DNA methylation age of blood predicts future onset of lung cancer in the women's health initiative

Epigenetic age predictions based on buccal swabs are more precise in combination with cell type-specific DNA methylation signatures

Aging is reflected by highly reproducible DNA methylation (DNAm) changes that open new perspectives for estimation of chronological age in legal medicine. DNA can be harvested non-invasively from cells at the inside of a person's cheek using buccal swabs - but these specimens resemble heterogeneous mixtures of buccal epithelial cells and leukocytes with different epigenetic makeup. In this study, we have trained an age predictor based on three age-associated CpG sites (associated with the genesPDE4C, ASPA, and ITGA2B) for swab samples to reach a mean absolute deviation (MAD) between predicted and chronological age of 4.3 years in a training set and of 7.03 years in a validation set. Subsequently, the composition of buccal epithelial cells versus leukocytes was estimated by two additional CpGs (associated with the genes CD6 and SERPINB5). Results of this "Buccal-Cell-Signature" correlated with cell counts in cytological stains (R2 = 0.94). Combination of cell type-specific and age-associated CpGs into one multivariate model enabled age predictions with MADs of 5.09 years and 5.12 years in two independent validation sets. Our results demonstrate that the cellular composition in buccal swab samples can be determined by DNAm at two cell type-specific CpGs to improve epigenetic age predictions.

A longitudinal study of DNA methylation as a potential mediator of age-related diabetes risk

DNA methylation (DNAm) has been found to show robust and widespread age-related changes across the genome. DNAm profiles from whole blood can be used to predict human aging rates with great accuracy. We sought to test whether DNAm-based predictions of age are related to phenotypes associated with type 2 diabetes (T2D), with the goal of identifying risk factors potentially mediated by DNAm. Our participants were 43 women enrolled in the Women's Health Initiative. We obtained methylation data via the Illumina 450K Methylation array on whole blood samples from participants at three timepoints, covering on average 16 years per participant. We employed the method and software of Horvath, which uses DNAm at 353 CpGs to form a DNAm-based estimate of chronological age. We then calculated the epigenetic age acceleration, or Δ_age, at each timepoint. We fit linear mixed models to characterize how Δ_age contributed to a longitudinal model of aging and diabetes-related phenotypes and risk factors. For most participants, Δ_age remained constant, indicating that age acceleration is generally stable over time. We found that Δ_age associated with body mass index (p = 0.0012), waist circumference (p = 0.033), and fasting glucose (p = 0.0073), with the relationship with BMI maintaining significance after correction for multiple testing. Replication in a larger cohort of 157 WHI participants spanning 3 years was unsuccessful, possibly due to the shorter time frame covered. Our results suggest that DNAm has the potential to act as a mediator between aging and diabetes-related phenotypes, or alternatively, may serve as a biomarker of these phenotypes.

An epigenetic aging clock for dogs and wolves

Several articles describe highly accurate age estimation methods based on human DNA-methylation data. It is not yet known whether similar epigenetic aging clocks can be developed based on blood methylation data from canids. Using Reduced Representation Bisulfite Sequencing, we assessed blood DNA-methylation data from 46 domesticated dogs (Canis familiaris) and 62 wild gray wolves (C. lupus). By regressing chronological dog age on the resulting CpGs, we defined highly accurate multivariate age estimators for dogs (based on 41 CpGs), wolves (67 CpGs), and both combined (115 CpGs). Age related DNA methylation changes in canids implicate similar gene ontology categories as those observed in humans suggesting an evolutionarily conserved mechanism underlying age-related DNA methylation in mammals.

Statistical and integrative system-level analysis of DNA methylation data

Epigenetics plays a key role in cellular development and function. Alterations to the epigenome are thought to capture and mediate the effects of genetic and environmental risk factors on complex disease. Currently, DNA methylation is the only epigenetic mark that can be measured reliably and genome-wide in large numbers of samples. This Review discusses some of the key statistical challenges and algorithms associated with drawing inferences from DNA methylation data, including cell-type heterogeneity, feature selection, reverse causation and system-level analyses that require integration with other data types such as gene expression, genotype, transcription factor binding and other epigenetic information.

Epigenetic clock analysis in long-term meditators

In this paper, we examined whether meditation practice influences the epigenetic clock, a strong and reproducible biomarker of biological aging, which is accelerated by cumulative lifetime stress and with age-related chronic diseases. Using the Illumina 450K array platform, we analyzed the DNA methylome from blood cells of long-term meditators and meditation-naïve controls to estimate their Intrinsic Epigenetic Age Acceleration (IEAA), using Horvath's calculator. IEAA was similar in both groups. However, controls showed a different IEAA trajectory with aging than meditators: older controls (age≥52) had significantly higher IEAAs compared with younger controls (age <52), while meditators were protected from this epigenetic aging effect. Notably, in the meditation group, we found a significant negative correlation between IEAA and the number of years of regular meditation practice. From our results, we hypothesize that the cumulative effects of a regular meditation practice may, in the long-term, help to slow the epigenetic clock and could represent a useful preventive strategy for age-related chronic diseases. Longitudinal randomized controlled trials in larger cohorts are warranted to confirm and further characterize these findings.

