Epigenetic clock for skin and blood cells applied to Hutchinson Gilford Progeria Syndrome and ex vivo studies

Younger epigenetic age is associated with higher cardiorespiratory fitness in individuals with airflow limitation

We hypothesized that increased cardiorespiratory fitness (CRF) slows down a person's aging, particularly in individuals with chronic airflow limitation (CAL). Participants aged ≥40 years (n = 78) had baseline blood DNA methylation profiled and underwent cardiopulmonary cycle exercise testing at baseline and at three years. Epigenetic clocks were calculated and tested for their association with CRF using linear regression. Differentially methylated genes associated with CRF were identified using a robust linear model. Higher CRF at baseline was associated with lower age acceleration in the epigenetic clocks DNAmAgeSkinBlood (p = 0.016), DNAmGrimAge (p = 0.012), and DNAmGrimAge2 (p = 0.011). These effects were consistent in individuals with CAL (DNAmGrimAge p = 0.009 and DNAmGrimAge2 p = 0.007). CRF at three years was associated with baseline DNAmGrimAge (p = 0.015) and DNAmGrimAge2 (p = 0.011). Differentially methylated genes associated with CRF enriched multiple aging-related pathways, including cellular senescence. Enhancing CRF may be one intervention that can slow biological aging and improve health outcomes in chronic respiratory diseases.

Retro-age: A unique epigenetic biomarker of aging captured by DNA methylation states of retroelements

Reactivation of retroelements in the human genome has been linked to aging. However, whether the epigenetic state of specific retroelements can predict chronological age remains unknown. We provide evidence that locus-specific retroelement DNA methylation can be used to create retroelement-based epigenetic clocks that accurately measure chronological age in the immune system, across human tissues, and pan-mammalian species. We also developed a highly accurate retroelement epigenetic clock compatible with EPICv.2.0 data that was constructed from CpGs that did not overlap with existing first- and second-generation epigenetic clocks, suggesting a unique signal for epigenetic clocks not previously captured. We found retroelement-based epigenetic clocks were reversed during transient epigenetic reprogramming, accelerated in people living with HIV-1, and responsive to antiretroviral therapy. Our findings highlight the utility of retroelement-based biomarkers of aging and support a renewed emphasis on the role of retroelements in geroscience.

Elevated homocysteine is associated with increased rates of epigenetic aging in a population with mild cognitive impairment

Elevated plasma total homocysteine (tHcy) is associated with the development of Alzheimer's disease and other forms of dementia. In this study, we report the relationship between tHcy and epigenetic age in older adults with mild cognitive impairment from the VITACOG study. Epigenetic age and rate of aging (ROA) were assessed using various epigenetic clocks, including those developed by Horvath and Hannum, DNAmPhenoAge, and with a focus on Index, a new principal component-based epigenetic clock that, like DNAmPhenoAge, is trained to predict an individual's "PhenoAge." We identified significant associations between tHcy levels and ROA, suggesting that hyperhomocysteinemic individuals were aging at a faster rate. Moreover, Index revealed a normalization of accelerated epigenetic aging in these individuals following treatment with tHcy-lowering B-vitamins. Our results indicate that elevated tHcy is a risk factor for accelerated epigenetic aging, and this can be ameliorated with B-vitamins. These findings have broad relevance for the sizable proportion of the worldwide population with elevated tHcy.

Discrepancies in readouts between Infinium MethylationEPIC v2.0 and v1.0 reflected in DNA methylation-based tools: implications and considerations for human population epigenetic studies

Background

The recently launched DNA methylation profiling platform, Illumina MethylationEPIC BeadChip Infinium microarray v2.0 (EPICv2), is highly correlated with measurements obtained from its predecessor MethylationEPIC BeadChip Infinium microarray v1.0 (EPICv1). However, the concordance between the two versions in the context of DNA methylation-based tools, including cell type deconvolution algorithms, epigenetic clocks, and inflammation and lifestyle biomarkers has not yet been investigated. To address this, we profiled DNA methylation on both EPIC versions using matched venous blood samples from individuals spanning early to late adulthood across four cohorts.

Findings

Within each cohort, samples primarily clustered by the EPIC version they were measured on. High concordance between EPIC versions at the array level, but variable concordance at the individual probe level was noted. Significant differences between versions in estimates from DNA methylation-based tools were observed, irrespective of the normalization method, with some nuanced differences across cohorts and tools. Adjusting for EPIC version or calculating estimates separately for each version largely mitigated these version-specific discordances.

Conclusions

Our work illustrates the importance of accounting for EPIC version differences in research scenarios, especially in meta-analyses and longitudinal studies, when samples profiled across different versions are harmonized. Alongside DNA methylation-based tools, our observations also have implications in interpretation of epigenome-wide association studies (EWAS) findings, when results obtained from one version are compared to another, particularly for probes that are poorly concordant between versions.

Interplay of education and DNA methylation age on cognitive impairment: insights from the Health and Retirement Study

Few studies have assessed the association of educational attainment on dementia and cognitive impairment through DNA methylation age acceleration, while accommodating exposure-mediator interaction effects. We evaluated the mediation role of six epigenetic clocks with dementia, cognitive impairment non-dementia, and normal cognition, while accommodating exposure-mediator interaction effects. To understand the joint association of low education (≤12 years) and DNA methylation age acceleration (yes/no) in relation to cognitive impairment, we used weighted logistic regression, adjusting for chronological age, sex, race/ethnicity, and cell type composition. We performed four-way mediation and interaction decomposition analysis. Analyses were conducted on 2016 venous blood study participants from the Health and Retirement Study (N = 3724). Both GrimAge acceleration (OR = 1.6 95%CI 1.3-2.1) and low educational attainment (OR = 2.4 95%CI 1.9-3.0) were associated with higher odds of cognitive impairment in a mutually adjusted logistic model. We found additive interaction associations between low education and GrimAge acceleration on dementia. We observed that 6-8% of the association of education on dementia was mediated through GrimAge acceleration. While mediation effects were small, the portion of the association of education on dementia due to additive interaction with GrimAge acceleration was between 23.6 and 29.2%. These results support the interplay of social disadvantage and biological aging processes on impaired cognition.

A longevity-specific bank of induced pluripotent stem cells from centenarians and their offspring

Centenarians provide a unique lens through which to study longevity, healthy aging, and resiliency. Moreover, models of human aging and resilience to disease that allow for the testing of potential interventions are virtually non-existent. We obtained and characterized over 96 centenarian and offspring peripheral blood samples including those connected to functional independence data highlighting resistance to disability and cognitive impairment. Targeted methylation arrays were used in molecular aging clocks to compare and contrast differences between biological and chronological age in these specialized subjects. Isolated peripheral blood mononuclear cells (PBMCs) from 20 of these subjects were then successfully reprogrammed into high-quality induced pluripotent stem cell (iPSC) lines which were functionally characterized for pluripotency, genomic stability, and the ability to undergo directed differentiation. The result of this work is a one-of-a-kind resource for studies of human longevity and resilience that can fuel the discovery and validation of novel therapeutics for aging-related disease.

Prenatal and early-life air pollutant exposure and epigenetic aging acceleration

BACKGROUND

This study investigated the association of prenatal and early childhood exposure to air pollution with epigenetic age acceleration (EAA) at six years of age using the Environment and Development of Children Cohort (EDC Cohort) MATERIALS & METHODS: Air pollution, including particulate matter [< 2.5 µm (PM2.5) and < 10 µm (PM10) in an aerodynamic diameter], nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO), and sulfur dioxide (SO2) were estimated based on the residential address for two periods: 1) during the whole pregnancy, and 2) for one year before the follow-up in children at six years of age. The methylation levels in whole blood at six years of age were measured, and the methylation clocks, including Horvath's clock, Horvath's skin and blood clock, PedBE, and Wu's clock, were estimated. Multivariate linear regression models were constructed to analyze the association between EAA and air pollutants.

RESULTS

A total of 76 children in EDC cohort were enrolled in this study. During the whole pregnancy, interquartile range (IQR) increases in exposure to PM2.5 (4.56 μg/m3) and CO (0.156 ppm) were associated with 0.406 years and 0.799 years of EAA (Horvath's clock), respectively. An IQR increase in PM2.5 (4.76 μg/m3) for one year before the child was six years of age was associated with 0.509 years of EAA (Horvath's clock) and 0.289 years of EAA (Wu's clock). PM10 (4.30 μg/m3) and O3 (0.003 ppm) exposure in the period were also associated with EAA in Horvath's clock (0.280 years) and EAA in Horvath's skin and blood clock (0.163 years), respectively.

CONCLUSION

We found that prenatal and childhood exposure to ambient air pollutants is associated with EAA among children. The results suggest that air pollution could induce excess biological aging even in prenatal and early life.

