Thanatological Epigenetics as a Postmortem Forensic Tool: A Contextual Call for Epigenetic Clock Research in Asia.

DNA methylation (DNAm) has been touted as a potential unified marker of the contributions of both inherited and environmental factors on an individual’s health. Changes in DNAm have been associated with several chronic diseases and mortality, and DNAm risk scores, or epigenetic clocks, have been proposed as metrics to quantify the process of ‘biological ageing’. Unfortunately, research involving epigenetic clocks is not free from the issues faced in other fields of genomic research. Namely, individuals of European ancestry make up the vast majority of epigenetic study participants and it is unclear whether epigenetic clocks will provide equitable benefits when applied in diverse populations. Although some studies have reported variation in DNAm between populations, it can be difficult to identify the mechanisms underlying these differences. This has implications for clinical application of epigenetic clocks. In this review, we discuss epigenetic clocks, missing diversity in epigenetic research and the potential consequences of the latter on the equitable translation of epigenetic clocks to diverse populations.

A systematic review of biological, social and environmental factors associated with epigenetic clock acceleration

Accelerated aging in psychiatric disorders: evidence from epigenetic clocks.

Epigenetic clocks are emerging as promising biomarkers of biological aging, yet their sensitivity to short-term interventions remains unclear. This pilot study investigates whether the GrimAge clock can capture the effects of a 6-month cycling-based endurance exercise training intervention, with cardiorespiratory fitness (VO2 max) and body composition as primary outcomes. We enrolled 42 adults aged 35-65, of whom 38 completed the study and 33 adhered to the protocol (> 66% adherence). Participants demonstrated significant improvements in VO2 max (+ 20%, P < 0.001) and body composition (P < 0.001). High-quality epigenetic data preprocessing yielded highly reproducible GrimAge estimates (< 2months measurement error), which strongly correlated with chronological age (R2 = 0.86, P < 0.001). On average, GrimAge decreased by 7.44months relative to the expected trajectory (P = 0.012), reflecting improvements in VO2 max (R2 = 0.27, P = 0.002) but not body composition changes. Notably, GrimAge changes strongly correlated with fluctuations in leukocyte composition, particularly neutrophil fraction (R2 = 0.74, P < 0.001). Adjusting for leukocyte composition improved consistency in GrimAge changes, aligning them with additional intervention outcomes and explaining up to 81% of variance. These findings demonstrate that GrimAge is responsive to short-term endurance training, serving as a meaningful biomarker of improved cardiorespiratory fitness, while also capturing immune system variability. This study supports the use of GrimAge in evaluating longevity interventions and highlights the importance of accounting for leukocyte composition in epigenetic aging research.

Numerous studies have shown that epigenetic age-an individual's degree of aging based on patterns of DNA methylation-can be computed and is associated with an array of factors including diet, lifestyle, genetics, and disease. One can expect that still further associations will emerge with additional aging research, but to what end? Prediction of age was an important first step, but-in our view-the focus must shift from chasing increasingly accurate age computations to understanding the links between the epigenome and the mechanisms and physiological changes of aging. Here, we outline emerging areas of epigenetic aging research that prioritize biological understanding and clinical application. First, we survey recent progress in epigenetic clocks, which are beginning to predict not only chronological age but aging outcomes such as all-cause mortality and onset of disease, or which integrate aging signals across multiple biological processes. Second, we discuss research that exemplifies how investigation of the epigenome is building a mechanistic theory of aging and informing clinical practice. Such examples include identifying methylation sites and the genes most strongly predictive of aging-a subset of which have shown strong potential as biomarkers of neurodegenerative disease and cancer; relating epigenetic clock predictions to hallmarks of aging; and using longitudinal studies of DNA methylation to characterize human disease, resulting in the discovery of epigenetic indications of type 1 diabetes and the propensity for psychotic experiences.

Recent advances in epigenetic research have enabled the development of epigenetic clocks, which have greatly enhanced our ability to investigate molecular processes that contribute to aging and age-related disease. These biomarkers offer the potential to measure the effect of environmental exposures linked to dynamic changes in DNA methylation, including nutrients, as factors in age-related disease. They also offer a compelling insight into how imbalances in the supply of nutrients, particularly B-vitamins, or polymorphisms in regulatory enzymes involved in 1-carbon metabolism, the key pathway that supplies methyl groups for epigenetic reactions, may influence epigenetic age and interindividual disease susceptibility. Evidence from recent studies is critically reviewed, focusing on the significant contribution of the epigenetic clock to nutritional epigenomics and its impact on health outcomes and age-related disease. Further longitudinal studies and randomized nutritional interventions are required to advance the field.

