DNA methylation age of blood predicts all-cause mortality in later life

Peripheral mitochondrial DNA, telomere length and DNA methylation as predictors of live birth in in vitro fertilization cycles

OBJECTIVE

To evaluate whether telomere length (TL), mitochondrial-DNA (mt-DNA) or epigenetic age estimators based on DNA methylation (DNAm) pattern could be considered reliable predictors of in-vitro-fertilization (IVF) success in terms of live birth rate.

DESIGN

Prospective cohort study.

SETTING

Infertility Unit of the Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico.

PATIENTS

181 women aged 37-39 years who underwent IVF at a single-centre between January 2017 and December 2018.

INTERVENTIONS

On the day of recruitment, blood samples were collected, and genomic DNA was isolated from white blood cells. TL, mt-DNA and DNAm assessment was performed using quantitative real-time polymerase chain reaction (qPCR). Biological age (DNAm age) was computed as the algorithm based on methylation pattern of five genes. Epigenetic age acceleration was estimated from the residuals of the linear model of epigenetic age regressed on chronological age. Long Interspersed Nuclear Elements (LINE)-1 methylation pattern was used as a surrogate for global DNA methylation.

MAIN OUTCOME MEASURES

This study investigated whether peripheral TL, mt-DNA and DNAm could predict live birth in IVF cycles.

RESULTS

TL, mt-DNA and LINE-1 methylation were not associated with IVF success. Conversely, DNAm age resulted significantly lower in women who had a live birth compared to women who did not (36.1 ± 4.2 and 37.3 ± 3.3 years, respectively, p = 0.04). For DNAm age, odds ratio (OR) for live birth per year of age was 0.90 (95%CI: 0.82-0.99, p = 0.036) after adjusting for FSH and antral follicle count (AFC) and 0.90 (95%CI: 0.82-0.99, p = 0.028) after adjusting also for number of oocytes retrieved. A significant association also emerged for epigenetic age acceleration after adjustments (OR = 0.91, 95%CI: 0.83-1.00, p = 0.048).

CONCLUSION

DNAm age is associated with IVF success but the magnitude of this association is insufficient to claim a clinical use. However, our findings are promising and warrant further investigation. Assessment of biological age using different epigenetic clocks or focusing on different tissues may reveal new predictors of IVF success.

A catalogue of omics biological ageing clocks reveals substantial commonality and associations with disease risk

Biological age (BA), a measure of functional capacity and prognostic of health outcomes that discriminates between individuals of the same chronological age (chronAge), has been estimated using a variety of biomarkers. Previous comparative studies have mainly used epigenetic models (clocks), we use ~1000 participants to compare fifteen omics ageing clocks, with correlations of 0.21-0.97 with chronAge, even with substantial sub-setting of biomarkers. These clocks track common aspects of ageing with 95% of the variance in chronAge being shared among clocks. The difference between BA and chronAge - omics clock age acceleration (OCAA) - often associates with health measures. One year’s OCAA typically has the same effect on risk factors/10-year disease incidence as 0.09/0.25 years of chronAge. Epigenetic and IgG glycomics clocks appeared to track generalised ageing while others capture specific risks. We conclude BA is measurable and prognostic and that future work should prioritise health outcomes over chronAge.

Blood-based epigenome-wide analyses of cognitive abilities

BACKGROUND

Blood-based markers of cognitive functioning might provide an accessible way to track neurodegeneration years prior to clinical manifestation of cognitive impairment and dementia.

RESULTS

Using blood-based epigenome-wide analyses of general cognitive function, we show that individual differences in DNA methylation (DNAm) explain 35.0% of the variance in general cognitive function (g). A DNAm predictor explains ~4% of the variance, independently of a polygenic score, in two external cohorts. It also associates with circulating levels of neurology- and inflammation-related proteins, global brain imaging metrics, and regional cortical volumes.

CONCLUSIONS

As sample sizes increase, the ability to assess cognitive function from DNAm data may be informative in settings where cognitive testing is unreliable or unavailable.

The Relationship between Epigenetic Age and Myocardial Infarction/Acute Coronary Syndrome in a Population-Based Nested Case-Control Study

We investigated the relationship between ‘epigenetic age’ (EA) derived from DNA methylation (DNAm) and myocardial infarction (MI)/acute coronary syndrome (ACS). A random population sample was examined in 2003/2005 (n = 9360, 45–69, the HAPIEE project) and followed up for 15 years. From this cohort, incident MI/ACS (cases, n = 129) and age- and sex-stratified controls (n = 177) were selected for a nested case-control study. Baseline EA (Horvath’s, Hannum’s, PhenoAge, Skin and Blood) and the differences between EA and chronological age (CA) were calculated (ΔAHr, ΔAHn, ΔAPh, ΔASB). EAs by Horvath’s, Hannum’s and Skin and Blood were close to CA (median absolute difference, MAD, of 1.08, –1.91 and –2.03 years); PhenoAge had MAD of −9.29 years vs. CA. The adjusted odds ratios (ORs) of MI/ACS per 1–year increments of ΔAHr, ΔAHn, ΔASB and ΔAPh were 1.01 (95% CI 0.95–1.07), 1.01 (95% CI 0.95–1.08), 1.02 (95% CI 0.97–1.06) and 1.01 (0.93–1.09), respectively. When classified into tertiles, only the highest tertile of ΔAPh showed a suggestion of increased risk of MI/ACS with OR 2.09 (1.11–3.94) independent of age and 1.84 (0.99–3.52) in the age- and sex-adjusted model. Metabolic modulation may be the likely mechanism of this association. In conclusion, this case-control study nested in a prospective population-based cohort did not find strong associations between accelerated epigenetic age markers and risk of MI/ACS. Larger cohort studies are needed to re-examine this important research question.

Association between Phenotypic Age and Mortality in Patients with Multivessel Coronary Artery Disease

Background

Chronological age (CA) is not a perfect proxy for the true biological aging status of the body. A new biological aging measure, phenotypic age (PhenoAge), has been shown to capture morbidity and mortality risk in the general US population and diverse subpopulations. This study was aimed at evaluating the association between PhenoAge and long-term outcome of patients with multivessel coronary artery disease (CAD).

Methods

A total of 609 multivessel CAD patients who received PCI attempt and with follow-up were enrolled. The clinical outcome was all-cause mortality on follow-up. PhenoAge was calculated using an equation constructed from CA and 9 clinical biomarkers. Cox proportional hazards regression models and receiver operating characteristic (ROC) curves were performed to evaluate the association between PhenoAge and mortality.

Results

Overall, patients with more diseases had older PhenoAge and phenotypic age acceleration (PhenoAgeAccel). After a median follow-up of 33.5 months, those with positive PhenoAgeAccel had a significantly higher incidence of all-cause mortality (P = 0.001). After adjusting for CA, Cox proportional hazards models showed that both PhenoAge and PhenoAgeAccel were significantly associated with all-cause mortality. Even after further adjusting for confounding factors, each 10-year increase in PhenoAge was also associated with a 51% increased mortality risk. ROC curves revealed that PhenoAge, with an area under the curve of 0.705, significantly outperformed CA, the individual clinical chemistry measure, and other risk factors. When reexamining the ROC curves using various combinations of variables, we found that PhenoAge provides additional predictive power to all models.

Conclusions

In conclusion, PhenoAge was strongly associated with all-cause mortality even after adjusting for CA. Our findings suggest that PhenoAge measure may be complementary in predicting mortality risk for patients with multivessel CAD.

Associations Between Blood Pressure and Accelerated DNA Methylation Aging

Background

Individuals of the same chronological age may exhibit diverse susceptibilities to death. However, few studies have investigated the associations between blood pressure and the accelerated aging.

Methods and Results

A cross‐sectional study was conducted in 288 adults aged ≥50 years. We assessed the DNA methylation‐based measures of biological age using CpG sites on the Illumina HumanMethylationEPIC BeadChip. Epigenetic age acceleration metrics were derived by regressing residuals (ΔAge) and ratios (aging rate) of DNA methylation age on chronological age. Dose‐response relationships between blood pressure and epigenetic age acceleration were quantified using multiple linear regression and restricted cubic regression models. We found that each 10–mm Hg increase in systolic blood pressure was associated with 0.608 (95% CI, 0.231–0.984) years increase in ΔAge and 0.007 (95% CI, 0.002–0.012) increase in aging rate; meanwhile, for pulse pressure, the increase was 1.12 (95% CI, 0.625–1.61) years for ΔAge and 0.013 (95% CI, 0.007–0.020) for aging rate. Subgroup analysis showed that the significant associations of systolic blood pressure and pulse pressure with epigenetic age acceleration appeared to be limited to women, although interactions between blood pressure and sex were not significant (P values for interaction >0.05). The combination of women and hypertension was associated with a much higher increase in ΔAge (β [95% CI], 4.05 [1.07–7.02]) and aging rate (β [95% CI], 0.047 [0.008–0.087]), compared with male participants without hypertension.

