Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing

Epigenetic biomarkers in aging and longevity: Current and future application

The aging process has been one of the most necessary research fields in the current century, and knowing different theories of aging and the role of different genetic, epigenetic, molecular, and environmental modulating factors in increasing the knowledge of aging mechanisms and developing appropriate diagnostic, therapeutic, and preventive ways would be helpful. One of the most conserved signs of aging is epigenetic changes, including DNA methylation, histone modifications, chromatin remodeling, noncoding RNAs, and extracellular RNAs. Numerous biological processes and hallmarks are vital in aging development, but epigenomic alterations are especially notable because of their importance in gene regulation and cellular identity. The mounting evidence points to a possible interaction between age-related epigenomic alterations and other aging hallmarks, like genome instability. To extend a healthy lifespan and possibly reverse some facets of aging and aging-related diseases, it will be crucial to comprehend global and locus-specific epigenomic modifications and recognize corresponding regulators of health and longevity. In the current study, we will aim to discuss the role of epigenomic mechanisms in aging and the most recent developments in epigenetic diagnostic biomarkers, which have the potential to focus efforts on reversing the destructive signs of aging and extending the lifespan.

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.

Towards Healthy Longevity: Comprehensive Insights from Molecular Targets and Biomarkers to Biological Clocks

Aging is a complex and time-dependent decline in physiological function that affects most organisms, leading to increased risk of age-related diseases. Investigating the molecular underpinnings of aging is crucial to identify geroprotectors, precisely quantify biological age, and propose healthy longevity approaches. This review explores pathways that are currently being investigated as intervention targets and aging biomarkers spanning molecular, cellular, and systemic dimensions. Interventions that target these hallmarks may ameliorate the aging process, with some progressing to clinical trials. Biomarkers of these hallmarks are used to estimate biological aging and risk of aging-associated disease. Utilizing aging biomarkers, biological aging clocks can be constructed that predict a state of abnormal aging, age-related diseases, and increased mortality. Biological age estimation can therefore provide the basis for a fine-grained risk stratification by predicting all-cause mortality well ahead of the onset of specific diseases, thus offering a window for intervention. Yet, despite technological advancements, challenges persist due to individual variability and the dynamic nature of these biomarkers. Addressing this requires longitudinal studies for robust biomarker identification. Overall, utilizing the hallmarks of aging to discover new drug targets and develop new biomarkers opens new frontiers in medicine. Prospects involve multi-omics integration, machine learning, and personalized approaches for targeted interventions, promising a healthier aging population.

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.

The influence of epigenetic biological age on key complications and outcomes in aneurysmal subarachnoid haemorrhage

BACKGROUND

We aimed to investigate the association between DNA-methylation biological age (B-age) calculated as age acceleration (ageAcc) and key aneurysmal subarachnoid haemorrhage (aSAH) complications such as vasospasm, delayed cerebral ischaemia (DCI), poor outcome, and mortality.

METHODS

We conducted a prospective study involving 277 patients with aSAH. B-age was determined in whole blood samples using five epigenetic clocks: Hannum's, Horvath's, Levine's and both versions of Zhang's clocks. Age acceleration was calculated as the residual obtained from regressing out the effect of C-age on the mismatch between C-age and B-age. We then tested the association between ageAcc and vasospasm, DCI and 12-month poor outcome (mRS 3-5) and mortality using linear regression models adjusted for confounders.

RESULTS

Average C-age was 55.0 years, with 66.8% being female. Vasospasm occurred in 143 cases (51.6%), DCI in 70 (25.3%) and poor outcomes in 99 (35.7%), with a mortality rate of 20.6%. Lower ageAcc was linked to vasospasm in Horvath's and Levine's clocks, whereas increased ageAcc was associated with 12-month mortality in Hannum's clock. No significant differences in ageAcc were found for DCI or poor outcome at 12 months with other clocks.

CONCLUSIONS

Our study indicates that B-age is independently associated with vasospasm and 12-month mortality in patients with aSAH. These findings underscore the potential role of epigenetics in understanding the pathophysiology of aSAH-related complications and outcomes.

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.

Aging clocks based on accumulating stochastic variation

Aging clocks have provided one of the most important recent breakthroughs in the biology of aging, and may provide indicators for the effectiveness of interventions in the aging process and preventive treatments for age-related diseases. The reproducibility of accurate aging clocks has reinvigorated the debate on whether a programmed process underlies aging. Here we show that accumulating stochastic variation in purely simulated data is sufficient to build aging clocks, and that first-generation and second-generation aging clocks are compatible with the accumulation of stochastic variation in DNA methylation or transcriptomic data. We find that accumulating stochastic variation is sufficient to predict chronological and biological age, indicated by significant prediction differences in smoking, calorie restriction, heterochronic parabiosis and partial reprogramming. Although our simulations may not explicitly rule out a programmed aging process, our results suggest that stochastically accumulating changes in any set of data that have a ground state at age zero are sufficient for generating aging clocks.

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.

Association of biological aging with frailty and post-transplant outcomes among adults with cirrhosis

Frailty is classically associated with advanced age but is also an important predictor of clinical outcomes in comparatively young adults with cirrhosis. We examined the association of biological aging with frailty and post-transplant outcomes in a pilot of adults with cirrhosis undergoing liver transplantation (LT). Frailty was measured via the Liver Frailty Index (LFI). The primary epigenetic clock DNA methylation (DNAm) PhenoAge was calculated from banked peripheral blood mononuclear cells; we secondarily explored two first-generation clocks (Hannum; Horvath) and two additional second-generation clocks (GrimAge; GrimAge2). Twelve adults were included: seven frail (LFI ≥ 4.4, mean age 55 years) and five robust (LFI < 3.2, mean age 55 years). Mean PhenoAge age acceleration (AgeAccel) was + 2.5 years (P = 0.23) for frail versus robust subjects. Mean PhenoAge AgeAccel was + 2.7 years (P = 0.19) for subjects who were readmitted or died within 30 days of discharge post-LT versus those without this outcome. When compared with first-generation clocks, the second-generation clocks demonstrated greater average AgeAccel for subjects with frailty or poor post-LT outcomes. Measuring biological age using DNAm-derived epigenetic clocks is feasible in adults undergoing LT. While frail and robust subjects had the same average chronological age, average biological age as measured by second-generation epigenetic clocks tended to be accelerated among those who were frail or experienced a poor post-LT outcome. These results suggest that frailty in these relatively young subjects with cirrhosis may involve similar aging mechanisms as frailty classically observed in chronologically older adults and warrant validation in a larger cohort.

Epigenetics in Alzheimer's Disease: A Critical Overview

Epigenetic modifications have been implicated in a number of complex diseases as well as being a hallmark of organismal aging. Several reports have indicated an involvement of these changes in Alzheimer's disease (AD) risk and progression, most likely contributing to the dysregulation of AD-related gene expression measured by DNA methylation studies. Given that DNA methylation is tissue-specific and that AD is a brain disorder, the limitation of these studies is the ability to identify clinically useful biomarkers in a proxy tissue, reflective of the tissue of interest, that would be less invasive, more cost-effective, and easily obtainable. The age-related DNA methylation changes have also been used to develop different generations of epigenetic clocks devoted to measuring the aging in different tissues that sometimes suggests an age acceleration in AD patients. This review critically discusses epigenetic changes and aging measures as potential biomarkers for AD detection, prognosis, and progression. Given that epigenetic alterations are chemically reversible, treatments aiming at reversing these modifications will be also discussed as promising therapeutic strategies for AD.