DNA methylation-based measures of biological age: meta-analysis predicting time to death

Estimates of biological age based on DNA methylation patterns, often referred to as "epigenetic age", "DNAm age", have been shown to be robust biomarkers of age in humans. We previously demonstrated that independent of chronological age, epigenetic age assessed in blood predicted all-cause mortality in four human cohorts. Here, we expanded our original observation to 13 different cohorts for a total sample size of 13,089 individuals, including three racial/ethnic groups. In addition, we examined whether incorporating information on blood cell composition into the epigenetic age metrics improves their predictive power for mortality. All considered measures of epigenetic age acceleration were predictive of mortality (p≤8.2x10^-9), independent of chronological age, even after adjusting for additional risk factors (p<5.4x10^-4), and within the racial/ethnic groups that we examined (non-Hispanic whites, Hispanics, African Americans). Epigenetic age estimates that incorporated information on blood cell composition led to the smallest p-values for time to death (p=7.5x10^-43). Overall, this study a) strengthens the evidence that epigenetic age predicts all-cause mortality above and beyond chronological age and traditional risk factors, and b) demonstrates that epigenetic age estimates that incorporate information on blood cell counts lead to highly significant associations with all-cause mortality.

Liquid biopsy for early detection of lung cancer

PURPOSE OF REVIEW

The possibility of complete recovery for a lung cancer patient depends on very early diagnosis, as it allows total surgical resection. Screening for this cancer in a high-risk population can be performed using a radiological approach, but this holds a certain number of limitations. Liquid biopsy could become an alternative and complementary screening approach to chest imaging for early diagnosis of lung cancer.

RECENT FINDINGS

Several circulating biomarkers indicative of lung cancer can be investigated in blood, such as circulating tumor cells, circulating free nucleic acids (RNA and DNA) and proteins. However, none of these biomarkers have yet been adopted in routine clinical practice and studies are ongoing to confirm or not the usefulness and practical interest in routine early diagnosis and screening for lung cancers.

SUMMARY

Several potential circulating biomarkers for the early detection of lung cancer exist. When coupled to thoracic imaging, these biomarkers must give diagnosis of a totally resectable lung cancer and potentially provide new recommendations for surveillance by imagery of high-risk populations without a detectable nodule. Optimization of the specificity and sensitivity of the detection methods as well as standardization of the techniques is essential before considering for daily practice a liquid biopsy as an early diagnostic tool, or possibly as a predictive test, of lung cancer.

Impacts of the Mitochondrial Genome on the Relationship of Long-Term Ambient Fine Particle Exposure with Blood DNA Methylation Age

The mitochondrial genome has long been implicated in age-related disease, but no studies have examined its role in the relationship of long-term fine particle (PM2.5) exposure and DNA methylation age (DNAm-age)-a novel measure of biological age. In this analysis based on 940 observations between 2000 and 2011 from 552 Normative Aging Study participants, we determined the roles of mitochondrial DNA haplogroup variation and mitochondrial genome abundance in the relationship of PM2.5 with DNAm-age. We used the GEOS-chem transport model to estimate address-specific, one-year PM2.5 levels for each participant. DNAm-age and mitochondrial DNA markers were measured from participant blood samples. Nine haplogroups (H, I, J, K, T, U, V, W, and X) were present in the population. In fully adjusted linear mixed-effects models, the association of PM2.5 with DNAm-age (in years) was significantly diminished in carriers of haplogroup V (Pinteraction = 0.01; β = 0.18, 95%CI: -0.41, 0.78) compared to noncarriers (β = 1.25, 95%CI: 0.58, 1.93). Mediation analysis estimated that decreases in mitochondrial DNA copy number, a measure of mitochondrial genome abundance, mediated 12% of the association of PM2.5 with DNAm-age. Our data suggests that the mitochondrial genome plays a role in DNAm-age relationships particularly in the context of long-term PM2.5 exposure.

