scholarly journals Human Epigenetic Aging is Logarithmic with Time across the Entire LifeSpan

2018 ◽  
Author(s):  
Sagi Snir ◽  
Matteo Pellegrini
Keyword(s):  
Methylation Status ◽  
Real Data ◽  
Chronological Age ◽  
Epigenetic Changes ◽  
Critical Question ◽  
Epigenetic Aging ◽  
Epigenetic Age ◽  
Non Linear ◽  
Development And Aging

AbstractIt is well established that organisms undergo epigenetic changes both during development and aging. Developmental changes have been extensively studied to characterize the differentiation of stem cells into diverse lineages. Epigenetic changes during aging have been characterized by multiple epigenetic clocks, that allow the prediction of chronological age based on methylation status. Despite their accuracy and utility, epigenetic age biomarkers leave many questions about epigenetic aging unanswered. Specifically, they do not permit the unbiased characterization of non-linear epigenetic aging trends across entire life spans, a critical question underlying this field of research. Here we a provide an integrated framework to address this question. Our model, inspired from evolutionary models, is able to account for acceleration/deceleration in epigenetic changes by fitting an individuals model age, the epigenetic age, which is related to chronological age in a non-linear fashion. We have devised a two stage procedure leveraging these model ages to infer aging trends over the entire lifespan of a population. Application of this procedure to real data measured across broad age ranges, from before birth to old age, and from two tissue types, suggests a universal logarithmic trend characterizes epigenetic aging across entire lifespans. This observation may have important implications for the development and application of future, more accurate, aging biomarkers.

Epigenetic Biomarkers Are Applicable for Risk Stratification in Myelodysplastic Syndromes

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 3194-3194 ◽  
Author(s):  
Anna Mies ◽  
Tanja Božić ◽  
Michael Kramer ◽  
Julia Franzen ◽  
Gerhard Ehninger ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Myelodysplastic Syndromes ◽  
Somatic Mutations ◽  
Chronological Age ◽  
Prognostic Impact ◽  
Cpg Site ◽  
Epigenetic Biomarkers ◽  
Epigenetic Aging ◽  
Epigenetic Age ◽  
Dnam Level

Abstract Introduction: Myelodysplastic syndromes (MDS) are frequently associated with somatic mutations in epigenetic modifiers such as de novo methyltransferase 3A (DNMT3A). However, so far the significance of specific epigenetic modifications for disease stratification remains largely unknown. In this study, we investigated if epigenetic biomarkers, which were previously described to be relevant in acute myeloid leukemia (AML), are also of prognostic impact in MDS. Methods: Peripheral blood samples of MDS patients (n=126; f/m=59/67; median age 66; range 26-93) equally distributed across all risk groups based on the revised International Prognostic Scoring System (IPSS-R; very low/low=43; int=37; high/very high=43; n.a.=3) were analyzed at initial diagnosis. Genomic DNA was isolated, bisulfite converted, and DNA methylation (DNAm) level at selected genomic regions were determined by pyrosequencing as described before: (1) hypermethylation at a CpG site in complement component 1 subcomponent R (C1R), (2) an epigenetic age prediction with an Epigenetic-Aging-Signature based on three CpG sites located in the genes ITGA2B, ASPA and PDE4C, and (3) an epimutation in the DNMT3A locus, mimicking somatic mutations of this gene, were all reported to correlate with overall survival (OS) in AML patients. Results were subsequently compared to clinical parameters such as IPSS-R, leukemic progression, and OS. Results: A clear tendency for longer OS of MDS patients was observed if DNAm level at C1R was above median (22%; two-year survival 67% [95% CI 53-84%] in hypo- vs. 84% [95% CI 74-95%] in hypermethylated samples; P=0.071), which is in line with previous findings in AML samples. The predicted epigenetic age determined by the Epigenetic-Aging-Signature correlated moderately with the chronological age of the investigated MDS patients (R=0.42) and their OS (P=0.029). This effect was also seen in a multivariable analysis of this cohort including predicted and chronological age (P=0.040). Finally, we stratified MDS patients by the DNAm level of 10% in DNMT3A. Similar to AML, also MDS patients with higher methylation at the CpG site represented on a microarray (cg23009818) showed in tendency shorter OS (two-year survival 79% [95% CI 69-89%] in hypo- vs. 65% [95% CI 45-93%] in hypermethylated samples; P=0.110). In fact, this association was even more pronounced at a neighboring CpG site (two-year survival 83% [95% CI 74-92%] in hypo- vs. 49% [95% CI 29-84%] in hypermethylated samples; P=0.009; Figure A). Moreover, increased DNAm level at this neighboring CpG site in DNMT3A was indicative for progression into AML (after two years: 15% [95% CI 6-24%] in hypo- vs. 44% [95% CI 12-76%] in hypermethylated samples; P=0.011; Figure B). Of note, none of these markers correlated with IPSS-R categories indicating that they might provide independent prognostic parameters. Conclusion: The analyzed epigenetic biomarkers revealed prognostic relevance in MDS patients and we suggest considering them in future risk stratification models. Particularly the aberrant hypermethylation of DNMT3A, which may also result in alternative splicing of DNMT3A transcripts, was associated with accelerated leukemic progression and shorter OS. Figure Figure. Disclosures Božić: Cygenia GmbH: Consultancy. Wagner:Cygenia GmbH: Equity Ownership.