Salivary DNA methylation derived estimates of biological aging, cellular frequency and protein expression as predictors of oral mucositis severity and survival in head and neck cancer patients

BACKGROUND

Oral mucositis is a painful and debilitating condition that occurs in the majority of head and neck cancer patients receiving radiation and/or chemotherapy. While some patient and treatment related factors are known to contribute to the incidence and severity of disease, reliable biomarkers remain elusive. In the following study, we investigated the association of salivary DNA methylation derived biological aging, cellular frequency and protein concentration measures with the severity of oral mucositis and overall survival in a cohort of head and neck cancer (HNC) patients (n = 103).

METHODS

DNA methylation profiling was performed on saliva samples obtained prior to treatment. Biological aging measures included Horvath2, PhenoAge, FitAge and GrimAge, and cellular frequency included epithelial and specific immune cell populations.

RESULTS

Severe mucositis (i.e. grade 3 or 4) occurred in nearly half of patients. For malignant HNC patients (n = 84), every 1-SD increase in GrimAge was associated with 2.62-times risk of severe mucositis (95 % CI: 1.38, 5.57), while a 1-SD increase in monocyte frequency was associated with a decreased risk (OR [95 %CI]: 0.40 [0.18, 0.80]). Over a median follow-up of 53 months, 39 of 103 participants died. Six protein scores (TNFSF14, GCSF, MATN3, GDF8, nCDase, TNF-β) were associated with survival at q < 0.15.

CONCLUSION

We provide evidence that the risk-related biological aging measure GrimAge may be a useful predictor of mucositis severity in HNC patients. Salivary monocyte frequency may be protective against mucositis, and this measure could be used as a predictive biomarker while also providing clues into the pathobiology of the disease.

Dissecting the impact of differentiation stage, replicative history, and cell type composition on epigenetic clocks

Epigenetic clocks, built on DNA methylation patterns of bulk tissues, are powerful age predictors, but their biological basis remains incompletely understood. Here, we conducted a comparative analysis of epigenetic age in murine muscle, epithelial, and blood cell types across lifespan. Strikingly, our results show that cellular subpopulations within these tissues, including adult stem and progenitor cells as well as their differentiated progeny, exhibit different epigenetic ages. Accordingly, we experimentally demonstrate that clocks can be skewed by age-associated changes in tissue composition. Mechanistically, we provide evidence that the observed variation in epigenetic age among adult stem cells correlates with their proliferative state, and, fittingly, forced proliferation of stem cells leads to increases in epigenetic age. Collectively, our analyses elucidate the impact of cell type composition, differentiation state, and replicative potential on epigenetic age, which has implications for the interpretation of existing clocks and should inform the development of more sensitive clocks.

Exploring the potential of epigenetic clocks in aging research

The process of aging is a notable risk factor for numerous age-related illnesses. Hence, a reliable technique for evaluating biological age or the pace of aging is crucial for understanding the aging process and its influence on the progression of disease. Epigenetic alterations are recognized as a prominent biomarker of aging, and epigenetic clocks formulated on this basis have been shown to provide precise estimations of chronological age. Extensive research has validated the effectiveness of epigenetic clocks in determining aging rates, identifying risk factors for aging, evaluating the impact of anti-aging interventions, and predicting the emergence of age-related diseases. This review provides a detailed overview of the theoretical principles underlying the development of epigenetic clocks and their utility in aging research. Furthermore, it explores the existing obstacles and possibilities linked to epigenetic clocks and proposes potential avenues for future studies in this field.

Potential Predictor of Epigenetic Age Acceleration in Men: 2D:4D Finger Pattern

OBJECTIVES

Second to fourth digit ratio is widely known indicator of prenatal sex hormones proportion. Higher prenatal androgenization results in longer fourth finger and lower 2D:4D index. The aim of this study was to determine whether the 2D:4D digit ratio is associated with DNA methylation (DNAm) age dependently on sex.

MATERIAL AND METHODS

The study included 182 adults (106 females and 76 males) with a mean age of 51.5 ± 13 years. The investigation consisted of three main parts: a survey, anthropometric dimensions measurements (fingers length) and methylome analysis using collected blood samples. Genome-wide methylation was analyzed using EPIC microarray technology. Epigenetic age and epigenetic age acceleration were calculated using several widely applied algorithms.

RESULTS

Males with the female left hand pattern had more accelerated epigenetic age than those with the male pattern as calculated with PhenoAge and DNAmTL clocks.

CONCLUSIONS

Finger female pattern 2D:4D above or equal to 1 in males is associated with epigenetic age acceleration, indicating that prenatal exposure to estrogens in males may be related to aging process in the later ontogenesis.

Epigenetic aging in major depressive disorder: Clocks, mechanisms and therapeutic perspectives

Depression, a chronic mental disorder characterized by persistent sadness, loss of interest, and difficulty in daily tasks, impacts millions globally with varying treatment options. Antidepressants, despite their long half-life and minimal effectiveness, leave half of patients undertreated, highlighting the need for new therapies to enhance well-being. Epigenetics, which studies genetic changes in gene expression or cellular phenotype without altering the underlying Deoxyribonucleic Acid (DNA) sequence, is explored in this article. This article delves into the intricate relationship between epigenetic mechanisms and depression, shedding light on how environmental stressors, early-life adversity, and genetic predispositions shape gene expression patterns associated with depression. We have also discussed Histone Deacetylase (HDAC) inhibitors, which enhance cognitive function and mood regulation in depression. Non-coding RNAs, (ncRNAs) such as Long Non-Coding RNAs (lncRNAs) and micro RNA (miRNAs), are highlighted as potential biomarkers for detecting and monitoring major depressive disorder (MDD). This article also emphasizes the reversible nature of epigenetic modifications and their influence on neuronal growth processes, underscoring the dynamic interplay between genetics, environment, and epigenetics in depression development. It explores the therapeutic potential of targeting epigenetic pathways in treating clinical depression. Additionally, it examines clinical findings related to epigenetic clocks and their role in studying depression and biological aging.

Exploring legal age estimation using DNA methylation

Minors (subjects under the legal age, established at this study at 18 years) benefit from a series of legal rights created to protect them and guarantee their welfare. However, throughout the world there are many minors who have no way to prove they are underaged, leading to a great interest in predicting legal age with the highest possible accuracy. Current methods, mainly involving X-ray analysis, are highly invasive, so new methods to predict legal age are being studied, such as DNA methylation. To further such studies, we created two age prediction models based on five epigenetic markers: cg21572722 (ELOVL2), cg02228185 (ASPA), cg06639320 (FHL2), cg19283806 (CCDC102B) and cg07082267 (no associated gene), that were analysed in blood samples to determine possible limitations regarding DNA methylation as an effective tool for legal age estimation. A wide age range prediction model was created using a broad set of samples (14-94 years) yielding a mean absolute error (MAE) of ±4.32 years. A second model, the constrained age prediction model, was created using a reduced range of samples (14-25 years) yielding an MAE of ±1.54 years. Both models, in addition to Horvath's Skin & Blood epigenetic clock, were evaluated using a test set comprising 732 pairs of 18-year-old twins (N=426 monozygotic (MZ) and N=306 dizygotic (DZ) pairs), representing a relevant age of study. Through analysis of the two former age prediction models, we found that constraining the age of the samples forming the training set around the desired age of study significantly reduced the prediction error (from MAE: ±4.07 and ±4.27 years for MZ and DZ twins, respectively; to ±1.31 and ±1.3 years). However, despite low prediction errors, DNA methylation models are still prone to classify same-aged individuals in different categories (minors or adults), despite each sample belonging to the same twin pair. Additional evaluation of Horvath's Skin & Blood model (391 CpGs) led to similar results in terms of age prediction errors than if using only five epigenetic markers (MAE: ±1.87 and ±1.99 years for MZ and DZ twins, respectively).

An epigenome-wide study of a needs-based family intervention for offspring of trauma-exposed mothers in Kosovo

INTRODUCTION

Maternal stress and trauma during pregnancy have been shown to influence cortisol levels and epigenetic patterns, including DNA methylation, in the offspring. This study aimed to determine whether a tailor-made family intervention could help reduce cortisol levels in children born to traumatized mothers, and to determine whether it effected offspring DNA methylation. The secondary aim was to determine whether the family intervention influenced DNA methylation aging, a marker of biological aging.

METHODS

A needs-based family intervention was designed to help address relational difficulties and family functioning, and included a focus on family strengths and problem-solving patterns. Women survivors of sexual violence during the Kosovar war in 1998-1999, and their families (children with or without partners) were randomly assigned to 10 sessions of a family therapy over a 3-5-month period, or to a waitlist control group. Both mothers and children completed assessments prior to and after the intervention phase. Children's blood samples collected at these two time points were used to measure cortisol and epigenome-wide DNA methylation patterns (Illumina EPIC array). Cortisol levels, and genome-wide DNA methylation changes pre-/postintervention were compared between children in the intervention and the waitlist groups. DNA methylation age and accelerated biological aging were calculated.