Editor's Highlight: Modifying Role of Endothelial Function Gene Variants on the Association of Long-Term PM2.5 Exposure With Blood DNA Methylation Age: The VA Normative Aging Study

Recent studies have reported robust associations of long-term PM2.5 exposure with DNA methylation-based measures of aging; yet, the molecular implications of these relationships remain poorly understood. We evaluated if genetic variation in 3 biological pathways implicated in PM2.5-related disease-oxidative stress, endothelial function, and metal processing-could modify the effect of PM2.5 on DNAm-age, one prominent DNA methylation-based measure of biological age. This analysis was based on 552 individuals from the Normative Aging Study with at least one visit between 2000 and 2011 (n = 940 visits). A genetic-score approach was used to calculate aging-risk variant scores for endothelial function, oxidative stress, and metal processing pathways. One-year PM2.5 and PM2.5 component (sulfate and ammonium) levels at participants' addresses were estimated using the GEOS-chem transport model. Blood DNAm-age was calculated using CpG sites on the Illumina HumanMethylation450 BeadChip. In fully-adjusted linear mixed-effects models, the effects of sulfate on DNAm-age (in years) were greater in individuals with high aging-risk endothelial function variant scores when compared with individuals with low aging-risk endothelial function variant scores (Pinteraction = 0.0007; βHigh = 1.09, 95% CIHigh: 0.70, 1.48; βLow = 0.40, 95% CILow: 0.14, 0.67). Similar trends were observed in fully adjusted models of ammonium and total PM2.5 alone. No effect modification was observed by oxidative stress and metal processing variant scores. Secondary analyses revealed significant associations of serum endothelial markers, intercellular adhesion molecule-1 (β = 0.01, 95% CI: 0.002, 0.012) and vascular cell adhesion molecule-1 (β = 0.002, 95% CI: 0.0005, 0.0026), with DNAm-age. Our results add novel evidence that endothelial physiology may be important to DNAm-age relationships, but further research is required to establish their generalizability.

DNA Methylation Profiling of Human Prefrontal Cortex Neurons in Heroin Users Shows Significant Difference between Genomic Contexts of Hyper- and Hypomethylation and a Younger Epigenetic Age

We employed Illumina 450 K Infinium microarrays to profile DNA methylation (DNAm) in neuronal nuclei separated by fluorescence-activated sorting from the postmortem orbitofrontal cortex (OFC) of heroin users who died from heroin overdose (N = 37), suicide completers (N = 22) with no evidence of heroin use and from control subjects who did not abuse illicit drugs and died of non-suicide causes (N = 28). We identified 1298 differentially methylated CpG sites (DMSs) between heroin users and controls, and 454 DMSs between suicide completers and controls (p < 0.001). DMSs and corresponding genes (DMGs) in heroin users showed significant differences in the preferential context of hyper and hypo DM. HyperDMSs were enriched in gene bodies and exons but depleted in promoters, whereas hypoDMSs were enriched in promoters and enhancers. In addition, hyperDMGs showed preference for genes expressed specifically by glutamatergic as opposed to GABAergic neurons and enrichment for axonogenesis- and synaptic-related gene ontology categories, whereas hypoDMGs were enriched for transcription factor activity- and gene expression regulation-related terms. Finally, we found that the DNAm-based “epigenetic age” of neurons from heroin users was younger than that in controls. Suicide-related results were more difficult to interpret. Collectively, these findings suggest that the observed DNAm differences could represent functionally significant marks of heroin-associated plasticity in the OFC.

Associations between long-term exposure to PM2.5 component species and blood DNA methylation age in the elderly: The VA normative aging study

BACKGROUND

Long-term PM2.5 exposure and aging have been implicated in multiple shared diseases; studying their relationship is a promising strategy to further understand the adverse impact of PM2.5 on human health.

OBJECTIVE

We assessed the relationship of major PM2.5 component species (ammonium, elemental carbon, organic carbon, nitrate, and sulfate) with Horvath and Hannum DNA methylation (DNAm) age, two DNA methylation-based predictors of chronological age.

METHODS

This analysis included 552 participants from the Normative Aging Study with multiple visits between 2000 and 2011 (n=940 visits). We estimated 1-year PM2.5 species levels at participants' addresses using the GEOS-chem transport model. Blood DNAm-age was calculated using CpG sites on the Illumina HumanMethylation450 BeadChip. We fit linear mixed-effects models, controlling for PM2.5 mass and lifestyle/environmental factors as fixed effects, with the adaptive LASSO penalty to identify PM2.5 species associated with DNAm-age.

RESULTS

Sulfate and ammonium were selected by the LASSO in the Horvath DNAm-age models. In a fully-adjusted multiple-species model, interquartile range increases in both 1-year sulfate (95%CI: 0.28, 0.74, P<0.0001) and ammonium (95%CI: 0.02, 0.70, P=0.04) levels were associated with at least a 0.36-year increase in Horvath DNAm-age. No PM2.5 species were selected by the LASSO in the Hannum DNAm-age models. Our findings persisted in sensitivity analyses including only visits with 1-year PM2.5 levels within US EPA national ambient air quality standards.

