapy (TF-CBT) and eye movement desensitization and reprocessing (EMDR) - and non-trauma-focused therapies, which also include some emerging identity-based approaches such as present-centered and compassion-focused therapies. Additionally, there are promising interventions that are neither psychological nor pharmacological, or that combine a pharmacological and a psychological approach, such as 3,4-methylenedioxymethamphetamine (MDMA)-assisted psychotherapy. We review advances in the priority areas of adapting interventions in resource-limited settings and across cultural contexts, and of community-based approaches. We conclude by identifying future directions for work on trauma and mental health.

Bidirectional relationship between epigenetic age and stroke, dementia, and late-life depression

Chronological age is an imperfect estimate of molecular aging. Epigenetic age, derived from DNA methylation data, provides a more nuanced representation of aging-related biological processes. We examine the bidirectional relationship between epigenetic age and brain health events (stroke, dementia, late-life depression) using data from 4,018 participants. Participants with a prior brain health event are 4% epigenetically older (β = 0.04, SE = 0.01), indicating these conditions are associated with accelerated aging beyond that captured by chronological age. Additionally, a one standard deviation increase in epigenetic age is associated with 70% higher odds of experiencing a brain health event in the next four years (OR = 1.70, 95% CI = 1.16-2.50), suggesting epigenetic age acceleration is not just a consequence but also a predictor of poor brain health. Mendelian Randomization analyses replicate these findings, supporting their causal nature. Our results support using epigenetic age as a biomarker to evaluate interventions aimed at preventing and promoting recovery after brain health events.

Integrated multiomic analyses: An approach to improve understanding of diabetic kidney disease

AIM

Diabetes is increasing in prevalence worldwide, with a 20% rise in prevalence predicted between 2021 and 2030, bringing an increased burden of complications, such as diabetic kidney disease (DKD). DKD is a leading cause of end-stage kidney disease, with significant impacts on patients, families and healthcare providers. DKD often goes undetected until later stages, due to asymptomatic disease, non-standard presentation or progression, and sub-optimal screening tools and/or provision. Deeper insights are needed to improve DKD diagnosis, facilitating the identification of higher-risk patients. Improved tools to stratify patients based on disease prognosis would facilitate the optimisation of resources and the individualisation of care. This review aimed to identify how multiomic approaches provide an opportunity to understand the complex underlying biology of DKD.

METHODS

This review explores how multiomic analyses of DKD are improving our understanding of DKD pathology, and aiding in the identification of novel biomarkers to detect disease earlier or predict trajectories.

RESULTS

Effective multiomic data integration allows novel interactions to be uncovered and empathises the need for harmonised studies and the incorporation of additional data types, such as co-morbidity, environmental and demographic data to understand DKD complexity. This will facilitate a better understanding of kidney health inequalities, such as social-, ethnicity- and sex-related differences in DKD risk, onset and progression.

CONCLUSION

Multiomics provides opportunities to uncover how lifetime exposures become molecularly embodied to impact kidney health. Such insights would advance DKD diagnosis and treatment, inform preventative strategies and reduce the global impact of this disease.

Transposable element 5mC methylation state of blood cells predicts age and disease

Transposable elements (TEs) are DNA sequences that expand selfishly in the genome, possibly causing severe cellular damage. While normally silenced, TEs have been shown to activate during aging. DNA 5-methylcytosine (5mC) is one of the main epigenetic modifications by which TEs are silenced and has been used to train highly accurate age predictors. Yet, one common criticism of such predictors is that they lack interpretability. In this study, we investigate the changes in TE 5mC methylation that occur during aging in human blood using published methylation array data. We find that evolutionarily young long interspersed nuclear elements 1 (L1s), the only known TEs capable of autonomous transposition in humans, undergo the fastest loss of 5mC methylation, suggesting an active mechanism of de-repression. The same young L1s also showed preferential gain in chromatin accessibility but not expression. The long terminal repeat retrotransposons THE1A and THE1C also showed very rapid 5mC loss. We then show that accurate age predictors can be trained on both 5mC methylation of individual TE copies and average methylation of TE families genome wide. Lastly, we show that while old L1s gradually lose 5mC during the entire lifespan, demethylation of young L1s only happens late in life and is associated with cancer.