Evaluation of DNAmAge in paired fresh, frozen, and formalin-fixed paraffin-embedded heart tissues

The continued development in methylome analysis has enabled a more precise assessment of DNA methylation, but treatment of target tissue prior to analysis may affect DNA analysis. Prediction of age based on methylation levels in the genome (DNAmAge) has gained much interest in disease predisposition (biological age estimation), but also in chronological donor age estimation in crime case samples. Various epigenetic clocks were designed to predict the age. However, it remains unknown how the storage of the tissues affects the DNAmAge estimation. In this study, we investigated the storage method impact of DNAmAge by the comparing the DNAmAge of the two commonly used storage methods, freezing and formalin-fixation and paraffin-embedding (FFPE) to DNAmAge of fresh tissue. This was carried out by comparing paired heart tissue samples of fresh tissue, samples stored by freezing and FFPE to chronological age and whole blood samples from the same individuals. Illumina EPIC beadchip array was used for methylation analysis and the DNAmAge was evaluated with the following epigenetic clocks: Horvath, Hannum, Levine, Horvath skin+blood clock (Horvath2), PedBE, Wu, BLUP, EN, and TL. We observed differences in DNAmAge among the storage conditions. FFPE samples showed a lower DNAmAge compared to that of frozen and fresh samples. Additionally, the DNAmAge of the heart tissue was lower than that of the whole blood and the chronological age. This highlights caution when evaluating DNAmAge for FFPE samples as the results were underestimated compared with fresh and frozen tissue samples. Furthermore, the study also emphasizes the need for a DNAmAge model based on heart tissue samples for an accurate age estimation.

methscore: a comprehensive R function for DNA methylation-based health predictors

MOTIVATION

DNA methylation-based predictors of various biological metrics have been widely published and are becoming valuable tools in epidemiologic studies of epigenetics and personalized medicine. However, generating these predictors from original source software and web servers is complex and time consuming. Furthermore, different predictors were often derived based on data from different types of arrays, where array differences and batch effects can make predictors difficult to compare across studies.

RESULTS

We integrate these published methods into a single R function to produce 158 previously published predictors for chronological age, biological age, exposures, lifestyle traits and serum protein levels using both classical and principal component-based methods. To mitigate batch and array differences, we also provide a modified RCP method (ref-RCP) that normalize input DNA methylation data to reference data prior to estimation. Evaluations in real datasets show that this approach improves estimate precision and comparability across studies.

AVAILABILITY AND IMPLEMENTATION

The function was included in software package ENmix, and is freely available from Bioconductor website (https://www.bioconductor.org/packages/release/bioc/html/ENmix.html).

Associations of depression and anxiety symptoms in childhood and adolescence with epigenetic aging

BACKGROUND

Childhood anxiety and depression symptoms are potential risk factors for accelerated biological aging. In child and adolescent twins, we tested whether these symptoms were associated with DNA methylation (DNAm) aging, a measure of biological aging.

METHODS

276 twins (135 pairs, 6 singletons) had DNAm assayed from saliva in middle childhood (mean = 7.8 years). Residuals of five different DNAm age estimates regressed on chronological age were used to indicate accelerated aging. Anxiety and depression symptoms were assessed in middle childhood and early adolescence using the Child Behavior Checklist. Mixed effect regression was used to examine potential relationships between anxiety or depression symptoms, and accelerated DNAm age. MZ twin difference analysis was then utilized to determine if associations were environmentally-driven or due to genetic or shared-environment confounding.

RESULTS

Anxiety and depression symptoms were not associated with accelerated DNAm aging in middle childhood. In early adolescence, only the Wu clock was significant and indicated that each one symptom increase in anxiety symptoms had an associated age acceleration of 0.03 years (~0.4 months; p = 0.019). MZ twin difference analysis revealed non-significant within-pair effects, suggesting genetic and shared environmental influences.

LIMITATIONS

Sample is predominantly male and white. Generalizability to other populations may be limited.

CONCLUSION

Accelerated DNAm aging of the Wu clock in middle childhood is associated with anxiety, but not depression, symptoms in early adolescence. Further, this association may be the result of shared genetic and environmental influences. Accelerated DNAm aging may serve as an early risk factor or predictor of later anxiety symptoms.

Uncovering Forensic Evidence: A Path to Age Estimation through DNA Methylation

Age estimation is a critical aspect of reconstructing a biological profile in forensic sciences. Diverse biochemical processes have been studied in their correlation with age, and the results have driven DNA methylation to the forefront as a promising biomarker. DNA methylation, an epigenetic modification, has been extensively studied in recent years for developing age estimation models in criminalistics and forensic anthropology. Epigenetic clocks, which analyze DNA sites undergoing hypermethylation or hypomethylation as individuals age, have paved the way for improved prediction models. A wide range of biomarkers and methods for DNA methylation analysis have been proposed, achieving different accuracies across samples and cell types. This review extensively explores literature from the past 5 years, showing scientific efforts toward the ultimate goal: applying age prediction models to assist in human identification.

Blood-Based Epigenetic Age Acceleration and Incident Colorectal Cancer Risk: Findings from a Population-Based Case-Control Study

This study investigates the association between epigenetic age acceleration (EAA) derived from DNA methylation and the risk of incident colorectal cancer (CRC). We utilized data from a random population sample of 9,360 individuals (men and women, aged 45-69) from the HAPIEE Study who had been followed up for 16 years. A nested case-control design yielded 35 incident CRC cases and 354 matched controls. Six baseline epigenetic age (EA) measures (Horvath, Hannum, PhenoAge, Skin and Blood (SB), BLUP, and Elastic Net (EN)) were calculated along with their respective EAAs. After adjustment, the odds ratios (ORs) for CRC risk per decile increase in EAA ranged from 1.20 (95% CI: 1.04-1.39) to 1.44 (95% CI: 1.21-1.76) for the Horvath, Hannum, PhenoAge, and BLUP measures. Conversely, the SB and EN EAA measures showed borderline inverse associations with ORs of 0.86-0.87 (95% CI: 0.76-0.99). Tertile analysis reinforced a positive association between CRC risk and four EAA measures (Horvath, Hannum, PhenoAge, and BLUP) and a modest inverse relationship with EN EAA. Our findings from a prospective population-based-case-control study indicate a direct association between incident CRC and four markers of accelerated baseline epigenetic age. In contrast, two markers showed a negative association or no association. These results warrant further exploration in larger cohorts and may have implications for CRC risk assessment and prevention.

A multi-organization epigenetic age prediction based on a channel attention perceptron networks

DNA methylation indicates the individual's aging, so-called Epigenetic clocks, which will improve the research and diagnosis of aging diseases by investigating the correlation between methylation loci and human aging. Although this discovery has inspired many researchers to develop traditional computational methods to quantify the correlation and predict the chronological age, the performance bottleneck delayed access to the practical application. Since artificial intelligence technology brought great opportunities in research, we proposed a perceptron model integrating a channel attention mechanism named PerSEClock. The model was trained on 24,516 CpG loci that can utilize the samples from all types of methylation identification platforms and tested on 15 independent datasets against seven methylation-based age prediction methods. PerSEClock demonstrated the ability to assign varying weights to different CpG loci. This feature allows the model to enhance the weight of age-related loci while reducing the weight of irrelevant loci. The method is free to use for academics at www.dnamclock.com/#/original.