CONCLUSION

Our results demonstrate that sulfate and ammonium were most associated with Horvath DNAm-age and suggest that DNAm-age measures differ in their sensitivity to ambient particle exposures and potentially disease.

Multi-tissue DNA methylation age predictor in mouse

Background

DNA methylation changes at a discrete set of sites in the human genome are predictive of chronological and biological age. However, it is not known whether these changes are causative or a consequence of an underlying ageing process. It has also not been shown whether this epigenetic clock is unique to humans or conserved in the more experimentally tractable mouse.

Results

We have generated a comprehensive set of genome-scale base-resolution methylation maps from multiple mouse tissues spanning a wide range of ages. Many CpG sites show significant tissue-independent correlations with age which allowed us to develop a multi-tissue predictor of age in the mouse. Our model, which estimates age based on DNA methylation at 329 unique CpG sites, has a median absolute error of 3.33 weeks and has similar properties to the recently described human epigenetic clock. Using publicly available datasets, we find that the mouse clock is accurate enough to measure effects on biological age, including in the context of interventions. While females and males show no significant differences in predicted DNA methylation age, ovariectomy results in significant age acceleration in females. Furthermore, we identify significant differences in age-acceleration dependent on the lipid content of the diet.

Conclusions

Here we identify and characterise an epigenetic predictor of age in mice, the mouse epigenetic clock. This clock will be instrumental for understanding the biology of ageing and will allow modulation of its ticking rate and resetting the clock in vivo to study the impact on biological age.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-017-1203-5) contains supplementary material, which is available to authorized users.

Acceleration of leukocytes' epigenetic age as an early tumor and sex-specific marker of breast and colorectal cancer

Changes in blood epigenetic age have been associated with several pathological conditions and have recently been described to anticipate cancer development. In this work, we analyze a publicly available leukocytes methylation dataset to evaluate the relation between DNA methylation age and the prospective development of specific types of cancer. We calculated DNA methylation age acceleration using five state-of-the-art estimators (three multi-site: Horvath, Hannum, Weidner; and two CpG specific: ELOV2 and FHL2) in a cohort including 845 subjects from the EPIC-Italy project and we compared 424 samples that remained cancer-free over the approximately ten years of follow-up with 235 and 166 subjects who developed breast and colorectal cancer, respectively. We show that the epigenetic age estimated from blood DNA methylation data is statistically significantly associated to future breast and male colorectal cancer development. These results are corroborated by survival analysis that shows significant association between age acceleration and cancer incidence suggesting that the chance of developing age-related diseases may be predicted by circulating epigenetic markers, with a dependence upon tumor type, sex and age estimator. These are encouraging results towards the non-invasive and perspective usage of epigenetic biomarkers.

DNA methylome analysis identifies accelerated epigenetic ageing associated with postmenopausal breast cancer susceptibility

AIM OF THE STUDY

A vast majority of human malignancies are associated with ageing, and age is a strong predictor of cancer risk. Recently, DNA methylation-based marker of ageing, known as 'epigenetic clock', has been linked with cancer risk factors. This study aimed to evaluate whether the epigenetic clock is associated with breast cancer risk susceptibility and to identify potential epigenetics-based biomarkers for risk stratification.

METHODS

Here, we profiled DNA methylation changes in a nested case-control study embedded in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort (n = 960) using the Illumina HumanMethylation 450K BeadChip arrays and used the Horvath age estimation method to calculate epigenetic age for these samples. Intrinsic epigenetic age acceleration (IEAA) was estimated as the residuals by regressing epigenetic age on chronological age.

RESULTS

We observed an association between IEAA and breast cancer risk (OR, 1.04; 95% CI, 1.007-1.076, P = 0.016). One unit increase in IEAA was associated with a 4% increased odds of developing breast cancer (OR, 1.04; 95% CI, 1.007-1.076). Stratified analysis based on menopausal status revealed that IEAA was associated with development of postmenopausal breast cancers (OR, 1.07; 95% CI, 1.020-1.11, P = 0.003). In addition, methylome-wide analyses revealed that a higher mean DNA methylation at cytosine-phosphate-guanine (CpG) islands was associated with increased risk of breast cancer development (OR per 1 SD = 1.20; 95 %CI: 1.03-1.40, P = 0.02) whereas mean methylation levels at non-island CpGs were indistinguishable between cancer cases and controls.

CONCLUSION

Epigenetic age acceleration and CpG island methylation have a weak, but statistically significant, association with breast cancer susceptibility.