Conclusions

Our findings suggested that high systolic blood pressure and pulse pressure were associated with the epigenetic age acceleration, providing important clues for relationships between blood pressure and epigenetic aging.

DNA methylation aging and transcriptomic studies in horses

Cytosine methylation patterns have not yet been thoroughly studied in horses. Here, we profile n = 333 samples from 42 horse tissue types at loci that are highly conserved between mammalian species using a custom array (HorvathMammalMethylChip40). Using the blood and liver tissues from horses, we develop five epigenetic aging clocks: a multi-tissue clock, a blood clock, a liver clock and two dual-species clocks that apply to both horses and humans. In addition, using blood methylation data from three additional equid species (plains zebra, Grevy's zebras and Somali asses), we develop another clock that applies across all equid species. Castration does not significantly impact the epigenetic aging rate of blood or liver samples from horses. Methylation and RNA data from the same tissues define the relationship between methylation and RNA expression across horse tissues. We expect that the multi-tissue atlas will become a valuable resource.

Overview of Polyamines as Nutrients for Human Healthy Long Life and Effect of Increased Polyamine Intake on DNA Methylation

Polyamines, spermidine and spermine, are synthesized in every living cell and are therefore contained in foods, especially in those that are thought to contribute to health and longevity. They have many physiological activities similar to those of antioxidant and anti-inflammatory substances such as polyphenols. These include antioxidant and anti-inflammatory properties, cell and gene protection, and autophagy activation. We have first reported that increased polyamine intake (spermidine much more so than spermine) over a long period increased blood spermine levels and inhibited aging-associated pathologies and pro-inflammatory status in humans and mice and extended life span of mice. However, it is unlikely that the life-extending effect of polyamines is exerted by the same bioactivity as polyphenols because most studies using polyphenols and antioxidants have failed to demonstrate their life-extending effects. Recent investigations revealed that aging-associated pathologies and lifespan are closely associated with DNA methylation, a regulatory mechanism of gene expression. There is a close relationship between polyamine metabolism and DNA methylation. We have shown that the changes in polyamine metabolism affect the concentrations of substances and enzyme activities involved in DNA methylation. I consider that the increased capability of regulation of DNA methylation by spermine is a key of healthy long life of humans.

Using Machine Learning to Predict Obesity Based on Genome-Wide and Epigenome-Wide Gene-Gene and Gene-Diet Interactions

Obesity is associated with many chronic diseases that impair healthy aging and is governed by genetic, epigenetic, and environmental factors and their complex interactions. This study aimed to develop a model that predicts an individual's risk of obesity by better characterizing these complex relations and interactions focusing on dietary factors. For this purpose, we conducted a combined genome-wide and epigenome-wide scan for body mass index (BMI) and up to three-way interactions among 402,793 single nucleotide polymorphisms (SNPs), 415,202 DNA methylation sites (DMSs), and 397 dietary and lifestyle factors using the generalized multifactor dimensionality reduction (GMDR) method. The training set consisted of 1,573 participants in exam 8 of the Framingham Offspring Study (FOS) cohort. After identifying genetic, epigenetic, and dietary factors that passed statistical significance, we applied machine learning (ML) algorithms to predict participants' obesity status in the test set, taken as a subset of independent samples (n = 394) from the same cohort. The quality and accuracy of prediction models were evaluated using the area under the receiver operating characteristic curve (ROC-AUC). GMDR identified 213 SNPs, 530 DMSs, and 49 dietary and lifestyle factors as significant predictors of obesity. Comparing several ML algorithms, we found that the stochastic gradient boosting model provided the best prediction accuracy for obesity with an overall accuracy of 70%, with ROC-AUC of 0.72 in test set samples. Top predictors of the best-fit model were 21 SNPs, 230 DMSs in genes such as CPT1A, ABCG1, SLC7A11, RNF145, and SREBF1, and 26 dietary factors, including processed meat, diet soda, French fries, high-fat dairy, artificial sweeteners, alcohol intake, and specific nutrients and food components, such as calcium and flavonols. In conclusion, we developed an integrated approach with ML to predict obesity using omics and dietary data. This extends our knowledge of the drivers of obesity, which can inform precision nutrition strategies for the prevention and treatment of obesity. Clinical Trial Registration: [www.ClinicalTrials.gov], the Framingham Heart Study (FHS), [NCT00005121].

Epigenetic biomarkers of ageing are predictive of mortality risk in a longitudinal clinical cohort of individuals diagnosed with oropharyngeal cancer

BACKGROUND

Epigenetic clocks are biomarkers of ageing derived from DNA methylation levels at a subset of CpG sites. The difference between age predicted by these clocks and chronological age, termed "epigenetic age acceleration", has been shown to predict age-related disease and mortality. We aimed to assess the prognostic value of epigenetic age acceleration and a DNA methylation-based mortality risk score with all-cause mortality in a prospective clinical cohort of individuals with head and neck cancer: Head and Neck 5000. We investigated two markers of intrinsic epigenetic age acceleration (IEAAHorvath and IEAAHannum), one marker of extrinsic epigenetic age acceleration (EEAA), one optimised to predict physiological dysregulation (AgeAccelPheno), one optimised to predict lifespan (AgeAccelGrim) and a DNA methylation-based predictor of mortality (ZhangScore). Cox regression models were first used to estimate adjusted hazard ratios (HR) and 95% confidence intervals (CI) for associations of epigenetic age acceleration with all-cause mortality in people with oropharyngeal cancer (n = 408; 105 deaths). The added prognostic value of epigenetic markers compared to a clinical model including age, sex, TNM stage and HPV status was then evaluated.

RESULTS

IEAAHannum and AgeAccelGrim were associated with mortality risk after adjustment for clinical and lifestyle factors (HRs per standard deviation [SD] increase in age acceleration = 1.30 [95% CI 1.07, 1.57; p = 0.007] and 1.40 [95% CI 1.06, 1.83; p = 0.016], respectively). There was weak evidence that the addition of AgeAccelGrim to the clinical model improved 3-year mortality prediction (area under the receiver operating characteristic curve: 0.80 vs. 0.77; p value for difference = 0.069).

CONCLUSION

In the setting of a large, clinical cohort of individuals with head and neck cancer, our study demonstrates the potential of epigenetic markers of ageing to enhance survival prediction in people with oropharyngeal cancer, beyond established prognostic factors. Our findings have potential uses in both clinical and non-clinical contexts: to aid treatment planning and improve patient stratification.

Emerging rejuvenation strategies-Reducing the biological age

Several interventions have recently emerged that were proposed to reverse rather than just attenuate aging, but the criteria for what it takes to achieve rejuvenation remain controversial. Distinguishing potential rejuvenation therapies from other longevity interventions, such as those that slow down aging, is challenging, and these anti-aging strategies are often referred to interchangeably. We suggest that the prerequisite for a rejuvenation intervention is a robust, sustained, and systemic reduction in biological age, which can be assessed by biomarkers of aging, such as epigenetic clocks. We discuss known and putative rejuvenation interventions and comparatively analyze them to explore underlying mechanisms.

Associations between alcohol use and accelerated biological ageing

Harmful alcohol use is a leading cause of premature death and is associated with age-related disease. Biological ageing is highly variable between individuals and may deviate from chronological ageing, suggesting that biomarkers of biological ageing (derived from DNA methylation or brain structural measures) may be clinically relevant. Here, we investigated the relationships between alcohol phenotypes and both brain and DNA methylation age estimates. First, using data from UK Biobank and Generation Scotland, we tested the association between alcohol consumption (units/week) or hazardous use (Alcohol Use Disorders Identification Test [AUDIT] scores) and accelerated brain and epigenetic ageing in 20,258 and 8051 individuals, respectively. Second, we used Mendelian randomisation (MR) to test for a causal effect of alcohol consumption levels and alcohol use disorder (AUD) on biological ageing. Alcohol use showed a consistent positive association with higher predicted brain age (AUDIT-C: β = 0.053, p = 3.16 × 10-13 ; AUDIT-P: β = 0.052, p = 1.6 × 10-13 ; total AUDIT score: β = 0.062, p = 5.52 × 10-16 ; units/week: β = 0.078, p = 2.20 × 10-16 ), and two DNA methylation-based estimates of ageing, GrimAge (units/week: β = 0.053, p = 1.48 × 10-7 ) and PhenoAge (units/week: β = 0.077, p = 2.18x10-10 ). MR analyses revealed limited evidence for a causal effect of AUD on accelerated brain ageing (β = 0.118, p = 0.044). However, this result should be interpreted cautiously as the significant effect was driven by a single genetic variant. We found no evidence for a causal effect of alcohol consumption levels on accelerated biological ageing. Future studies investigating the mechanisms associating alcohol use with accelerated biological ageing are warranted.