RESULTS

Sixty-two women-child dyads completed the study, 30 were assigned first to the intervention group, and 32 to the waitlist control group. In adjusted linear regression, the family intervention was associated with a significant decline in cortisol levels compared to the waitlist control (β = -124.72, 95% confidence interval [CI]: -197.4 to -52.1, p = .001). Children in the intervention group, compared to the waitlist control group, showed >1% differential methylation degree at 5819 CpG (5'-C-phosphate-G-3') sites across the genome (p < .01), with the largest methylation difference being 21%. However, none of these differences reached genome-wide significant levels. There was no significant difference in DNA methylation aging between the two groups.

CONCLUSION

We find evidence that a tailored family-based intervention reduced stress levels in the children (based on cortisol levels), and modified DNA methylation levels at a number of sites across the genome. This study provides some preliminary evidence to suggest the potential for tailored interventions to help break the intergenerational transmission of trauma, however, large studies powered to detect associations at genome-wide significant levels are needed.

Developmental origins of psycho-cardiometabolic multimorbidity in adolescence and their underlying pathways through methylation markers: a two-cohort study

Understanding the biological mechanisms behind multimorbidity patterns in adolescence is important as they may act as intermediary risk factor for long-term health. We aimed to explore relationship between prenatal exposures and adolescent's psycho-cardiometabolic intermediary traits mediated through epigenetic biomarkers, using structural equation modeling (SEM). We used data from mother-child dyads from pregnancy and adolescents at 16-17 years from two prospective cohorts: Northern Finland Birth Cohort 1986 (NFBC1986) and Raine Study from Australia. Factor analysis was applied to generate two different latent factor structures: (a) prenatal exposures and (b) adolescence psycho-cardiometabolic intermediary traits. Furthermore, three types of epigenetic biomarkers were included: (1) DNA methylation score for maternal smoking during pregnancy (DNAmMSS), (2) DNAm age estimate PhenoAge and (3) DNAm estimate for telomere length (DNAmTL). Similar factor structure was observed between both cohorts yielding three prenatal factors, namely BMI (Body Mass Index), SOP (Socio-Obstetric-Profile), and Lifestyle, and four adolescent factors: Anthropometric, Insulin-Triglycerides, Blood Pressure, and Mental health. In the SEM pathways, stronger direct effects of F1prenatal-BMI (NFBC1986 = β: 0.27; Raine = β: 0.39) and F2prenatal-SOP (β: -0.11) factors were observed on adolescent psycho-cardiometabolic multimorbidity. We observed an indirect effect of prenatal latent factors through epigenetic markers on a psycho-cardiometabolic multimorbidity factor in Raine study (P < 0.05). The present study exemplifies an evidence-based approach in two different birth cohorts to demonstrate similar composite structure of prenatal exposures and psycho-cardiometabolic traits (despite cultural, social, and genetic differences) and a common plausible pathway between them through underlying epigenetic markers.

Epigenetic age prediction using costal cartilage for the investigation of disaster victims and missing persons

The DNA intelligence tool, DNA methylation-based age prediction, can help identify disaster victims and suspects in criminal investigations. In this study, we developed a costal cartilage-based age prediction tool that uses massive parallel sequencing (MPS) of age-associated DNA methylation markers. Costal cartilage samples were obtained from 85 deceased Koreans, aged between 26 and 89 years. An MPS library was prepared using two rounds of multiplex polymerase chain reaction of nine genes (TMEM51, MIR29B2CHG, EDARADD, FHL2, TRIM59, ELOVL2, KLF14, ASPA, and PDE4C). The DNA methylation status of 45 CpG sites was determined and used to train an age prediction model via stepwise regression analysis. Nine CpGs in MIR29B2CHG, FHL2, TRIM59, ELOVL2, KLF14, and ASPA were selected for regression model construction. A leave-one-out cross-validation analysis revealed the high performance of the age prediction model, with a mean absolute error (MAE) and root mean square error of 4.97 and 6.43 years, respectively. Additionally, our model showed good performance with a MAE of 6.06 years in the analysis of data of 181 costal cartilage samples collected from Europeans. Our model effectively estimates the age of deceased individuals using costal cartilage samples; therefore, it can be a valuable forensic tool for disaster victim and missing person investigation.

Prediction of Multiple Degenerative Diseases Based on DNA Methylation in a Co-Physiology Mechanisms Perspective

Degenerative diseases oftentimes occur within the continuous process of aging, and the corresponding clinical manifestations may be neurodegeneration, neoplastic diseases, or various human complex diseases. DNA methylation provides the opportunity to explore aging and degenerative diseases as epigenetic traits. It has already been applied to age prediction and disease diagnosis. It has been shown that various degenerative diseases share co-physiology mechanisms with each other, clues of which may be gained from studying the aging process. Here, we endeavor to predict the risk of degenerative diseases in an aging-relevant comorbid mechanism perspective. Firstly, an epigenetic clock method was implemented based on a multi-scale convolutional neural network, and a Shapley feature attribution analysis was applied to discover the aging-related CpG sites. Then, these sites were further screened to a smaller subset composed of 196 sites by using biomics analysis according to their biological functions and mechanisms. Finally, we constructed a multilayer perceptron (MLP)-based degenerative disease risk prediction model, Mlp-DDR, which was well trained and tested to accurately classify nine degenerative diseases. Recent studies also suggest that DNA methylation plays a significant role in conditions like osteoporosis and osteoarthritis, broadening the potential applications of our model. This approach significantly advances the ability to understand degenerative diseases and represents a substantial shift from traditional diagnostic methods. Despite the promising results, limitations regarding model complexity and dataset diversity suggest directions for future research, including the development of tissue-specific epigenetic clocks and the inclusion of a wider range of diseases.

Map of epigenetic age acceleration: A worldwide analysis

We present a systematic analysis of epigenetic age acceleration based on by far the largest collection of publicly available DNA methylation data for healthy samples (93 datasets, 23 K samples), focusing on the geographic (25 countries) and ethnic (31 ethnicities) aspects around the world. We employed the most popular epigenetic tools for assessing age acceleration and examined their quality metrics and ability to extrapolate to epigenetic data from different tissue types and age ranges different from the training data of these models. In most cases, the models proved to be inconsistent with each other and showed different signs of age acceleration, with the PhenoAge model tending to systematically underestimate and different versions of the GrimAge model tending to systematically overestimate the age prediction of healthy subjects. Referring to data availability and consistency, most countries and populations are still not represented in GEO, moreover, different datasets use different criteria for determining healthy controls. Because of this, it is difficult to fully isolate the contribution of "geography/environment", "ethnicity" and "healthiness" to epigenetic age acceleration. Among the explored metrics, only the DunedinPACE, which measures aging rate, appears to adequately reflect the standard of living and socioeconomic indicators in countries, although it has a limited application to blood methylation data only. Invariably, by epigenetic age acceleration, males age faster than females in most of the studied countries and populations.

Epigenetic age acceleration in follicular fluid and markers of ovarian response among women undergoing IVF

STUDY QUESTION

Are markers of epigenetic age acceleration in follicular fluid associated with outcomes of ovarian stimulation?

SUMMARY ANSWER

Increased epigenetic age acceleration of follicular fluid using the Horvath clock, but not other epigenetic clocks (GrimAge and Granulosa Cell), was associated with lower peak estradiol levels and decreased number of total and mature oocytes.

WHAT IS KNOWN ALREADY

In granulosa cells, there are inconsistent findings between epigenetic age acceleration and ovarian response outcomes.

STUDY DESIGN, SIZE, DURATION

Our study included 61 women undergoing IVF at an academic fertility clinic in the New England area who were part of the Environment and Reproductive Health Study (2006-2016).

PARTICIPANTS/MATERIALS, SETTING, METHODS

Participants provided a follicular fluid sample during oocyte retrieval. DNA methylation of follicular fluid was assessed using a genome-wide methylation screening tool. Three established epigenetic clocks (Horvath, GrimAge, and Granulosa Cell) were used to predict DNA-methylation-based epigenetic age. To calculate the age acceleration, we regressed epigenetic age on chronological age and extracted the residuals. The association between epigenetic age acceleration and ovarian response outcomes (peak estradiol levels, follicle stimulation hormone, number of total, and mature oocytes) was assessed using linear and Poisson regression adjusted for chronological age, three surrogate variables (to account for cellular heterogeneity), race, smoking status, initial infertility diagnosis, and stimulation protocol.