Biological Age Predictors

The search for reliable indicators of biological age, rather than chronological age, has been ongoing for over three decades, and until recently, largely without success. Advances in the fields of molecular biology have increased the variety of potential candidate biomarkers that may be considered as biological age predictors. In this review, we summarize current state-of-the-art findings considering six potential types of biological age predictors: epigenetic clocks, telomere length, transcriptomic predictors, proteomic predictors, metabolomics-based predictors, and composite biomarker predictors. Promising developments consider multiple combinations of these various types of predictors, which may shed light on the aging process and provide further understanding of what contributes to healthy aging. Thus far, the most promising, new biological age predictor is the epigenetic clock; however its true value as a biomarker of aging requires longitudinal confirmation.

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Telomere length is the most well studied biological age predictor, but many new predictors are emerging.

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The epigenetic clock is currently the best biological age predictor, as it correlates well with age and predicts mortality.

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The various biological age predictors tend to reflect different aspects of the aging process.

Methylated DNA/RNA in Body Fluids as Biomarkers for Lung Cancer

DNA/RNA methylation plays an important role in lung cancer initiation and progression. Liquid biopsy makes use of cells, nucleotides and proteins released from tumor cells into body fluids to help with cancer diagnosis and prognosis. Methylation of circulating tumor DNA (ctDNA) has gained increasing attention as biomarkers for lung cancer. Here we briefly introduce the biological basis and detection method of ctDNA methylation, and review various applications of methylated DNA in body fluids in lung cancer screening, diagnosis, prognosis, monitoring and treatment prediction. We also discuss the emerging role of RNA methylation as biomarkers for cancer.

Epigenetic clock analysis of diet, exercise, education, and lifestyle factors

Behavioral and lifestyle factors have been shown to relate to a number of health-related outcomes, yet there is a need for studies that examine their relationship to molecular aging rates. Toward this end, we use recent epigenetic biomarkers of age that have previously been shown to predict all-cause mortality, chronic conditions, and age-related functional decline. We analyze cross-sectional data from 4,173 postmenopausal female participants from the Women's Health Initiative, as well as 402 male and female participants from the Italian cohort study, Invecchiare nel Chianti.Extrinsic epigenetic age acceleration (EEAA) exhibits significant associations with fish intake (p=0.02), moderate alcohol consumption (p=0.01), education (p=3x10-5), BMI (p=0.01), and blood carotenoid levels (p=1x10-5)-an indicator of fruit and vegetable consumption, whereas intrinsic epigenetic age acceleration (IEAA) is associated with poultry intake (p=0.03) and BMI (p=0.05). Both EEAA and IEAA were also found to relate to indicators of metabolic syndrome, which appear to mediate their associations with BMI. Metformin-the first-line medication for the treatment of type 2 diabetes-does not delay epigenetic aging in this observational study. Finally, longitudinal data suggests that an increase in BMI is associated with increase in both EEAA and IEAA.Overall, the epigenetic age analysis of blood confirms the conventional wisdom regarding the benefits of eating a high plant diet with lean meats, moderate alcohol consumption, physical activity, and education, as well as the health risks of obesity and metabolic syndrome.

Linking the Epigenome with Exposure Effects and Susceptibility: The Epigenetic Seed and Soil Model

The epigenome is a dynamic mediator of gene expression that shapes the way that cells, tissues, and organisms respond to their environment. Initial studies in the emerging field of "toxicoepigenetics" have described either the impact of an environmental exposure on the epigenome or the association of epigenetic signatures with the onset or progression of disease; however, the majority of these pioneering studies examined the relationship between discrete epigenetic modifications and the effects of a single environmental factor. Although these data provide critical blocks with which we construct our understanding of the role of the epigenome in susceptibility and disease, they are akin to individual letters in a complex alphabet that is used to compose the language of the epigenome. Advancing the use of epigenetic data to gain a more comprehensive understanding of the mechanisms underlying exposure effects, identify susceptible populations, and inform the next generation risk assessment depends on our ability to integrate these data in a way that accounts for their cumulative impact on gene regulation. Here we will review current examples demonstrating associations between the epigenetic impacts of intrinsic factors, such as such as age, genetics, and sex, and environmental exposures shape the epigenome and susceptibility to exposure effects and disease. We will also demonstrate how the "epigenetic seed and soil" model can be used as a conceptual framework to explain how epigenetic states are shaped by the cumulative impacts of intrinsic and extrinsic factors and how these in turn determine how an individual responds to subsequent exposure to environmental stressors.

Epigenetic Aging and Immune Senescence in Women With Insomnia Symptoms: Findings From the Women's Health Initiative Study

BACKGROUND

Insomnia symptoms are associated with vulnerability to age-related morbidity and mortality. Cross-sectional data suggest that accelerated biological aging may be a mechanism through which sleep influences risk. A novel method for determining age acceleration using epigenetic methylation to DNA has demonstrated predictive utility as an epigenetic clock and prognostic of age-related morbidity and mortality.