Epigenetic age oscillates during the day

Since their introduction, epigenetic clocks have been extensively used in aging, human disease, and rejuvenation studies. In this article, we report an intriguing pattern: epigenetic age predictions display a 24-h periodicity. We tested a circadian blood sample collection using 17 epigenetic clocks addressing different aspects of aging. Thirteen clocks exhibited significant oscillations with the youngest and oldest age estimates around midnight and noon, respectively. In addition, daily oscillations were consistent with the changes of epigenetic age across different times of day observed in an independant populational dataset. While these oscillations can in part be attributed to variations in white blood cell type composition, cell count correction methods might not fully resolve the issue. Furthermore, some epigenetic clocks exhibited 24-h periodicity even in the purified fraction of neutrophils pointing at plausible contributions of intracellular epigenomic oscillations. Evidence for circadian variation in epigenetic clocks emphasizes the importance of the time-of-day for obtaining accurate estimates of epigenetic age.

Pregnancy is linked to faster epigenetic aging in young women

A central prediction of evolutionary theory is that energy invested into reproduction comes at the expense of somatic maintenance and repair, accelerating biological aging. Supporting this prediction are findings that high fertility among women predicts shorter lifespan and poorer health later in life. However, biological aging is thought to begin before age-related health declines, limiting the applicability of morbidity and mortality for studying the aging process earlier in life. Here, we examine the relationship between reproductive history and biological aging in a sample of young (20 to 22yo) men and women from the Cebu Longitudinal Health and Nutrition Survey, located in the Philippines (n = 1,735). We quantify biological aging using six measures, collectively known as epigenetic clocks, reflecting various facets of cellular aging, health, and mortality risk. In a subset of women, we test whether longitudinal changes in gravidity between young and early-middle adulthood (25 to 31yo) are associated with changes in epigenetic aging during that time. Cross-sectionally, gravidity was associated with all six measures of accelerated epigenetic aging in women (n = 825). Furthermore, longitudinal increases in gravidity were linked to accelerated epigenetic aging in two epigenetic clocks (n = 331). In contrast, the number of pregnancies a man reported fathering was not associated with epigenetic aging among same-aged cohort men (n = 910). These effects were robust to socioecological, environmental, and immunological factors, consistent with the hypothesis that pregnancy accelerates biological aging and that these effects can be detected in young women in a high-fertility context.

Epigenomic insights into common human disease pathology

The epigenome-the chemical modifications and chromatin-related packaging of the genome-enables the same genetic template to be activated or repressed in different cellular settings. This multi-layered mechanism facilitates cell-type specific function by setting the local sequence and 3D interactive activity level. Gene transcription is further modulated through the interplay with transcription factors and co-regulators. The human body requires this epigenomic apparatus to be precisely installed throughout development and then adequately maintained during the lifespan. The causal role of the epigenome in human pathology, beyond imprinting disorders and specific tumour suppressor genes, was further brought into the spotlight by large-scale sequencing projects identifying that mutations in epigenomic machinery genes could be critical drivers in both cancer and developmental disorders. Abrogation of this cellular mechanism is providing new molecular insights into pathogenesis. However, deciphering the full breadth and implications of these epigenomic changes remains challenging. Knowledge is accruing regarding disease mechanisms and clinical biomarkers, through pathogenically relevant and surrogate tissue analyses, respectively. Advances include consortia generated cell-type specific reference epigenomes, high-throughput DNA methylome association studies, as well as insights into ageing-related diseases from biological 'clocks' constructed by machine learning algorithms. Also, 3rd-generation sequencing is beginning to disentangle the complexity of genetic and DNA modification haplotypes. Cell-free DNA methylation as a cancer biomarker has clear clinical utility and further potential to assess organ damage across many disorders. Finally, molecular understanding of disease aetiology brings with it the opportunity for exact therapeutic alteration of the epigenome through CRISPR-activation or inhibition.

Meta-analysis of epigenetic aging in schizophrenia reveals multifaceted relationships with age, sex, illness duration, and polygenic risk

BACKGROUND

The study of biological age acceleration may help identify at-risk individuals and reduce the rising global burden of age-related diseases. Using DNA methylation (DNAm) clocks, we investigated biological aging in schizophrenia (SCZ), a mental illness that is associated with an increased prevalence of age-related disabilities and morbidities. In a whole blood DNAm sample of 1090 SCZ cases and 1206 controls across four European cohorts, we performed a meta-analysis of differential aging using three DNAm clocks (i.e., Hannum, Horvath, and Levine). To dissect how DNAm aging contributes to SCZ, we integrated information on duration of illness and SCZ polygenic risk, as well as stratified our analyses by chronological age and biological sex.

RESULTS

We found that blood-based DNAm aging is significantly altered in SCZ independent from duration of the illness since onset. We observed sex-specific and nonlinear age effects that differed between clocks and point to possible distinct age windows of altered aging in SCZ. Most notably, intrinsic cellular age (Horvath clock) is decelerated in SCZ cases in young adulthood, while phenotypic age (Levine clock) is accelerated in later adulthood compared to controls. Accelerated phenotypic aging was most pronounced in women with SCZ carrying a high polygenic burden with an age acceleration of + 3.82 years (CI 2.02-5.61, P = 1.1E-03). Phenotypic aging and SCZ polygenic risk contributed additively to the illness and together explained up to 14.38% of the variance in disease status.

CONCLUSIONS

Our study contributes to the growing body of evidence of altered DNAm aging in SCZ and points to intrinsic age deceleration in younger adulthood and phenotypic age acceleration in later adulthood in SCZ. Since increased phenotypic age is associated with increased risk of all-cause mortality, our findings indicate that specific and identifiable patient groups are at increased mortality risk as measured by the Levine clock. Our study did not find that DNAm aging could be explained by the duration of illness of patients, but we did observe age- and sex-specific effects that warrant further investigation. Finally, our results show that combining genetic and epigenetic predictors can improve predictions of disease outcomes and may help with disease management in schizophrenia.

A blood biomarker of the pace of aging is associated with brain structure: replication across three cohorts

Biological aging is the correlated decline of multi-organ system integrity central to the etiology of many age-related diseases. A novel epigenetic measure of biological aging, DunedinPACE, is associated with cognitive dysfunction, incident dementia, and mortality. Here, we tested for associations between DunedinPACE and structural MRI phenotypes in three datasets spanning midlife to advanced age: the Dunedin Study (age=45 years), the Framingham Heart Study Offspring Cohort (mean age=63 years), and the Alzheimer's Disease Neuroimaging Initiative (mean age=75 years). We also tested four additional epigenetic measures of aging: the Horvath clock, the Hannum clock, PhenoAge, and GrimAge. Across all datasets (total N observations=3380; total N individuals=2322), faster DunedinPACE was associated with lower total brain volume, lower hippocampal volume, greater burden of white matter microlesions, and thinner cortex. Across all measures, DunedinPACE and GrimAge had the strongest and most consistent associations with brain phenotypes. Our findings suggest that single timepoint measures of multi-organ decline such as DunedinPACE could be useful for gauging nervous system health.