MAIN RESULTS AND ROLE OF CHANCE

Compared to the median chronological age of our participants (34 years), the Horvath clock predicted, on an average, a younger epigenetic age (median: 24.2 years) while the GrimAge (median: 38.6 years) and Granulosa Cell (median: 39.0 years) clocks predicted, on an average, an older epigenetic age. Age acceleration based on the Horvath clock was associated with lower peak estradiol levels (-819.4 unit decrease in peak estradiol levels per standard deviation increase; 95% CI: -1265.7, -373.1) and fewer total (% change in total oocytes retrieved per standard deviation increase: -21.8%; 95% CI: -37.1%, -2.8%) and mature oocytes retrieved (% change in mature oocytes retrieved per standard deviation increase: -23.8%; 95% CI: -39.9%, -3.4%). The age acceleration based on the two other epigenetic clocks was not associated with markers of ovarian response.

LIMITATIONS, REASONS FOR CAUTION

Our sample size was small and we did not specifically isolate granulosa cells from follicular fluid samples so our samples could have included mixed cell types.

WIDER IMPLICATIONS OF THE FINDINGS

Our results highlight that certain epigenetic clocks may be predictive of ovarian stimulation outcomes when applied to follicular fluid; however, the inconsistent findings for specific clocks across studies indicate a need for further research to better understand the clinical utility of epigenetic clocks to improve IVF treatment.

STUDY FUNDING/COMPETING INTEREST(S)

The study was supported by grants ES009718, ES022955, ES000002, and ES026648 from the National Institute of Environmental Health Sciences (NIEHS) and a pilot grant from the NIEHS-funded HERCULES Center at Emory University (P30 ES019776). RBH was supported by the Emory University NIH Training Grant (T32-ES012870).

TRIAL REGISTRATION NUMBER

N/A.

A systematic review of phenotypic and epigenetic clocks used for aging and mortality quantification in humans

Aging is the leading driver of disease in humans and has profound impacts on mortality. Biological clocks are used to measure the aging process in the hopes of identifying possible interventions. Biological clocks may be categorized as phenotypic or epigenetic, where phenotypic clocks use easily measurable clinical biomarkers and epigenetic clocks use cellular methylation data. In recent years, methylation clocks have attained phenomenal performance when predicting chronological age and have been linked to various age-related diseases. Additionally, phenotypic clocks have been proven to be able to predict mortality better than chronological age, providing intracellular insights into the aging process. This review aimed to systematically survey all proposed epigenetic and phenotypic clocks to date, excluding mitotic clocks (i.e., cancer risk clocks) and those that were modeled using non-human samples. We reported the predictive performance of 33 clocks and outlined the statistical or machine learning techniques used. We also reported the most influential clinical measurements used in the included phenotypic clocks. Our findings provide a systematic reporting of the last decade of biological clock research and indicate possible avenues for future research.

Epigenetic age acceleration and mortality risk prediction in U.S. adults

Background

Epigenetic clocks have emerged as novel measures of biological age and potential predictors of mortality. We aimed to test whether epigenetic age acceleration (EAA) estimated using different epigenetic clocks predict long-term overall, cardiovascular or cancer mortality.

Methods

We analyzed data from 2,105 participants to the 1999-2002 National Health and Nutrition Examination Survey aged ≥50 years old who were followed for mortality through 2019. EAAs was calculated from the residuals of Horvath, Hannum, SkinBlood, Pheno, Zhang, Lin, Weidner, Vidal-Bralo and Grim epigenetic clocks regressed on chronological age. Using cox proportional hazards regression, we estimated the hazard ratio (HR) and 95% confidence interval (CI) for the association of EAA (per 5-year) and the DunedinPoAm pace of aging (per 10% increase) with overall, cardiovascular and cancer mortality, adjusting for covariates and white blood cell composition.

Results

During a median follow-up of 17.5 years, 998 deaths occurred, including 272 from cardiovascular disease and 209 from cancer. Overall mortality was most significantly predicted by Grim EAA (P < 0.0001; HR: 1.50, 95% CI: 1.32-1.71) followed by Hannum (P = 0.001; HR: 1.16, 95% CI: 1.07-1.27), Pheno (P = 0.001; HR: 1.13, 95% CI: 1.05-1.21), Horvath (P = 0.007; HR: 1.13, 95% CI: 1.04-1.22) and Vidal-Bralo (P = 0.008; HR: 1.13, 95% CI: 1.03-1.23) EAAs. Grim EAA predicted cardiovascular mortality (P < 0.0001; HR: 1.55, 95% CI: 1.29-1.86), whereas Hannum (P = 0.006; HR: 1.24, 95% CI: 1.07-1.44), Horvath (P = 0.02; HR: 1.18, 95% CI: 1.02-1.35) and Grim (P = 0.049; HR: 1.37, 95% CI: 1.00-1.87) EAAs predicted cancer mortality. DunedinPoAm pace of aging was associated with overall (P = 0.003; HR: 1.23, 95% CI: 1.08-1.38) and cardiovascular (P = 0.04; HR: 1.25, 95% CI: 1.01-1.55) mortality.

Conclusions

In a U.S. representative sample, Horvath, Hannum, Pheno, Vidal-Bralo and Grim EAA all predicted overall mortality but only Grim EAA predicted cardiovascular mortality and Horvath, Hannum or Grim EAA predicted cancer mortality. Pace of aging predicted overall and cardiovascular mortality.

Common DNA sequence variation influences epigenetic aging in African populations

Aging is associated with genome-wide changes in DNA methylation in humans, facilitating the development of epigenetic age prediction models. However, most of these models have been trained primarily on European-ancestry individuals, and none account for the impact of methylation quantitative trait loci (meQTL). To address these gaps, we analyzed the relationships between age, genotype, and CpG methylation in 3 understudied populations: central African Baka (n = 35), southern African ‡Khomani San (n = 52), and southern African Himba (n = 51). We find that published prediction methods yield higher mean errors in these cohorts compared to European-ancestry individuals, and find that unaccounted-for DNA sequence variation may be a significant factor underlying this loss of accuracy. We leverage information about the associations between DNA genotype and CpG methylation to develop an age predictor that is minimally influenced by meQTL, and show that this model remains accurate across a broad range of genetic backgrounds. Intriguingly, we also find that the older individuals and those exhibiting relatively lower epigenetic age acceleration in our cohorts tend to carry more epigenetic age-reducing genetic variants, suggesting a novel mechanism by which heritable factors can influence longevity.

Epigenetic patterns, accelerated biological aging, and enhanced epigenetic drift detected 6 months following COVID-19 infection: insights from a genome-wide DNA methylation study

BACKGROUND

The epigenetic status of patients 6-month post-COVID-19 infection remains largely unexplored. The existence of long-COVID, or post-acute sequelae of SARS-CoV-2 infection (PASC), suggests potential long-term changes. Long-COVID includes symptoms like fatigue, neurological issues, and organ-related problems, regardless of initial infection severity. The mechanisms behind long-COVID are unclear, but virus-induced epigenetic changes could play a role.

METHODS AND RESULTS

Our study explores the lasting epigenetic impacts of SARS-CoV-2 infection. We analyzed genome-wide DNA methylation patterns in an Italian cohort of 96 patients 6 months after COVID-19 exposure, comparing them to 191 healthy controls. We identified 42 CpG sites with significant methylation differences (FDR < 0.05), primarily within CpG islands and gene promoters. Dysregulated genes highlighted potential links to glutamate/glutamine metabolism, which may be relevant to PASC symptoms. Key genes with potential significance to COVID-19 infection and long-term effects include GLUD1, ATP1A3, and ARRB2. Furthermore, Horvath's epigenetic clock showed a slight but significant age acceleration in post-COVID-19 patients. We also observed a substantial increase in stochastic epigenetic mutations (SEMs) in the post-COVID-19 group, implying potential epigenetic drift. SEM analysis identified 790 affected genes, indicating dysregulation in pathways related to insulin resistance, VEGF signaling, apoptosis, hypoxia response, T-cell activation, and endothelin signaling.

CONCLUSIONS

Our study provides valuable insights into the epigenetic consequences of COVID-19. Results suggest possible associations with accelerated aging, epigenetic drift, and the disruption of critical biological pathways linked to insulin resistance, immune response, and vascular health. Understanding these epigenetic changes could be crucial for elucidating the complex mechanisms behind long-COVID and developing targeted therapeutic interventions.