scholarly journals Long-term aspirin use and epigenetic mitotic clocks for cancer risk prediction: findings in healthy colon mucosa and recommendations for future epigenetic aging studies

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Jamaji C. Nwanaji-Enwerem ◽  
Chijioke Nze ◽  
Andres Cardenas
Keyword(s):  
Cancer Risk ◽  
Risk Prediction ◽  
Proximal Colon ◽  
Chronological Age ◽  
Strongly Correlated ◽  
Cell Divisions ◽  
Epigenetic Aging ◽  
Epigenetic Age ◽  
Mitotic Clock

Abstract Background Despite the known role of mitosis in colorectal cancer, previous associations of long-term aspirin use with suppressed cancer-related epigenetic aging did not involve epigenetic mitotic clocks. We investigated these relationships using three epigenetic mitotic clocks developed for cancer risk prediction: EpiTOC, EpiTOC2, and MiAge. We utilized publicly available HumanMethylationEPIC BeadChip data from 112 healthy colon (proximal and distal) mucosal samples taken at baseline (T1) and at 10-years follow-up (T2) from a screening cohort of 28 Polish women (11 non-users and 17 long-term [≥ 2 years] aspirin users). Mitotic clock values were divided by chronological age at each timepoint to obtain intrinsic rates (IRs). We evaluated differences in residuals of the mitotic clock IRs taken from linear mixed effects models adjusted for BMI, polyp status, and DNA methylation batch. Findings EpiTOC, EpiTOC2, and MiAge were significantly correlated with chronological age (P < 0.05) with correlations ranging from 0.41 to 0.63. The EpiTOC, EpiTOC2, and MiAge clocks were strongly correlated with each other in proximal and distal samples (r > 0.79, P < 0.0001). We observed proximal within group median clock IR deceleration for EpiTOC (-0.0004 DNAm, P = 0.008), EpiTOC2 (− 16 cell divisions, P = 0.009), and MiAge (− 3 cell divisions, P = 0.002) for long-term aspirin users from T1 to T2 but not for non-users. In distal samples, only the long-term user MiAge IR was significantly deaccelerated (− 3 cell divisions, P = 0.009). Conclusions Our observed findings support previously reported longitudinal associations of aspirin use with deceleration of other epigenetic age measures in the proximal colon. Our mitotic clock results suggest that cell proliferation could play a role in some aspirin relationships with epigenetic aging. Furthermore, the findings provide added impetus for establishing gold standards for epigenetic aging and consensus guidelines for more comprehensive reporting in future epigenetic aging cancer studies.