The Pace of Biological Aging Predicts Nonspecific Chronic Low Back Pain Severity

This study aimed to determine if and how the pace of biological aging was associated with nonspecific chronic low back pain (cLBP) and compare what measure of epigenetic age acceleration most strongly predicts cLBP outcomes. We used the Dunedin Pace of Aging from the Epigenome (DunedinPACE), Horvath's, Hannum's, and PhenoAge clocks to determine the pace of biological aging in 69 cLBP, and 49 pain-free controls (PFCs) adults, ages 18 to 85 years. On average, participants with cLBP had higher DunedinPACE (P < .001) but lower Horvath (P = .04) and Hannum (P = .02) accelerated epigenetic age than PFCs. There was no significant difference in PhenoAge acceleration between the cLBP and PFC groups (P = .97). DunedinPACE had the largest effect size (Cohen's d = .78) on group differences. In univariate regressions, a unit increase in DunedinPACE score was associated with 265.98 times higher odds of cLBP than the PFC group (P < .001). After controlling for sex, race, and body mass index (BMI), the odds ratio of cLBP to PFC group was 149.62 (P < .001). Furthermore, among participants with cLBP, DunedinPACE scores positively correlated with pain severity (rs = .385, P = .001) and interference (rs = .338, P = .005). Epigenetic age acceleration from Horvath, Hannum, and PhenoAge clocks were not significant predictors of cLBP. The odds of a faster pace of biological aging are higher among adults with cLBP, and this was associated with greater pain severity and disability. Future interventions to slow the pace of biological aging may improve cLBP outcomes. PERSPECTIVE: Accelerated epigenetic aging is common among adults with nonspecific cLBP. Higher DunedinPACE scores positively correlate with pain severity and interference, and better predict cLBP than other DNA methylation clocks. Interventions to slow the pace of biological aging may be viable targets for improving pain outcomes.

pyaging: a Python-based compendium of GPU-optimized aging clocks

MOTIVATION

Aging is intricately linked to diseases and mortality. It is reflected in molecular changes across various tissues which can be leveraged for the development of biomarkers of aging using machine learning models, known as aging clocks. Despite advancements in the field, a significant challenge remains: the lack of robust, Python-based software tools for integrating and comparing these diverse models. This gap highlights the need for comprehensive solutions that can handle the complexity and variety of data in aging research.

RESULTS

To address this gap, I introduce pyaging, a comprehensive open-source Python package designed to facilitate aging research. pyaging harmonizes dozens of aging clocks, covering a range of molecular data types such as DNA methylation, transcriptomics, histone mark ChIP-Seq, and ATAC-Seq. The package is not limited to traditional model types; it features a diverse array, from linear and principal component models to neural networks and automatic relevance determination models. Thanks to a PyTorch-based backend that enables GPU acceleration, pyaging is capable of rapid inference, even when dealing with large datasets and complex models. In addition, the package's support for multi-species analysis extends its utility across various organisms, including humans, various mammals, and Caenorhabditis elegans.

AVAILABILITY AND IMPLEMENTATION

pyaging is accessible on GitHub, at https://github.com/rsinghlab/pyaging, and the distribution is available on PyPi, at https://pypi.org/project/pyaging/. The software is also archived on Zenodo, at https://zenodo.org/doi/10.5281/zenodo.10335011.

Building Minimized Epigenetic Clock by iPlex MassARRAY Platform

Epigenetic clocks are valuable tools for estimating both chronological and biological age by assessing DNA methylation levels at specific CpG dinucleotides. While conventional epigenetic clocks rely on genome-wide methylation data, targeted approaches offer a more efficient alternative. In this study, we explored the feasibility of constructing a minimized epigenetic clock utilizing data acquired through the iPlex MassARRAY technology. The study enrolled a cohort of relatively healthy individuals, and their methylation levels of eight specific CpG dinucleotides in genes SLC12A5, LDB2, FIGN, ACSS3, FHL2, and EPHX3 were evaluated using the iPlex MassARRAY system and the Illumina EPIC array. The methylation level of five studied CpG sites demonstrated significant correlations with chronological age and an acceptable convergence of data obtained by the iPlex MassARRAY and Illumina EPIC array. At the same time, the methylation level of three CpG sites showed a weak relationship with age and exhibited a low concordance between the data obtained from the two technologies. The construction of the epigenetic clock involved the utilization of different machine-learning models, including linear models, deep neural networks (DNN), and gradient-boosted decision trees (GBDT). The results obtained from these models were compared with each other and with the outcomes generated by other well-established epigenetic clocks. In our study, the TabNet architecture (deep tabular data learning architecture) exhibited the best performance (best MAE = 5.99). Although our minimized epigenetic clock yielded slightly higher age prediction errors compared to other epigenetic clocks, it still represents a viable alternative to the genome-wide epigenotyping array.

The Sociodemographic and Lifestyle Correlates of Epigenetic Aging in a Nationally Representative U.S. Study of Younger Adults

Importance

Epigenetic clocks represent molecular evidence of disease risk and aging processes and have been used to identify how social and lifestyle characteristics are associated with accelerated biological aging. However, most of this research is based on older adult samples who already have measurable chronic disease.

Objective

To investigate whether and how sociodemographic and lifestyle characteristics are related to biological aging in a younger adult sample across a wide array of epigenetic clock measures.

Design

Nationally representative prospective cohort study.

Setting

United States (U.S.).

Participants

Data come from the National Longitudinal Study of Adolescent to Adult Health, a national cohort of adolescents in grades 7-12 in U.S. in 1994 followed for 25 years over five interview waves. Our analytic sample includes participants followed-up through Wave V in 2016-18 who provided blood samples for DNA methylation (DNAm) testing (n=4237) at Wave V.

Exposure

Sociodemographic (sex, race/ethnicity, immigrant status, socioeconomic status, geographic location) and lifestyle (obesity status, exercise, tobacco, and alcohol use) characteristics.

Main Outcome

Biological aging assessed from blood DNAm using 16 epigenetic clocks when the cohort was aged 33-44 in Wave V.

Results

While there is considerable variation in the mean and distribution of epigenetic clock estimates and in the correlations among the clocks, we found sociodemographic and lifestyle factors are more often associated with biological aging in clocks trained to predict current or dynamic phenotypes (e.g., PhenoAge, GrimAge and DunedinPACE) as opposed to clocks trained to predict chronological age alone (e.g., Horvath). Consistent and strong associations of faster biological aging were found for those with lower levels of education and income, and those with severe obesity, no weekly exercise, and tobacco use.

Conclusions and Relevance

Our study found important social and lifestyle factors associated with biological aging in a nationally representative cohort of younger-aged adults. These findings indicate that molecular processes underlying disease risk can be identified in adults entering midlife before disease is manifest and represent useful targets for interventions to reduce social inequalities in heathy aging and longevity.

Key Points

Question: Are epigenetic clocks, measures of biological aging developed mainly on older-adult samples, meaningful for younger adults and associated with sociodemographic and lifestyle characteristics in expected patterns found in prior aging research?Findings: Sociodemographic and lifestyle factors were associated with biological aging in clocks trained to predict morbidity and mortality showing accelerated aging among those with lower levels of education and income, and those with severe obesity, no weekly exercise, and tobacco use.Meaning: Age-related molecular processes can be identified in younger-aged adults before disease manifests and represent potential interventions to reduce social inequalities in heathy aging and longevity.

Clarifying the biological and statistical assumptions of cross-sectional biological age predictors: an elaborate illustration using synthetic and real data

BACKGROUND

There is divergence in the rate at which people age. The concept of biological age is postulated to capture this variability, and hence to better represent an individual's true global physiological state than chronological age. Biological age predictors are often generated based on cross-sectional data, using biochemical or molecular markers as predictor variables. It is assumed that the difference between chronological and predicted biological age is informative of one's chronological age-independent aging divergence ∆.

METHODS

We investigated the statistical assumptions underlying the most popular cross-sectional biological age predictors, based on multiple linear regression, the Klemera-Doubal method or principal component analysis. We used synthetic and real data to illustrate the consequences if this assumption does not hold.

RESULTS

The most popular cross-sectional biological age predictors all use the same strong underlying assumption, namely that a candidate marker of aging's association with chronological age is directly informative of its association with the aging rate ∆. We called this the identical-association assumption and proved that it is untestable in a cross-sectional setting. If this assumption does not hold, weights assigned to candidate markers of aging are uninformative, and no more signal may be captured than if markers would have been assigned weights at random.