Genetic variation influencing DNA methylation provides insights into molecular mechanisms regulating genomic function

We determined the relationships between DNA sequence variation and DNA methylation using blood samples from 3,799 Europeans and 3,195 South Asians. We identify 11,165,559 SNP-CpG associations (methylation quantitative trait loci (meQTL), P < 10-14), including 467,915 meQTL that operate in trans. The meQTL are enriched for functionally relevant characteristics, including shared chromatin state, High-throuhgput chromosome conformation interaction, and association with gene expression, metabolic variation and clinical traits. We use molecular interaction and colocalization analyses to identify multiple nuclear regulatory pathways linking meQTL loci to phenotypic variation, including UBASH3B (body mass index), NFKBIE (rheumatoid arthritis), MGA (blood pressure) and COMMD7 (white cell counts). For rs6511961 , chromatin immunoprecipitation followed by sequencing (ChIP-seq) validates zinc finger protein (ZNF)333 as the likely trans acting effector protein. Finally, we used interaction analyses to identify population- and lineage-specific meQTL, including rs174548 in FADS1, with the strongest effect in CD8+ T cells, thus linking fatty acid metabolism with immune dysregulation and asthma. Our study advances understanding of the potential pathways linking genetic variation to human phenotype.

Epigenetic aging biomarkers and occupational exposure to benzene, trichloroethylene and formaldehyde

Epigenetic aging biomarkers are associated with increased morbidity and mortality. We evaluated if occupational exposure to three established chemical carcinogens is associated with acceleration of epigenetic aging. We studied workers in China occupationally exposed to benzene, trichloroethylene (TCE) or formaldehyde by measuring personal air exposures prior to blood collection. Unexposed controls matched by age and sex were selected from nearby factories. We measured leukocyte DNA methylation (DNAm) in peripheral white blood cells using the Infinium HumanMethylation450 BeadChip to calculate five epigenetic aging clocks and DNAmTL, a biomarker associated with leukocyte telomere length and cell replication. We tested associations between exposure intensity and epigenetic age acceleration (EAA), defined as the residuals of regressing the DNAm aging biomarker on chronological age, matching factors and potential confounders. Median differences in EAA between exposure groups were tested using a permutation test with exact p-values. Epigenetic clocks were strongly correlated with age (Spearman r > 0.8) in all three occupational studies. There was a positive exposure-response relationship between benzene and the Skin-Blood Clock EAA biomarker: median EAA was -0.91 years in controls (n = 44), 0.78 years in workers exposed to <10 ppm (n = 41; mean benzene = 1.35 ppm; p = 0.034 vs. controls), and 2.10 years in workers exposed to ≥10 ppm (n = 9; mean benzene = 27.3 ppm; p = 0.019 vs. controls; p_trend = 0.0021). In the TCE study, control workers had a median Skin-Blood Clock EAA of -0.54 years (n = 71) compared to 1.63 years among workers exposed to <10 ppm of TCE (n = 27; mean TCE = 4.22 ppm; p = 0.035). We observed no evidence of EAA associations with formaldehyde exposure (39 controls, 31 exposed). Occupational benzene and TCE exposure were associated with increased epigenetic age acceleration measured by the Skin-Blood Clock. For TCE, there was some evidence of epigenetic age acceleration for lower exposures compared to controls. Our results suggest that some chemical carcinogens may accelerate epigenetic aging.

Unstable Childhood, Adult Adversity, and Smoking Accelerate Biological Aging Among Middle-Age African Americans: Similar Findings for GrimAge and PoAm

Objectives: The recent biological clocks GrimAge and PoAm are robust predictors of morbidity and mortality. Little research, however, has investigated the factors that influence their ticking speed. No study has used multivariate analyses to examine whether childhood adversity, adult hardship, lifestyle practices, or some combination of these factors best explains acceleration of these indices. Methods: Using a sample of 506 middle-age African Americans, the present study investigated the extent to which childhood instability, adult adversity, and lifestyle predict accelerated GrimAge and PoAm. Results: The two clocks were highly correlated and the pattern of findings was very similar for the two measures. Childhood instability, adult financial hardship, and smoking were significant predictors of both clocks. Discussion: The findings support a life course perspective where both the long arm of childhood as well as later life conditions influence speed of aging. Similar results across the two clocks enhance confidence in the findings.

Epigenetic models developed for plains zebras predict age in domestic horses and endangered equids

Effective conservation and management of threatened wildlife populations require an accurate assessment of age structure to estimate demographic trends and population viability. Epigenetic aging models are promising developments because they estimate individual age with high accuracy, accurately predict age in related species, and do not require invasive sampling or intensive long-term studies. Using blood and biopsy samples from known age plains zebras (Equus quagga), we model epigenetic aging using two approaches: the epigenetic clock (EC) and the epigenetic pacemaker (EPM). The plains zebra EC has the potential for broad application within the genus Equus given that five of the seven extant wild species of the genus are threatened. We test the EC's ability to predict age in sister taxa, including two endangered species and the more distantly related domestic horse, demonstrating high accuracy in all cases. By comparing chronological and estimated age in plains zebras, we investigate age acceleration as a proxy of health status. An interaction between chronological age and inbreeding is associated with age acceleration estimated by the EPM, suggesting a cumulative effect of inbreeding on biological aging throughout life.

Genome-wide DNA methylation profiling in nonagenarians suggests an effect of PM20D1 in late onset Alzheimer's disease

Background

The aim of this study is to identify differentially methylated regions (DMRs) in the genomes of a sample of cognitively healthy individuals and a sample of individuals with LOAD, all of them nonagenarians from Costa Rica.

Methods

In this study, we compared whole blood DNA methylation profiles of 32 individuals: 21 cognitively healthy and 11 with LOAD, using the Infinium MethylationEPIC BeadChip. First, we calculated the epigenetic age of the participants based on Horvath’s epigenetic clock. DMRcate and Bumphunter were used to identify DMRs. After in silico and knowledge-based filtering of the DMRs, we performed a methylation quantitative loci (mQTL) analysis (rs708727 and rs960603).

Results

On average, the epigenetic age was 73 years in both groups, which represents a difference of over 20 years between epigenetic and chronological age in both affected and unaffected individuals. Methylation analysis revealed 11 DMRs between groups, which contain six genes and two pseudogenes. These genes are involved in cell cycle regulation, embryogenesis, synthesis of ceramides, and migration of interneurons to the cerebral cortex. One of the six genes is PM20D1, for which altered expression has been reported in LOAD. After genotyping previously reported mQTL SNPs for the gene, we found that average methylation in the PM20D1 DMR differs between genotypes for rs708727, but not for rs960603.

Conclusions

This work supports the possible role of PM20D1 in protection against AD, by showing differential methylation in blood of affected and unaffected nonagenarians. Our results also support the influence of genetic factors on PM20D1 methylation levels.

DNA methylation in genes associated with the evolution of ageing and disease: A critical review

Ageing is characterised by a physical decline in biological functioning which results in a progressive risk of mortality with time. As a biological phenomenon, it is underpinned by the dysregulation of a myriad of complex processes. Recently, however, ever-increasing evidence has associated epigenetic mechanisms, such as DNA methylation (DNAm) with age-onset pathologies, including cancer, cardiovascular disease, and Alzheimer's disease. These diseases compromise healthspan. Consequently, there is a medical imperative to understand the link between epigenetic ageing, and healthspan. Evolutionary theory provides a unique way to gain new insights into epigenetic ageing and health. This review will: (1) provide a brief overview of the main evolutionary theories of ageing; (2) discuss recent genetic evidence which has revealed alleles that have pleiotropic effects on fitness at different ages in humans; (3) consider the effects of DNAm on pleiotropic alleles, which are associated with age related disease; (4) discuss how age related DNAm changes resonate with the mutation accumulation, disposable soma and programmed theories of ageing; (5) discuss how DNAm changes associated with caloric restriction intersect with the evolution of ageing; and (6) conclude by discussing how evolutionary theory can be used to inform investigations which quantify age-related DNAm changes which are linked to age onset pathology.

