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

Immigrant status and citizenship relationships with epigenetic aging in a representative sample of United States adults

BACKGROUND

Immigrant status and citizenship influence health and well-being, yet their associations with DNA methylation (DNAm)-based biomarkers of aging - key predictors of healthspan and lifespan, also known as epigenetic aging - remain underexplored.

METHODS

Using a representative sample of 2,336 United States (U.S.) adults from the 1999-2000 and 2001-2002 cycles of the National Health and Nutrition Examination Survey (NHANES), we analyzed cross-sectional associations of immigrant status and U.S. citizenship with seven epigenetic aging biomarkers: HannumAge, HorvathAge, SkinBloodAge, PhenoAge, GrimAge2, DNAm Telomere Length, and DunedinPoAm.

RESULTS

After adjusting for demographic factors, immigrants had 2.53-year lower GrimAge2 measures (95%CI: -3.44, -1.63, p < 0.001) compared to non-immigrants. U.S. citizens had 1.98-year higher GrimAge2 measures (95%CI: 0.66, 3.30, p = 0.005) compared to non-citizens. The GrimAge2 associations with immigrant status (β = -1.04-years, 95%CI: -1.87, -0.21, p = 0.02) and citizenship (β = 1.35-years, 95%CI: 0.38, 2.32, p = 0.02) were attenuated after adjusting for other lifestyle/health variables. Immigrant status and citizenship were associated with estimated levels of several GrimAge2 DNAm component proteins, including adrenomedullin and C-reactive protein.

CONCLUSION

Our results support the paradigm of the immigrant mortality advantage and highlight the potential value of epigenetic age measures in studying socioeconomic and broader factors influencing citizen and immigrant health.

Slowed epigenetic aging in Olympic champions compared to non-champions

The lifestyle patterns of top athletes are highly disciplined, featuring strict exercise regimens, nutrition plans, and mental preparation, often beginning at a young age. Recently, it was shown that physically active individuals exhibit slowed epigenetic aging and better age-related outcomes. Here, we investigate whether the extreme intensity of physical activity of Olympic champions still has a beneficial effect on epigenetic aging. To test this hypothesis, we examined the epigenetic aging of 59 Hungarian Olympic champions and of the 332 control subjects, 205 were master rowers. We observed that Olympic champions exhibit slower epigenetic aging, applying seven state-of-the-art epigenetic aging clocks. Additionally, male champions who won any medal within the last 10 years showed slower epigenetic aging compared to other male champions, while female champions exhibited the opposite trend. We also found that wrestlers had higher age acceleration compared to gymnasts, fencers, and water polo players. We identified the top 20 genes that showed the most remarkable difference in promoter methylation between Olympic champions and non-champions. The hypo-methylated genes are involved in synaptic health, glycosylation, metal ion membrane transfer, and force generation. Most of the hyper-methylated genes were associated with cancer promotion. The data suggest that rigorous and long-term exercise from adolescence to adulthood has beneficial effects on epigenetic aging.

Epigenetic heterogeneity hotspots in human liver disease progression

BACKGROUND AND AIMS

Disruption of the epigenome is a hallmark of human disease, including liver cirrhosis and HCC. While genetic heterogeneity is an established effector of pathologic phenotypes, epigenetic heterogeneity is less well understood. Environmental exposures alter the liver-specific DNA methylation landscape and influence the onset of liver cancer. Given that currently available treatments are unable to target frequently mutated genes in HCC, there is an unmet need for novel therapeutics to prevent or reverse liver damage leading to hepatic tumorigenesis, which the epigenome may provide.

APPROACH AND RESULTS

We performed genome-wide profiling of DNA methylation, copy number, and gene expression from multiple liver regions from 31 patients with liver disease to examine their crosstalk and define the individual and combinatorial contributions of these processes to liver disease progression. We identified epigenetic heterogeneity hotspots that are conserved across patients. Elevated epigenetic heterogeneity is associated with increased gene expression heterogeneity. Cirrhotic regions comprise 2 distinct cohorts-one exclusively epigenetic, and the other where epigenetic and copy number variations collaborate. Epigenetic heterogeneity hotspots are enriched for genes central to liver function (eg, HNF1A ) and known tumor suppressors (eg, RASSF1A ). These hotspots encompass genes including ACSL1 , ACSL5 , MAT1A , and ELFN1 , which have phenotypic effects in functional screens, supporting their relevance to hepatocarcinogenesis. Moreover, epigenetic heterogeneity hotspots are linked to clinical measures of outcome.

CONCLUSIONS

Substantial epigenetic heterogeneity arises early in liver disease development, targeting key pathways in the progression and initiation of both cirrhosis and HCC. Integration of epigenetic and transcriptional heterogeneity unveils putative epigenetic regulators of hepatocarcinogenesis.

Major experiences of perceived discrimination across life and biological aging

Perceived lifetime discrimination may accelerate aspects of biological aging, but it is unknown whether there are life stages during which experiencing discrimination has the greatest biological impacts. In this study, we tested the effects of total forms of perceived lifetime discrimination experienced both across life and in specific lifespan stages on biological aging. Health and Retirement Study participants (N = 2986, Mage=68 years, 46.2 % Male, 73.4 % White) reported most recent experiences of perceived lifetime discrimination events and their years of occurrence; events were summed across one's life (total forms of perceived lifetime discrimination across life) and in the following life stages: childhood (0-17 years), young adulthood (18-39), midlife (40-59), and late adulthood (60 +). Blood drawn after survey completion (average 5.89 years later) was used to measure biological aging outcomes, including inflammation (CRP, IL-6, and sTNFR-1) and epigenetic age. In multilevel models adjusted for age, sex, BMI, smoking status, and the time interval between completing the discrimination questionnaire and blood draw, those who experienced greater total forms of perceived lifetime discrimination had higher levels of CRP (γ=0.08, p < 0.001) and IL-6 (γ=0.07, p < 0.001). When testing each life stage in separate models, more perceived lifetime discrimination events in young adulthood were associated with higher IL-6 (γ=0.05, p = 0.015). When comparing the effects of the life stages within the same model among adults age 45 + (n = 2978), more perceived lifetime discrimination events in young adulthood were independently associated with higher IL-6 (γ=0.07, p = 0.001) and in midlife with higher CRP (γ=0.06, p = 0.011) and IL-6 (γ=0.07, p = 0.002). Perceived lifetime discrimination was not associated with sTNFR-1 or epigenetic age. More perceived lifetime discrimination events - both across one's life and in certain adult developmental life stages - are associated with higher levels of later-life inflammation. In particular, young adulthood and midlife may be sensitive periods during which experiencing perceived lifetime discrimination has the greatest immunological impacts.

A SNaPshot Assay for Epigenetic Age Prediction of Costal Cartilage

Estimating age at death narrows the pool of potential donors in mass disasters and criminal investigations. In this study, we developed a capillary electrophoresis-based SNaPshot assay for age prediction of costal cartilage and used it to analyze DNA methylation at 11 CpG sites across six genes in 136 samples from deceased Koreans aged 28-84 years. To develop the predictive model, DNA methylation levels at these sites from a training set of 83 samples were analyzed using multivariate linear regression in five ways. We then compared the performance parameters calculated from the training set and a test set of 53 samples. Considering experimental simplicity, we selected a model that incorporates four CpGs (MIR29B2CHG_C2, FHL2_C4, TRIM59_C3, and KLF14_C3) as the optimal age prediction model, demonstrating high performance with a mean absolute error of 4.60 years and a root mean square error of 5.41 years in the test set. Subsequently, we developed a multiplex SNaPshot system covering CpGs included in the optimal model, requiring a minimum of 4 ng of bisulfite-converted DNA for reliable prediction and demonstrating multi-tissue applicability, particularly in blood and buccal swabs. We believe this tool will support forensic investigations, including the identification of victims and missing persons.