CONCLUSIONS

Cross-sectional methods for predicting biological age commonly use the untestable identical-association assumption, which previous literature in the field had never explicitly acknowledged. These methods have inherent limitations and may provide uninformative results, highlighting the importance of researchers exercising caution in the development and interpretation of cross-sectional biological age predictors.

Where are we in the implementation of tissue-specific epigenetic clocks?

Introduction: DNA methylation clocks presents advantageous characteristics with respect to the ambitious goal of identifying very early markers of disease, based on the concept that accelerated ageing is a reliable predictor in this sense. Methods: Such tools, being epigenomic based, are expected to be conditioned by sex and tissue specificities, and this work is about quantifying this dependency as well as that from the regression model and the size of the training set. Results: Our quantitative results indicate that elastic-net penalization is the best performing strategy, and better so when-unsurprisingly-the data set is bigger; sex does not appear to condition clocks performances and tissue specific clocks appear to perform better than generic blood clocks. Finally, when considering all trained clocks, we identified a subset of genes that, to the best of our knowledge, have not been presented yet and might deserve further investigation: CPT1A, MMP15, SHROOM3, SLIT3, and SYNGR. Conclusion: These factual starting points can be useful for the future medical translation of clocks and in particular in the debate between multi-tissue clocks, generally trained on a large majority of blood samples, and tissue-specific clocks.

Deciphering the role of immune cell composition in epigenetic age acceleration: Insights from cell-type deconvolution applied to human blood epigenetic clocks

Aging is a significant risk factor for various human disorders, and DNA methylation clocks have emerged as powerful tools for estimating biological age and predicting health-related outcomes. Methylation data from blood DNA has been a focus of more recently developed DNA methylation clocks. However, the impact of immune cell composition on epigenetic age acceleration (EAA) remains unclear as only some clocks incorporate partial cell type composition information when analyzing EAA. We investigated associations of 12 immune cell types measured by cell-type deconvolution with EAA predicted by six widely-used DNA methylation clocks in data from >10,000 blood samples. We observed significant associations of immune cell composition with EAA for all six clocks tested. Across the clocks, nine or more of the 12 cell types tested exhibited significant associations with EAA. Higher memory lymphocyte subtype proportions were associated with increased EAA, and naïve lymphocyte subtypes were associated with decreased EAA. To demonstrate the potential confounding of EAA by immune cell composition, we applied EAA in rheumatoid arthritis. Our research maps immune cell type contributions to EAA in human blood and offers opportunities to adjust for immune cell composition in EAA studies to a significantly more granular level. Understanding associations of EAA with immune profiles has implications for the interpretation of epigenetic age and its relevance in aging and disease research. Our detailed map of immune cell type contributions serves as a resource for studies utilizing epigenetic clocks across diverse research fields, including aging-related diseases, precision medicine, and therapeutic interventions.

Insights into ageing rates comparison across tissues from recalibrating cerebellum DNA methylation clock

DNA methylation (DNAm)-based age clocks have been studied extensively as a biomarker of human ageing and a risk factor for age-related diseases. Despite different tissues having vastly different rates of proliferation, it is still largely unknown whether they age at different rates. It was previously reported that the cerebellum ages slowly; however, this claim was drawn from a single clock using a relatively small sample size and so warrants further investigation. We collected the largest cerebellum DNAm dataset (N = 752) to date. We found the respective epigenetic ages are all severely underestimated by six representative DNAm age clocks, with the underestimation effects more pronounced in the four clocks whose training datasets do not include brain-related tissues. We identified 613 age-associated CpGs in the cerebellum, which accounts for only 14.5% of the number found in the middle temporal gyrus from the same population (N = 404). From the 613 cerebellum age-associated CpGs, we built a highly accurate age prediction model for the cerebellum named CerebellumClockspecific (Pearson correlation=0.941, MAD=3.18 years). Ageing rate comparisons based on the two tissue-specific clocks constructed on the 201 overlapping age-associated CpGs support the cerebellum has younger DNAm age. Nevertheless, we built BrainCortexClock to prove a single DNAm clock is able to unbiasedly estimate DNAm ages of both cerebellum and cerebral cortex, when they are adequately and equally represented in the training dataset. Comparing ageing rates across tissues using DNA methylation multi-tissue clocks is flawed. The large underestimation of age prediction for cerebellums by previous clocks mainly reflects the improper usage of these age clocks. There exist strong and consistent ageing effects on the cerebellar methylome, and we suggest the smaller number of age-associated CpG sites in cerebellum is largely attributed to its extremely low average cell replication rates.

Biologically informed deep learning for explainable epigenetic clocks

Ageing is often characterised by progressive accumulation of damage, and it is one of the most important risk factors for chronic disease development. Epigenetic mechanisms including DNA methylation could functionally contribute to organismal aging, however the key functions and biological processes may govern ageing are still not understood. Although age predictors called epigenetic clocks can accurately estimate the biological age of an individual based on cellular DNA methylation, their models have limited ability to explain the prediction algorithm behind and underlying key biological processes controlling ageing. Here we present XAI-AGE, a biologically informed, explainable deep neural network model for accurate biological age prediction across multiple tissue types. We show that XAI-AGE outperforms the first-generation age predictors and achieves similar results to deep learning-based models, while opening up the possibility to infer biologically meaningful insights of the activity of pathways and other abstract biological processes directly from the model.

Open Genes-a new comprehensive database of human genes associated with aging and longevity

The Open Genes database was created to enhance and simplify the search for potential aging therapy targets. We collected data on 2402 genes associated with aging and developed convenient tools for searching and comparing gene features. A comprehensive description of genes has been provided, including lifespan-extending interventions, age-related changes, longevity associations, gene evolution, associations with diseases and hallmarks of aging, and functions of gene products. For each experiment, we presented the necessary structured data for evaluating the experiment's quality and interpreting the study's findings. Our goal was to stay objective and precise while connecting a particular gene to human aging. We distinguished six types of studies and 12 criteria for adding genes to our database. Genes were classified according to the confidence level of the link between the gene and aging. All the data collected in a database are provided both by an API and a user interface. The database is publicly available on a website at https://open-genes.org/.

Identifying epigenetic aging moderators using the epigenetic pacemaker

Epigenetic clocks are DNA methylation-based chronological age prediction models that are commonly employed to study age-related biology. The difference between the predicted and observed age is often interpreted as a form of biological age acceleration, and many studies have measured the impact of environmental and disease-associated factors on epigenetic age. Most epigenetic clocks are fit using approaches that minimize the error between the predicted and observed chronological age, and as a result, they may not accurately model the impact of factors that moderate the relationship between the actual and epigenetic age. Here, we compare epigenetic clocks that are constructed using penalized regression methods to an evolutionary framework of epigenetic aging with the epigenetic pacemaker (EPM), which directly models DNA methylation as a function of a time-dependent epigenetic state. In simulations, we show that the value of the epigenetic state is impacted by factors such as age, sex, and cell-type composition. Next, in a dataset aggregated from previous studies, we show that the epigenetic state is also moderated by sex and the cell type. Finally, we demonstrate that the epigenetic state is also moderated by toxins in a study on polybrominated biphenyl exposure. Thus, we find that the pacemaker provides a robust framework for the study of factors that impact epigenetic age acceleration and that the effect of these factors may be obscured in traditional clocks based on linear regression models.