scholarly journals Dysfunctional epigenetic aging of the normal colon and colorectal cancer risk

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Ting Wang ◽  
Sean K. Maden ◽  
Georg E. Luebeck ◽  
Christopher I. Li ◽  
Polly A. Newcomb ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Risk Group ◽  
Risk Groups ◽  
Biological Age ◽  
Chronological Age ◽  
Epigenetic Clock ◽  
Normal Colon ◽  
Epigenetic Aging ◽  
Epigenetic Age ◽  
Epigenetic Age Acceleration

Abstract Background Chronological age is a prominent risk factor for many types of cancers including colorectal cancer (CRC). Yet, the risk of CRC varies substantially between individuals, even within the same age group, which may reflect heterogeneity in biological tissue aging between people. Epigenetic clocks based on DNA methylation are a useful measure of the biological aging process with the potential to serve as a biomarker of an individual’s susceptibility to age-related diseases such as CRC. Methods We conducted a genome-wide DNA methylation study on samples of normal colon mucosa (N = 334). Subjects were assigned to three cancer risk groups (low, medium, and high) based on their personal adenoma or cancer history. Using previously established epigenetic clocks (Hannum, Horvath, PhenoAge, and EpiTOC), we estimated the biological age of each sample and assessed for epigenetic age acceleration in the samples by regressing the estimated biological age on the individual’s chronological age. We compared the epigenetic age acceleration between different risk groups using a multivariate linear regression model with the adjustment for gender and cell-type fractions for each epigenetic clock. An epigenome-wide association study (EWAS) was performed to identify differential methylation changes associated with CRC risk. Results Each epigenetic clock was significantly correlated with the chronological age of the subjects, and the Horvath clock exhibited the strongest correlation in all risk groups (r > 0.8, p < 1 × 10−30). The PhenoAge clock (p = 0.0012) revealed epigenetic age deceleration in the high-risk group compared to the low-risk group. Conclusions Among the four DNA methylation-based measures of biological age, the Horvath clock is the most accurate for estimating the chronological age of individuals. Individuals with a high risk for CRC have epigenetic age deceleration in their normal colons measured by the PhenoAge clock, which may reflect a dysfunctional epigenetic aging process.

scholarly journals Deconvolution of the epigenetic age discloses distinct inter-personal variability in epigenetic aging patterns

2021 ◽  
Author(s):  
Tamar Shahal ◽  
Elad Segev ◽  
Thomas Konstantinovsky ◽  
Yonit Marcus ◽  
Gabi Shefer ◽  
...  
Keyword(s):  
Healthy Subjects ◽  
Interindividual Variability ◽  
Chronological Age ◽  
Biological Aging ◽  
Epigenetic Clock ◽  
Cpg Sites ◽  
Epigenetic Aging ◽  
Epigenetic Age ◽  
Shed Light

Epigenetic age not only correlates with chronological age but predicts morbidity and mortality. We assumed that deconvolution of epigenetic age to its individual components could shed light on the diversity of epigenetic, and by inference, biological aging. Using the Horvath original epigenetic clock, we identified several CpG sites linked to distinct genes that quantitatively explain much of the interpersonal variability in epigenetic aging, with secretagogin and malin showing the most dominant effects. The analysis shows that the same epigenetic age for any given chronological age can be accounted for by variable contributions of identifiable CpG sites; that old epigenetic relative to chronological age is mostly explained by the same CpG sites, mapped to genes showing the highest interindividual variability differences in healthy subjects but not in subjects with type 2 diabetes. This paves the way to form personalized aging cards indicating the sources of accelerated/decelerated epigenetic aging in each examinee, en route to targeting specific sites as indicators, and perhaps treatment targets of personal undesirable age drifting.

scholarly journals The Epigenetic Pacemaker is a more sensitive tool than penalized regression for identifying moderators of epigenetic aging