Nonlinear ageing gero-marker dynamics of transcriptomic profile during calcific aortic valve mouse modeling

The prevention and management of degenerative heart disease remain challenging and could potentially be significantly improved by understanding of ageing biomarker dynamics. In this study, we constructed the calcific aortic valve mouse model at different age points, measured valve function degeneration along with valve calcification, and investigated the nonlinear dynamics using sequencing data and deep learning models. In C57BL/6 N mouse model, the older mice had higher levels of peak transvalvular jet velocity in terms of valve function. Regarding valve calcification, collagen and elastic fiber calcification in the middle layer increased significantly at 48-week-old (p < 0.001), and the calcification spread to the inner endothelial cells at 72-week-old (p < 0.0001). RNA sequencing illustrated that 30 genes, including Acadsb, L2hgdh, and Cpped1, showed increased expression with age. Among them, four genes, namely Hipk2, 9430069I07Rik, Peli3, and Slc22a12, increased more than threefold in aortic tissues in 72-week-old mice compared to 6-week-old mice. Moreover, a large proportion of genes changed in a nonlinear pattern (6,325 out of 12,160, 52%). In conclusion, both linear and nonlinear gero-markers were found in the calcific aortic valve mouse modeling, which highlighted specific periods of significant wave with accelerated ageing (48-week-old in mice).

Alcohol and aging: Next-generation epigenetic clocks predict biological age acceleration in individuals with alcohol use disorder

BACKGROUND

Chronic heavy alcohol use is a major risk factor for premature aging and age-related diseases. DNA methylation (DNAm)-based epigenetic clocks are novel tools for predicting biological age. However, the newest configurations, causality-enriched epigenetic clocks, have not been assessed in the context of alcohol consumption and alcohol use disorder (AUD).

METHODS

Epigenetic aging was evaluated in a sample of 615 individuals (372 AUD patients and 243 healthy controls) by applying the GrimAge Version 1 (V1) and Version 2 (V2) clocks alongside three causality-enriched clocks (CausAge, DamAge, and AdaptAge). A linear model controlling for AUD diagnosis, sex, race, BMI, smoking status, and five blood cell types was leveraged to test associations between alcohol-related metrics and age-adjusted epigenetic clocks.

RESULTS

GrimAge V1 and V2 maintained significant associations with AUD and drinking behavior measures within the total sample and both the young (<40 years old) and old (≥40 years old) subgroups. Generally, GrimAge V2 slightly outperformed GrimAge V1, while none of the causality-enriched epigenetic clocks demonstrated significant associations with AUD. However, in the young subgroup, DamAge had a significant association with the total number of drinks. Across the total sample and age subgroups, with liver function enzymes, GrimAge V2 consistently sustained stronger associations compared with GrimAge V1. Among fourth-generation clocks, DamAge exhibited significant associations with gamma-glutamyl transferase (GGT) and aspartate aminotransferase in the total sample and young subgroup; CausAge displayed a significant association with GGT in the total sample. Examining clinical biomarkers, GrimAge V2 showed improved associations with C-reactive protein compared to GrimAge V1 in the total sample and age subgroups.

CONCLUSIONS

Overall, we observed moderately improved performance of GrimAge V2 compared with GrimAge V1 with the majority of the parameters tested. The causality-enriched epigenetic clocks lacked significant associations but demonstrate the complexities of aging and inspire further research of AUD and drinking dynamics.

Impact of DNA-Methylation Age Acceleration on Long-Term Mortality Among US Adults With Cardiovascular-Kidney-Metabolic Syndrome

BACKGROUND

The association between DNA methylation age acceleration (DNAmAA) and cardiovascular-kidney-metabolic (CKM) syndrome stages and long-term mortality in the population with CKM syndrome remains unclear.

METHODS AND RESULTS

This cohort study included 1889 participants from the National Health and Nutrition Examination Survey (1999-2002) with CKM stages and DNA methylation age data. DNAmAA was calculated as residuals from the regression of DNA methylation age on chronological age. The primary outcome was all-cause mortality, with cardiovascular and noncardiovascular mortality as secondary outcomes. Proportional odds models assessed the associations between DNAmAAs and CKM stages, and Cox proportional hazards regression models estimated the associations between DNAmAAs and mortality. Significant associations were found between DNAmAAs and advanced CKM stages, particularly for GrimAge2Mort acceleration (GrimAA) (odds ratio [OR], 1.547 [95% CI, 1.316-1.819]). Over an average follow-up of 14 years, 1015 deaths occurred. Each 5-unit increase in GrimAA was associated with a 50% increase in all-cause mortality (95% CI, 1.39-1.63), a 77% increase in cardiovascular mortality (95% CI, 1.46-2.15), and a 42% increase in noncardiovascular mortality (95% CI, 1.27-1.59). With the lowest GrimAA tertile as a reference, the highest GrimAA tertile showed hazard ratios of 1.95 (95% CI, 1.56-2.45) for all-cause mortality, 3.06 (95% CI, 2.13-4.40) for cardiovascular mortality, and 1.65 (95% CI, 1.20-2.29) for noncardiovascular mortality. Mediation analysis indicated that GrimAA mediates the association between various exposures (including physical activity, Healthy Eating Index-2015 score, hemoglobin A1c, etc.) and mortality.

CONCLUSIONS

GrimAA may serve as a valuable biomarker for assessing CKM stages and mortality risk in individuals with CKM syndrome, thereby informing personalized management strategies.

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.

Dietary factors and DNA methylation-based markers of ageing in 5310 middle-aged and older Australian adults

The role of nutrition in healthy ageing is acknowledged but details of optimal dietary composition are still uncertain. We aimed to investigate the cross-sectional associations between dietary exposures, including macronutrient composition, food groups, specific foods, and overall diet quality, with methylation-based markers of ageing. Blood DNA methylation data from 5310 participants (mean age 59 years) in the Melbourne Collaborative Cohort Study were used to calculate five methylation-based measures of ageing: PCGrimAge, PCPhenoAge, DunedinPACE, ZhangAge, TelomereAge. For a range of dietary exposures, we estimated (i) the 'equal-mass substitution effect', which quantifies the effect of adding the component of interest to the diet while keeping overall food mass constant, and (ii) the 'total effect', which quantifies the effect of adding the component of interest to the current diet. For 'equal-mass substitution effects', the strongest association for macronutrients was for fibre intake (e.g. DunedinPACE, per 12 g/day - 0.10 [standard deviations]; 95%CI - 0.15, - 0.05, p < 0.001). Associations were positive for protein (e.g. PCGrimAge, per 33 g/day 0.04; 95%CI 0.01-0.08, p = 0.005). For food groups, the evidence tended to be weak, though sugar-sweetened drinks showed positive associations, as did artificially-sweetened drinks (e.g. DunedinPACE, per 91 g/day 0.06, 95%CI 0.03-0.08, p < 0.001). 'Total effect' estimates were generally very similar. Scores reflecting overall diet quality suggested that healthier diets were associated with lower levels of ageing markers. High intakes of fibre and low intakes of protein and sweetened drinks, as well as overall healthy diets, showed the most consistent associations with lower methylation-based ageing in our study.

Forensic Age Estimation From Blood Samples by Combining DNA Methylation and MicroRNA Markers Using Droplet Digital PCR

Age estimation is important in criminal investigations and forensic practice, and extensive studies have focused on age determination based on DNA methylation (DNAm) and miRNA markers. Interestingly, it has been reported that combining different types of molecular omics data helps build more accurate predictive models. However, few studies have compared the application of combined DNAm and miRNA data to predict age in the same cohort. In this study, a novel multiplex droplet digital PCR (ddPCR) system that allows for the simultaneous detection of age-associated DNAm and miRNA markers, including KLF14, miR-106b-5p, and two reference genes (C-LESS-C1 and RNU6B), was developed. Next, we examined and calculated the methylation levels of KLF14 and relative expression levels of miR-106b-5p in 132 blood samples. The collected data were used to establish age prediction models. Finally, the optimal models were evaluated using bloodstain samples. The results revealed that the random forest (RF) model had a minimum mean absolute deviation (MAD) value of 3.51 years and a maximum R2 of 0.84 for the validation sets in the combined age prediction models. However, the MAD was 5.66 years and the absolute error ranged from 3.16 to 10.54 years for bloodstain samples. Larger sample sizes and validation datasets are required to confirm these results in future studies. Overall, a stable method for the detection of KLF14, miR-106b-5p, C-LESS-C1, and RNU6B by 4-plex ddPCR was successfully established, and our study suggests that combining DNAm and miRNA data can improve the accuracy of age prediction, which has potential applications in forensic science.