Application of array-based age prediction models to post-mortem tissue samples

Since DNA methylation at specific CpG sites exhibits a strong age association, researchers have developed numerous age prediction models based on the methylation BeadChip array. These models harness epigenetic clocks that hold the potential to narrow down the search range for unknown suspects and unidentified victims. This study collected 180 post-mortem tissue samples comprising nine tissue types (blood, brain, heart, lung, liver, kidney, muscle, epidermis, and dermis) from autopsies of 20 Koreans aged 18-78. Subsequently, DNA methylation profiling was conducted using the Infinium MethylationEPIC array. We tested several array-based age prediction models using the data obtained from various tissues. The pan-tissue clock exhibited a moderately accurate prediction across all nine tissue types (MAE = 8.7 years, r = 0.88). Notably, the DNAm ages of the Hannum clock, the skin & blood clock, and the Zhang clock strongly correlated with the actual age in blood samples (MAE < approximately 5 years, r > 0.9). PhenoAge yielded an MAE of 10.1 years and an r-value of 0.92. The muscle-specific epigenetic clock, the MEAT package, demonstrated high prediction accuracy in muscle samples (MAE = 4.7 years, r = 0.93). Those previously reported array-based age prediction models were mainly constructed in Europeans but performed well in Koreans. In addition, tests involving various quantities of DNA and fragmented DNA have shown that DNA quantity and quality affected methylation measurements and age prediction results. However, robust age prediction models exist under low amounts of DNA and fragmented DNA conditions.

Exercise is associated with younger methylome and transcriptome profiles in human skeletal muscle

Exercise training prevents age-related decline in muscle function. Targeting epigenetic aging is a promising actionable mechanism and late-life exercise mitigates epigenetic aging in rodent muscle. Whether exercise training can decelerate, or reverse epigenetic aging in humans is unknown. Here, we performed a powerful meta-analysis of the methylome and transcriptome of an unprecedented number of human skeletal muscle samples (n = 3176). We show that: (1) individuals with higher baseline aerobic fitness have younger epigenetic and transcriptomic profiles, (2) exercise training leads to significant shifts of epigenetic and transcriptomic patterns toward a younger profile, and (3) muscle disuse "ages" the transcriptome. Higher fitness levels were associated with attenuated differential methylation and transcription during aging. Furthermore, both epigenetic and transcriptomic profiles shifted toward a younger state after exercise training interventions, while the transcriptome shifted toward an older state after forced muscle disuse. We demonstrate that exercise training targets many of the age-related transcripts and DNA methylation loci to maintain younger methylome and transcriptome profiles, specifically in genes related to muscle structure, metabolism, and mitochondrial function. Our comprehensive analysis will inform future studies aiming to identify the best combination of therapeutics and exercise regimes to optimize longevity.

The ticking of aging clocks

Computational models that measure biological age and aging rate regardless of chronological age are called aging clocks. The underlying counting mechanisms of the intrinsic timers of these clocks are still unclear. Molecular mediators and determinants of aging rate point to the key roles of DNA damage, epigenetic drift, and inflammation. Persistent DNA damage leads to cellular senescence and the senescence-associated secretory phenotype (SASP), which induces cytotoxic immune cell infiltration; this further induces DNA damage through reactive oxygen and nitrogen species (RONS). I discuss the possibility that DNA damage (or the response to it, including epigenetic changes) is the fundamental counting unit of cell cycles and cellular senescence, that ultimately accounts for cell composition changes and functional decline in tissues, as well as the key intervention points.

ResnetAge: A Resnet-Based DNA Methylation Age Prediction Method

Aging is a significant contributing factor to degenerative diseases such as cancer. The extent of DNA methylation in human cells indicates the aging process and screening for age-related methylation sites can be used to construct epigenetic clocks. Thereby, it can be a new aging-detecting marker for clinical diagnosis and treatments. Predicting the biological age of human individuals is conducive to the study of physical aging problems. Although many researchers have developed epigenetic clock prediction methods based on traditional machine learning and even deep learning, higher prediction accuracy is still required to match the clinical applications. Here, we proposed an epigenetic clock prediction method based on a Resnet neuro networks model named ResnetAge. The model accepts 22,278 CpG sites as a sample input, supporting both the Illumina 27K and 450K identification frameworks. It was trained using 32 public datasets containing multiple tissues such as whole blood, saliva, and mouth. The Mean Absolute Error (MAE) of the training set is 1.29 years, and the Median Absolute Deviation (MAD) is 0.98 years. The Mean Absolute Error (MAE) of the validation set is 3.24 years, and the Median Absolute Deviation (MAD) is 2.3 years. Our method has higher accuracy in age prediction in comparison with other methylation-based age prediction methods.

ExplaiNAble BioLogical Age (ENABL Age): an artificial intelligence framework for interpretable biological age

BACKGROUND

Biological age is a measure of health that offers insights into ageing. The existing age clocks, although valuable, often trade off accuracy and interpretability. We introduce ExplaiNAble BioLogical Age (ENABL Age), a computational framework that combines machine-learning models with explainable artificial intelligence (XAI) methods to accurately estimate biological age with individualised explanations.

METHODS

To construct the ENABL Age clock, we first predicted an age-related outcome (eg, all-cause or cause-specific mortality), and then rescaled these predictions to estimate biological age, using UK Biobank and National Health and Nutrition Examination Survey (NHANES) datasets. We adapted existing XAI methods to decompose individual ENABL Ages into contributing risk factors. For broad accessibility, we developed two versions: ENABL Age-L, based on blood tests, and ENABL Age-Q, based on questionnaire characteristics. Finally, we validated diverse ageing mechanisms captured by each ENABL Age clock through genome-wide association studies (GWAS) association analyses.

FINDINGS

Our ENABL Age clock was significantly correlated with chronological age (r=0·7867, p<0·0001 for UK Biobank; r=0·7126, p<0·0001 for NHANES). These clocks distinguish individuals who are healthy (ie, their ENABL Age is lower than their chronological age) from those who are unhealthy (ie, their ENABL Age is higher than their chronological age), predicting mortality more effectively than existing clocks. Groups of individuals who were unhealthy showed approximately three to 12 times higher log hazard ratio than healthy groups, as per ENABL Age. The clocks achieved high mortality prediction power with an area under the receiver operating characteristic curve of 0·8179 for 5-year mortality and 0·8115 for 10-year mortality on the UK Biobank dataset, and 0·8935 for 5-year mortality and 0·9107 for 10-year mortality on the NHANES dataset. The individualised explanations that revealed the contribution of specific characteristics to ENABL Age provided insights into the important characteristics for ageing. An association analysis with risk factors and ageing-related morbidities and GWAS results on ENABL Age clocks trained on different mortality causes showed that each clock captures distinct ageing mechanisms.

INTERPRETATION

ENABL Age brings an important leap forward in the application of XAI for interpreting biological age clocks. ENABL Age also carries substantial potential in practical settings, assisting medical professionals in untangling the complexity of ageing mechanisms, and potentially becoming a valuable tool in informed clinical decision-making processes.

FUNDING

National Science Foundation and National Institutes of Health.

Epigenetic clocks in neurodegenerative diseases: a systematic review

BACKGROUND

Biological ageing is one of the principal risk factors for neurodegenerative diseases. It is becoming increasingly clear that acceleration of DNA methylation age, as measured by the epigenetic clock, is closely associated with many age-related diseases.

METHODS

We searched the PubMed and Web of Science databases to identify eligible studies reporting epigenetic clocks in several neurodegenerative diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS) and Huntington's disease (HD).

RESULTS

Twenty-three studies (12 for AD, 4 for PD, 5 for ALS, and 2 for HD) were included. We systematically summarised the clinical utility of 11 epigenetic clocks (based on blood and brain tissues) in assessing the risk factors, age of onset, diagnosis, progression, prognosis and pathology of AD, PD, ALS and HD. We also critically described our current understandings to these evidences, and further discussed key challenges, potential mechanisms and future perspectives of epigenetic ageing in neurodegenerative diseases.