Sex-Specific Associations between Prenatal Exposure to Bisphenols and Phthalates and Infant Epigenetic Age Acceleration

We examined whether prenatal exposure to two classes of endocrine-disrupting chemicals (EDCs) was associated with infant epigenetic age acceleration (EAA), a DNA methylation biomarker of aging. Participants included 224 maternal-infant pairs from a Canadian pregnancy cohort study. Two bisphenols and 12 phthalate metabolites were measured in maternal second trimester urines. Buccal epithelial cell cheek swabs were collected from 3 month old infants and DNA methylation was profiled using the Infinium MethylationEPIC BeadChip. The Pediatric-Buccal-Epigenetic tool was used to estimate EAA. Sex-stratified robust regressions examined individual chemical associations with EAA, and Bayesian kernel machine regression (BKMR) examined chemical mixture effects. Adjusted robust models showed that in female infants, prenatal exposure to total bisphenol A (BPA) was positively associated with EAA (B = 0.72, 95% CI: 0.21, 1.24), and multiple phthalate metabolites were inversely associated with EAA (Bs from -0.36 to -0.66, 95% CIs from -1.28 to -0.02). BKMR showed that prenatal BPA was the most important chemical in the mixture and was positively associated with EAA in both sexes. No overall chemical mixture effects or male-specific associations were noted. These findings indicate that prenatal EDC exposures are associated with sex-specific deviations in biological aging, which may have lasting implications for child health and development.

Development of an epigenetic clock resistant to changes in immune cell composition

Epigenetic clocks are age predictors that use machine-learning models trained on DNA CpG methylation values to predict chronological or biological age. Increases in predicted epigenetic age relative to chronological age (epigenetic age acceleration) are connected to aging-associated pathologies, and changes in epigenetic age are linked to canonical aging hallmarks. However, epigenetic clocks rely on training data from bulk tissues whose cellular composition changes with age. Here, we found that human naive CD8+ T cells, which decrease in frequency during aging, exhibit an epigenetic age 15-20 years younger than effector memory CD8+ T cells from the same individual. Importantly, homogenous naive T cells isolated from individuals of different ages show a progressive increase in epigenetic age, indicating that current epigenetic clocks measure two independent variables, aging and immune cell composition. To isolate the age-associated cell intrinsic changes, we created an epigenetic clock, the IntrinClock, that did not change among 10 immune cell types tested. IntrinClock shows a robust predicted epigenetic age increase in a model of replicative senescence in vitro and age reversal during OSKM-mediated reprogramming.

GrimAge is elevated in older adults with mild COVID-19 an exploratory analysis

COVID-19 has been contained; however, the side effects associated with its infection continue to be a challenge for public health, particularly for older adults. On the other hand, epigenetic status contributes to the inter-individual health status and is associated with COVID-19 severity. Nevertheless, current studies focus only on severe COVID-19. Considering that most of the worldwide population developed mild COVID-19 infection. In the present exploratory study, we aim to analyze the association of mild COVID-19 with epigenetic ages (HorvathAge, HannumAge, GrimAge, PhenoAge, SkinAge, and DNAmTL) and clinical variables obtained from a Mexican cohort of older adults. We found that all epigenetic ages significantly differ from the chronological age, but only GrimAge is elevated. Additionally, both the intrinsic epigenetic age acceleration (IEAA) and the extrinsic epigenetic age acceleration (EEAA) are accelerated in all patients. Moreover, we found that immunological estimators and DNA damage were associated with PhenoAge, SkinBloodHorvathAge, and HorvathAge, suggesting that the effects of mild COVID-19 on the epigenetic clocks are mainly associated with inflammation and immunology changes. In conclusion, our results show that the effects of mild COVID-19 on the epigenetic clock are mainly associated with the immune system and an increase in GrimAge, IEAA, and EEAA.

Epigenetic and biological age acceleration in children with atopic dermatitis

Background

Atopic dermatitis (AD) is a chronic inflammatory skin disease resulting from the complex interplay of genetic and environmental factors, meriting exploration using temporally dynamic biomarkers. DNA methylation-based algorithms have been trained to accurately estimate biological age, and deviation of predicted age from true age (epigenetic age acceleration) has been implicated in several inflammatory diseases, including asthma.

Objective

We sought to determine the role of epigenetic and biological aging, telomere length, and epigenetically inferred abundance of 7 inflammatory biomarkers in AD.

Methods

We performed DNA methylation-based analyses in a pediatric AD cohort (n = 24, mean ± standard deviation [SD] age 2.56 ± 0.28 years) and age-matched healthy subjects (n = 24, age 2.09 [0.15] years) derived from blood using 5 validated algorithms that assess epigenetic age (Horvath, Skin&Blood) and biological age (PhenoAge, GrimAge), telomere length (TelomereLength), and inflammatory biomarker levels.

Results

Epigenetic and biological age, but not telomere length, were accelerated in AD patients for 4 algorithms: Horvath (+0.88 years; 95% confidence interval [CI], 0.33 to 1.4; P = 2.3 × 10-3), Skin&Blood (+0.95 years; 95% CI, 0.67 to 1.2; P = 1.8 × 10-8), PhenoAge (+8.2 years; 95% CI, 3.4 to 13.0; P = 1.3 × 10-3), and GrimAge (+1.8 years 95% CI, 0.22 to 3.3; P = .026). Moreover, patients had increased levels of β2 microglobulin (+47,584.4 ng/mL; P = .029), plasminogen activation inhibitor 1 (+3,432.9 ng/mL; P = 1.1 × 10-5), and cystatin C (+31,691 ng/mL; P = 4.0 × 10-5), while levels of tissue inhibitor metalloproteinase 1 (-370.7 ng/mL; P = 7.5 × 10-4) were decreased compared to healthy subjects.

Conclusion

DNA methylation changes associated with epigenetic and biological aging, and inflammatory proteins appear early in life in pediatric AD and may be relevant clinical biomarkers of pathophysiology.

Is exposure to pesticides associated with biological aging? A systematic review and meta-analysis

OBJECTIVE

Exposure to pesticides is a risk factor for various diseases, yet its association with biological aging remains unclear. We aimed to systematically investigate the relationship between pesticide exposure and biological aging.

METHODS

PubMed, Embase and Web of Science were searched from inception to August 2023. Observational studies investigating the association between pesticide exposure and biomarkers of biological aging were included. Three-level random-effect meta-analysis was used to synthesize the data. Risk of bias was assessed by the Newcastle-Ottawa Scale.

RESULTS

Twenty studies evaluating the associations between pesticide exposure and biomarkers of biological aging in 10,368 individuals were included. Sixteen reported telomere length and four reported epigenetic clocks. Meta-analysis showed no statistically significant associations between pesticide exposure and the Hannum clock (pooled β = 0.27; 95 %CI: -0.25, 0.79), or telomere length (pooled Hedges'g = -0.46; 95 %CI: -1.10, 0.19). However, the opposite direction of effects for the two outcomes showed an indication of possible accelerated biological aging. After removal of influential effect sizes or low-quality studies, shorter telomere length was found in higher-exposed populations.

CONCLUSION

The existing evidence for associations between pesticide exposure and biological aging is limited due to the scarcity of studies on epigenetic clocks and the substantial heterogeneity across studies on telomere length. High-quality studies incorporating more biomarkers of biological aging, focusing more on active chemical ingredients of pesticides and accounting for potential confounders are needed to enhance our understanding of the impact of pesticides on biological aging.

Determining the relationship of p16INK4a and additional molecular markers of aging with clinical frailty in hematologic malignancy

PURPOSE

Older adults with hematologic malignancies (HM) have unique challenges due to age and fitness. The primary aim of this pilot study was to benchmark the ability of multiple biomarkers of aging (p16, epigenetic clocks, T cell gene expression profiles, and T cell receptor excision circles (TREC) to identify frailty as measured by a clinical impairment index (I2) in patients with HM.

METHODS

70 patients newly diagnosed with HM had peripheral blood T lymphocytes (PBTL) analyzed for p16INK4a expression using the OSU_Senescence Nanostring CodeSet. PBTL epigenetic age was measured using 7 epigenetic clocks, and TREC were quantified by qRT-PCR. A composite clinical impairment index (I2) was generated by combining values from 11 geriatric metrics (Independent Activities of Daily Living (iADL), physical health score, Short Physical Performance Battery (SPPB), Body Mass Index (BMI), Eastern Cooperative Oncology Group (ECOG) performance status, self-reported KPS, Blessed Orientation Memory Concentration (BOMC), polypharmacy, Mental Health Inventory (MHI)-17, Medical Outcomes Study (MOS) subscales). Clinical frailty was defined as a score of 7 or greater on the I2.