Methylomic signature of current cannabis use in two first-episode psychosis cohorts

The rising prevalence and legalisation of cannabis worldwide have underscored the need for a comprehensive understanding of its biological impact, particularly on mental health. Epigenetic mechanisms, specifically DNA methylation, have gained increasing recognition as vital factors in the interplay between risk factors and mental health. This study aimed to explore the effects of current cannabis use and high-potency cannabis on DNA methylation in two independent cohorts of individuals experiencing first-episode psychosis (FEP) compared to control subjects. The combined sample consisted of 682 participants (188 current cannabis users and 494 never users). DNA methylation profiles were generated on blood-derived DNA samples using the Illumina DNA methylation array platform. A meta-analysis across cohorts identified one CpG site (cg11669285) in the CAVIN1 gene that showed differential methylation with current cannabis use, surpassing the array-wide significance threshold, and independent of the tobacco-related epigenetic signature. Furthermore, a CpG site localised in the MCU gene (cg11669285) achieved array-wide significance in an analysis of the effect of high-potency (THC = > 10%) current cannabis use. Pathway and regional analyses identified cannabis-related epigenetic variation proximal to genes linked to immune and mitochondrial function, both of which are known to be influenced by cannabinoids. Interestingly, a model including an interaction term between cannabis use and FEP status identified two sites that were significantly associated with current cannabis use with a nominally significant interaction suggesting that FEP status might moderate how cannabis use affects DNA methylation. Overall, these findings contribute to our understanding of the epigenetic impact of current cannabis use and highlight potential molecular pathways affected by cannabis exposure.

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.

Epigenetic biomarkers of socioeconomic status are associated with age-related chronic diseases and mortality in older adults

Later-life health is patterned by socioeconomic influences across the lifecourse. However, the pathways underlying the biological embedding of socioeconomic status (SES) and its consequences on downstream morbidity and mortality are not fully understood. Epigenetic markers like DNA methylation (DNAm) may be promising surrogates of underlying biological processes that can enhance our understanding of how SES shapes population health. Studies have shown that SES is associated with epigenetic aging measures, but few have examined relationships between early and later-life SES and DNAm sites across the epigenome. In this study, we trained and tested DNAm-based surrogates, or "biomarkers," of childhood and adult SES in two large, multiracial/ethnic samples of older adults-the Health and Retirement Study (n = 3,527) and the Multi-Ethnic Study of Atherosclerosis (n = 1,182). Both biomarkers were associated with downstream morbidity and mortality, and these associations persisted after controlling for measured SES, and in some cases, epigenetic aging clocks. Both childhood and adult SES biomarker CpG sites were enriched for genomic features that regulate gene expression (e.g. DNAse hypersensitivity sites and enhancers) and were implicated in prior epigenome-wide studies of inflammation, aging, and chronic disease. Distinct patterns also emerged between childhood CpGs and immune system dysregulation and adult CpGs and metabolic functioning, health behaviors, and cancer. Results suggest DNAm-based surrogate biomarkers of SES may be useful proxies for unmeasured social exposures that can augment our understanding of the biological mechanisms between social disadvantage and downstream health.

Assessing transcriptomic signatures of aging: Testing an mRNA marker panel for forensic age estimation of blood samples

Estimating the age of an unknown perpetrator can be a valuable tool in narrowing down a group of suspects. Research efforts to estimate the age of a stain donor have mainly focused on epigenetic modifications, but there is evidence that RNA expression patterns, i.e. the composition of the transcriptome, change with increasing age, which could be a promising molecular alternative for age prediction. In a previous study, we identified a total of 508 mRNA markers with age related expression from two blood whole transcriptome sequencing data sets, using differential expression analysis with DESeq2 and marker selection with lasso regression. For this study, the selected markers from both approaches were combined into an RNA-specific targeted MPS assay for the Ion Torrent platform and evaluated with 100 EDTA blood samples from healthy donors (aged between 23 and 73 years). We compared three different normalization methods for the obtained sequencing data and investigated the performance of various regression techniques for age prediction. The model based on elastic net regression and dSVA-normalized data exhibited the most robust performance, achieving an MAE of 9.29 years and a correlation of 0.57 between the chronological and predicted age. Although the use of a targeted approach instead of RNA-Seq offers several advantages in a forensic setting, we observed a considerable amount of unwanted variation in the targeted sequencing data. We conclude that it is challenging to detect distinct signals associated with chronological age.

Plasma proteomics-based organ-specific aging for all-cause mortality and cause-specific mortality: a prospective cohort study

Individual's aging rates vary across organs. However, there are few methods for assessing aging at organ levels and whether they contribute differently to mortalities remains unknown. We analyzed data from 45,821 adults in the UK Biobank, using plasma proteomics and machine learning to estimate biological ages for 12 major organs. The differences between biological age and chronological age, referred to as "age gaps," were calculated for each organ. Partial correlation analyses were used to assess the association between age gaps and modifiable factors. Adjusted multivariable Cox regression models were applied to examine the association of age gaps with all-cause mortality, cause-specific mortalities, and cancer-specific mortalities. We reveal a complex network of varied associations between multi-organ aging and modifiable factors. All age gaps increase the risk of all-cause mortality by 6-60%. The risk of death varied from 5.54 to 29.18 times depending on the number of aging organs. Cause-specific mortalities are associated with certain organs' aging. For mental diseases mortality, and nervous system mortality, only brain aging exhibited a significant increased risk of HR 2.38 (per SD, 95% CI: 2.06-2.74) and 1.99 (per SD, 95% CI: 1.84-2.16), respectively. Age gaps of stomach were also a specific indicator for gastric cancer. Eventually, we find that an organ's biological age selectively influences the aging of other organ systems. Our study demonstrates that accelerated aging in specific organs increases the risk of mortality from various causes. This provides a potential tool for early identification of at-risk populations, offering a relatively objective method for precision medicine.

DNA methylation-based semen age prediction using the markers identified in Koreans and Europeans

In the forensic field, sexual assaults have consistently been the important issue, with semen frequently serving as the primary evidence. When the suspect is unidentified, estimating the perpetrator's age using investigating semen can provide important information. The VISAGE consortium conducted research on the semen age prediction focused on European semen samples, but the age prediction model has remained undisclosed. Additionally, several studies have reported methylation differences across populations, indicating that the European semen age prediction model might not be broadly applicable to other groups. A study did explore semen age prediction in Koreans using Illumina's Infinium Methylation450K BeadChip array, however recent developments in technology could enhance this approach. To address this, we conducted a study on Korean males aged 18-70 years. We initially analyzed 49 samples utilizing Illumina's Infinium MethylationEPIC BeadChip array to identify age-related CpG sites. From this analysis, we identified 9 age-related CpG markers, excluding one due to difficulties in locus-specific analysis. As a result, we used 11 markers including 8 newly identified CpGs from the EPIC array and 3 CpG markers from previous research utilizing the SNaPshot assay. Furthermore, we incorporated 13 CpG markers from the European study to analyze a total of 159 semen samples using the Illumina Nextera MPS system. This approach enabled us to test age-related markers identified in Europeans within the Korean population and to construct a more accurate age prediction model using markers from both Korean and European sources.

Cross-tissue comparison of epigenetic aging clocks in humans

Epigenetic clocks are a common group of tools used to measure biological aging-the progressive deterioration of cells, tissues, and organs. Epigenetic clocks have been trained almost exclusively using blood-based tissues, but there is growing interest in estimating epigenetic age using less-invasive oral-based tissues (i.e., buccal or saliva) in both research and commercial settings. However, differentiated cell types across body tissues exhibit unique DNA methylation landscapes and age-related alterations to the DNA methylome. Applying epigenetic clocks derived from blood-based tissues to estimate epigenetic age of oral-based tissues may introduce biases. We tested the within-person comparability of common epigenetic clocks across five tissue types: buccal epithelial, saliva, dry blood spots, buffy coat (i.e., leukocytes), and peripheral blood mononuclear cells. We tested 284 distinct tissue samples from 83 individuals aged 9-70 years. Overall, there were significant within-person differences in epigenetic clock estimates from oral-based versus blood-based tissues, with average differences of almost 30 years observed in some age clocks. In addition, most epigenetic clock estimates of blood-based tissues exhibited low correlation with estimates from oral-based tissues despite controlling for cellular proportions and other technical factors. Notably, the Skin and Blood clock exhibited the greatest concordance across all tissue types, indicating its unique ability to estimate chronological age in oral- and blood-based tissues. Our findings indicate that application of blood-derived epigenetic clocks in oral-based tissues may not yield comparable estimates of epigenetic age, highlighting the need for careful consideration of tissue type when estimating epigenetic age.