Epidemiological and genetic overlap among biological aging clocks: New challenges in biogerontology

Estimators of biological age (BA) - defined as the hypothetical underlying age of an organism - have attracted more and more attention in the last years, especially after the advent of new algorithms based on machine learning and genetic markers. While different aging clocks reportedly predict mortality in the general population, very little is known on their overlap. Here we review the evidence reported so far to support the existence of a partial overlap among different BA acceleration estimators, both from an epidemiological and a genetic perspective. On the epidemiological side, we review evidence supporting shared and independent influence on mortality risk of different aging clocks - including telomere length, brain, blood and epigenetic aging - and provide an overview of how an important exposure like diet may affect the different aging systems. On the genetic side, we apply linkage disequilibrium score regression analyses to support the existence of partly shared genomic overlap among these aging clocks. Through multivariate analysis of published genetic associations with these clocks, we also identified the most associated variants, genes, and pathways, which may affect common mechanisms underlying biological aging of different systems within the body. Based on our analyses, the most implicated pathways were involved in inflammation, lipid and carbohydrate metabolism, suggesting them as potential molecular targets for future anti-aging interventions. Overall, this review is meant as a contribution to the knowledge on the overlap of aging clocks, trying to clarify their shared biological basis and epidemiological implications.

Characterization of cerebrospinal fluid DNA methylation age during the acute recovery period following aneurysmal subarachnoid hemorrhage

Background

Biological aging may occur at different rates than chronological aging due to genetic, social, and environmental factors. DNA methylation (DNAm) age is thought to be a reliable measure of accelerated biological aging which has been linked to an array of poor health outcomes. Given the importance of chronological age in recovery following aneurysmal subarachnoid hemorrhage (aSAH), a type of stroke, DNAm age may also be an important biomarker of outcomes, further improving predictive models. Cerebrospinal fluid (CSF) is a unique tissue representing the local central nervous system environment post-aSAH. However, the validity of CSF DNAm age is unknown, and it is unclear which epigenetic clock is ideal to compute CSF DNAm age, particularly given changes in cell type heterogeneity (CTH) during the acute recovery period. Further, the stability of DNAm age post-aSAH, specifically, has not been examined and may improve our understanding of patient recovery post-aSAH. Therefore, the purpose of this study was to characterize CSF DNAm age over 14 days post-aSAH using four epigenetic clocks.

Results

Genome-wide DNAm data were available for two tissues: (1) CSF for N = 273 participants with serial sampling over 14 days post-aSAH (N = 850 samples) and (2) blood for a subset of n = 72 participants at one time point post-aSAH. DNAm age was calculated using the Horvath, Hannum, Levine, and “Improved Precision” (Zhang) epigenetic clocks. “Age acceleration” was computed as the residuals of DNAm age regressed on chronological age both with and without correcting for CTH. Using scatterplots, Pearson correlations, and group-based trajectory analysis, we examined the relationships between CSF DNAm age and chronological age, the concordance between DNAm ages calculated from CSF versus blood, and the stability (i.e., trajectories) of CSF DNAm age acceleration over time during recovery from aSAH. We observed moderate to strong correlations between CSF DNAm age and chronological age (R = 0.66 [Levine] to R = 0.97 [Zhang]), moderate to strong correlations between DNAm age in CSF versus blood (R = 0.69 [Levine] to R = 0.98 [Zhang]), and stable CSF age acceleration trajectories over 14 days post-aSAH in the Horvath and Zhang clocks (unadjusted for CTH), as well as the Hannum clock (adjusted for CTH).

Conclusions

CSF DNAm age was generally stable post-aSAH. Although correlated, CSF DNAm age differs from blood DNAm age in the Horvath, Hannum, and Levine clocks, but not in the Zhang clock. Taken together, our results suggest that, of the clocks examined here, the Zhang clock is the most robust to CTH and is recommended for use in complex tissues such as CSF.

The "cognitive clock": A novel indicator of brain health

INTRODUCTION

We identified a "cognitive clock," a novel indicator of brain health that provides person-specific estimates of cognitive age, and tested the hypothesis that cognitive age is a better predictor of brain health than chronological age in two independent datasets.

METHODS

The initial analyses were based on 1057 participants from the Rush Memory and Aging Project and the Religious Orders Study who began without impairment and underwent cognitive assessments up to 24 years. A shape invariant model characterized the latent pattern of cognitive decline, conceptualized here as the "cognitive clock," and yielded person-specific estimates of cognitive age. Survival analyses examined cognitive versus chronological age for predicting Alzheimer's disease dementia, mild cognitive impairment and mortality, and regression analyses examined associations of cognitive versus chronological age with neuropathology and brain atrophy. Finally, we applied the cognitive clock to an independent validation sample of 2592 participants from the Chicago Health and Aging Project, a biracial population-based study, to confirm the predictive utility of cognitive age.

RESULTS

The "cognitive clock" showed that cognition remained stable until a cognitive age of about 80, then declined moderately until 90, then declined precipitously. In the initial dataset, cognitive age was a better predictor of dementia, mild cognitive impairment and mortality than chronological age, and was more strongly associated with neuropathology and brain atrophy. Application of the cognitive clock to the independent validation sample provided further support for the utility of cognitive age as a strong prognostic indicator of adverse outcomes.

DISCUSSION

Cognitive age is a robust prognostic indicator of adverse health outcomes and may serve as a useful biomarker in aging research.

The Interplay of Vitamin D Deficiency and Cellular Senescence in The Pathogenesis of Obesity-Related Co-Morbidities

This scoping review aims to clarify the interplay between obesity, vitamin D deficiency, cellular senescence, and obesity-related metabolic consequences, mainly subclinical atherosclerosis, and non-alcoholic fatty liver disease (NAFLD). Obesity is a significant global health problem that involves cellular, environmental, behavioral, and genetic elements. The fundamental cause of obesity throughout all life stages is an energy imbalance, and its consequences are countless and, foremost, very common. Obesity has been comprehensively studied in the literature given its association with low serum vitamin D, with many proposed mechanisms linking the two conditions. Moreover, markers of exaggerated cellular senescence have been proven to accumulate in obese individuals. Subclinical atherosclerosis initiates an early stage that ends in serious cardiac events, and obesity, low vitamin D, and senescent cells largely contribute to its associated chronic low-grade inflammation. Furthermore, NAFLD signifies the hepatic manifestation of metabolic syndrome, and studies have highlighted the important role of obesity, vitamin D deficiency, and cellular senescence in its development. Therefore, we outlined the most important mechanisms tying these conditions to one another.

DNA Methylation in Genetic and Sporadic Forms of Neurodegeneration: Lessons from Alzheimer's, Related Tauopathies and Genetic Tauopathies

Genetic and sporadic forms of tauopathies, the most prevalent of which is Alzheimer's Disease, are a scourge of the aging society, and in the case of genetic forms, can also affect children and young adults. All tauopathies share ectopic expression, mislocalization, or aggregation of the microtubule associated protein TAU, encoded by the MAPT gene. As TAU is a neuronal protein widely expressed in the CNS, the overwhelming majority of tauopathies are neurological disorders. They are characterized by cognitive dysfunction often leading to dementia, and are frequently accompanied by movement abnormalities such as parkinsonism. Tauopathies can lead to severe neurological deficits and premature death. For some tauopathies there is a clear genetic cause and/or an epigenetic contribution. However, for several others the disease etiology is unclear, with few tauopathies being environmentally triggered. Here, we review current knowledge of tauopathies listing known genetic and important sporadic forms of these disease. Further, we discuss how DNA methylation as a major epigenetic mechanism emerges to be involved in the disease pathophysiology of Alzheimer's, and related genetic and non-genetic tauopathies. Finally, we debate the application of epigenetic signatures in peripheral blood samples as diagnostic tools and usages of epigenetic therapy strategies for these diseases.

Predicting physiological aging rates from a range of quantitative traits using machine learning

It is widely thought that individuals age at different rates. A method that measures “physiological age” or physiological aging rate independent of chronological age could therefore help elucidate mechanisms of aging and inform an individual’s risk of morbidity and mortality. Here we present machine learning frameworks for inferring individual physiological age from a broad range of biochemical and physiological traits including blood phenotypes (e.g., high-density lipoprotein), cardiovascular functions (e.g., pulse wave velocity) and psychological traits (e.g., neuroticism) as main groups in two population cohorts SardiNIA (~6,100 participants) and InCHIANTI (~1,400 participants). The inferred physiological age was highly correlated with chronological age (R2 > 0.8). We further defined an individual’s physiological aging rate (PAR) as the ratio of the predicted physiological age to the chronological age. Notably, PAR was a significant predictor of survival, indicating an effect of aging rate on mortality. Our trait-based PAR was correlated with DNA methylation-based epigenetic aging score (r = 0.6), suggesting that both scores capture a common aging process. PAR was also substantially heritable (h2~0.3), and a subsequent genome-wide association study of PAR identified significant associations with two genetic loci, one of which is implicated in telomerase activity. Our findings support PAR as a proxy for an underlying whole-body aging mechanism. PAR may thus be useful to evaluate the efficacy of treatments that target aging-related deficits and controllable epidemiological factors.