RESULTS

Age-adjusted p16INK4a was similar in newly diagnosed patients and healthy controls (p > 0.1). PBTL p16INK4a levels correlated positively with the Hannum [r = 0.35, 95% CI (0.09-0.75); p adj. = 0.04] and PhenoAge [r = 0.37, 95% CI (0.11-0.59); p adj. = 0.04] epigenetic clocks. The discrimination ability of the I2 model was calculated using the area under the receiver operating characteristic curve (AUC). After adjusting for chronologic age and disease group, baseline p16INK4a [AUC = 0.76, 95% CI (0.56-0.98); p = 0.01], Hannum [AUC = 0.70, 95% CI (0.54-0.85); p = 0.01], PhenoAge [AUC = 0.71, 95% CI (0.55-0.86); p = 0.01], and DunedinPACE [AUC = 0.73, 95% CI (0.57-0.88); p =  < 0.01] measures showed the greatest potential to identify clinical frailty using the I2.

CONCLUSIONS

Our pilot data suggest that multiple blood-based aging biomarkers have potential to identify frailty in older adults with HM.

IMPLICATIONS FOR CANCER SURVIVORS

We developed the I2 index to quantify impairments across geriatric domains and discovered that PBTL p16, Hannum, PhenoAge, and DunedinPACE are promising indicators of frailty in HM.

Frailty and biological age. Which best describes our aging and longevity?

Frailty and Biological Age are two closely related concepts; however, frailty is a multisystem geriatric syndrome that applies to elderly subjects, whereas biological age is a gerontologic way to describe the rate of aging of each individual, which can be used from the beginning of the aging process, in adulthood. If frailty reaches less consensus on the definition, it is a term much more widely used than this of biological age, which shows a clearer definition but is scarcely employed in social and medical fields. In this review, we suggest that this Biological Age is the best to describe how we are aging and determine our longevity, and several examples support our proposal.

Epigenome-metabolism nexus in the retina: implications for aging and disease

Intimate links between epigenome modifications and metabolites allude to a crucial role of cellular metabolism in transcriptional regulation. Retina, being a highly metabolic tissue, adapts by integrating inputs from genetic, epigenetic, and extracellular signals. Precise global epigenomic signatures guide development and homeostasis of the intricate retinal structure and function. Epigenomic and metabolic realignment are hallmarks of aging and highlight a link of the epigenome-metabolism nexus with aging-associated multifactorial traits affecting the retina, including age-related macular degeneration and glaucoma. Here, we focus on emerging principles of epigenomic and metabolic control of retinal gene regulation, with emphasis on their contribution to human disease. In addition, we discuss potential mitigation strategies involving lifestyle changes that target the epigenome-metabolome relationship for maintaining retinal function.

Unveiling the epigenetic impact of vegan vs. omnivorous diets on aging: insights from the Twins Nutrition Study (TwiNS)

BACKGROUND

Geroscience focuses on interventions to mitigate molecular changes associated with aging. Lifestyle modifications, medications, and social factors influence the aging process, yet the complex molecular mechanisms require an in-depth exploration of the epigenetic landscape. The specific epigenetic clock and predictor effects of a vegan diet, compared to an omnivorous diet, remain underexplored despite potential impacts on aging-related outcomes.

METHODS

This study examined the impact of an entirely plant-based or healthy omnivorous diet over 8 weeks on blood DNA methylation in paired twins. Various measures of epigenetic age acceleration (PC GrimAge, PC PhenoAge, DunedinPACE) were assessed, along with system-specific effects (Inflammation, Heart, Hormone, Liver, and Metabolic). Methylation surrogates of clinical, metabolite, and protein markers were analyzed to observe diet-specific shifts.

RESULTS

Distinct responses were observed, with the vegan cohort exhibiting significant decreases in overall epigenetic age acceleration, aligning with anti-aging effects of plant-based diets. Diet-specific shifts were noted in the analysis of methylation surrogates, demonstrating the influence of diet on complex trait prediction through DNA methylation markers. An epigenome-wide analysis revealed differentially methylated loci specific to each diet, providing insights into the affected pathways.

CONCLUSIONS

This study suggests that a short-term vegan diet is associated with epigenetic age benefits and reduced calorie intake. The use of epigenetic biomarker proxies (EBPs) highlights their potential for assessing dietary impacts and facilitating personalized nutrition strategies for healthy aging. Future research should explore the long-term effects of vegan diets on epigenetic health and overall well-being, considering the importance of proper nutrient supplementation.

TRIAL REGISTRATION

Clinicaltrials.gov identifier: NCT05297825.

Epistemic uncertainty challenges aging clock reliability in predicting rejuvenation effects

Epigenetic aging clocks have been widely used to validate rejuvenation effects during cellular reprogramming. However, these predictions are unverifiable because the true biological age of reprogrammed cells remains unknown. We present an analytical framework to consider rejuvenation predictions from the uncertainty perspective. Our analysis reveals that the DNA methylation profiles across reprogramming are poorly represented in the aging data used to train clock models, thus introducing high epistemic uncertainty in age estimations. Moreover, predictions of different published clocks are inconsistent, with some even suggesting zero or negative rejuvenation. While not questioning the possibility of age reversal, we show that the high clock uncertainty challenges the reliability of rejuvenation effects observed during in vitro reprogramming before pluripotency and throughout embryogenesis. Conversely, our method reveals a significant age increase after in vivo reprogramming. We recommend including uncertainty estimation in future aging clock models to avoid the risk of misinterpreting the results of biological age prediction.

Biological aging of different blood cell types

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

DNA Methylation Age Acceleration Mediates the Relationship between Systemic Inflammation and Cognitive Impairment

Background

Chronic inflammation and DNA methylation are potential mechanisms in dementia etiology. The linkage between inflammation and DNA methylation age acceleration in shaping dementia risk is understudied. We explored the association of inflammatory cytokines with cognitive impairment and whether DNA methylation age acceleration mediates this relationship.

Methods

In a subset of the 2016 wave of the Health and Retirement Study (n=3,346, age>50), we employed logistic regression to estimate the associations between each inflammatory cytokine (interleukin-6 (IL-6), C-reactive protein (CRP), and insulin-like growth factor-1 (IGF-1)), and both Langa-Weir classified cognitive impairment non-dementia and dementia, respectively. We calculated DNA methylation age acceleration residuals by regressing GrimAge on chronologic age. We tested if DNA methylation age acceleration mediated the relationship between systemic inflammation and cognitive impairment, adjusting for sociodemographic, behavioral factors, chronic conditions, and cell type proportions.

Results

The prevalence of cognitive impairment was 16%. In the fully-adjusted model, participants with a doubling of IL-6 levels had 1.12 (95% CI: 1.02-1.22) times higher odds of cognitive impairment. Similar associations were found for CRP and IGF-1. Participants with a doubling of IL-6 levels had 0.77 (95% CI: 0.64, 0.90) years of GrimAge acceleration. In mediation analyses with each cytokine as predictor separately, 17.7% (95% CI: 7.0%, 50.9%) of the effect of IL-6 on cognitive impairment was mediated through DNA methylation age acceleration. Comparable mediated estimates were found for CRP and IGF-1.

Conclusions

Systemic inflammation is associated with cognitive impairment, with suggestive evidence that this relationship is partially mediated through DNA methylation age acceleration.

DNA methylation clocks for estimating biological age in Chinese cohorts

Epigenetic clocks are accurate predictors of human chronological age based on the analysis of DNA methylation (DNAm) at specific CpG sites. However, a systematic comparison between DNA methylation data and other omics datasets has not yet been performed. Moreover, available DNAm age predictors are based on datasets with limited ethnic representation. To address these knowledge gaps, we generated and analyzed DNA methylation datasets from two independent Chinese cohorts, revealing age-related DNAm changes. Additionally, a DNA methylation aging clock (iCAS-DNAmAge) and a group of DNAm-based multi-modal clocks for Chinese individuals were developed, with most of them demonstrating strong predictive capabilities for chronological age. The clocks were further employed to predict factors influencing aging rates. The DNAm aging clock, derived from multi-modal aging features (compositeAge-DNAmAge), exhibited a close association with multi-omics changes, lifestyles, and disease status, underscoring its robust potential for precise biological age assessment. Our findings offer novel insights into the regulatory mechanism of age-related DNAm changes and extend the application of the DNAm clock for measuring biological age and aging pace, providing the basis for evaluating aging intervention strategies.

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. 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.

Epigenome-wide association study of lung cancer among never smokers in two prospective cohorts in Shanghai, China

BACKGROUND

The aetiology of lung cancer among individuals who never smoked remains elusive, despite 15% of lung cancer cases in men and 53% in women worldwide being unrelated to smoking. Epigenetic alterations, particularly DNA methylation (DNAm) changes, have emerged as potential drivers. Yet, few prospective epigenome-wide association studies (EWAS), primarily focusing on peripheral blood DNAm with limited representation of never smokers, have been conducted.