A novel framework to build saliva-based DNA methylation biomarkers: Quantifying systemic chronic inflammation as a case study

Accessible and non-invasive biomarkers that measure human ageing processes and the risk of developing age-related disease are paramount in preventative healthcare. Here, we describe a novel framework to train saliva-based DNA methylation (DNAm) biomarkers that are reproducible and biologically interpretable. By leveraging a reliability dataset with replicates across tissues, we demonstrate that it is possible to transfer knowledge from blood DNAm to saliva DNAm data using DNAm proxies of blood proteins (EpiScores). We apply these methods to create a new saliva-based epigenetic clock (InflammAge) that quantifies systemic chronic inflammation (SCI) in humans. Using a large blood DNAm human cohort with linked electronic health records and over 18,000 individuals (Generation Scotland), we demonstrate that InflammAge significantly associates with all-cause mortality, disease outcomes, lifestyle factors, and immunosenescence; in many cases outperforming the widely used SCI biomarker C-reactive protein (CRP). We propose that our biomarker discovery framework and InflammAge will be useful to improve understanding of the molecular mechanisms underpinning human ageing and to assess the impact of gero-protective interventions.

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.

Epigenetic editing at individual age-associated CpGs affects the genome-wide epigenetic aging landscape

Aging is reflected by genome-wide DNA methylation changes, which form the basis of epigenetic clocks, but it is largely unclear how these epigenetic modifications are regulated and whether they directly affect the aging process. In this study, we performed epigenetic editing at age-associated CpG sites to explore the consequences of interfering with epigenetic clocks. CRISPR-guided editing targeted at individual age-related CpGs evoked genome-wide bystander effects, which were highly reproducible and enriched at other age-associated regions. 4C-sequencing at age-associated sites revealed increased interactions with bystander modifications and other age-related CpGs. Subsequently, we multiplexed epigenetic editing in human T cells and mesenchymal stromal cells at five genomic regions that become either hypermethylated or hypomethylated upon aging. While targeted methylation seemed more stable at age-hypermethylated sites, both approaches induced bystander modifications at CpGs with the highest correlations with chronological age. Notably, these effects were simultaneously observed at CpGs that gain and lose methylation with age. Our results demonstrate that epigenetic editing can extensively modulate the epigenetic aging network and interfere with epigenetic clocks.

Decoding brain aging trajectory: predictive discrepancies, genetic susceptibilities, and emerging therapeutic strategies

The global extension of human lifespan has intensified the focus on aging, yet its underlying mechanisms remain inadequately understood. The article highlights aspects of genetic susceptibility to impaired brain bioenergetics, trends in age-related gene expression related to neuroinflammation and brain senescence, and the impact of stem cell exhaustion and quiescence on accelerated brain aging. We also review the accumulation of senescent cells, mitochondrial dysfunction, and metabolic disturbances as central pathological processes in aging, emphasizing how these factors contribute to inflammation and disrupt cellular competition defining the aging trajectory. Furthermore, we discuss emerging therapeutic strategies and the future potential of integrating advanced technologies to refine aging assessments. The combination of several methods including genetic analysis, neuroimaging techniques, cognitive tests and digital twins, offer a novel approach by simulating and monitoring individual health and aging trajectories, thereby providing more accurate and personalized insights. Conclusively, the accurate estimation of brain aging trajectories is crucial for understanding and managing aging processes, potentially transforming preventive and therapeutic strategies to improve health outcomes in aging populations.

Epigenetic and accelerated age in captive olive baboons (Papio anubis), and relationships with walking speed and fine motor performance

Epigenetic age, estimated by DNA methylation across the genome, reflects biological age. Accelerated age (i.e., an older methylation age than expected given chronological age) is an accepted aging biomarker in humans, showing robust associations with deleterious health outcomes, longevity, and mortality. However, data regarding age acceleration in nonhuman primates (NHPs), and relationships between NHP epigenetic age and behavioral indicators of aging, such as walking speed and fine motor performance, are sparse. We measured DNA methylation of 140 captive olive baboons (Papio anubis) (84% female, 3-20 years-old), estimated their epigenetic ages, and classified them as showing age acceleration or deceleration. We found that epigenetic age was strongly correlated with chronological age, and that approximately 27% of the sample showed age acceleration and 28% showed age deceleration. We subsequently examined relationships between epigenetic and accelerated age and walking speed (N=129) and fine motor performance (N=39). Older animals showed slower speeds and poorer motor performance. However, the difference between the epigenetic age and chronological age, referred to as delta age, was not a consistent predictor of walking speed or fine motor performance. These data highlight the need for further examination of age acceleration across NHP species, and the ways that age acceleration may (not) be related to indicators of aging in NHP models.

Epigenetic Aging Acceleration in Obesity Is Slowed Down by Nutritional Ketosis Following Very Low-Calorie Ketogenic Diet (VLCKD): A New Perspective to Reverse Biological Age

Background/Objectives: Epigenetic clocks have emerged as a tool to quantify biological age, providing a more accurate estimate of an individual's health status than chronological age, helping to identify risk factors for accelerated aging and evaluating the reversibility of therapeutic strategies. This study aimed to evaluate the potential association between epigenetic acceleration of biological age and obesity, as well as to determine whether nutritional interventions for body weight loss could slow down this acceleration. Methods: Biological age was estimated using three epigenetic clocks (Horvath (Hv), Hannum (Hn), and Levine (Lv)) based on the leukocyte methylome analysis of individuals with normal weight (n = 20), obesity (n = 24), and patients with obesity following a VLCKD (n = 10). We analyzed differences in biological age estimates, the relationship between age acceleration and obesity, and the impact of VLCKD. Correlations were assessed between age acceleration, BMI, and various metabolic parameters. Results: Analysis of the epigenetic clocks revealed an acceleration of biological age in individuals with obesity (Hv = +3.4(2.5), Hn = +5.7(3.2), Lv = +3.9(2.7)) compared to a slight deceleration in individuals with normal weight. This epigenetic acceleration correlated with BMI (p < 0.0001). Interestingly, patients with obesity following a VLCKD showed a deceleration in estimated biological age, both in nutritional ketosis (Hv = -3.3(4.0), Hn = -6.3(5.3), Lv = -8.8(4.5)) and at endpoint (Hv = -1.1(4.3), Hn = -7.4(5.6), Lv = -8.2(5.3)). Relevantly, this slowdown in age is associated with BMI (p < 0.0001), ketonemia (p ≤ 0.001), and metabolic parameters (p < 0.05). Conclusions: Our findings highlight the applicability of epigenetic clocks to monitor obesity-related biological aging in precision medicine and show the potential efficacy of the VLCKD in slowing obesity-related epigenetic aging.

The Effect of SGLT2 Inhibition on Brain-related Phenotypes and Aging: A Drug Target Mendelian Randomization Study

INTRODUCTION

An observational study suggested sodium-glucose cotransporter 2 (SGLT2) inhibitors might promote healthy aging. However, whether brain-related phenotypes mediate this association is still a question. We applied Mendelian randomization (MR) to investigate the effect of SGLT2 inhibition on chronological age, biological age, and cognition and explore the mediation effects of brain imaging-derived phenotypes (IDPs).