Assessment of relationships between epigenetic age acceleration and multiple sclerosis: a bidirectional mendelian randomization study

BACKGROUND

The DNA methylation-based epigenetic clocks are increasingly recognized for their precision in predicting aging and its health implications. Although prior research has identified connections between accelerated epigenetic aging and multiple sclerosis, the chronological and causative aspects of these relationships are yet to be elucidated. Our research seeks to clarify these potential causal links through a bidirectional Mendelian randomization study.

METHODS

This analysis employed statistics approaches from genome-wide association studies related to various epigenetic clocks (GrimAge, HannumAge, PhenoAge, and HorvathAge) and multiple sclerosis, utilizing robust instrumental variables from the Edinburgh DataShare (n = 34,710) and the International Multiple Sclerosis Genetics Consortium (including 24,091 controls and 14,498 cases). We applied the inverse-variance weighted approach as our main method for Mendelian randomization, with additional sensitivity analyses to explore underlying heterogeneity and pleiotropy.

RESULTS

Using summary-based Mendelian randomization, we found that HannumAge was associated with multiple sclerosis (OR = 1.071, 95%CI:1.006-1.140, p = 0.033, by inverse-variance weighted). The results suggest that an increase in epigenetic age acceleration of HannumAge promotes the risk of multiple sclerosis. In reverse Mendelian randomization analysis, no evidence of a clear causal association of multiple sclerosis on epigenetic age acceleration was identified.

CONCLUSIONS

Our Mendelian randomization analysis revealed that epigenetic age acceleration of HannumAge was causally associated with multiple sclerosis, and provided novel insights for further mechanistic and clinical studies of epigenetic age acceleration-mediated multiple sclerosis.

Greater residential greenness is associated with reduced epigenetic aging in adults

Potential pathways linking urban green spaces to improved health include relaxation, stress alleviation, and improved immune system functioning. Epigenetic age acceleration (EAA) is a composite biomarker of biological aging based on DNA methylation measurements; it is predictive of morbidity and mortality. This cross-sectional study of 116 adult residents of a metropolitan area in central North Carolina investigated associations between exposure to residential green spaces and EAA using four previously developed epigenetic age formulas. DNA methylation tests of white blood cells were conducted using Illumina MethylationEPIC v1.0 assays. EAA values were calculated as residuals from the linear regression model of epigenetic age on chronological age. Residential greenness was characterized using tree cover, total vegetated land cover, and normalized difference vegetation index (NDVI) data. An interquartile range (IQR) increase in distance-to-residence weighted average greenness within 500 m of residence was consistently associated with a reduced EAA adjusted for sociodemographic covariates, smoking status, white blood cell fractions, and the two-dimensional spline function of geographic coordinates. The reduction in the EAA estimates for the four EAA measures ranged from - 1.0 to - 1.6 years for tree cover, from - 1.2 to - 1.5 years for vegetated land cover, and from - 0.9 to - 1.3 years for the NDVI; 11 of the 12 associations were statistically significant (p < 0.05). This study produced new evidence linking reduced epigenetic aging to greater greenness near residences.

Cigarette smoke and biological age induce degenerative heterogeneity in retinal pigment epithelium

Environmental exposure such as cigarette smoke induces epigenetic changes that can induce degenerative heterogeneity and accelerate aging. In early age-related macular degeneration (AMD), the leading worldwide cause of blindness among the elderly, retinal pigment epithelial (RPE) cell heterogeneity is a key change. Since smoking is the strongest environmental risk factor for AMD, we hypothesized that cigarette smoke induces degenerative RPE heterogeneity through epigenetic changes that are distinct from aging, and that with aging, the RPE becomes vulnerable to cigarette smoke insult. We administered cigarette smoke condensate (CSC) intravitreally to young and aged mice and performed snRNA-seq and snATAC-seq on the RPE/choroid. This analysis identified separate cell clusters corresponding to healthy and abnormal, dedifferentiated RPE in both aged vehicle-treated and young CSC-treated mice. The dedifferentiated RPE were characterized by a global decrease in chromatin accessibility and decreased expression of genes in functional categories that were linked to hallmarks of aging. Notably, young, dedifferentiated RPE also exhibited a compensatory upregulation of hallmarks of aging-related genes, specifically those related to mitochondrial function and proteostasis. In contrast, aged dedifferentiated RPE did not express these compensatory changes, and did not survive CSC treatment, as experimentally verified with TUNEL labeling. These changes are relevant to early AMD because we identified through scRNA-seq, similar dedifferentiated and healthy macular RPE clusters in a donor who smoked and another with early AMD, but not from a nonsmoker. Degenerative cellular heterogeneity can include an abnormal cluster that jeopardizes cell survival and may represent an additional hallmark of ocular aging.