METHODS

We conducted a nested case-control study of 80 never-smoking incident lung cancer cases and 83 never-smoking controls within the Shanghai Women's Health Study and Shanghai Men's Health Study. DNAm was measured in prediagnostic oral rinse samples using Illumina MethylationEPIC array. Initially, we conducted an EWAS to identify differentially methylated positions (DMPs) associated with lung cancer in the discovery sample of 101 subjects. The top 50 DMPs were further evaluated in a replication sample of 62 subjects, and results were pooled using fixed-effect meta-analysis.

RESULTS

Our study identified three DMPs significantly associated with lung cancer at the epigenome-wide significance level of p<8.22×10-8. These DMPs were identified as cg09198866 (MYH9; TXN2), cg01411366 (SLC9A10) and cg12787323. Furthermore, examination of the top 1000 DMPs indicated significant enrichment in epithelial regulatory regions and their involvement in small GTPase-mediated signal transduction pathways. Additionally, GrimAge acceleration was identified as a risk factor for lung cancer (OR=1.19 per year; 95% CI 1.06 to 1.34).

CONCLUSIONS

While replication in a larger sample size is necessary, our findings suggest that DNAm patterns in prediagnostic oral rinse samples could provide novel insights into the underlying mechanisms of lung cancer in never smokers.

The Gut and Skin Microbiome and Its Association with Aging Clocks

Aging clocks are predictive models of biological age derived from age-related changes, such as epigenetic changes, blood biomarkers, and, more recently, the microbiome. Gut and skin microbiota regulate more than barrier and immune function. Recent studies have shown that human microbiomes may predict aging. In this narrative review, we aim to discuss how the gut and skin microbiomes influence aging clocks as well as clarify the distinction between chronological and biological age. A literature search was performed on PubMed/MEDLINE databases with the following keywords: "skin microbiome" OR "gut microbiome" AND "aging clock" OR "epigenetic". Gut and skin microbiomes may be utilized to create aging clocks based on taxonomy, biodiversity, and functionality. The top contributing microbiota or metabolic pathways in these aging clocks may influence aging clock predictions and biological age. Furthermore, gut and skin microbiota may directly and indirectly influence aging clocks through the regulation of clock genes and the production of metabolites that serve as substrates or enzymatic regulators. Microbiome-based aging clock models may have therapeutic potential. However, more research is needed to advance our understanding of the role of microbiota in aging clocks.

An expedited screening platform for the discovery of anti-ageing compounds in vitro and in vivo

BACKGROUND

Restraining or slowing ageing hallmarks at the cellular level have been proposed as a route to increased organismal lifespan and healthspan. Consequently, there is great interest in anti-ageing drug discovery. However, this currently requires laborious and lengthy longevity analysis. Here, we present a novel screening readout for the expedited discovery of compounds that restrain ageing of cell populations in vitro and enable extension of in vivo lifespan.

METHODS

Using Illumina methylation arrays, we monitored DNA methylation changes accompanying long-term passaging of adult primary human cells in culture. This enabled us to develop, test, and validate the CellPopAge Clock, an epigenetic clock with underlying algorithm, unique among existing epigenetic clocks for its design to detect anti-ageing compounds in vitro. Additionally, we measured markers of senescence and performed longevity experiments in vivo in Drosophila, to further validate our approach to discover novel anti-ageing compounds. Finally, we bench mark our epigenetic clock with other available epigenetic clocks to consolidate its usefulness and specialisation for primary cells in culture.

RESULTS

We developed a novel epigenetic clock, the CellPopAge Clock, to accurately monitor the age of a population of adult human primary cells. We find that the CellPopAge Clock can detect decelerated passage-based ageing of human primary cells treated with rapamycin or trametinib, well-established longevity drugs. We then utilise the CellPopAge Clock as a screening tool for the identification of compounds which decelerate ageing of cell populations, uncovering novel anti-ageing drugs, torin2 and dactolisib (BEZ-235). We demonstrate that delayed epigenetic ageing in human primary cells treated with anti-ageing compounds is accompanied by a reduction in senescence and ageing biomarkers. Finally, we extend our screening platform in vivo by taking advantage of a specially formulated holidic medium for increased drug bioavailability in Drosophila. We show that the novel anti-ageing drugs, torin2 and dactolisib (BEZ-235), increase longevity in vivo.

CONCLUSIONS

Our method expands the scope of CpG methylation profiling to accurately and rapidly detecting anti-ageing potential of drugs using human cells in vitro, and in vivo, providing a novel accelerated discovery platform to test sought after anti-ageing compounds and geroprotectors.

Current evidence supporting associations of DNA methylation measurements with survivorship burdens in cancer survivors: A scoping review

INTRODUCTION

Identifying reliable biomarkers that reflect cancer survivorship symptoms remains a challenge for researchers. DNA methylation (DNAm) measurements reflecting epigenetic changes caused by anti-cancer therapy may provide needed insights. Given lack of consensus describing utilization of DNAm data to predict survivorship issues, a review evaluating the current landscape is warranted.

OBJECTIVE

Provide an overview of current studies examining associations of DNAm with survivorship burdens in cancer survivors.

METHODS

A literature review was conducted including studies if they focused on cohorts of cancer survivors, utilized peripheral blood cell DNAm data, and evaluated the associations of DNAm and survivorship issues.

RESULTS

A total of 22 studies were identified, with majority focused on breast (n = 7) or childhood cancer (n = 9) survivors, and half studies included less than 100 patients (n = 11). Survivorship issues evaluated included those related to neurocognition (n = 5), psychiatric health (n = 3), general wellness (n = 9), chronic conditions (n = 5), and treatment specific toxicities (n = 4). Studies evaluated epigenetic age metrics (n = 10) and DNAm levels at individual CpG sites or regions (n = 12) for their associations with survivorship issues in cancer survivors along with relevant confounding factors. Significant associations of measured DNAm in the peripheral blood samples of cancer survivors and survivorship issues were identified.

DISCUSSION/CONCLUSION

Studies utilizing epigenetic age metrics and differential methylation analysis demonstrated significant associations of DNAm measurements with survivorship burdens. Associations were observed encompassing diverse survivorship outcomes and timeframes relative to anti-cancer therapy initiation. These findings underscore the potential of these measurements as useful biomarkers in survivorship care and research.

Towards a Novel Frontier in the Use of Epigenetic Clocks in Epidemiology

Health problems associated with aging are a major public health concern for the future. Aging is a complex process with wide intervariability among individuals. Therefore, there is a need for innovative public health strategies that target factors associated with aging and the development of tools to assess the effectiveness of these strategies accurately. Novel approaches to measure biological age, such as epigenetic clocks, have become relevant. These clocks use non-sequential variable information from the genome and employ mathematical algorithms to estimate biological age based on DNA methylation levels. Therefore, in the present study, we comprehensively review the current status of the epigenetic clocks and their associations across the human phenome. We emphasize the potential utility of these tools in an epidemiological context, particularly in evaluating the impact of public health interventions focused on promoting healthy aging. Our review describes associations between epigenetic clocks and multiple traits across the life and health span. Additionally, we highlighted the evolution of studies beyond mere associations to establish causal mechanisms between epigenetic age and disease. We explored the application of epigenetic clocks to measure the efficacy of interventions focusing on rejuvenation.

Bidirectional relationship between epigenetic age and brain health events

Chronological age offers an imperfect estimate of the molecular changes that occur with aging. Epigenetic age, which is derived from DNA methylation data, provides a more nuanced representation of aging-related biological processes. This study examines the bidirectional relationship between epigenetic age and the occurrence of brain health events (stroke, dementia, and late-life depression). Using data from the Health and Retirement Study, we analyzed blood samples from over 4,000 participants to determine how epigenetic age relates to past and future brain health events. Study participants with a prior brain health event prior to blood collection were 4% epigenetically older (beta 0.04, SE 0.01), suggesting that these conditions are associated with faster aging than that captured by chronological age. Furthermore, a one standard deviation increase in epigenetic age was associated with 70% higher odds of experiencing a brain health event in the next four years after blood collection (OR 1.70, 95%CI 1.16-2.50), indicating that epigenetic age is not just a consequence but also a predictor of poor brain health. Both results were replicated through Mendelian Randomization analyses, supporting their causal nature. Our findings support the utilization of epigenetic age as a useful biomarker to evaluate the role of interventions aimed at preventing and promoting recovery after a brain health event.

Maximizing Insights from Longitudinal Epigenetic Age Data: Simulations, Applications, and Practical Guidance

Background

Epigenetic Age (EA) is an age estimate, developed using DNA methylation (DNAm) states of selected CpG sites across the genome. Although EA and chronological age are highly correlated, EA may not increase uniformly with time. Departures, known as epigenetic age acceleration (EAA), are common and have been linked to various traits and future disease risk. Limited by available data, most studies investigating these relationships have been cross-sectional - using a single EA measurement. However, the recent growth in longitudinal DNAm studies has led to analyses of associations with EA over time. These studies differ in (i) their choice of model; (ii) the primary outcome (EA vs. EAA); and (iii) in their use of chronological age or age-independent time variables to account for the temporal dynamic. We evaluated the robustness of each approach using simulations and tested our results in two real-world examples, using biological sex and birthweight as predictors of longitudinal EA.