METHODS

We selected genetic variants associated with both expression levels of SLC5A2 (Genotype-Tissue Expression and eQTLGen data; n = 129 to 31 684) and hemoglobin A1c (HbA1c) levels (UK Biobank; n = 344 182) and used them to proxy the effect of SGLT2 inhibition. Aging-related outcomes, including parental longevity (n = 389 166) and epigenetic clocks (n = 34 710), and cognitive phenotypes, including cognitive function (n = 300 486) and intelligence (n = 269 867) were derived from genome-wide association studies. Two-step MR was conducted to explore the associations between SGLT2 inhibition, IDPs, and aging outcomes and cognition.

RESULTS

SGLT2 inhibition was associated with longer father's attained age [years of life increase per SD (6.75 mmol/mol) reduction in HbA1c levels = 6.21, 95% confidence interval (CI) 1.27-11.15], better cognitive function (beta = .17, 95% CI 0.03-0.31), and higher intelligence (beta = .47, 95% CI 0.19-0.75). Two-step MR identified 2 IDPs as mediators linking SGLT2 inhibition with chronological age (total proportion of mediation = 22.6%), where 4 and 5 IDPs were mediators for SGLT2 inhibition on cognitive function and intelligence, respectively (total proportion of mediation = 61.6% and 68.6%, respectively).

CONCLUSION

Our study supported that SGLT2 inhibition increases father's attained age, cognitive function, and intelligence, which was mediated through brain images of different brain regions. Future studies are needed to investigate whether a similar effect could be observed for users of SGLT2 inhibitors.

Healthy and premature aging of monocytes and macrophages

Aging is associated with immunosenescence, a decline in immune functions, but also with inflammaging, a chronic, low-grade inflammation, contributing to immunosenescence. Monocytes and macrophages belong to the innate immune system and aging has a profound impact on these cells, leading to functional changes and most importantly, to the secretion of pro-inflammatory cytokines and thereby contributing to inflammaging. Rheumatoid arthritis (RA) is an autoimmune disease and age is an important risk factor for developing RA. RA is associated with the early development of age-related co-morbidities like cardiovascular manifestations and osteoporosis. The immune system of RA patients shows signs of premature aging like age-inappropriate increased production of myeloid cells, accelerated telomeric erosion, and the uncontrolled production of pro-inflammatory cytokines. In this review we discuss the influence of aging on monocytes and macrophages during healthy aging and premature aging in rheumatoid arthritis.

Role of genetic variants and DNA methylation of lipid metabolism-related genes in metabolic dysfunction-associated steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD), is one of the most common chronic liver diseases, which encompasses a spectrum of diseases, from metabolic dysfunction-associated steatotic liver (MASL) to metabolic dysfunction-associated steatohepatitis (MASH), and may ultimately progress to MASH-related cirrhosis and hepatocellular carcinoma (HCC). MASLD is a complex disease that is influenced by genetic and environmental factors. Dysregulation of hepatic lipid metabolism plays a crucial role in the development and progression of MASLD. Therefore, the focus of this review is to discuss the links between the genetic variants and DNA methylation of lipid metabolism-related genes and MASLD pathogenesis. We first summarize the interplay between MASLD and the disturbance of hepatic lipid metabolism. Next, we focus on reviewing the role of hepatic lipid related gene loci in the onset and progression of MASLD. We summarize the existing literature around the single nucleotide polymorphisms (SNPs) associated with MASLD identified by genome-wide association studies (GWAS) and candidate gene analyses. Moreover, based on recent evidence from human and animal studies, we further discussed the regulatory function and associated mechanisms of changes in DNA methylation levels in the occurrence and progression of MASLD, with a particular emphasis on its regulatory role of lipid metabolism-related genes in MASLD and MASH. Furthermore, we review the alterations of hepatic DNA and blood DNA methylation levels associated with lipid metabolism-related genes in MASLD and MASH patients. Finally, we introduce potential value of the genetic variants and DNA methylation profiles of lipid metabolism-related genes in developing novel prognostic biomarkers and therapeutic targets for MASLD, intending to provide references for the future studies of MASLD.

Epigenetic age acceleration and mortality risk prediction in US adults

Epigenetic clocks have emerged as novel measures of biological aging and potential predictors of mortality. We examined all-cause, cardiovascular, and cancer mortality prediction by epigenetic age acceleration (EAA) estimated using different epigenetic clocks. Among 2105 participants to the 1999-2002 National Health and Nutrition Examination Survey aged ≥ 50 years old and followed for mortality through 2019, we calculated EAAs from the residuals of nine epigenetic clocks regressed on chronological age. We assessed the association of EAAs and pace of aging with mortality adjusting for covariates. During 17.5 years of median follow-up, 998 deaths occurred, including 272 from cardiovascular disease and 209 from cancer. Overall mortality was most significantly predicted by Grim EAA (P < 0.0001) followed by Hannum (P = 0.005), Pheno (P = 0.004), Horvath (P = 0.03), and Vidal-Bralo (P = 0.04) EAAs. Grim EAA predicted cardiovascular mortality (P < 0.0001), whereas Hannum (P = 0.006), Horvath (P = 0.009), and Grim (P = 0.01) EAAs predicted cancer mortality. Overall mortality prediction differed by race/ethnicity between non-Hispanic White and White participants for Horvath (Pinteraction = 0.048), Hannum (Pinteraction = 0.01), and Grim (Pinteraction = 0.04) EAAs. Hannum prediction of cancer mortality also differed between the two races/ethnicities (Pinteraction = 0.007). Despite being predictive in non-Hispanic White participants, Horvath (P = 0.75), Hannum (P = 0.84), and Grim (P = 0.10) EAAs failed to predict overall mortality in Hispanic participants, and Hannum EAA was not associated with cancer mortality in Hispanic participants (P = 0.18). In a US representative sample, Horvath, Hannum, SkinBlood, Pheno, Vidal-Bralo, and Grim EAAs as well as pace of aging predict mortality. Howbeit, Horvath, Hannum, and Grim EAAs were less predictive in Hispanic participants.

A new robust AI/ML based model for accurate forensic age estimation using DNA methylation markers

CpG sites are regions of DNA where a cytosine nucleotide is followed by a guanine nucleotide in the 5' → 3' direction. Epigenetic markers based on methylation values at CpG sites are valuable for accurate age prediction and have become essential in forensic science, supporting criminal investigations and human identification. The present study identified 12 CpG sites from a collection of 476,366 CpG sites based on the following criteria: (a) CpG sites were retained if the Pearson correlation coefficient between the methylation values and the chronological age of the individual is greater than 0.85, and (b) if the mutual correlation coefficient between a pair of selected CpG sites is greater than 0.15, only one of them is retained. The identified CpG sites are associated with genes FHL2, ELOVL2, TRIM59, PCDHB1, KLF14, C1orf132, ACSS3, and CCDC102B. To ensure that the predictive accuracy is intrinsic to the selected CpG sites and not model dependent, the identified CpG sites were passed to three different Neural network models. All models achieved comparable accuracy across diverse populations, genders, and health conditions. The model's accuracy and reliability were validated through age predictions on independent datasets. By utilizing a minimal set of CpG sites, this approach offers a robust and efficient solution for forensic age estimation, significantly enhancing the precision and reliability of forensic investigations.

Effects of a natural ingredients-based intervention targeting the hallmarks of aging on epigenetic clocks, physical function, and body composition: a single-arm clinical trial

Aging interventions have progressed in recent years due to the growing curiosity about how lifestyle impacts longevity. This study assessed the effects of SRW Laboratories' Cel System nutraceutical range on epigenetic methylation patterns, inflammation, physical performance, body composition, and epigenetic biomarkers of aging. A 1-year study was conducted with 51 individuals, collecting data at baseline, 3 months, 6 months, and 12 months. Participants were encouraged to walk 10 minutes and practice 5 minutes of mindfulness daily. Significant improvements in muscle strength, body function, and body composition metrics were observed. Epigenetic clock analysis showed a decrease in biological age with significant reductions in stem cell division rates. Immune cell subset analysis indicated significant changes, with increases in eosinophils and CD8T cells and decreases in B memory, CD4T memory, and T-regulatory cells. Predicted epigenetic biomarker proxies (EBPs) showed significant changes in retinol/TTHY, a regulator of cell growth, proliferation, and differentiation, and deoxycholic acid glucuronide levels, a metabolite of deoxycholic acid generated in the liver. Gene ontology analysis revealed significant CpG methylation changes in genes involved in critical biological processes related to aging, such as oxidative stress-induced premature senescence, pyrimidine deoxyribonucleotide metabolic process, TRAIL binding, hyaluronan biosynthetic process, neurotransmitter loading into synaptic vesicles, pore complex assembly, collagen biosynthetic process, protein phosphatase 2A binding activity, and activation of transcription factor binding. Our findings suggest that the Cel System supplement range may effectively reduce biological age and improve health metrics, warranting further investigation into its mechanistic pathways and long-term efficacy.