DNA methylation as a tool to explore ageing in wild roe deer populations

DNA methylation‐based biomarkers of ageing (epigenetic clocks) promise to lead to new insights into evolutionary biology of ageing. Relatively little is known about how the natural environment affects epigenetic ageing effects in wild species. In this study, we took advantage of a unique long‐term (>40 years) longitudinal monitoring of individual roe deer (Capreolus capreolus) living in two wild populations (Chizé and Trois‐Fontaines, France) facing different ecological contexts, to investigate the relationship between chronological age and levels of DNA methylation (DNAm). We generated novel DNA methylation data from n = 94 blood samples, from which we extracted leucocyte DNA, using a custom methylation array (HorvathMammalMethylChip40). We present three DNA methylation‐based estimators of age (DNAm or epigenetic age), which were trained in males, females, and both sexes combined. We investigated how sex differences influenced the relationship between DNAm age and chronological age using sex‐specific epigenetic clocks. Our results highlight that old females may display a lower degree of biological ageing than males. Further, we identify the main sites of epigenetic alteration that have distinct ageing patterns between the two sexes. These findings open the door to promising avenues of research at the crossroads of evolutionary biology and biogerontology.

Exposure to ionizing radiation disrupts normal epigenetic aging in Japanese medaka

Alterations to the epigenome are a hallmark of biological aging and age-dependent patterning of the DNA methylome ("epigenetic aging") can be modeled to produce epigenetic age predictors. Rates of epigenetic aging vary amongst individuals and correlate to the onset of age-related disease and all-cause mortality. Yet, the origins of epigenetic-to-chronological age discordance are not empirically resolved. Here, we investigate the relationship between aging, DNA methylation, and environmental exposures in Japanese medaka (Oryzias latipes). We find age-associated DNA methylation patterning enriched in genomic regions of low CpG density and that, similar to mammals, most age-related changes occur during early life. We construct an epigenetic clock capable of predicting chronological age with a mean error of 61.1 days (~8.4% of average lifespan). To test the role of environmental factors in driving epigenetic age variation, we exposed medaka to chronic, environmentally relevant doses of ionizing radiation. Because most organisms share an evolutionary history with ionizing radiation, we hypothesized that exposure would reveal fundamental insights into environment-by-epigenetic aging interactions. Radiation exposure disrupted epigenetic aging by accelerating and decelerating normal age-associated patterning and was most pronounced in cytosines that were moderately associated with age. These findings empirically demonstrate the role of DNA methylation in integrating environmental factors into aging trajectories.

Healthy eating patterns and epigenetic measures of biological age

BACKGROUND

Healthy eating is associated with lower risks of disease and mortality, but the mechanisms underlying these associations are unclear. Age is strongly related to health outcomes, and biological age can be estimated using the blood methylome.

OBJECTIVES

To determine whether healthy eating patterns are associated with methylation-based measures of biological age.

METHODS

Among women in the Sister Study, we calculated scores on 4 recommendation-based healthy eating indexes [Dietary Approaches to Stop Hypertension diet, Healthy Eating Index-2015, Alternative Healthy Eating Index (aHEI-2010), and the Alternative Mediterranean diet] using a validated 110-item Block FFQ completed at enrollment. Genome-wide DNA methylation data were generated using the HumanMethylation450 BeadChip on whole blood samples collected at enrollment from a case-cohort sample of 2694 women and were used to calculate 4 measures of epigenetic age acceleration (Hannum AgeAccel, Horvath AgeAccel, PhenoAgeAccel, and GrimAgeAccel). Linear regression models, adjusted for covariates and cohort sampling weights, were used to examine cross-sectional associations between eating patterns and measures of biological age.

RESULTS

All 4 healthy eating indexes had inverse associations with epigenetic age acceleration, most notably with PhenoAgeAccel and GrimAgeAccel. Of these, the strongest associations were for aHEI-2010 [per 1-SD increase in diet quality, PhenoAgeAccel β = -0.5 y (95% CI: -0.8 to -0.2 y) and GrimAgeAccel β = -0.4 y (95% CI: -0.6 to -0.3 y)]. Although effect modification was not observed for most lifestyle factors, in analyses stratified by physical activity, the benefits of a healthy diet on epigenetic age acceleration were more pronounced among women who did not meet physical activity guidelines (reporting <2.5 h/wk of exercise).

CONCLUSIONS

Higher diet quality is inversely associated with methylation-based measures of biological age. Improving diet could have the most benefits in lowering biological age among women with lower levels of physical activity. This trial was registered at clinicaltrials.gov as NCT00047970.

Idiopathic early ovarian aging: is there a relation with premenopausal accelerated biological aging in young women with diminished response to ART?

PURPOSE

To evaluate whether young women with idiopathic early ovarian aging, as defined by producing fewer oocytes than expected for a given age over multiple in vitro fertilization (IVF) cycles, have changes in telomere length and epigenetic age indicating accelerated biological aging (i.e., increased risk of morbidity and mortality).

METHODS

A prospective cohort study was conducted at two Danish public fertility clinics. A total of 55 young women (≤ 37 years) with at least two IVF cycles with ≤ 5 harvested oocytes despite sufficient stimulation with follicle-stimulating hormone (FSH) were included in the early ovarian aging group. As controls, 52 young women (≤ 37 years) with normal ovarian function, defined by at least eight harvested oocytes, were included. Relative telomere length (rTL) and epigenetic age acceleration (AgeAccel) were measured in white blood cells as markers of premenopausal accelerated biological aging.

RESULTS

rTL was comparable with a mean of 0.46 (± SD 0.12) in the early ovarian aging group and 0.47 (0.14) in the normal ovarian aging group. The AgeAccel of the early ovarian aging group was, insignificantly, 0.5 years older, but this difference disappeared when adjusting for chronological age. Sub-analysis using Anti-Müllerian hormone (AMH) as selection criterion for the two groups did not change the results.

CONCLUSION

We did not find any indications of accelerated aging in whole blood from young women with idiopathic early ovarian aging. Further investigations in a similar cohort of premenopausal women or other tissues are needed to fully elucidate the potential relationship between premenopausal accelerated biological aging and early ovarian aging.

Epigenetic clock and methylation studies in the rhesus macaque

Methylation levels at specific CpG positions in the genome have been used to develop accurate estimators of chronological age in humans, mice, and other species. Although epigenetic clocks are generally species-specific, the principles underpinning them appear to be conserved at least across the mammalian class. This is exemplified by the successful development of epigenetic clocks for mice and several other mammalian species. Here, we describe epigenetic clocks for the rhesus macaque (Macaca mulatta), the most widely used nonhuman primate in biological research. Using a custom methylation array (HorvathMammalMethylChip40), we profiled n = 281 tissue samples (blood, skin, adipose, kidney, liver, lung, muscle, and cerebral cortex). From these data, we generated five epigenetic clocks for macaques. These clocks differ with regard to applicability to different tissue types (pan-tissue, blood, skin), species (macaque only or both humans and macaques), and measure of age (chronological age versus relative age). Additionally, the age-based human-macaque clock exhibits a high age correlation (R = 0.89) with the vervet monkey (Chlorocebus sabaeus), another Old World species. Four CpGs within the KLF14 promoter were consistently altered with age in four tissues (adipose, blood, cerebral cortex, skin). Future studies will be needed to evaluate whether these epigenetic clocks predict age-related conditions in the rhesus macaque.

DNA methylation age analysis of rapamycin in common marmosets

Human DNA methylation data have previously been used to develop highly accurate biomarkers of aging ("epigenetic clocks"). Subsequent studies demonstrate that similar epigenetic clocks can also be developed for mice and many other mammals. Here, we describe epigenetic clocks for common marmosets (Callithrix jacchus) based on novel DNA methylation data generated from highly conserved mammalian CpGs that were profiled using a custom Infinium array (HorvathMammalMethylChip40). From these, we developed and present here two epigenetic clocks for marmosets that are applicable to whole blood samples. We find that the human-marmoset clock for relative age exhibits moderately high age correlations in two other non-human primate species: vervet monkeys and rhesus macaques. In a separate cohort of marmosets, we tested whether intervention with rapamycin, a drug shown to extend lifespan in mice, would alter the epigenetic age of marmosets, as measured by the marmoset epigenetic clocks. These clocks did not detect significant effects of rapamycin on the epigenetic age of marmoset blood. The common marmoset stands out from other mammals in that it is not possible to build accurate estimators of sex based on DNA methylation data: the accuracy of a random forest predictor of sex (66%) was substantially lower than that observed for other mammals (which is close to 100%). Overall, the epigenetic clocks developed here for the common marmoset are expected to be useful for age estimation of wild-born animals and for anti-aging studies in this species.