TimeFlies: an snRNA-seq aging clock for the fruit fly head sheds light on sex-biased aging

Although multiple high-performing epigenetic aging clocks exist, few are based directly on gene expression. Such transcriptomic aging clocks allow us to extract age-associated genes directly. However, most existing transcriptomic clocks model a subset of genes and are limited in their ability to predict novel biomarkers. With the growing popularity of single-cell sequencing, there is a need for robust single-cell transcriptomic aging clocks. Moreover, clocks have yet to be applied to investigate the elusive phenomenon of sex differences in aging. We introduce TimeFlies, a pan-cell-type scRNA-seq aging clock for the Drosophila melanogaster head. TimeFlies uses deep learning to classify the donor age of cells based on genome-wide gene expression profiles. Using explainability methods, we identified key marker genes contributing to the classification, with lncRNAs showing up as highly enriched among predicted biomarkers. The top biomarker gene across cell types is lncRNA:roX1, a regulator of X chromosome dosage compensation, a pathway previously identified as a top biomarker of aging in the mouse brain. We validated this finding experimentally, showing a decrease in survival probability in the absence of roX1 in vivo. Furthermore, we trained sex-specific TimeFlies clocks and noted significant differences in model predictions and explanations between male and female clocks, suggesting that different pathways drive aging in males and females.

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.

Painful diabetic neuropathy is associated with accelerated epigenetic aging

About one out of two diabetic patients develop diabetic neuropathy (DN), of these 20% experience neuropathic pain (NP) leading to individual, social, and health-economic burden. Risk factors for NP are largely unknown; however, premature aging was recently associated with several chronic pain disorders. DNA methylation-based biological age (DNAm) is associated with disease risk, morbidity, and mortality in different clinical settings. The purpose of this work was to study, for the first time, whether biological age is involved in pain development in a huge cohort of DN patients with neuropathy assessed by anatomopathological assay (99 painful (PDN), 132 painless (PLDN) patients, 84 controls (CTRL)). Six subsets of DNAm biomarkers were calculated to evaluate NP-associated changes in epigenetic aging, telomere shortening, blood cell count estimates, and plasma protein surrogates. We observed pain-related acceleration of epigenetic age (DNAmAgeHannum, DNAmGrimAgeBasedOnPredictedAge, DNAmAgeSkinBloodClock), pace of aging (DunedinPoAm), and shortening of telomeres between PDN and PLDN patients. PDN showed decreased predicted counts of B lymphocytes, naive and absolute CD8 T cells, and increased granulocyte counts. Several surrogates of plasma proteins were significantly different (GHR, MMP1, THBS2, PAPPA, TGF-α, GDF8, EDA, MPL, CCL21) in PDNs compared to PLDNs. These results provide the first evidence of an acceleration of biological aging in patients with painful compared to painless DN. This achievement has been possible thanks to the state of the art clinical phenotyping of the enrolled patients. Our findings indicate that the aging process may be directly involved in the PDN progression and in general health degeneration in the T2DM patients. Therefore, it is possible to hypothesize that the administration of effective antiaging drugs could slow down or even block the disease advancement.

EpiAge: a next-generation sequencing-based ELOVL2 epigenetic clock for biological age assessment in saliva and blood across health and disease

This study introduces EpiAgePublic, a new method to estimate biological age using only three specific sites on the gene ELOVL2, known for its connection to aging. Unlike traditional methods that require complex and extensive data, our model uses a simpler approach that is well-suited for next-generation sequencing technology, which is a more advanced method of analyzing DNA methylation. This new model overcomes some of the common challenges found in older methods, such as errors due to sample quality and processing variations. We tested EpiAgePublic with a large and varied group of over 4,600 people to ensure its accuracy. It performed on par with, and sometimes better than, more complicated models that use much more data for age estimation. We examined its effectiveness in understanding how factors like HIV infection and stress affect aging, confirming its usefulness in real-world clinical settings. Our results prove that our simple yet effective model, EpiAgePublic, can capture the subtle signs of aging with high accuracy. We also used this model in a study involving patients with Alzheimer's Disease, demonstrating the practical benefits of next-generation sequencing in making precise age-related assessments. This study lays the groundwork for future research on aging mechanisms and assessing how different interventions might impact the aging process using this clock.