Comparing Veteran and Nonveteran Epigenetic Aging in a Representative Sample of United States Adults

INTRODUCTION

Military service can significantly impact human health, with research showing that veterans experience higher mortality rates than the general population. However, limited data exist on the relationships of veteran status with biomarkers of aging that may precede clinical illness and mortality.

METHODS

Using survey-design weighted generalized linear regression models, we examined the cross-sectional relationship of self-reported veteran status with DNA methylation (DNAm)-based biomarkers of aging (epigenetic age) in a representative sample of 2344 U.S. adults participating in the 1999-2000 and 2001-2002 cycles of the National Health and Nutrition Examination Survey. We tested 7 epigenetic aging markers: HannumAge, HorvathAge, SkinBloodAge, PhenoAge, GrimAge2, DNAm Telomere Length (TL), and DunedinPoAm.

RESULTS

After adjusting for basic demographics, veterans had marginally greater SkinBloodAge (β = 0.86 years, 95% CI: -0.10, 1.81, P = .08) and GrimAge2 (β = 0.71 years, 95% CI: -0.07, 1.49, P = .07) measures when compared to nonveterans. Similar SkinBloodAge (β = 1.00 years, 95% CI: -0.01, 2.00, P = .05) and GrimAge2 (β = 0.69 years, 95% CI: -0.14, 1.52, P = .09) relationships were observed in fully-adjusted models where missing health and lifestyle covariates were imputed. Compared to nonveterans, veterans also had higher DNAm-estimated blood levels of GrimAge2-components hemoglobin A1c (β = 0.006, 95% CI: 0.0005, 0.01, P = .03) and protein TIMP1 (β = 71.14, 95% CI: 8.28, 134.01, P = .03) in basic demographic-adjusted models. In fully-adjusted imputed models (β = 96.40, 95% CI: -15.05, 207.85, P = .08) and complete case models (β = 98.66, 95% CI: -25.24, 222.55, P = .099), the TIMP1 relationships remained marginally significant.

CONCLUSIONS

Our marginal results support existing veteran morbidity and mortality literature while suggesting a modest utility of epigenetic aging biomarkers for further understanding veteran health. As veterans represent an important subset of the population and are a priority in federal government budgets, future research in this area holds the potential for significant public health and policy impact.

Biological age prediction and NAFLD risk assessment: a machine learning model based on a multicenter population in Nanchang, Jiangxi, China

BACKGROUND

The objective was to develop a biological age prediction model (NC-BA) for the Chinese population to enrich the relevant studies in this population. And to investigate the association between accelerated age and NAFLD.

METHODS

On the basis of the physical examination data of people without noninfectious chronic diseases (PWNCDs) in Nanchang, Jiangxi, China, the biological age measurement method was developed via three feature selection methods (all-subset regression, LASSO regression (LR), and recursive feature elimination) and three machine learning algorithms (generalized linear model (GLM), support vector machine, and deep generalized linear model (deep GLM)). Model performance was evaluated by the coefficient of determination (R²) and mean absolute error (MAE). National Health and Nutrition Examination Survey (NHANES) data were used to verify the model's generalizability. The standardized age deviation (SAD) was calculated to explore the associations between age acceleration and the risk of morbidity and mortality from NAFLD.

RESULTS

The physical examination data of 26,356 PWNCDs were collected in Nanchang. Among the 26 biomarkers, 26 and 24 biomarkers were associated with chronological age in the male and female groups, respectively (P < 0.05). The model combining the LR and deep GLM algorithms provided the most accurate measurement of chronological age (r = 0.58, MAE = 5.33) and was named the Nanchang-biological age (NC-BA) model. The generalizability of the NC-BA model was verified in the NHANES dataset (r = 0.57, MAE = 7.12). There was a significant correlation between NC-BA and existing biological age indicators (Klemera-Doubal method biological age (KDM-BA), PhenoAge, and homeostatic dysregulation (HD), r = 0.42-0.66, P < 0.05). The physical examination data of 1,663 and 1,445 patients with NAFLD from the Nanchang population and NHANES, respectively, were obtained. The SAD values of NAFLD patients were significantly greater than those of PWNCDs (P < 0.001). The SAD values of NAFLD patients with younger chronological ages were greater (P < 0.001). Higher SAD values were associated with a greater risk of all-cause mortality (HR = 1.73, P = 0.005).

CONCLUSIONS

This study provides a new model for biological age measurement in the Chinese population. There is a clear link between NAFLD and age acceleration.

DNA methylation entropy is a biomarker for aging

The dynamic nature of epigenetic modifications has been leveraged to construct epigenetic clocks that accurately predict an individual's age based on DNA methylation levels. Here we explore whether the accumulation of epimutations, which can be quantified by Shannon's entropy, changes reproducibly with age. Using targeted bisulfite sequencing, we analyzed the associations between age, entropy, and methylation levels in human buccal swab samples. We find that epigenetic clocks based on the entropy of methylation states predict chronological age with similar accuracy as common approaches that are based on methylation levels of individual cytosines. Our approach suggests that across many genomic loci, methylation entropy changes reproducibly with age.

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.

Workshop Report-Heterogeneity and Successful Aging Part I: Heterogeneity in Aging-Challenges and Opportunities

Historically, aging research has focused primarily on the study of differences in means of varied measures obtained at different ages. However, growing evidence has shown that for many parameters, variability in measurements obtained both between- and within-age groups increases with aging. Moreover, growing heterogeneity may become especially apparent when examined via longitudinal as opposed to cross-sectional aging data. Efforts to deconvolute and better understand such heterogeneity present remarkable translational opportunities for developing targeted and more effective interventions into aging. Here, we present Part I, a summary of the NIA Heterogeneity and Successful Aging workshop virtually held in May 2023.

Association between cardiometabolic index and biological ageing among adults: a population-based study

BACKGROUND

Cardiovascular health (CVH) is closely associated with ageing. This study aimed to investigate the association between cardiometabolic index (CMI), a novel indicator of cardiometabolic status, and biological ageing.

METHODS

Cross-sectional data were obtained from participants with comprehensive CMI and biological age data in the National Health and Nutrition Examination Survey from 2011 to 2018. Biological age acceleration (BioAgeAccel) is calculated as the differences between biological age and chronological age, and that biological age is derived from a model incorporating eight biomarkers. Weighted multivariable regression, sensitivity analysis, and smoothing curve fitting were performed to explore the independent association between CMI and the acceleration of biological age. Subgroup and interaction analyses were performed to investigate whether this association was consistent across populations.

RESULTS

In 4282 subjects ≥ 20 years of age, there was a positive relationship between CMI and biological age. The BioAgeAccel increased 1.16 years for each unit CMI increase [1.16 (1.02, 1.31)], and increased 0.99 years for per SD increase in CMI [0.99 (0.87, 1.11)]. Participants in the highest CMI quartile had a BioAgeAccel that was 2.49 years higher than participants in the lowest CMI quartile [2.49 (2.15, 2.83)]. In stratified studies, the positive correlation between CMI and biological age acceleration was not consistent across strata. This positive correlation was stronger in female, diabetes, and non-hypertension populations.

CONCLUSIONS

CMI is positively correlated with biological ageing in adults in the United States. Prospective studies with larger sample sizes are required to validate our findings.

Dissecting the causal association of periodontitis with biological aging and its underlying mechanisms: findings from Mendelian randomization and integrative genetic analysis

PURPOSE

Chronic low-grade inflammation is linked to the biology of aging; however, evidence supporting a causal relationship between periodontitis-a dysbiotic biofilm-initiated inflammatory disease-and accelerated aging remains limited. This study investigated the causality between periodontitis and biological aging and identified potentially shared genomic loci, genes, and pathways.