Epigenetic clock and DNA methylation analysis of porcine models of aging and obesity

DNA-methylation profiles have been used successfully to develop highly accurate biomarkers of age, epigenetic clocks, for many species. Using a custom methylation array, we generated DNA methylation data from n = 238 porcine tissues including blood, bladder, frontal cortex, kidney, liver, and lung, from domestic pigs (Sus scrofa domesticus) and minipigs (Wisconsin Miniature Swine™). Samples used in this study originated from Large White X Landrace crossbred pigs, Large White X Minnesota minipig crossbred pigs, and Wisconsin Miniature Swine™. We present 4 epigenetic clocks for pigs that are distinguished by their compatibility with tissue type (pan-tissue and blood clock) and species (pig and human). Two dual-species human-pig pan-tissue clocks accurately measure chronological age and relative age, respectively. We also characterized CpGs that differ between minipigs and domestic pigs. Strikingly, several genes implicated by our epigenetic studies of minipig status overlap with genes (ADCY3, TFAP2B, SKOR1, and GPR61) implicated by genetic studies of body mass index in humans. In addition, CpGs with different levels of methylation between the two pig breeds were identified proximal to genes involved in blood LDL levels and cholesterol synthesis, of particular interest given the minipig's increased susceptibility to cardiovascular disease compared to domestic pigs. Thus, breed-specific differences of domestic and minipigs may potentially help to identify biological mechanisms underlying weight gain and aging-associated diseases. Our porcine clocks are expected to be useful for elucidating the role of epigenetics in aging and obesity, and the testing of anti-aging interventions.

Epigenetic clock and methylation studies in cats

Human DNA methylation profiles have been used successfully to develop highly accurate biomarkers of aging ("epigenetic clocks"). Although these human epigenetic clocks are not immediately applicable to all species of the animal kingdom, the principles underpinning them appear to be conserved even in animals that are evolutionarily far removed from humans. This is exemplified by recent development of epigenetic clocks for mice and other mammalian species. Here, we describe epigenetic clocks for the domestic cat (Felis catus), based on methylation profiles of CpGs with flanking DNA sequences that are highly conserved between multiple mammalian species. Methylation levels of these CpGs are measured using a custom-designed Infinium array (HorvathMammalMethylChip40). From these, we present 3 epigenetic clocks for cats; of which, one applies only to blood samples from cats, while the remaining two dual-species human-cat clocks apply both to cats and humans. We demonstrate that these domestic cat clocks also lead to high age correlations in cheetahs, tigers, and lions. It is expected that these epigenetic clocks for cats possess the potential to be further developed for monitoring feline health as well as being used for identifying and validating anti-aging interventions.

[The presence of a psychiatric diagnosis could alter the epigenetic clock in monozygotic twins]

BACKGROUND

The acceleration of the epigenetic clock has been associated with a reduction in life expectancy. Individuals diagnosed with a psychiatric disorder have a reduction in life expectancy, and some studies have correlated it with accelerated aging.

OBJECTIVE

The present study aims to explore whether the presence of any psychiatric disorder could accelerate the epigenetic clock in monozygotic twins.

METHODS

A total of 15 pairs of monozygotic twins were included. Epigenetic age in peripheral blood cells was estimated by previously published algorithms, using the 450K Beadchip microarray.

RESULTS

We found that in twins with a diagnosis of a psychiatric disorder, the epigenetic clock could increase compared to their twin without a psychiatric disorder.

DISCUSSION AND CONCLUSION

The presence of some psychiatric disorder could accelerate the epigenetic clock in homozygous twins, but studies with larger samples are required to clarify this relationship.

Investigating the DNA methylation profile of e-cigarette use

BACKGROUND

Little evidence exists on the health effects of e-cigarette use. DNA methylation may serve as a biomarker for exposure and could be predictive of future health risk. We aimed to investigate the DNA methylation profile of e-cigarette use.

RESULTS

Among 117 smokers, 117 non-smokers and 116 non-smoking vapers, we evaluated associations between e-cigarette use and epigenome-wide methylation from saliva. DNA methylation at 7 cytosine-phosphate-guanine sites (CpGs) was associated with e-cigarette use at p < 1 × 10-5 and none at p < 5.91 × 10-8. 13 CpGs were associated with smoking at p < 1 × 10-5 and one at p < 5.91 × 10-8. CpGs associated with e-cigarette use were largely distinct from those associated with smoking. There was strong enrichment of known smoking-related CpGs in the smokers but not the vapers. We also tested associations between e-cigarette use and methylation scores known to predict smoking and biological ageing. Methylation scores for smoking and biological ageing were similar between vapers and non-smokers. Higher levels of all smoking scores and a biological ageing score (GrimAge) were observed in smokers. A methylation score for e-cigarette use showed poor prediction internally (AUC 0.55, 0.41-0.69) and externally (AUC 0.57, 0.36-0.74) compared with a smoking score (AUCs 0.80) and was less able to discriminate lung squamous cell carcinoma from adjacent normal tissue (AUC 0.64, 0.52-0.76 versus AUC 0.73, 0.61-0.85).

CONCLUSIONS

The DNA methylation profile for e-cigarette use is largely distinct from that of cigarette smoking, did not replicate in independent samples, and was unable to discriminate lung cancer from normal tissue. The extent to which methylation related to long-term e-cigarette use translates into chronic effects requires further investigation.

DNA Methylation Age Drift Is Associated with Poor Outcomes and De-Differentiation in Papillary and Follicular Thyroid Carcinomas

Simple Summary

Normal human tissues contain an epigenetic clock resulting from the age-dependent DNA methylation signature, which is the so-called DNA methylation (DNAm) age and can be used to precisely predict chronological age of healthy individuals. Abnormal DNAm age drift has been implicated in cancer risk and pathogenesis. Here, we observed that highly drifted DNAm age (HDDA) occurred in approximately 1/3 tumors derived from patients with papillary and follicular thyroid carcinomas. HDDA is significantly associated with dedifferentiation of tumor cells and poor patient outcomes. Thus, HDDA may serve as a new prognostic factor for thyroid carcinoma.

Abstract

Alterations in global DNA methylation play a critical role in both aging and cancer, and DNA methylation (DNAm) age drift has been implicated in cancer risk and pathogenesis. In the present study, we analyzed the TCGA cohort of papillary and follicular thyroid carcinoma (PTC and FTC) for their DNAm age and association with clinic-pathological features. In 54 noncancerous thyroid (NT) samples, DNAm age was highly correlated with patient chronological age (R² = 0.928, p = 2.6 × 10⁻³¹), but drifted to younger than chronological age in most specimens, especially those from patients >50 years old. DNAm age in 502 tumors was also correlated with patient chronological age, but to a much lesser extent (R² = 0.403). Highly drifted DNAm age (HDDA) was identified in 161 tumors, among which were 101 with DNAm age acceleration while 60 with DNAm age deceleration. Tumors with HDDA were characterized by the robust aberrations in metabolic activities, extracellular microenvironment components and inflammation/immunology responses, and dedifferentiation. Importantly, HDDA in tumors independently predicted shorter disease-free survival of patients. Collectively, NT thyroids from TC patients have younger DNAm age, while HDDA frequently occurs in TCs, and contributes to the TC progression and poor patient outcomes. HDDA may serve as a new prognostic factor for TCs.

Epigenetic clocks in the pediatric population: when and why they tick?

ABSTRACT

Recent research efforts have provided compelling evidence of genome-wide DNA methylation alterations in pediatrics. It is currently well established that epigenetic clocks, composed of DNA methylation sites, can estimate the gestational and chronological age of cells and tissues from different ages. Also, extensive research is aimed at their correlation with early life exposure and pediatric diseases. This review aimed to systematically summarize the epigenetic clocks in the pediatric population. Publications were collected from PubMed and Web of Science databases up to Apr 2021. Epigenetic clocks, DNA methylation clocks, epigenetic age acceleration or deceleration, pediatric and the pediatric population were used as search criteria. Here, we first review the currently applicative pediatric epigenetic clocks. We then highlight the interpretation for epigenetic age deviations in the pediatric population and their association with external factors, developmental trajectories, and pediatric diseases. Considering the remaining unknown of pediatric clocks, research strategies into them are also discussed. In all, pediatric epigenetic clocks may act as potent tools to understand development, growth and diseases in early life.

How Are Epigenetic Modifications Related to Cardiovascular Disease in Older Adults?