Investigating aging has become a subject of intense biomedical focus. This hascoincided with an unprecedented rise in epigenetic research. DNA methylation(DNAm) is the most comprehensively investigated epigenetic process. Epigeneticclocks are capable of statistically correlating DNAm changes with chronologicalage. DNAm changes are also proving to be a worthwhile biomarker of age-relateddisease, while emerging evidence suggests this epigenetic mechanism could be aneffective diagnostic tool for disease detection. Such investigative progress hassignificant implications for health care. In this brief review we examine somerecent findings in this area. The overarching aim and scope of the work is toaddress the relationship between aging, DNAm, and health. We commence by brieflyintroducing aging. Next, DNAm and age-related disease are discussed. Thirdly, wecritically examine epigenetic clocks. We conclude by exploring recent advancesin the use of biosensors for measuring DNAm and disease detection.

The Y chromosome plays a crucial role in understanding the overall landscape of male health. Incorporating the Y chromosome into genomic and epigenomic research may elucidate the male-specific mechanisms behind aging and the pathogenesis of certain conditions, both acute and chronic. Present epigenetic research focuses on the effects of modifications like methylation on autosomal chromosomes. However, little research has been conducted to further these investigations in sex chromosomes, especially the Y chromosome. Epigenetic analyses can identify age-associated CpG sites that may offer potential biomarkers for age estimation and disease risk assessment, among others. This review emphasizes interdisciplinary efforts that have been made in the construction of an assembly and the application of “epigenetic clocks” to the Y chromosome. The studies reviewed here examined the effects of aging on genes such as NLGN4Y, DDX3Y, and TBL1Y, and on male-specific health disparities and disease etiologies, as well as the potential for the use of these genes to assess the diagnostic and age algorithmic potential of Y-specific genes.

Accurately quantifying biological age is crucial for understanding the mechanisms of aging and developing effective interventions. Molecular aging clocks, particularly epigenetic clocks that use DNA methylation data to estimate biological age, have become essential tools in this area of research. However, the lack of a comprehensive, publicly accessible database with uniformly formatted DNA methylation datasets across various ages and tissues complicates the investigation of epigenetic clocks. Researchers face significant challenges in locating relevant datasets, accessing key information from raw data, and managing inconsistent data formats and metadata annotations. Additionally, there is a lack of dedicated resources for aging-related differentially methylated sites (DMSs, also named differentially methylated positions or differentially methylated cytosines) and regions (DMRs), which hinders progress in understanding the epigenetic mechanisms of aging. To address these challenges, we developed MethAgingDB, a comprehensive DNA methylation database for aging biology. MethAgingDB includes 93 datasets, with 11474 profiles from 13 distinct human tissues and 1361 profiles from 9 distinct mouse tissues. The database provides preprocessed DNA methylation data in a consistent matrix format, along with tissue-specific DMSs and DMRs, gene-centric aging insights, and an extensive collection of epigenetic clocks. Together, MethAgingDB is expected to streamline aging-related epigenetic research and support the development of robust, biologically informed aging biomarkers.

BackgroundDNA methylation-based predictors of phenotypic traits including leukocyte proportions, smoking activity, biological aging, and circulating levels of plasma proteins are widely used as biomarkers in public health research. However, limited racial and ethnic diversity of research participants is an ongoing issue for epigenetics research, and the potential downstream impacts of limited diversity in training samples on the performance of epigenetic predictors remains poorly understood. We examined the performance of epigenetic predictors of chronological age (also known as epigenetic clocks), telomere length, cell proportions, and plasma proteins within a diverse sample of adult NHANES participants during the 1999–2000 and 2001–2002 survey cycles, both overall and stratified by self-reported race/ethnicity and sex. We utilized correlation coefficients and median absolute errors (MAE) to judge predictor performance, and bootstrapping and multivariate regression to assess the significance of differences between groups.ResultsAll epigenetic predictors were significantly associated with their corresponding phenotypic traits in the overall population, with particularly high correlations for the epigenetic clocks and cell proportion estimates. Several significant differences in performance were observed between racial/ethnic groups, particularly for the plasma protein predictors, with a reoccurring trend of lower correlation in Mexican American and non-Hispanic Black participants compared to non-Hispanic White participants. Sex-differences in performance for several predictors were also identified but were not as pronounced. Multivariate regression models indicated that disparities in epigenetic predictor performance persisted after accounting for overall differences in epigenetic predictions related to race/ethnicity and sex, as well as further adjustment for estimated cell proportions and SES variables.ConclusionsWe found evidence for substantial disparities in epigenetic predictor performance, with each predictor exhibiting at least one significant difference in correlation or MAE related to race, ethnicity, or sex.

A new mitotic clock and mathematical approach that incorporates DNA methylation biology common among human cell types provides a new tool for cancer epigenetics research.

Pan-tissue methylation aging clock: Recalibrated and a method to analyze and interpret the selected features