METHODS

We examined the association of epigenetic age and immune cell aging with sleep in the Women's Health Initiative study (N = 2078; mean 64.5 ± 7.1 years of age) with assessment of insomnia symptoms (restlessness, difficulty falling asleep, waking at night, trouble getting back to sleep, and early awakenings), sleep duration (short sleep 5 hours or less; long sleep greater than 8 hours), epigenetic age, naive T cell (CD8+CD45RA+CCR7+), and late differentiated T cells (CD8+CD28-CD45RA-).

RESULTS

Insomnia symptoms were related to advanced epigenetic age (β ± SE = 1.02 ± 0.37, p = .005) after adjustments for covariates. Insomnia symptoms were also associated with more late differentiated T cells (β ± SE = 0.59 ± 0.21, p = .006), but not with naive T cells. Self-reported short and long sleep duration were unrelated to epigenetic age. Short sleep, but not long sleep, was associated with fewer naive T cells (p < .005) and neither was related to late differentiated T cells.

CONCLUSIONS

Symptoms of insomnia were associated with increased epigenetic age of blood tissue and were associated with higher counts of late differentiated CD8+ T cells. Short sleep was unrelated to epigenetic age and late differentiated cell counts, but was related to a decline in naive T cells. In this large population-based study of women in the United States, insomnia symptoms are implicated in accelerated aging.

DNA methylation-based variation between human populations

Several studies have proved that DNA methylation affects regulation of gene expression and development. Epigenome-wide studies have reported variation in methylation patterns between populations, including Caucasians, non-Caucasians (Blacks), Hispanics, Arabs, and numerous populations of the African continent. Not only has DNA methylation differences shown to impact externally visible characteristics, but is also a potential biomarker for underlying racial health disparities between human populations. Ethnicity-related methylation differences set their mark during early embryonic development. Genetic variations, such as single-nucleotide polymorphisms and environmental factors, such as age, dietary folate, socioeconomic status, and smoking, impacts DNA methylation levels, which reciprocally impacts expression of phenotypes. Studies show that it is necessary to address these external influences when attempting to differentiate between populations since the relative impacts of these factors on the human methylome remain uncertain. The present review summarises several reported attempts to establish the contribution of differential DNA methylation to natural human variation, and shows that DNA methylation could represent new opportunities for risk stratification and prevention of several diseases amongst populations world-wide. Variation of methylation patterns between human populations is an exciting prospect which inspires further valuable research to apply the concept in routine medical and forensic casework. However, trans-generational inheritance needs to be quantified to decipher the proportion of variation contributed by DNA methylation. The future holds thorough evaluation of the epigenome to understand quantification, heritability, and the effect of DNA methylation on phenotypes. In addition, methylation profiling of the same ethnic groups across geographical locations will shed light on conserved methylation differences in populations.

Increased epigenetic age and granulocyte counts in the blood of Parkinson's disease patients

It has been a long standing hypothesis that blood tissue of PD Parkinson's disease (PD) patients may exhibit signs of accelerated aging. Here we use DNA methylation based biomarkers of aging (“epigenetic clock”) to assess the aging rate of blood in two ethnically distinct case-control data sets. Using n=508 Caucasian and n=84 Hispanic blood samples, we assess a) the intrinsic epigenetic age acceleration of blood (IEAA), which is independent of blood cell counts, and b) the extrinsic epigenetic age acceleration rate of blood (EEAA) which is associated with age dependent changes in blood cell counts. Blood of PD subjects exhibits increased age acceleration according to both IEAA (p=0.019) and EEAA (p=6.1×10⁻³). We find striking differences in imputed blood cell counts between PD cases and controls. Compared to control subjects, PD subjects contains more granulocytes (p=1.0×10⁻⁹ in Caucasians, p=0.00066 in Hispanics) but fewer T helper cells (p=1.4×10⁻⁶ in Caucasians, p=0.0024 in Hispanics) and fewer B cells (p=1.6×10⁻⁵ in Caucasians, p=4.5×10⁻⁵ in Hispanics). Overall, this study shows that the epigenetic age of the immune system is significantly increased in PD patients and that granulocytes play a significant role.

An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease

Background

Epigenetic biomarkers of aging (the “epigenetic clock”) have the potential to address puzzling findings surrounding mortality rates and incidence of cardio-metabolic disease such as: (1) women consistently exhibiting lower mortality than men despite having higher levels of morbidity; (2) racial/ethnic groups having different mortality rates even after adjusting for socioeconomic differences; (3) the black/white mortality cross-over effect in late adulthood; and (4) Hispanics in the United States having a longer life expectancy than Caucasians despite having a higher burden of traditional cardio-metabolic risk factors.