The rate of aging has increased globally during recent decades and has led to a rising burden of age-related diseases such as cardiovascular disease (CVD). At the molecular level, epigenetic modifications have been shown recently to alter gene expression during the life course and impair cellular function. In this regard, several CVD risk factors, such as lifestyle and environmental factors, have emerged as key factors in epigenetic modifications within the cardiovascular system. In this study, we attempted to summarized recent evidence related to epigenetic modification, inflammation response, and CVD in older adults as well as the effect of lifestyle modification as a preventive strategy in this age group. Recent evidence showed that lifestyle and environmental factors may affect epigenetic mechanisms, such as DNA methylation, histone acetylation, and miRNA expression. Several substances or nutrients such as selenium, magnesium, curcumin, and caffeine (present in coffee and some teas) could regulate epigenetics. Similarly, physical inactivity, alcohol consumption, air pollutants, psychological stress, and shift working are well-known modifiers of epigenetic patterns. Understanding the exact ways that lifestyle and environmental factors could affect the expression of genes could help to influence the time of incidence and severity of aging-associated diseases. This review highlighted that a healthy lifestyle throughout the life course, such as a healthy diet rich in fibers, vitamins, and essential elements, and specific fatty acids, adequate physical activity and sleep, smoking cessation, and stress control, could be useful tools in preventing epigenetic changes that lead to impaired cardiovascular function.

Cellular reprogramming and epigenetic rejuvenation

Ageing is an inevitable condition that afflicts all humans. Recent achievements, such as the generation of induced pluripotent stem cells, have delivered preliminary evidence that slowing down and reversing the ageing process might be possible. However, these techniques usually involve complete dedifferentiation, i.e. somatic cell identity is lost as cells are converted to a pluripotent state. Separating the rejuvenative properties of reprogramming from dedifferentiation is a promising prospect, termed epigenetic rejuvenation. Reprogramming-induced rejuvenation strategies currently involve using Yamanaka factors (typically transiently expressed to prevent full dedifferentiation) and are promising candidates to safely reduce biological age. Here, we review the development and potential of reprogramming-induced rejuvenation as an anti-ageing strategy.

Epigenetic age prediction

Advanced age is the main common risk factor for cancer, cardiovascular disease and neurodegeneration. Yet, more is known about the molecular basis of any of these groups of diseases than the changes that accompany ageing itself. Progress in molecular ageing research was slow because the tools predicting whether someone aged slowly or fast (biological age) were unreliable. To understand ageing as a risk factor for disease and to develop interventions, the molecular ageing field needed a quantitative measure; a clock for biological age. Over the past decade, a number of age predictors utilising DNA methylation have been developed, referred to as epigenetic clocks. While they appear to estimate biological age, it remains unclear whether the methylation changes used to train the clocks are a reflection of other underlying cellular or molecular processes, or whether methylation itself is involved in the ageing process. The precise aspects of ageing that the epigenetic clocks capture remain hidden and seem to vary between predictors. Nonetheless, the use of epigenetic clocks has opened the door towards studying biological ageing quantitatively, and new clocks and applications, such as forensics, appear frequently. In this review, we will discuss the range of epigenetic clocks available, their strengths and weaknesses, and their applicability to various scientific queries.

A DNA methylation clock associated with age-related illnesses and mortality is accelerated in men with combat PTSD

DNA methylation patterns at specific cytosine-phosphate-guanine (CpG) sites predictably change with age and can be used to derive "epigenetic age", an indicator of biological age, as opposed to merely chronological age. A relatively new estimator, called "DNAm GrimAge", is notable for its superior predictive ability in older populations regarding numerous age-related metrics like time-to-death, time-to-coronary heart disease, and time-to-cancer. PTSD is associated with premature mortality and frequently has comorbid physical illnesses suggestive of accelerated biological aging. This is the first study to assess DNAm GrimAge in PTSD patients. We investigated the acceleration of GrimAge relative to chronological age, denoted "AgeAccelGrim" in combat trauma-exposed male veterans with and without PTSD using cross-sectional and longitudinal data from two independent well-characterized veteran cohorts. In both cohorts, AgeAccelGrim was significantly higher in the PTSD group compared to the control group (N = 162, 1.26 vs -0.57, p = 0.001 and N = 53, 0.93 vs -1.60 Years, p = 0.008), suggesting accelerated biological aging in both cohorts with PTSD. In 3-year follow-up study of individuals initially diagnosed with PTSD (N = 26), changes in PTSD symptom severity were correlated with AgeAccelGrim changes (r = 0.39, p = 0.049). In addition, the loss of CD28 cell surface markers on CD8 + T cells, an indicator of T-cell senescence/exhaustion that is associated with biological aging, was positively correlated with AgeAccelGrim, suggesting an immunological contribution to the accelerated biological aging. Overall, our findings delineate cellular correlates of biological aging in combat-related PTSD, which may help explain the increased medical morbidity and mortality seen in this disease.

Re(Setting) Epigenetic Clocks: An Important Avenue Whereby Social Conditions Become Biologically Embedded across the Life Course

Research on biological embedding of the social environment has been expedited by increased availability of biomarkers. Recently, this arsenal of measures has been expanded to include epigenetic clocks that indicate in years the extent to which an individual is older or younger than their chronological age. These measures of biological aging, especially GrimAge, are robust predictors of both illness and time to death. Importantly for sociologists, several studies have linked social conditions to these indices of aging. The present study extends this research using longitudinal data from a sample of 223 black women participating in the Family and Community Health Study. We find that changes in income and living arrangements over an 11-year period predict changes in speed of biological aging. These results provide further support for the idea that epigenetic aging is a mechanism whereby social conditions become biologically embedded. The utility of epigenetic clocks for sociological studies of health are discussed.

Exploring domains, clinical implications and environmental associations of a deep learning marker of biological ageing

Deep Neural Networks (DNN) have been recently developed for the estimation of Biological Age (BA), the hypothetical underlying age of an organism, which can differ from its chronological age (CA). Although promising, these population-specific algorithms warrant further characterization and validation, since their biological, clinical and environmental correlates remain largely unexplored. Here, an accurate DNN was trained to compute BA based on 36 circulating biomarkers in an Italian population (N = 23,858; age ≥ 35 years; 51.7% women). This estimate was heavily influenced by markers of metabolic, heart, kidney and liver function. The resulting Δage (BA-CA) significantly predicted mortality and hospitalization risk for all and specific causes. Slowed biological aging (Δage < 0) was associated with higher physical and mental wellbeing, healthy lifestyles (e.g. adherence to Mediterranean diet) and higher socioeconomic status (educational attainment, household income and occupational status), while accelerated aging (Δage > 0) was associated with smoking and obesity. Together, lifestyles and socioeconomic variables explained ~48% of the total variance in Δage, potentially suggesting the existence of a genetic basis. These findings validate blood-based biological aging as a marker of public health in adult Italians and provide a robust body of knowledge on its biological architecture, clinical implications and potential environmental influences.

Epigenetic Clocks Are Not Accelerated in COVID-19 Patients

Age is a major risk factor for severe outcome of the 2019 coronavirus disease (COVID-19). In this study, we followed the hypothesis that particularly patients with accelerated epigenetic age are affected by severe outcomes of COVID-19. We investigated various DNA methylation datasets of blood samples with epigenetic aging signatures and performed targeted bisulfite amplicon sequencing. Overall, epigenetic clocks closely correlated with the chronological age of patients, either with or without acute respiratory distress syndrome. Furthermore, lymphocytes did not reveal significantly accelerated telomere attrition. Thus, these biomarkers cannot reliably predict higher risk for severe COVID-19 infection in elderly patients.

Impact of excessive alcohol abuse on age prediction using the VISAGE enhanced tool for epigenetic age estimation in blood

DNA methylation-based clocks provide the most accurate age estimates with practical implications for clinical and forensic genetics. However, the effects of external factors that may influence the estimates are poorly studied. Here, we evaluated the effect of alcohol consumption on epigenetic age prediction in a cohort of extreme alcohol abusers. Blood samples from deceased alcohol abusers and age- and sex-matched controls were analyzed using the VISAGE enhanced tool for age prediction from somatic tissues that enables examination of 44 CpGs within eight age markers. Significantly altered DNA methylation was recorded for alcohol abusers in MIR29B2CHG. This resulted in a mean predicted age of 1.4 years higher compared to the controls and this trend increased in older individuals. The association of alcohol abuse with epigenetic age acceleration, as determined by the prediction analysis performed based on MIR29B2CHG, was small but significant (β = 0.190; P-value = 0.007). However, the observed alteration in DNA methylation of MIR29B2CHG had a non-significant effect on age estimation with the VISAGE age prediction model. The mean absolute error in the alcohol-abusing cohort was 3.1 years, compared to 3.3 years in the control group. At the same time, upregulation of MIR29B2CHG expression may have a biological function, which merits further studies.