2021 ◽  
Author(s):  
Colin Farrell ◽  
Kalsuda Lapborisuth ◽  
Chanyue Hu ◽  
Kyle Pu ◽  
Sagi Snir ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Penalized Regression ◽  
Chronological Age ◽  
Data Set ◽  
Epigenetic State ◽  
Age Related ◽  
Epigenetic Aging ◽  
Epigenetic Age ◽  
The Impact ◽  
The Relationship

Epigenetic clocks, DNA methylation based chronological age prediction models, are commonly employed to study age related biology. The error between the predicted and observed age is often interpreted as a form of biological age acceleration and many studies have measured the impact of environmental and other factors on epigenetic age. Epigenetic clocks are fit using approaches that minimize the error between the predicted and observed chronological age and as a result they reduce the impact of factors that may moderate the relationship between actual and epigenetic age. Here we compare the standard methods used to construct epigenetic clocks to an evolutionary framework of epigenetic aging, the epigenetic pacemaker (EPM) that directly models DNA methylation as a function of a time dependent epigenetic state. We show that the EPM is more sensitive than epigenetic clocks for the detection of factors that moderate the relationship between actual age and epigenetic state (ie epigenetic age). Specifically, we show that the EPM is more sensitive at detecting sex and cell type effects in a large aggregate data set and in an example case study is more sensitive sensitive at detecting age related methylation changes associated with polybrominated biphenyl exposure. Thus we find that the pacemaker provides a more robust framework for the study of factors that impact epigenetic age acceleration than traditional clocks based on linear regression models.

scholarly journals Epigenetic Age Acceleration Reflects Long-Term Cardiovascular Health

2021 ◽  
Author(s):  
Brian Joyce ◽  
Tao Gao ◽  
Yinan Zheng ◽  
Jiantao Ma ◽  
Shih-Jen Hwang ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Dna Methylation ◽  
Cardiovascular Health ◽  
Clinical Score ◽  
Chronological Age ◽  
Cvd Risk ◽  
Cross Sectional ◽  
Age Related ◽  
Epigenetic Aging ◽  
Epigenetic Age

Rationale: Epigenetic aging is a novel measure of biological age, reflecting exposures and disease risks independent of chronological age. It may serve as a useful biomarker of cardiovascular health (CVH) and/or cardiovascular disease (CVD) risk for early detection or prevention. Objective: To examine associations between GrimAge acceleration (GrimAA), a measure of epigenetic aging calculated from the residuals of GrimAge regressed on chronological age, and two repeated CVH measures: a full score for the AHA "Life's Simple 7" (diet, smoking, physical activity, BMI, blood pressure, total cholesterol, and glucose) and a clinical CVH score (BMI, blood pressure, cholesterol, and glucose). Methods and Results: We used Illumina array DNA methylation data from two prospective cohort studies: The Coronary Artery Risk Development in Young Adults (CARDIA) study and Framingham Heart Study (FHS), to calculate GrimAA and model associations with CVH. CARDIA randomly selected 1,118 participants for assays at Y15 (2000-2001; mean age 40) and/or Y20 (2005-2006); in FHS, 2,106 Offspring participants had DNA methylation measured at exam 8 (2005-2008; mean age 66). We examined multiple cross-sectional and longitudinal models of GrimAA and each CVH score measured at CARDIA Y0-Y20 and FHS exams 7-8. In CARDIA clinical CVH score from Y0-Y20 was associated with Y15 and Y20 GrimAA (β range -0.41 to -0.21 years per 1-point increase in CVH; p range <0.01 to 0.01), as was full score (β range -0.65 to -0.67 years; p<0.01 for all). These findings were validated in FHS (clinical score β range -0.51 to -0.54 years; full score β range -0.76 to -0.83 years; p<0.01 for all). Conclusions: Our data demonstrate that faster GrimAA is associated with the loss of CVH from young age. Epigenetic age may be a useful biomarker of CVD risk and provides biological insight into the role of epigenetic mechanisms linking age-related CVH loss and CVD.

scholarly journals A pilot prospective study of sleep patterns and DNA methylation-characterized epigenetic aging in young adults

