scholarly journals Recalibrating the epigenetic clock: implications for assessing biological age in the human cortex

Brain ◽  
2020 ◽  
Author(s):  
Gemma L Shireby ◽  
Jonathan P Davies ◽  
Paul T Francis ◽  
Joe Burrage ◽  
Emma M Walker ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Age Distribution ◽  
Biological Age ◽  
Supervised Machine Learning ◽  
Tissue Type ◽  
Chronological Age ◽  
Epigenetic Clock ◽  
Methylation Data ◽  
Human Cortex ◽  
Dna Methylation Age

Abstract Human DNA methylation data have been used to develop biomarkers of ageing, referred to as ‘epigenetic clocks’, which have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks have been shown to be highly accurate in blood but are less precise when used in older samples or in tissue types not included in training the model, including brain. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life course (n = 1397, ages = 1 to 108 years). This dataset was split into ‘training’ and ‘testing’ samples (training: n = 1047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel independent human cortex dataset (n = 1221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination, optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically outperformed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts.

scholarly journals Recalibrating the Epigenetic Clock: Implications for Assessing Biological Age in the Human Cortex

2020 ◽  
Author(s):  
Gemma L Shireby ◽  
Jonathan P Davies ◽  
Paul T Francis ◽  
Joe Burrage ◽  
Emma M Walker ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Age Distribution ◽  
Biological Age ◽  
Supervised Machine Learning ◽  
Tissue Type ◽  
Chronological Age ◽  
Epigenetic Clock ◽  
Methylation Data ◽  
Human Cortex ◽  
Dna Methylation Age

AbstractHuman DNA-methylation data have been used to develop biomarkers of ageing - referred to ‘epigenetic clocks’ - that have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks are highly accurate in blood but are less precise when used in older samples or on brain tissue. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life-course (n = 1,397, ages = 1 to 104 years). This dataset was split into ‘training’ and ‘testing’ samples (training: n = 1,047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel human cortex dataset (n = 1,221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1,175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically out-performed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts.

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 Biological age of the endometrium using DNA methylation

Reproduction ◽  
2018 ◽  
Vol 155 (2) ◽  
pp. 165-170 ◽  
Author(s):  
Mia S Olesen ◽  
Anna Starnawska ◽  
Jonas Bybjerg-Grauholm ◽  
Alexandra P Bielfeld ◽  
Inge Agerholm ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Menstrual Cycle ◽  
Endometrial Biopsy ◽  
Human Endometrium ◽  
Biological Age ◽  
Fertility Treatment ◽  
Chronological Age ◽  
Epigenetic Clock ◽  
Mathematical Algorithm ◽  
Time Point

Age has a detrimental effect on reproduction and as an increasing number of women postpone motherhood, it is imperative to assess biological age in terms of fertility prognosis and optimizing fertility treatment individually. Horvath’s epigenetic clock is a mathematical algorithm that calculates the biological age of human cells, tissues or organs based on DNA methylation levels. The clock, however, was previously shown to be highly inaccurate for the human endometrium, most likely because of the hormonal responsive nature of this tissue. The aim of this study was to determine if epigenetically based biological age of the human endometrium correlated with chronological age, when strictly timed to the same time point in the menstrual cycle. Endometrial biopsies from nine women were obtained in two consecutive cycles, both strictly timed to the LH surge (LH + 7) and additionally, peripheral whole blood samples were analyzed. Using the Illumina HumanMethylation 450 K array and Horvath’s epigenetic clock, we found a significant correlation between the biological age of the endometrium and the chronological age of the participants, although the endometrial biological age was accelerated by comparison with blood and chronological age. Moreover, similar biological ages were found in pairs of consecutive biopsies, indicating that an endometrial biopsy does not alter the biological age in the following cycle. In conclusion, as long as endometrial samples are timed to the same time point in the menstrual cycle, Horvath’s epigenetic clock could be a powerful new biomarker of reproductive aging in the human endometrium.

scholarly journals Multi-tissue DNA methylation age predictor in mouse

2017 ◽  
Author(s):  
Thomas M. Stubbs ◽  
Marc Jan Bonder ◽  
Anne-Katrien Stark ◽  
Felix Krueger ◽  
Ferdinand von Meyenn ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Absolute Error ◽  
Biological Age ◽  
Epigenetic Clock ◽  
Cpg Sites ◽  
Mouse Tissues ◽  
Wide Range ◽  
Dna Methylation Age ◽  
The Impact

AbstractBackgroundDNA-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.ResultsWe 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 and 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 offspring diet.ConclusionsHere we identify and characterize 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.

scholarly journals Epigenetic clock detected a breast cancer mitosis subtype with improved immunotherapy