METHODS

We conducted a 2-sample Mendelian randomization (MR) analysis to explore the causality of periodontitis on age acceleration measures (DNAm PhenoAge acceleration, GrimAge acceleration, Hannum age acceleration, and intrinsic epigenetic age acceleration) using a dataset from genome-wide association studies of European ancestry populations. Independent genetic variants associated with each trait were used as instrumental variables. The inverse variance-weighted (IVW) method served as the primary MR approach, supplemented by sensitivity testing. We also performed additional statistical genetic analyses to identify pleiotropic loci, shared functional genes, and potential biological pathways, integrating large-scale expression quantitative trait loci data from blood samples.

RESULTS

The MR analysis indicated a causal relationship between periodontitis and DNAm PhenoAge acceleration (IVW β=0.308; 95% confidence interval, 0.056-0.561; P=0.017), a finding corroborated by sensitivity analyses. There was a significant genetic overlap between periodontitis and age acceleration. Pleiotropic analysis revealed 24 shared SNPs associated with 242 genes, predominantly involved in immune functions and pathways related to cellular processes. Further integration analysis showed that 91 of these pleiotropic genes were causally linked to both conditions, with C6orf183 identified as a potential mediator.

CONCLUSIONS

This study presents compelling genetic evidence supporting a causal relationship between periodontitis and accelerated aging. Further research is required to validate these findings and investigate the underlying mechanisms.

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.

Predicting mental health disorder onsets with Fibonacci sequencing: A genetic and epigenetic perspective

This study explores a novel intersection between molecular biology, genomics, and mathematical modeling to predict the onset patterns of mental health disorders. By investigating the alignment between the Fibonacci sequence and the timing of genetic and epigenetic events, this research seeks to uncover whether these patterns can serve as a predictive model for the onset of disorders such as schizophrenia, bipolar disorder, and major depressive disorder. Leveraging epidemiological data and advanced time-series analysis, the study examines how the temporal progression of molecular markers corresponds to clinical manifestation ages in mental health disorders. Findings indicate that specific ages of disorder onset show significant alignment with Fibonacci harmonics, suggesting a potential natural synchrony within biological processes. This interdisciplinary approach could enhance predictive accuracy, supporting early intervention and personalized mental health strategies, and offering a new perspective on the molecular underpinnings of psychiatric conditions.

Independent evaluation of an 11-CpG panel for age estimation in blood

DNA methylation patterns have emerged as reliable markers for age estimation, offering potential applications in forensic investigations, namely, in cases where there is no information about a possible suspect, in the identification of victims of mass disasters, or in immigration cases when assessing the age of individuals seeking asylum. This study aimed to evaluate the 11-CpG panel proposed by Aliferi et al. (2022) for age estimation. During the implementation phase, the ELOVL2 amplicon from the original work was replaced with a shorter fragment, and the two PCR multiplexes were optimized by changing the amplicons and primer conditions of each multiplex. The technical performance of the optimised assay was assessed using artificially methylated DNA standards. Robust quantification of the methylation levels at the 11 CpG sites was observed. Sensitivity tests demonstrated that DNA inputs down to 10 ng could produce reliable methylation quantification. Using the optimised panel, 148 Danish blood samples (18 - 68 years of age) were typed for their methylation status at the 11 CpG sites. Results showed that the DNA methylation at the 11 CpG loci was significantly correlated with age (0.68 ≤ r ≤ 0.88) in the Danish sample set, confirming the potential of the 11 CpGs in age prediction. A Danish age prediction model was constructed using 108 of the Danish blood samples and a support vector machine with polynomial function (SVMp). The performances of the new model and the original model based on UK individuals were compared using the remaining 40 Danish blood samples. Comparing the published model to the one developed in this study gave similar results with mean absolute errors (MAE) of 3.28 and 3.35, respectively. However, the original model showed a bias in the age predictions, underestimating the age by an average of 1.53 years in the Danish samples. This bias towards underestimation was not observed in the newly developed age prediction model based on Danish individuals. In summary, this assay provides a reasonably accurate age estimation of a single-source donor, if the sample material is blood and more than 10 ng of nuclear DNA can be extracted from the sample.

Association between epigenetic aging and atrioventricular block: a two-sample Mendelian randomization study

AIMS

Atrioventricular block (AVB) is a prevalent bradyarrhythmia. This study aims to investigate the causal effects of epigenetic aging, as inferred from DNA methylation profiles on the prevalence of AVB by Mendelian randomization (MR) analysis.

METHODS

Genetic instruments for epigenetic aging and AVB were obtained from genome-wide association study data in the Edinburgh DataShare and FinnGen biobanks. Univariable and multivariable MR analyses were conducted to evaluate causal associations. Additionally, we employed sensitivity tests to assess the robustness of the MR findings.

RESULTS

MR analysis showed that genetically predicted GrimAge acceleration was significantly associated with a higher risk of AVB (inverse variance-weighted: p = 0.010, 95% confidence interval (CI) = 1.024-1.196; weighted median: p = 0.031, 95% CI = 1.009-1.215). However, no evidence supported a causal relationship between AVB and epigenetic aging. The association between epigenetic aging and AVB was established using multivariate MR analysis after adjusting for various risk factors. Sensitivity analyses confirmed the reliability and robustness of the results.

CONCLUSION

Our findings suggest that epigenetic aging in GrimAge may increase the risk of AVB, emphasizing the importance of addressing epigenetic aging in strategies for AVB prevention.

Causal Associations of Epigenetic Age Acceleration With Stroke and Its Functional Outcome: A Two-Sample, Two-Step Mendelian Randomization Study

BACKGROUND

Emerging evidence from observational studies suggested that epigenetic age acceleration may result in an increased incidence of stroke and poorer functional outcomes after a stroke. However, the causality of these associations remains controversial and may be confounded by bias. We aimed to investigate the causal effects of epigenetic age on stroke and its functional outcomes.

METHODS

We conducted a two-sample Mendelian randomization (MR) analysis to explore the causal relationships between epigenetic age and stroke and its outcomes. Additionally, a two-step MR analysis was performed to investigate whether lifestyle factors affect stroke via epigenetic age. Datasets of epigenetic age were obtained from a recent meta-analysis (n = 34,710), while those of stroke and its outcomes were sourced from the MEGASTROKE (n = 520,000) consortium and Genetics of Ischaemic Stroke Functional Outcome (GISCOME) network (n = 6165).

RESULTS

Two-sample MR analysis revealed a causal relationship between PhenoAge and small vessel stroke (SVS) (OR = 1.07; 95% CI, 1.03-1.12; p = 2.01 × 10-3). Mediation analysis through two-step MR indicated that the increased risk of SVS due to smoking initiation was partially mediated by PhenoAge, with a mediation proportion of 9.5% (95% CI, 1.6%-20.6%). No causal relationships were identified between epigenetic age and stroke outcomes.

CONCLUSIONS

Our study supports using epigenetic age as a biomarker to predict stroke occurrence. Interventions specifically aimed at decelerating epigenetic aging, such as specific lifestyle changes, offer effective strategies for reducing stroke risk.

Sleep Duration Polygenic Risk and Phenotype: Associations with Biomarkers of Accelerated Aging in the Baltimore Longitudinal Study of Aging

We sought to explore whether genetic risk for, and self-reported, short sleep are associated with biological aging and whether age and sex moderate these associations. Participants were a subset of individuals from the Baltimore Longitudinal Study of Aging who had complete data on self-reported sleep (n = 567) or genotype (n = 367). Outcomes included: Intrinsic Horvath age, Hannum age, PhenoAge, GrimAge, and DNAm-based estimates of plasminogen activator inhibitor-1 (PAI-1) and granulocyte count. Results demonstrated that polygenic risk for short sleep was positively associated with granulocyte count; compared to those reporting <6 hr sleep, those reporting >7 hr demonstrated faster PhenoAge and GrimAge acceleration and higher estimated PAI-1. Polygenic risk for short sleep and self-reported sleep duration interacted with age and sex in their associations with some of the outcomes. Findings highlight that polygenic risk for short sleep and self-reported long sleep is associated with variation in the epigenetic landscape and subsequently aging.