Results

We analyzed blood, saliva, and brain samples from seven different racial/ethnic groups. We assessed the intrinsic epigenetic age acceleration of blood (independent of blood cell counts) and the extrinsic epigenetic aging rates of blood (dependent on blood cell counts and tracks the age of the immune system). In blood, Hispanics and Tsimane Amerindians have lower intrinsic but higher extrinsic epigenetic aging rates than Caucasians. African-Americans have lower extrinsic epigenetic aging rates than Caucasians and Hispanics but no differences were found for the intrinsic measure. Men have higher epigenetic aging rates than women in blood, saliva, and brain tissue.

Conclusions

Epigenetic aging rates are significantly associated with sex, race/ethnicity, and to a lesser extent with CHD risk factors, but not with incident CHD outcomes. These results may help elucidate lower than expected mortality rates observed in Hispanics, older African-Americans, and women.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-016-1030-0) contains supplementary material, which is available to authorized users.

Life stress, glucocorticoid signaling, and the aging epigenome: Implications for aging-related diseases

Life stress has been associated with accelerated cellular aging and increased risk for developing aging-related diseases; however, the underlying molecular mechanisms remain elusive. A highly relevant process that may underlie this association is epigenetic regulation. In this review, we build upon existing evidence to propose a model whereby exposure to life stress, in part via its effects on the hypothalamic-pituitary axis and the glucocorticoid signaling system, may alter the epigenetic landscape across the lifespan and, consequently, influence genomic regulation and function in ways that are conducive to the development of aging-related diseases. This model is supported by recent studies showing that life stressors and stress-related phenotypes can accelerate epigenetic aging, a measure that is based on DNA methylation prediction of chronological age and has been associated with several aging-related disease phenotypes. We discuss the implications of this model for the prevention and treatment of aging-related diseases, as well as the challenges and limitations of this line of research.

Epigenetic age acceleration predicts cancer, cardiovascular, and all-cause mortality in a German case cohort

BACKGROUND

Previous studies have developed models predicting methylation age from DNA methylation in blood and other tissues (epigenetic clock) and suggested the difference between DNA methylation and chronological ages as a marker of healthy aging. The goal of this study was to confirm and expand such observations by investigating whether different concepts of the epigenetic clocks in a population-based cohort are associated with cancer, cardiovascular, and all-cause mortality.

RESULTS

DNA methylation age was estimated in a cohort of 1863 older people, and the difference between age predicted by DNA methylation and chronological age (Δage) was calculated. A case-cohort design and weighted proportional Cox hazard models were used to estimate associations of Δage with cancer, cardiovascular, and all-cause mortality. Hazard ratios for Δage (per 5 years) calculated using the epigenetic clock developed by Horvath were 1.23 (95 % CI 1.10-1.38) for all-cause mortality, 1.22 (95 % CI 1.03-1.45) for cancer mortality, and 1.19 (95 % CI 0.98-1.43) for cardiovascular mortality after adjustment for batch effects, age, sex, educational level, history of chronic diseases, hypertension, smoking status, body mass index, and leucocyte distribution. Associations were similar but weaker for Δage calculated using the epigenetic clock developed by Hannum.

CONCLUSIONS

These results show that age acceleration in terms of the difference between age predicted by DNA methylation and chronological age is an independent predictor of all-cause and cause-specific mortality and may be useful as a general marker of healthy aging.

Epigenetic drift, epigenetic clocks and cancer risk

It is well-established that the DNA methylation landscape of normal cells undergoes a gradual modification with age, termed as 'epigenetic drift'. Here, we review the current state of knowledge of epigenetic drift and its potential role in cancer etiology. We propose a new terminology to help distinguish the different components of epigenetic drift, with the aim of clarifying the role of the epigenetic clock, mitotic clocks and active changes, which accumulate in response to environmental disease risk factors. We further highlight the growing evidence that epigenetic changes associated with cancer risk factors may play an important causal role in cancer development, and that monitoring these molecular changes in normal cells may offer novel risk prediction and disease prevention strategies.

Long-term ambient particle exposures and blood DNA methylation age: findings from the VA normative aging study

BACKGROUND

Ambient particles have been shown to exacerbate measures of biological aging; yet, no studies have examined their relationships with DNA methylation age (DNAm-age), an epigenome-wide DNA methylation based predictor of chronological age.

OBJECTIVE

We examined the relationship of DNAm-age with fine particulate matter (PM2.5), a measure of total inhalable particle mass, and black carbon (BC), a measure of particles from vehicular traffic.