Results

Our simulations showed most accurate effect sizes in a linear mixed model or generalized estimating equation, using chronological age as the time variable. The use of EA versus EAA as an outcome did not strongly impact estimates. Applying the optimal model in real-world data uncovered an accelerated EA rate in males and an advanced EA that decelerates over time in children with higher birthweight.

Conclusion

Our results can serve as a guide for forthcoming longitudinal EA studies, aiding in methodological decisions that may determine whether an association is accurately estimated, overestimated, or potentially overlooked.

CheekAge: a next-generation buccal epigenetic aging clock associated with lifestyle and health

Epigenetic aging clocks are computational models that predict age using DNA methylation information. Initially, first-generation clocks were developed to make predictions using CpGs that change with age. Over time, next-generation clocks were created using CpGs that relate to both age and health. Since existing next-generation clocks were constructed in blood, we sought to develop a next-generation clock optimized for prediction in cheek swabs, which are non-invasive and easy to collect. To do this, we collected MethylationEPIC data as well as lifestyle and health information from 8045 diverse adults. Using a novel simulated annealing approach that allowed us to incorporate lifestyle and health factors into training as well as a combination of CpG filtering, CpG clustering, and clock ensembling, we constructed CheekAge, an epigenetic aging clock that has a strong correlation with age, displays high test-retest reproducibility across replicates, and significantly associates with a plethora of lifestyle and health factors, such as BMI, smoking status, and alcohol intake. We validated CheekAge in an internal dataset and multiple publicly available datasets, including samples from patients with progeria or meningioma. In addition to exploring the underlying biology of the data and clock, we provide a free online tool that allows users to mine our methylomic data and predict epigenetic age.

Food insecurity and epigenetic aging in middle-aged and older adults

BACKGROUND

Food insecurity is recognized as a key social determinant of health for older adults. While food insecurity has been associated with morbidity and mortality, few studies have examined how it may contribute to accelerated biological aging. A potential mechanism by which food insecurity may contribute to aging is via epigenetic alterations. We examined the relationship between food insecurity and epigenetic aging, a novel measure of biological aging, in a nationally representative sample of middle-aged and older adults in the United States.

METHODS

Cross-sectional analysis of adults 50 years of age and older from the 2016 Health and Retirement Study (HRS). Financial food insecurity was self-reported via two questions that ascertained having enough money for food or eating less than they felt they should. Epigenetic aging was measured via epigenetic clocks based on DNA methylation patterns that predict aging correlates of morbidity and mortality. Linear regressions were performed to test for differences in the epigenetic clocks, adjusting for biological, socioeconomic, and behavioral factors.

RESULTS

The analysis consisted of 3875 adults with mean age of 68.5 years. A total of 8.1% reported food insecurity. Food insecurity was associated with several characteristics, including younger age, race/ethnic minority, lower income, total wealth, and educational attainment, higher BMI, and less physical activity. Food insecurity was associated with accelerated epigenetic aging compared to food security, as measured via second (Zhang, PhenoAge, GrimAge) and third (DunedinPoAm) generation epigenetic clocks. In particular, food insecurity remained significantly associated with accelerated Zhang (B = 0.09, SE = 0.03, p = 0.011) and GrimAge (B = 0.57, SE = 0.24, p = 0.022) in the fully adjusted models.

CONCLUSIONS

Food insecurity is associated with accelerated epigenetic aging among middle-aged and older adults in the United States. Food insecurity may contribute to DNA methylation alterations across the genome and biological age acceleration. These findings add to a growing understanding of the influence of socioeconomic status on the epigenome and health in aging.

Quantifying the stochastic component of epigenetic aging

DNA methylation clocks can accurately estimate chronological age and, to some extent, also biological age, yet the process by which age-associated DNA methylation (DNAm) changes are acquired appears to be quasi-stochastic, raising a fundamental question: how much of an epigenetic clock's predictive accuracy could be explained by a stochastic process of DNAm change? Here, using DNAm data from sorted immune cells, we build realistic simulation models, subsequently demonstrating in over 22,770 sorted and whole-blood samples from 25 independent cohorts that approximately 66-75% of the accuracy underpinning Horvath's clock could be driven by a stochastic process. This fraction increases to 90% for the more accurate Zhang's clock, but is lower (63%) for the PhenoAge clock, suggesting that biological aging is reflected by nonstochastic processes. Confirming this, we demonstrate that Horvath's age acceleration in males and PhenoAge's age acceleration in severe coronavirus disease 2019 cases and smokers are not driven by an increased rate of stochastic change but by nonstochastic processes. These results significantly deepen our understanding and interpretation of epigenetic clocks.

Retirement Makes You Old? Causal Effect of Retirement on Biological Age

Retirement is a critical life event for older people. Health scholars have scrutinized the health effects of retirement, but its consequences on age-related diseases and mortality are unclear. We extend this body of research by integrating measurements of biological age, representing the physiological decline preceding disease onset. Using data from the UK Biobank and a fuzzy regression discontinuity design, we estimated the effects of retirement on two biomarker-based biological age measures. Results showed that retirement significantly increases biological age for those induced to retire by the State Pension eligibility by 0.871-2.503 years, depending on sex and specific biological age measurement. Given the emerging scientific discussion about direct interventions to biological age to achieve additional improvements in population health, the positive effect of retirement on biological age has important implications for an increase in the State Pension eligibility age and its potential consequences on population health, public health care policy, and older people's labor force participation. Overall, this study provides novel empirical evidence contributing to the question of what social factors make people old.

Atherosclerotic Plaque Epigenetic Age Acceleration Predicts a Poor Prognosis and Is Associated With Endothelial-to-Mesenchymal Transition in Humans

BACKGROUND

Epigenetic age estimators (clocks) are predictive of human mortality risk. However, it is not yet known whether the epigenetic age of atherosclerotic plaques is predictive for the risk of cardiovascular events.

METHODS

Whole-genome DNA methylation of human carotid atherosclerotic plaques (n=485) and of blood (n=93) from the Athero-Express endarterectomy cohort was used to calculate epigenetic age acceleration (EAA). EAA was linked to clinical characteristics, plaque histology, and future cardiovascular events (n=136). We studied whole-genome DNA methylation and bulk and single-cell transcriptomics to uncover molecular mechanisms of plaque EAA. We experimentally confirmed our in silico findings using in vitro experiments in primary human coronary endothelial cells.

RESULTS

Male and female patients with severe atherosclerosis had a median chronological age of 69 years. The median epigenetic age was 65 years in females (median EAA, -2.2 [interquartile range, -4.3 to 2.2] years) and 68 years in males (median EAA, -0.3 [interquartile range, -2.9 to 3.8] years). Patients with diabetes and a high body mass index had higher plaque EAA. Increased EAA of plaque predicted future events in a 3-year follow-up in a Cox regression model (univariate hazard ratio, 1.7; P=0.0034) and adjusted multivariate model (hazard ratio, 1.56; P=0.02). Plaque EAA predicted outcome independent of blood EAA (hazard ratio, 1.3; P=0.018) and of plaque hemorrhage (hazard ratio, 1.7; P=0.02). Single-cell RNA sequencing in plaque samples from 46 patients in the same cohort revealed smooth muscle and endothelial cells as important cell types in plaque EAA. Endothelial-to-mesenchymal transition was associated with EAA, which was experimentally confirmed by TGFβ-triggered endothelial-to-mesenchymal transition inducing rapid epigenetic aging in coronary endothelial cells.

CONCLUSIONS

Plaque EAA is a strong and independent marker of poor outcome in patients with severe atherosclerosis. Plaque EAA was linked to mesenchymal endothelial and smooth muscle cells. Endothelial-to-mesenchymal transition was associated with EAA, which was experimentally validated. Epigenetic aging mechanisms may provide new targets for treatments that reduce atherosclerosis complications.

The Latent Aging of Cells

As epigenetic clocks have evolved from powerful estimators of chronological aging to predictors of mortality and disease risk, it begs the question of what role DNA methylation plays in the aging process. We hypothesize that while it has the potential to serve as an informative biomarker, DNA methylation could also be a key to understanding the biology entangled between aging, (de)differentiation, and epigenetic reprogramming. Here we use an unsupervised approach to analyze time associated DNA methylation from both in vivo and in vitro samples to measure an underlying signal that ties these phenomena together. We identify a methylation pattern shared across all three, as well as a signal that tracks aging in tissues but appears refractory to reprogramming, suggesting that aging and reprogramming may not be fully mirrored processes.