DNA methylation clocks struggle to distinguish inflammaging from healthy aging, but feature rectification improves coherence and enhances detection of inflammaging

Biological age estimation from DNA methylation and determination of relevant biomarkers is an active research problem which has predominantly been tackled with black-box penalized regression. Machine learning is used to select a small subset of features from hundreds of thousands of CpG probes and to increase generalizability typically lacking with ordinary least-squares regression. Here, we show that such feature selection lacks biological interpretability and relevance in the clocks of the first and next generations and clarify the logic by which these clocks systematically exclude biomarkers of aging and age-related disease. Moreover, in contrast to the assumption that regularized linear regression is needed to prevent overfitting, we demonstrate that hypothesis-driven selection of biologically relevant features in conjunction with ordinary least squares regression yields accurate, well-calibrated, generalizable clocks with high interpretability. We further demonstrate that the interplay of inflammaging-related shifts of predictor values and their corresponding weights, which we term feature shifts, contributes to the lack of resolution between health and inflammaging in conventional linear models. Lastly, we introduce a method of feature rectification, which aligns these shifts to improve the distinction of age predictions for healthy people vs. patients with various chronic inflammation diseases.

Characterizing biomarkers of ageing in Singaporeans: the ABIOS observational study protocol

Ageing is the primary driver of age-associated chronic diseases and conditions. Asian populations have traditionally been underrepresented in studies understanding age-related diseases. Thus, the Ageing BIOmarker Study in Singaporeans (ABIOS) aims to characterise biomarkers of ageing in Singaporeans, exploring associations between molecular, physiological, and digital biomarkers of ageing. This is a single-centre, cross-sectional study that recruits healthy community-dwelling adults (≥ 21 years) from three different ethnic groups (Chinese, Malay, and Indian). Molecular biomarkers of ageing include multi-omics approaches, such as DNA methylation analysis and metabolic and inflammatory proteomic profiling in blood, saliva, and stool. Physiological biomarkers of ageing include bone density, body composition, skin autofluorescence, arterial stiffness, physical performance (e.g., muscle strength and flexibility), cognition, and nutritional status. Digital biomarkers of ageing include three-dimensional facial morphology and objectively measured physical activity. Additional measures, such as habitual physical activity, dietary patterns, and medical history, are also examined. The associations between the molecular, physiological, and digital phenotypes will be explored. This study is expected to generate a comprehensive profile of molecular, physiological, and digital biomarkers of ageing in Chinese, Malay, and Indian populations in Singapore. By integrating diverse age-related biomarkers, clinical indicators, and lifestyle factors, ABIOS will offer unique insights into the ageing process specific to Southeast Asian populations. These findings can help identify markers of biological ageing, uncover ethnic-specific patterns, and reveal modifiable lifestyle factors for healthier ageing. The results could inform evidence-based health policies, personalized interventions, and future cross-ethnic comparative studies to enhance understanding of ageing biology across diverse populations.