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Mary A. Carskadon ◽  
Kenneth R. Chappell ◽  
David H. Barker ◽  
Anne C. Hart ◽  
Kayla Dwyer ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Sleep Duration ◽  
Sleep Time ◽  
Chronological Age ◽  
Age Difference ◽  
Regularity Index ◽  
Epigenetic Aging ◽  
Epigenetic Age ◽  
Sleep Log ◽  
Healthy Sample

Abstract Objective Molecular markers in DNA methylation at a subset of CpG sites are affected by the environment and contribute to biological (epigenetic) age. We hypothesized that shorter sleep duration and possibly irregular sleep would be associated with accelerated epigenetic aging. We examined epigenetic vs. chronological age in 12 young women selected as shorter or longer sleepers studied prospectively across the first 9 weeks of college using a daily online sleep log. Genomic DNA was isolated from two blood samples spanning the interval, and DNA methylation levels were determined and used to measure epigenetic age. Results Epigenetic vs. chronological age differences averaged 2.07 at Time 1 and 1.21 at Time 2. Sleep duration was computed as average daily total sleep time and sleep regularity was indexed using the Sleep Regularity Index. Participants with longer and more regular sleep showed reduced age difference: mean = − 2.48 [95% CI − 6.11; 1.15]; those with shorter and more irregular sleep showed an increased age difference: 3.03 [0.02; 6.03]; and those with either shorter or more irregular sleep averaged no significant change: − 0.49 [− 3.55; 2.56]. These pilot data suggest that short and irregular sleep, even in a young healthy sample, may be associated with accelerated epigenetic aging.

scholarly journals The Epigenetic Pacemaker: modeling epigenetic states under an evolutionary framework

2020 ◽  
Vol 36 (17) ◽  
pp. 4662-4663
Author(s):  
Colin Farrell ◽  
Sagi Snir ◽  
Matteo Pellegrini
Keyword(s):  
Mutation Rate ◽  
Expectation Maximization ◽  
Conditional Expectation ◽  
Expectation Maximization Algorithm ◽  
Accurate Estimation ◽  
Epigenetic Changes ◽  
Rates Of Change ◽  
Epigenetic State ◽  
Epigenetic Aging ◽  
Non Linear

Abstract Summary Epigenetic rates of change, much as evolutionary mutation rate along a lineage, vary during lifetime. Accurate estimation of the epigenetic state has vast medical and biological implications. To account for these non-linear epigenetic changes with age, we recently developed a formalism inspired by the Pacemaker model of evolution that accounts for varying rates of mutations with time. Here, we present a python implementation of the Epigenetic Pacemaker (EPM), a conditional expectation maximization algorithm that estimates epigenetic landscapes and the state of individuals and may be used to study non-linear epigenetic aging. Availability and Implementation The EPM is available at https://pypi.org/project/EpigeneticPacemaker/ under the MIT license. The EPM is compatible with python version 3.6 and above.

scholarly journals The aging DNA methylome reveals environment-by-aging interactions in a model teleost

2021 ◽  
Author(s):  
Emily M Bertucci ◽  
Marilyn W Mason ◽  
Olin E Rhodes ◽  
Benjamin B Parrott
Keyword(s):  
Dna Methylation ◽  
Environmental Factors ◽  
Environmental Drivers ◽  
Chronological Age ◽  
Epigenetic Clock ◽  
Ongoing Research ◽  
Dna Methylome ◽  
Age Related ◽  
Epigenetic Aging ◽  
Epigenetic Age