Stress-induced aberrations in sensory processing predict worse cognitive outcomes in healthy aging adults

It is well recognized that not all individuals age equivalently, with functional dependence attributable, at least in part, to stress accumulated across the lifespan. Amongst these dependencies are age-related declines in cognitive function, which may be the result of impaired inhibitory processing (e.g., sensory gating). Herein, we examined the unique roles of life and biological stress on somatosensory gating dynamics in 74 adults (22-72 years old). Participants completed a sensory gating paired-pulse electrical stimulation paradigm of the right median nerve during magnetoencephalography (MEG) and data were subjected to advanced oscillatory and time-domain analysis methods. We observed separable mechanisms by which increasing levels of life and biological stress predicted higher oscillatory gating ratios, indicative of age-related impairments in inhibitory function. Specifically, elevations in life stress significantly modulated the neural response to the first stimulation in the pair, while elevations in biological stress significantly modulated the neural response to the second stimulation in the pair. In contrast, neither elevations in life nor biological stress significantly predicted the gating of time-domain neural activity in the somatosensory cortex. Finally, our study is the first to link stress-induced decline in sensory gating to cognitive dysfunction, suggesting that gating paradigms may hold promise for detecting discrepant functional trajectories in age-related pathologies in the future.

Frailty and Biological Age

A reliable model of biological age is instrumental in the field of geriatrics and gerontology. This model should account for the heterogeneity and plasticity of aging and also accurately predict aging-related adverse outcomes. Epigenetic age models are based on DNA methylation levels at selected genomic sites and can be significant predictors of mortality and healthy/unhealthy aging. However, the biological function of DNA methylation at selected sites is yet to be determined. Frailty is a syndrome resulting from decreased physiological reserves and resilience. The frailty index is a probability-based extension of the concept of frailty. Defined as the proportion of health deficits, the frailty index quantifies the progression of unhealthy aging. The frailty index is currently the best predictor of mortality. It is associated with various biological factors and provides insight into the biological processes of aging. Investigation of the multi-omics factors associated with the frailty index will provide further insight.

GrimAge Outperforms Other Epigenetic Clocks in the Prediction of Age-Related Clinical Phenotypes and All-Cause Mortality

The aging process is characterized by the presence of high interindividual variation between individuals of the same chronical age prompting a search for biomarkers that capture this heterogeneity. Epigenetic clocks measure changes in DNA methylation levels at specific CpG sites that are highly correlated with calendar age. The discrepancy resulting from the regression of DNA methylation age on calendar age is hypothesized to represent a measure of biological aging with a positive/negative residual signifying age acceleration (AA)/deceleration, respectively. The present study examines the associations of 4 epigenetic clocks-Horvath, Hannum, PhenoAge, GrimAge-with a wide range of clinical phenotypes (walking speed, grip strength, Fried frailty, polypharmacy, Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MOCA), Sustained Attention Reaction Time, 2-choice reaction time), and with all-cause mortality at up to 10-year follow-up, in a sample of 490 participants in the Irish Longitudinal Study on Ageing (TILDA). HorvathAA and HannumAA were not predictive of health; PhenoAgeAA was associated with 4/9 outcomes (walking speed, frailty MOCA, MMSE) in minimally adjusted models, but not when adjusted for other social and lifestyle factors. GrimAgeAA by contrast was associated with 8/9 outcomes (all except grip strength) in minimally adjusted models, and remained a significant predictor of walking speed, .polypharmacy, frailty, and mortality in fully adjusted models. Results indicate that the GrimAge clock represents a step-improvement in the predictive utility of the epigenetic clocks for identifying age-related decline in an array of clinical phenotypes promising to advance precision medicine.

Prediction of biological age and all-cause mortality by 12-lead electrocardiogram in patients without structural heart disease

Background

There is a well-established relationship between 12-lead electrocardiogram (ECG) and age and mortality. Furthermore, there is increasing evidence that ECG can be used to predict biological age. However, the utility of biological age from ECG for predicting mortality remains unclear.

Methods

This was a single-center cohort study from a cardiology specialized hospital. A total of 19,170 patients registered in this study from February 2010 to March 2018. ECG was analyzed in a final 12,837 patients after excluding those with structural heart disease or with pacing beats, atrial or ventricular tachyarrhythmia, or an indeterminate axis (R axis > 180°) on index ECG. The models for biological age were developed by principal component analysis (BA) and the Klemera and Doubal’s method (not adjusted for age [BA_E] and adjusted for age [BA_EC]) using 438 ECG parameters. The predictive capability for all-cause death and cardiovascular death by chronological age (CA) and biological age using the three algorithms were evaluated by receiver operating characteristic analysis.

Results

During the mean follow-up period of 320.4 days, there were 55 all-cause deaths and 23 cardiovascular deaths. The predictive capabilities for all-cause death by BA, BA_E, and BA_EC using area under the curves were 0.731, 0.657, and 0.685, respectively, which were comparable to 0.725 for CA (p = 0.760, 0.141, and 0.308, respectively). The predictive capabilities for cardiovascular death by BA, BA_E, and BA_EC were 0.682, 0.685, and 0.692, respectively, which were also comparable to 0.674 for CA (p = 0.775, 0.839, and 0.706, respectively). In patients aged 60–74 years old, the area under the curves for all-cause death by BA, BA_E, and BA_EC were 0.619, 0.702, and 0.697, respectively, which tended to be or were significantly higher than 0.482 for CA (p = 0.064, 0.006, and 0.005, respectively).

Conclusion

Biological age by 12-lead ECG showed a similar predictive capability for mortality compared to CA among total patients, but partially showed a significant increase in predictive capability among patients aged 60–74 years old.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-021-02391-8.

Childhood growth and development and DNA methylation age in mid-life

BACKGROUND

In the first study of its kind, we examine the association between growth and development in early life and DNAm age biomarkers in mid-life.

METHODS

Participants were from the Medical Research Council National Survey of Health and Development (n = 1376). Four DNAm age acceleration (AgeAccel) biomarkers were measured when participants were aged 53 years: AgeAccelHannum; AgeAccelHorvath; AgeAccelLevine; and AgeAccelGrim. Exposure variables included: relative weight gain (standardised residuals from models of current weight z-score on current height, and previous weight and height z-scores); and linear growth (standardised residuals from models of current height z-score on previous height and weight z-scores) during infancy (0-2 years, weight gain only), early childhood (2-4 years), middle childhood (4-7 years) and late childhood to adolescence (7-15 years); age at menarche; and pubertal stage for men at 14-15 years. The relationship between relative weight gain and linear growth and AgeAccel was investigated using conditional growth models. We replicated analyses from the late childhood to adolescence period and pubertal timing among 240 participants from The National Child and Development Study (NCDS).

RESULTS

A 1SD increase in relative weight gain in late childhood to adolescence was associated with 0.50 years (95% CI 0.20, 0.79) higher AgeAccelGrim. Although the CI includes the null, the estimate was similar in NCDS [0.57 years (95% CI - 0.01, 1.16)] There was no strong evidence that relative weight gain and linear growth in childhood was associated with any other AgeAccel biomarker. There was no relationship between pubertal timing in men and AgeAccel biomarkers. Women who reached menarche ≥ 12 years had 1.20 years (95% CI 0.15, 2.24) higher AgeAccelGrim on average than women who reached menarche < 12 years; however, this was not replicated in NCDS and was not statistically significant after Bonferroni correction.

CONCLUSIONS

Our findings generally do not support an association between growth and AgeAccel biomarkers in mid-life. However, we found rapid weight gain during pubertal development, previously related to higher cardiovascular disease risk, to be associated with older AgeAccelGrim. Given this is an exploratory study, this finding requires replication.

An epigenetic predictor of death captures multi-modal measures of brain health

Individuals of the same chronological age exhibit disparate rates of biological ageing. Consequently, a number of methodologies have been proposed to determine biological age and primarily exploit variation at the level of DNA methylation (DNAm). A novel epigenetic clock, termed 'DNAm GrimAge' has outperformed its predecessors in predicting the risk of mortality as well as many age-related morbidities. However, the association between DNAm GrimAge and cognitive or neuroimaging phenotypes remains unknown. We explore these associations in the Lothian Birth Cohort 1936 (n = 709, mean age 73 years). Higher DNAm GrimAge was strongly associated with all-cause mortality over the eighth decade (Hazard Ratio per standard deviation increase in GrimAge: 1.81, P < 2.0 × 10-16). Higher DNAm GrimAge was associated with lower age 11 IQ (β = -0.11), lower age 73 general cognitive ability (β = -0.18), decreased brain volume (β = -0.25) and increased brain white matter hyperintensities (β = 0.17). There was tentative evidence for a longitudinal association between DNAm GrimAge and cognitive decline from age 70 to 79. Sixty-nine of 137 health- and brain-related phenotypes tested were significantly associated with GrimAge. Adjusting all models for childhood intelligence attenuated to non-significance a small number of associations (12/69 associations; 6 of which were cognitive traits), but not the association with general cognitive ability (33.9% attenuation). Higher DNAm GrimAge associates with lower cognitive ability and brain vascular lesions in older age, independently of early-life cognitive ability. This epigenetic predictor of mortality associates with different measures of brain health and may aid in the prediction of age-related cognitive decline.