METHODS

We used validated spatiotemporal models to generate 1-year PM2.5 and BC exposure levels at the addresses of 589 older men participating in the VA Normative Aging Study with 1-3 visits between 2000 and 2011 (n = 1032 observations). Blood DNAm-age was calculated using 353 CpG sites from the Illumina HumanMethylation450 BeadChip. We estimated associations of PM2.5 and BC with DNAm-age using linear mixed effects models adjusted for age, lifestyle/environmental factors, and aging-related diseases.

RESULTS

After adjusting for covariates, a 1-µg/m3 increase in PM2.5 (95% CI: 0.30, 0.75, P<0.0001) was significantly associated with a 0.52-year increase in DNAm-age. Adjusted BC models showed similar patterns of association (β = 3.02, 95% CI: 0.48, 5.57, P = 0.02). Only PM2.5 (β = 0.54, 95% CI: 0.24, 0.84, P = 0.0004) remained significantly associated with DNAm-age in two-particle models. Methylation levels from 20 of the 353 CpGs contributing to DNAm-age were significantly associated with PM2.5 levels in our two-particle models. Several of these CpGs mapped to genes implicated in lung pathologies including LZTFL1, PDLIM5, and ATPAF1.

CONCLUSION

Our results support an association of long-termambient particle levels with DNAm-age and suggest that DNAm-age is a biomarker of particle-related physiological processes.

Blood Epigenetic Age may Predict Cancer Incidence and Mortality

Biological measures of aging are important for understanding the health of an aging population, with epigenetics particularly promising. Previous studies found that tumor tissue is epigenetically older than its donors are chronologically. We examined whether blood Δ_age (the discrepancy between epigenetic and chronological ages) can predict cancer incidence or mortality, thus assessing its potential as a cancer biomarker. In a prospective cohort, Δ_age and its rate of change over time were calculated in 834 blood leukocyte samples collected from 442 participants free of cancer at blood draw. About 3–5 years before cancer onset or death, Δ_age was associated with cancer risks in a dose-responsive manner (P = 0.02) and a one-year increase in Δ_age was associated with cancer incidence (HR: 1.06, 95% CI: 1.02–1.10) and mortality (HR: 1.17, 95% CI: 1.07–1.28). Participants with smaller Δ_age and decelerated epigenetic aging over time had the lowest risks of cancer incidence (P = 0.003) and mortality (P = 0.02). Δ_age was associated with cancer incidence in a ‘J-shaped’ manner for subjects examined pre-2003, and with cancer mortality in a time-varying manner. We conclude that blood epigenetic age may mirror epigenetic abnormalities related to cancer development, potentially serving as a minimally invasive biomarker for cancer early detection.

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We prospectively examined blood Δ_age and its ability to predict cancer risks.

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Epigenetic age older than chronological age elevated cancer risk.

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Δ_age predicted cancer incidence and mortality in a dose-responsive manner.

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The Δ_age–cancer relationship was a nonlinear ‘J-shape’ for subjects measured before 2003.

Blood-based epigenetic age is a potential biomarker for cancer early detection.

This study studies a way to calculate your body's age, not based on how old you are, but by measuring a number of markers in your blood — called epigenetic age. This paper also looks at how good epigenetic age over time is at predicting whether you'll get cancer, and whether you'll die from it. The authors found that epigenetic age could be good at predicting both of these things, which means that someday it could be developed into a blood test for diagnosing cancer, and for helping patients figure out how long they'll live.

Epigenetic age of the pre-frontal cortex is associated with neuritic plaques, amyloid load, and Alzheimer's disease related cognitive functioning

There is an urgent need to develop molecular biomarkers of brain age in order to advance our understanding of age related neurodegeneration. Recently, we developed a highly accurate epigenetic biomarker of tissue age (known as epigenetic clock) which is based on DNA methylation levels. Here we use n=700 dorsolateral prefrontal cortex (DLPFC) samples from Caucasian subjects of the Religious Order Study and the Rush Memory and Aging Project to examine the association between epigenetic age and Alzheimer's disease (AD) related cognitive decline, and AD related neuropathological markers. Epigenetic age acceleration of DLPFC is correlated with several neuropathological measurements including diffuse plaques (r=0.12, p=0.0015), neuritic plaques (r=0.11, p=0.0036), and amyloid load (r=0.091, p=0.016). Further, it is associated with a decline in global cognitive functioning (β=-0.500, p=0.009), episodic memory (β=-0.411, p=0.009) and working memory (β=-0.405, p=0.011) among individuals with AD. The neuropathological markers may mediate the association between epigenetic age and cognitive decline. Genetic complex trait analysis (GCTA) revealed that epigenetic age acceleration is heritable (h2=0.41) and has significant genetic correlations with diffuse plaques (r=0.24, p=0.010) and possibly working memory (r=-0.35, p=0.065). Overall, these results suggest that the epigenetic clock may lend itself as a molecular biomarker of brain age.