The rate at which individuals age underlies variation in life history and attendant health and disease trajectories. Age specific patterning of the DNA methylome (epigenetic aging) is strongly correlated with chronological age in humans and can be modeled to produce epigenetic age predictors. However, epigenetic age estimates vary among individuals of the same age, and this mismatch is correlated to the onset of age-related disease and all-cause mortality. Yet, the origins of epigenetic-to-chronological age discordance are not resolved. In an effort to develop a tractable model in which environmental drivers of epigenetic aging can be assessed, we investigate the relationship between aging and DNA methylation in a small teleost, medaka (Oryzias latipes). We find that age-associated DNA methylation patterning occurs broadly across the genome, with the majority of age-related changes occurring during early life. By modeling the stereotypical nature of age-associated DNA methylation dynamics, we built an epigenetic clock, which predicts chronological age with a mean error of 29.1 days (~4% of average lifespan). Characterization of clock loci suggests that aspects of epigenetic aging are functionally similar across vertebrates. To understand how environmental factors interact with epigenetic aging, we exposed medaka to four doses of ionizing radiation for seven weeks, hypothesizing that exposure to such an environmental stressor would accelerate epigenetic aging. While the epigenetic clock was not significantly affected, radiation exposure accelerated and decelerated patterns of normal epigenetic aging, with radiation-induced epigenetic alterations enriched at loci that become hypermethylated with age. Together, our findings advance ongoing research attempting to elucidate the functional role of DNA methylation in integrating environmental factors into the rate of biological aging.

Abstract P106: Epigenetic Age Acceleration and Postprandial Lipemia in the Genetics of Lipid Lowering Drugs and Diet Network Study

Circulation ◽  
2017 ◽  
Vol 135 (suppl_1) ◽  
Author(s):  
Marguerite R Irvin ◽  
Bertha Hidalgo ◽  
Degui Zhi ◽  
Stella Aslibekyan ◽  
Hemant K Tiwari ◽  
...  
Keyword(s):  
Dna Methylation ◽  
T Cell ◽  
Density Lipoprotein ◽  
Lipid Lowering ◽  
Chronological Age ◽  
Lipid Levels ◽  
Lipid Lowering Drugs ◽  
Epigenetic Aging ◽  
Epigenetic Age ◽  
Epigenetic Age Acceleration

Background: Calculated ‘epigenetic age,’ a novel biomarker based on DNA methylation levels of 353 CpGs, has been demonstrated to accurately predict chronological age across a broad spectrum of tissues and cell types. Recently epigenetic age acceleration or older epigenetic age in comparison to chronological age has been robustly associated with all-cause mortality independent of chronological age in multiple human cohorts. However, accelerated epigenetic aging has not been associated with lipids levels, including postprandial lipid levels which are linked to prothrombotic and proinflammatory processes that may precipitate aging. In the current study we aimed to evaluate the association between epigenetic age acceleration and lipid levels. Methods: We used the Horvath DNA methylation age calculator to estimate epigenetic age in 988 Caucasian participants from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) using Illumina Infinium HumanMethylation450 BeadChip array data derived from CD4+ T-cell DNA. GOLDN participants did not take lipid lowering drugs for at least four weeks prior to enrollment and underwent a standardized high fat meal challenge after fasting for at least 8 hours followed by timed blood draws at 3.5 and 6 hours following the meal. Epigenetic age acceleration was calculated as the residual from regressing methylation age on chronological age. We used linear mixed models to examine the association of age acceleration quartiles with fasting and postrandial (3.5 and 6 hour time points) low density lipoprotein (LDL), high density lipoprotein (HDL) and triglyceride (TG) levels after adjusting for age, study site, sex, fasting lipid level (if applicable), deconvolution estimated T-cell type percentages and a random effect of family relationship. Results: The correlation between calculated methylation age and chronological age was 0.91. The difference between methylation age and chronological age (methylation age - chronological age) was on average -5.8 (5.9), -0.5 (4.7), 2.9 (4.3), and 7.8 (5.0) years for the first through fourth quartiles of age acceleration, respectively. After adjustment for covariates neither fasting nor postprandial lipids were associated with age acceleration quartile. Conclusions: Evidence from the current study suggests lipid levels in the fasting and postprandial state are not related to accelerated epigenetic aging, however given the association between epigenetic age acceleration and mortality observed in previous studies the relationship of other metabolic parameters with age acceleration may be worthy of investigation.

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