Accelerated epigenetic ageing after burn injury

Individuals who suffer a major burn injury are at higher risk of developing a range of age-associated diseases prematurely leading to an increase in mortality in adult and juvenile burn injury survivors. One possible explanation is that injury is accelerating the biological ageing process. To test this hypothesis, we analysed DNA methylation in peripheral blood mononuclear cells from adult burn-injured patients (> 5%TBSA) upon admission to hospital and 6 months later, to calculate an epigenetic clock value which can be used to determine biological age. Fifty-three burn-injured participants (mean age 45.43 years, 49 male, mean TBSA 37.65%) were recruited at admission and 34 again 6 months post injury (mean age 40.4 years, 34 male, mean TBSA 30.91%). Twenty-nine healthy controls (mean age 43.69 years, 24 male) were also recruited. Epigenetic age acceleration at admission by PhenoAge was + 7.2 years (P = 8.31e-5) but by month 6 was not significantly different from healthy controls. PCGrimAge acceleration was + 9.23 years at admission (P = 5.79e-11) and remained 4.18 years higher than in controls by month 6 (P = 2.64e-6). At admission, the burn-injured participants had a Dunedin PACE of ageing score 31.65% higher than the control group (P = 2.14e-12), the equivalent of + 115 days per year of biological ageing. Six months post injury the Dunedin PACE of ageing remained significantly higher (+ 11.36%, 41 days/year) than in the control group (P = 3.99e-5). No differences were seen using the Horvath and Hannum clocks. Enrichment analysis revealed that key pathways enriched with burn injury related to immune function, activation, and inflammation. The results reveal that epigenetic age, specifically the PACE of ageing and PCGrimAge, was accelerated in burn-injured adults at admission, with some return towards control values by 6 months. That these two clocks are built upon morbidity outcomes suggests that the injury is invoking a biological response that increases the risk of disease. Burn injury in adults induces epigenetic changes suggestive of an acceleration of the ageing process, which may contribute to the increased morbidity and mortality in these patients.

Deep learning and generative artificial intelligence in aging research and healthy longevity medicine

With the global population aging at an unprecedented rate, there is a need to extend healthy productive life span. This review examines how Deep Learning (DL) and Generative Artificial Intelligence (GenAI) are used in biomarker discovery, deep aging clock development, geroprotector identification and generation of dual-purpose therapeutics targeting aging and disease. The paper explores the emergence of multimodal, multitasking research systems highlighting promising future directions for GenAI in human and animal aging research, as well as clinical application in healthy longevity medicine.

Treating Metabolic Dysregulation and Senescence by Caloric Restriction: Killing Two Birds with One Stone?

Cellular senescence is a state of permanent cell cycle arrest accompanied by metabolic activity and characteristic phenotypic changes. This process is crucial for developing age-related diseases, where excessive calorie intake accelerates metabolic dysfunction and aging. Overnutrition disturbs key metabolic pathways, including insulin/insulin-like growth factor signaling (IIS), the mammalian target of rapamycin (mTOR), and AMP-activated protein kinase. The dysregulation of these pathways contributes to insulin resistance, impaired autophagy, exacerbated oxidative stress, and mitochondrial dysfunction, further enhancing cellular senescence and systemic metabolic derangements. On the other hand, dysfunctional endothelial cells and adipocytes contribute to systemic inflammation, reduced nitric oxide production, and altered lipid metabolism. Numerous factors, including extracellular vesicles, mediate pathological communication between the vascular system and adipose tissue, amplifying metabolic imbalances. Meanwhile, caloric restriction (CR) emerges as a potent intervention to counteract overnutrition effects, improve mitochondrial function, reduce oxidative stress, and restore metabolic balance. CR modulates pathways such as IIS, mTOR, and sirtuins, enhancing glucose and lipid metabolism, reducing inflammation, and promoting autophagy. CR can extend the health span and mitigate age-related diseases by delaying cellular senescence and improving healthy endothelial-adipocyte interactions. This review highlights the crosstalk between endothelial cells and adipocytes, emphasizing CR potential in counteracting overnutrition-induced senescence and restoring vascular homeostasis.

estiMAge: development of a DNA methylation clock to estimate the methylation age of single cells

Motivation

Since their introduction about 10 years ago, methylation clocks have provided broad insights into the biological age of different species, tissues, and in the context of several diseases or aging. However, their application to single-cell methylation data remains a major challenge, because of the inherent sparsity of such data, as many CpG sites are not covered. A methylation clock applicable on single-cell level could help to further disentangle the processes that drive the ticking of epigenetic clocks.

Results

We have developed estiMAge ("estimation of Methylation Age"), a framework that exploits redundancy in methylation data to substitute missing CpGs of trained methylation clocks in single cells. Using Euclidean distance as a measure of similarity, we determine which CpGs covary with the required CpG sites of an epigenetic clock and can be used as surrogates for clock CpGs not covered in single-cell experiments. estiMAge is thus a tool that can be applied to standard epigenetic clocks built on elastic net regression, to achieve bulk and single-cell resolution. We show that estiMAge can accurately predict the ages of young and old hepatocytes and can be used to generate single-cell versions of publicly available epigenetic clocks.

Availability and implementation

The source code and instructions for usage of estiMAge are available at https://github.com/DivEpigenetics/estiMAge.