Sex differences in epigenetic ageing for older people living with HIV

BACKGROUND

HIV-1 infection impacts biological ageing, and epigenetic clocks highlight epigenetic age acceleration in people with HIV. Despite evidence indicating sex differences in clinical, immunological, and virological measures, females have been underrepresented in most HIV epigenetic studies. Hence, we generated a more representative epigenetic dataset to examine sex differences in epigenetic ageing and relationships to clinical phenotypes and proteomics.

METHODS

We calculated first, second, and third-generation epigenetic ages using DNA methylation data in an observational cohort of 52 females and 106 males with HIV age 50 and over. We profiled plasma biomarkers with Olink high-throughput proteomics to test associations with epigenetic age acceleration. Survival was ascertained over 5 years.

FINDINGS

Epigenetic age acceleration measured by three principal-component based chronological epigenetic age clocks (p = 0.0029, 0.021, 0.010) and one epigenetic mortality risk clock was significantly lower in females living with HIV compared to males (p = 0.0011). Additionally, sex was significantly associated with epigenetic biomarker scores for proportion of naïve CD4+ T cells (p = 0.0006), physical fitness including DNAmGait (p = 0.0010), DNAmGrip (p < 0.0001), and DNAmV02 max (p < 0.0001). We found epigenetic age acceleration associated with plasma proteomic markers involved in inflammation, senescence, immune regulation, kidney function, and tissue homoeostasis (p < 0.0001). Higher epigenetic frailty risk scores were associated with lower CD4 T cell counts (p = 0.0072) and lower CD4/CD8 ratio (p = 0.0017). Slower gait (p = 0.0017), greater frailty (p = 0.0074), and history of smoking (p = 0.042) were associated with increased DNAmFitAge. Risk of death was increased in females with PCPhenoAge acceleration over a 5-year timespan compared to men with PCPhenoAge acceleration (p = 0.03).

INTERPRETATION

These results highlight the importance of studying sex-specific differences in epigenetic ageing biomarkers for HIV-related geroscience research.

FUNDING

National Institute on Aging (K23AG072960), National Center for Advancing Translational Sciences (UL1TR000457), National Institute of Mental Health (R21 MH115821).

Epigenetic age acceleration in idiopathic pulmonary fibrosis revealed by DNA methylation clocks

In this research, we delve into the association between epigenetic aging and idiopathic pulmonary fibrosis (IPF), a debilitating lung disease that progresses over time. Utilizing the Illumina MethylationEPIC array, we assessed DNA methylation levels in donated human lung tissue from patients with IPF, categorizing the disease into mild, moderate, and severe stages based on clinical assessments. We used seven epigenetic clocks to determine age acceleration, which is the discrepancy between biological (epigenetic) and chronological age. Our findings revealed a notable acceleration of biological aging in IPF tissues compared with healthy controls, with four clocks-Horvath's, Hannum's, PhenoAge, and DunedinPACE-showing significant correlations. DunedinPACE, in particular, indicated a more rapid aging process in the more severe regions within the lungs of IPF cases. These results suggest that the biological aging process in IPF is expedited and closely tied to the severity of the disease. The study underscores the potential of DNA methylation as a biomarker for IPF, providing valuable insights into the underlying methylation patterns and the dynamics of epigenetic aging in affected lung tissue. This research supports the broader application of epigenetic clocks in clinical prognosis and highlights the critical role of biological age in the context of medical research and healthcare.NEW & NOTEWORTHY Using epigenetic clocks, we found a notable acceleration of biological aging in IPF tissues, particularly in DunedinPACE, suggesting that the biological aging process in IPF is accelerated and closely related to the severity of the disease. The study also underscores DNA methylation's potential as a biomarker for IPF, as well as the dynamics of epigenetic aging and the need to consider biological age in medical research and healthcare.

Epigenetic Clock Analysis for National Institutes of Health Stroke Scale in Patients With Ischemic Stroke

AIM

Strokes are the second most common cause of mortality and disability worldwide. Ischemic strokes account for the main part of strokes. Recently, the epigenetic changes that occur during biological aging through DNA methylation have gained attention. The National Institutes of Health Stroke Scale (NIHSS) scores measure physical and cognitive function. We hypothesized that there are associations between acute changes in the NIHSS score and biological aging in patients with ischemic stroke. We conducted epigenetic clock analyses to investigate the association between the difference in NIHSS (dNIHSS) and epigenetic clock in patients with ischemic stroke.

METHODS

We used two publicly available DNA methylation data sets from Caucasian patients with ischemic stroke in Spain. The discovery data set consists of 59 patients with ischemic stroke, and the replication dataset consists of 62. Acceleration of several epigenetic clocks (HorvathAge, HannumAge, SkinBloodAge, PhenoAge, GrimAge, GrimAge2, DNA methylation-based telomere length, and DunedinPACE), GrimAge components, and GrimAge2 components was analyzed with standard multiple regression analyses with dNIHSS. We obtained information on dNIHSS between discharge and baseline for each patient. We integrated these results from the two data sets using meta-analyses.

RESULTS

There was no significant association in the epigenetic age acceleration. The predictive value of only Cystatin C showed a significant association with dNIHSS in the GrimAge components.

CONCLUSIONS

We could not find a significant association between the severity during the acute phase of ischemic stroke and epigenetic clocks. We may be able to find different findings with a larger sample size and longitudinal data such as NIHSS scores at fixed intervals.

Individual and additive effects of vitamin D, omega-3 and exercise on DNA methylation clocks of biological aging in older adults from the DO-HEALTH trial

While observational studies and small pilot trials suggest that vitamin D, omega-3 and exercise may slow biological aging, larger clinical trials testing these treatments individually or in combination are lacking. Here, we report the results of a post hoc analysis among 777 participants of the DO-HEALTH trial on the effect of vitamin D (2,000 IU per day) and/or omega-3 (1 g per day) and/or a home exercise program on four next-generation DNA methylation (DNAm) measures of biological aging (PhenoAge, GrimAge, GrimAge2 and DunedinPACE) over 3 years. Omega-3 alone slowed the DNAm clocks PhenoAge, GrimAge2 and DunedinPACE, and all three treatments had additive benefits on PhenoAge. Overall, from baseline to year 3, standardized effects ranged from 0.16 to 0.32 units (2.9-3.8 months). In summary, our trial indicates a small protective effect of omega-3 treatment on slowing biological aging over 3 years across several clocks, with an additive protective effect of omega-3, vitamin D and exercise based on PhenoAge.

Trends of genetic contributions on epigenetic clocks and related methylation sites with aging: A population-based adult twin study

Several crucial acceleration periods exist during aging process. Epigenetic clocks, serving as indicators of aging, are influenced by genetic factors. Investigating how the genetic contributions on these clocks change with age may provide novel insights into the aging process. In this study, based on 1084 adult twins from the Chinese National Twin Registry (CNTR), we established structural equation models (SEMs) to evaluate the trends in genetic influence with aging for epigenetic clocks, which include PC-Horvath, PC-Hannum, PC-PhenoAge, PC-GrimAge, and DunedinPACE. A decline in overall heritability was observed for all five clocks from ages 31 to 70, with a relatively stable trend at first. Subsequently, apart from PC-GrimAge, the other four clocks displayed a more evident drop in heritability: DunedinPACE and PC-PhenoAge experienced a clear decline between 55 and 65 years, while PC-Horvath and PC-Hannum showed a similar decrease between 60 and 70 years. In contrast, the heritability of PC-GrimAge remained stable throughout. An analysis of methylation sites (CpGs) from these clocks identified 41, 26, 4, and 36 CpG sites potentially underlying heritability changes in DunedinPACE, PC-Horvath, PC-Hannum, and PC-PhenoAge, respectively. Data from the CNTR were collected through two surveys in 2013 and 2018. Based on 308 twins with longitudinal data, declines in genetic components were observed at follow-up compared to baseline, with significant decreases in the four PC-clocks. DunedinPACE peaked in 5-year longitudinal genetic contribution changes at age 55-60, while PC-clocks consistently peaked at age 50-55. These findings may offer novel insights into the role of genetic variations in aging.

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.