CONCLUSIONS

Epigenetic clocks hold great potential in neurodegenerative diseases. Further research is encouraged to evaluate the clinical utility and promote the application.

PROSPERO REGISTRATION NUMBER

CRD42022365233.

Cumulative stress, PTSD, and emotion dysregulation during pregnancy and epigenetic age acceleration in Hispanic mothers and their newborn infants

Pregnancy can exacerbate or prompt the onset of stress-related disorders, such as post-traumatic stress disorder (PTSD). PTSD is associated with heightened stress responsivity and emotional dysregulation, as well as increased risk of chronic disorders and mortality. Further, maternal PTSD is associated with gestational epigenetic age acceleration in newborns, implicating the prenatal period as a developmental time period for the transmission of effects across generations. Here, we evaluated the associations between PTSD symptoms, maternal epigenetic age acceleration, and infant gestational epigenetic age acceleration in 89 maternal-neonatal dyads. Trauma-related experiences and PTSD symptoms in mothers were assessed during the third trimester of pregnancy. The MethylationEPIC array was used to generate DNA methylation data from maternal and neonatal saliva samples collected within 24 h of infant birth. Maternal epigenetic age acceleration was calculated using Horvath's multi-tissue clock, PhenoAge and GrimAge. Gestational epigenetic age was estimated using the Haftorn clock. Maternal cumulative past-year stress (GrimAge: p = 3.23e-04, PhenoAge: p = 9.92e-03), PTSD symptoms (GrimAge: p = 0.019), and difficulties in emotion regulation (GrimAge: p = 0.028) were associated with accelerated epigenetic age in mothers. Maternal PTSD symptoms were associated with lower gestational epigenetic age acceleration in neonates (p = 0.032). Overall, our results suggest that maternal cumulative past-year stress exposure and trauma-related symptoms may increase the risk for age-related problems in mothers and developmental problems in their newborns.

Histopathologic brain age estimation via multiple instance learning

Understanding age acceleration, the discordance between biological and chronological age, in the brain can reveal mechanistic insights into normal physiology as well as elucidate pathological determinants of age-related functional decline and identify early disease changes in the context of Alzheimer's and other disorders. Histopathological whole slide images provide a wealth of pathologic data on the cellular level that can be leveraged to build deep learning models to assess age acceleration. Here, we used a collection of digitized human post-mortem hippocampal sections to develop a histological brain age estimation model. Our model predicted brain age within a mean absolute error of 5.45 ± 0.22 years, with attention weights corresponding to neuroanatomical regions vulnerable to age-related changes. We found that histopathologic brain age acceleration had significant associations with clinical and pathologic outcomes that were not found with epigenetic based measures. Our results indicate that histopathologic brain age is a powerful, independent metric for understanding factors that contribute to brain aging.

Epigenetics insights from perceived facial aging

Facial aging is the most visible manifestation of aging. People desire to look younger than others of the same chronological age. Hence, perceived age is often used as a visible marker of aging, while biological age, often estimated by methylation markers, is used as an objective measure of age. Multiple epigenetics-based clocks have been developed for accurate estimation of general biological age and the age of specific organs, including the skin. However, it is not clear whether the epigenetic biomarkers (CpGs) used in these clocks are drivers of aging processes or consequences of aging. In this proof-of-concept study, we integrate data from GWAS on perceived facial aging and EWAS on CpGs measured in blood. By running EW Mendelian randomization, we identify hundreds of putative CpGs that are potentially causal to perceived facial aging with similar numbers of damaging markers that causally drive or accelerate facial aging and protective methylation markers that causally slow down or protect from aging. We further demonstrate that while candidate causal CpGs have little overlap with known epigenetics-based clocks, they affect genes or proteins with known functions in skin aging, such as skin pigmentation, elastin, and collagen levels. Overall, our results suggest that blood methylation markers reflect facial aging processes, and thus can be used to quantify skin aging and develop anti-aging solutions that target the root causes of aging.

Endometrial receptivity in women of advanced age: an underrated factor in infertility

BACKGROUND

Modern lifestyle has led to an increase in the age at conception. Advanced age is one of the critical risk factors for female-related infertility. It is well known that maternal age positively correlates with the deterioration of oocyte quality and chromosomal abnormalities in oocytes and embryos. The effect of age on endometrial function may be an equally important factor influencing implantation rate, pregnancy rate, and overall female fertility. However, there are only a few published studies on this topic, suggesting that this area has been under-explored. Improving our knowledge of endometrial aging from the biological (cellular, molecular, histological) and clinical perspectives would broaden our understanding of the risks of age-related female infertility.

OBJECTIVE AND RATIONALE

The objective of this narrative review is to critically evaluate the existing literature on endometrial aging with a focus on synthesizing the evidence for the impact of endometrial aging on conception and pregnancy success. This would provide insights into existing gaps in the clinical application of research findings and promote the development of treatment options in this field.

SEARCH METHODS

The review was prepared using PubMed (Medline) until February 2023 with the keywords such as 'endometrial aging', 'receptivity', 'decidualization', 'hormone', 'senescence', 'cellular', 'molecular', 'methylation', 'biological age', 'epigenetic', 'oocyte recipient', 'oocyte donation', 'embryo transfer', and 'pregnancy rate'. Articles in a language other than English were excluded.

OUTCOMES

In the aging endometrium, alterations occur at the molecular, cellular, and histological levels suggesting that aging has a negative effect on endometrial biology and may impair endometrial receptivity. Additionally, advanced age influences cellular senescence, which plays an important role during the initial phase of implantation and is a major obstacle in the development of suitable senolytic agents for endometrial aging. Aging is also accountable for chronic conditions associated with inflammaging, which eventually can lead to increased pro-inflammation and tissue fibrosis. Furthermore, advanced age influences epigenetic regulation in the endometrium, thus altering the relation between its epigenetic and chronological age. The studies in oocyte donation cycles to determine the effect of age on endometrial receptivity with respect to the rates of implantation, clinical pregnancy, miscarriage, and live birth have revealed contradictory inferences indicating the need for future research on the mechanisms and corresponding causal effects of women's age on endometrial receptivity.

WIDER IMPLICATIONS

Increasing age can be accountable for female infertility and IVF failures. Based on the complied observations and synthesized conclusions in this review, advanced age has been shown to have a negative impact on endometrial functioning. This information can provide recommendations for future research focusing on molecular mechanisms of age-related cellular senescence, cellular composition, and transcriptomic changes in relation to endometrial aging. Additionally, further prospective research is needed to explore newly emerging therapeutic options, such as the senolytic agents that can target endometrial aging without affecting decidualization. Moreover, clinical trial protocols, focusing on oocyte donation cycles, would be beneficial in understanding the direct clinical implications of endometrial aging on pregnancy outcomes.

Smoking affects epigenetic ageing of lung bronchoalveolar lavage cells in Multiple Sclerosis

BACKGROUND

A compelling body of evidence implicates cigarette smoking and lung inflammation in Multiple Sclerosis (MS) susceptibility and progression. Previous studies have reported epigenetic age (DNAm age) acceleration in blood immune cells and in glial cells of people with MS (pwMS) compared to healthy controls (HC).

OBJECTIVES

We aimed to examine biological ageing in lung immune cells in the context of MS and smoking.

METHODS

We analyzed age acceleration residuals in lung bronchoalveolar lavage (BAL) cells, constituted of mainly alveolar macrophages, from 17 pwMS and 22 HC in relation to smoking using eight DNA methylation-based clocks, namely AltumAge, Horvath, GrimAge, PhenoAge, Zhang, SkinBlood, Hannum, Monocyte clock as well as two RNA-based clocks, which capture different aspects of biological ageing.

RESULTS

After adjustment for covariates, five epigenetic clocks showed significant differences between the groups. Four of them, Horvath (Padj = 0.028), GrimAge (Padj = 4.28 × 10-7), SkinBlood (Padj = 0.001) and Zhang (Padj = 0.02), uncovered the sole effect of smoking on ageing estimates, irrespective of the clinical group. The Horvath, SkinBlood and Zhang clocks showed a negative impact of smoking while GrimAge detected smoking-associated age acceleration in BAL cells. On the contrary, the AltumAge clock revealed differences between pwMS and HC and indicated that, in the absence of smoking, BAL cells of pwMS were epigenetically 5.4 years older compared to HC (Padj = 0.028). Smoking further affected epigenetic ageing in BAL cells of pwMS specifically as non-smoking pwMS exhibited a 10.2-year AltumAge acceleration compared to pwMS smokers (Padj = 0.0049). Of note, blood-derived monocytes did not show any MS-specific or smoking-related AltumAge differences. The difference between BAL cells of pwMS smokers and non-smokers was attributable to the differential methylation of 114 AltumAge-CpGs (Padj < 0.05) affecting genes involved in innate immune processes such as cytokine production, defense response and cell motility. These changes functionally translated into transcriptional differences in BAL cells between pwMS smokers and non-smokers.

CONCLUSIONS

BAL cells of pwMS display inflammation-related and smoking-dependent changes associated to epigenetic ageing captured by the AltumAge clock. Future studies examining potential confounders, such as the distribution of distinct BAL myeloid cell types in pwMS compared to control individuals in relation to smoking may clarify the varying performance and DNAm age estimations among epigenetic clocks.

Open access-enabled evaluation of epigenetic age acceleration in colorectal cancer and development of a classifier with diagnostic potential

Aberrant DNA methylation (DNAm) is known to be associated with the aetiology of cancer, including colorectal cancer (CRC). In the past, the availability of open access data has been the main driver of innovative method development and research training. However, this is increasingly being eroded by the move to controlled access, particularly of medical data, including cancer DNAm data. To rejuvenate this valuable tradition, we leveraged DNAm data from 1,845 samples (535 CRC tumours, 522 normal colon tissues adjacent to tumours, 72 colorectal adenomas, and 716 normal colon tissues from healthy individuals) from 14 open access studies deposited in NCBI GEO and ArrayExpress. We calculated each sample's epigenetic age (EA) using eleven epigenetic clock models and derived the corresponding epigenetic age acceleration (EAA). For EA, we observed that most first- and second-generation epigenetic clocks reflect the chronological age in normal tissues adjacent to tumours and healthy individuals [e.g., Horvath (r = 0.77 and 0.79), Zhang elastic net (EN) (r = 0.70 and 0.73)] unlike the epigenetic mitotic clocks (EpiTOC, HypoClock, MiAge) (r < 0.3). For EAA, we used PhenoAge, Wu, and the above mitotic clocks and found them to have distinct distributions in different tissue types, particularly between normal colon tissues adjacent to tumours and cancerous tumours, as well as between normal colon tissues adjacent to tumours and normal colon tissue from healthy individuals. Finally, we harnessed these associations to develop a classifier using elastic net regression (with lasso and ridge regularisations) that predicts CRC diagnosis based on a patient's sex and EAAs calculated from histologically normal controls (i.e., normal colon tissues adjacent to tumours and normal colon tissue from healthy individuals). The classifier demonstrated good diagnostic potential with ROC-AUC = 0.886, which suggests that an EAA-based classifier trained on relevant data could become a tool to support diagnostic/prognostic decisions in CRC for clinical professionals. Our study also reemphasises the importance of open access clinical data for method development and training of young scientists. Obtaining the required approvals for controlled access data would not have been possible in the timeframe of this study.

Aging and Age-Related Epigenetic Drift in the Pathogenesis of Leukemia and Lymphomas: New Therapeutic Targets

One of the traits of cancer cells is abnormal DNA methylation patterns. The idea that age-related epigenetic changes may partially explain the increased risk of cancer in the elderly is based on the observation that aging is also accompanied by comparable changes in epigenetic patterns. Lineage bias and decreased stem cell function are signs of hematopoietic stem cell compartment aging. Additionally, aging in the hematopoietic system and the stem cell niche have a role in hematopoietic stem cell phenotypes linked with age, such as leukemia and lymphoma. Understanding these changes will open up promising pathways for therapies against age-related disorders because epigenetic mechanisms are reversible. Additionally, the development of high-throughput epigenome mapping technologies will make it possible to identify the "epigenomic identity card" of every hematological disease as well as every patient, opening up the possibility of finding novel molecular biomarkers that can be used for diagnosis, prediction, and prognosis.

Accurate age prediction from blood using a small set of DNA methylation sites and a cohort-based machine learning algorithm

Chronological age prediction from DNA methylation sheds light on human aging, health, and lifespan. Current clocks are mostly based on linear models and rely upon hundreds of sites across the genome. Here, we present GP-age, an epigenetic non-linear cohort-based clock for blood, based upon 11,910 methylomes. Using 30 CpG sites alone, GP-age outperforms state-of-the-art models, with a median accuracy of ∼2 years on held-out blood samples, for both array and sequencing-based data. We show that aging-related changes occur at multiple neighboring CpGs, with implications for using fragment-level analysis of sequencing data in aging research. By training three independent clocks, we show enrichment of donors with consistent deviation between predicted and actual age, suggesting individual rates of biological aging. Overall, we provide a compact yet accurate alternative to array-based clocks for blood, with applications in longitudinal aging research, forensic profiling, and monitoring epigenetic processes in transplantation medicine and cancer.

Accelerated aging in bipolar disorders: An exploratory study of six epigenetic clocks

Bipolar disorder (BD) is a chronic and severe psychiatric disorder associated with significant medical morbidity and reduced life expectancy. In this study, we assessed accelerated epigenetic aging in individuals with BD using various DNA methylation (DNAm)-based markers. For this purpose, we used five epigenetic clocks (Horvath, Hannum, EN, PhenoAge, and GrimAge) and a DNAm-based telomere length clock (DNAmTL). DNAm profiles were obtained using Infinium MethylationEPIC Arrays from whole-blood samples of 184 individuals with BD. We also estimated blood cell counts based on DNAm levels for adjustment. Significant correlations between chronological age and each epigenetic age estimated using the six different clocks were observed. Following adjustment for blood cell counts, we found that the six epigenetic AgeAccels (age accelerations) were significantly associated with the body mass index. GrimAge AgeAccel was significantly associated with male sex, smoking status and childhood maltreatment. DNAmTL AgeAccel was significantly associated with smoking status. Overall, this study showed that distinct epigenetic clocks are sensitive to different aspects of aging process in BD. Further investigations with comprehensive epigenetic clock analyses and large samples are required to confirm our findings of potential determinants of an accelerated epigenetic aging in BD.

Biomarkers of aging for the identification and evaluation of longevity interventions

With the rapid expansion of aging biology research, the identification and evaluation of longevity interventions in humans have become key goals of this field. Biomarkers of aging are critically important tools in achieving these objectives over realistic time frames. However, the current lack of standards and consensus on the properties of a reliable aging biomarker hinders their further development and validation for clinical applications. Here, we advance a framework for the terminology and characterization of biomarkers of aging, including classification and potential clinical use cases. We discuss validation steps and highlight ongoing challenges as potential areas in need of future research. This framework sets the stage for the development of valid biomarkers of aging and their ultimate utilization in clinical trials and practice.