2021 ◽  
Author(s):  
Shipeng Shang ◽  
Xin Li ◽  
Yue Gao ◽  
Shuang Guo ◽  
Hanxiao Zhou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Breast Tissue ◽  
Pearson Correlation ◽  
Breast Cancer Subtypes ◽  
Chronological Age ◽  
Epigenetic Clock ◽  
Methylation Data ◽  
Cancer Subtypes ◽  
Epigenetic Age

Abstract Background Epigenetic clock based on DNA methylation can estimate the epigenetic age of tissue and cell that can describe the process of aging. However, the exploration of diseases by the epigenetic clock is still an uncharted territory. Our objective was to assess the role of the epigenetic clock in breast cancer. Methods In this study, DNA methylation data of breast tissue sample was download from TCGA and GEO database. DNA methylation level of CpG sites and age of samples was calculated by using pearson correlation test. Differentially expressed genes were identified using the limma package and Kruskal-Wallis test was used for the difference between cancer subtypes. Results We developed a workflow to construct the Breast Epigenetic Clock (BEpiC) that could accurately predict the chronological age of normal breast tissue. Furthermore, the BEpiC was applied to breast cancer to identify three breast cancer subtypes (including development, homeostasis, and mitosis) by using the deviation between epigenetic age and chronological age. Interestingly, the prognosis of the three breast cancer subtypes is significantly different. In addition, the three breast cancer subtypes had distinct differences in multiple immune cells and the mitosis subtype had the highest tumor mutation burden that was used to estimate response to checkpoint inhibitors. Conclusion Our model highlights that epigenetic age of breast cancer samples had an important impact on immunotherapy. We constructed a BEpiC web server (http://bio-bigdata.hrbmu.edu.cn/BEpiC/) where users submit DNA methylation data and age information to predict the epigenetic age of breast tissue and breast cancer subtypes. Trial registration Not applicable

scholarly journals DNA methylation age acceleration and risk factors for Alzheimer’s disease

2018 ◽  
Author(s):  
Daniel L McCartney ◽  
Anna J Stevenson ◽  
Rosie M Walker ◽  
Jude Gibson ◽  
Stewart W Morris ◽  
...  
Keyword(s):  
Risk Factors ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Dna Methylation ◽  
Environmental Risk ◽  
Environmental Risk Factors ◽  
Biological Age ◽  
Chronological Age ◽  
Epigenetic Clock ◽  
The Relationship

AbstractINTRODUCTIONThe ‘epigenetic clock’ is a DNA methylation-based estimate of biological age and is correlated with chronological age – the greatest risk factor for Alzheimer’s disease (AD). Genetic and environmental risk factors exist for AD, several of which are potentially modifiable. Here, we assess the relationship associations between the epigenetic clock and AD risk factors.METHODSLinear mixed modelling was used to assess the relationship between age acceleration (the residual of biological age regressed onto chronological age) and AD risk factors relating to cognitive reserve, lifestyle, disease, and genetics in the Generation Scotland study (n=5,100).RESULTSWe report significant associations between the epigenetic clock and BMI, total:HDL cholesterol ratios, socioeconomic status, and smoking behaviour (Bonferroni-adjusted P<0.05).DISCUSSIONAssociations are present between environmental risk factors for AD and age acceleration. Measures to modify such risk factors might improve the risk profile for AD and the rate of biological ageing. Future longitudinal analyses are therefore warranted.

scholarly journals Racial Differences in DNA Methylation-Based Age Acceleration in Preeclamptic and Normotensive Pregnancy

2020 ◽  
Author(s):  
Lacey W. Heinsberg ◽  
Mitali Ray ◽  
Yvette P. Conley ◽  
James M. Roberts ◽  
Arun Jeyabalan ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Racial Disparities ◽  
Small Sample ◽  
Cellular Aging ◽  
Neonatal Outcomes ◽  
Chronological Age ◽  
Biological Aging ◽  
Cell Type ◽  
Increased Risk ◽  
Dna Methylation Age

ABSTRACTBackgroundPreeclampsia is a leading cause of maternal and neonatal morbidity and mortality. Chronological age and race are associated with increased risk of preeclampsia; however, the pathophysiology of preeclampsia and how these risk factors impact its development, are not entirely understood. This gap precludes clinical interventions to prevent preeclampsia occurrence or to address stark racial disparities in maternal and neonatal outcomes. Of note, cellular aging rates can differ between individuals and chronological age is often a poor surrogate of biological age. DNA methylation age provides a marker of biological aging, and those with a DNA methylation age greater than their chronological age have ‘age acceleration’. Examining age acceleration in the context of preeclampsia status, and race, could strengthen our understanding of preeclampsia pathophysiology, inform future interventions to improve maternal/neonatal outcomes, and provide insight to racial disparities across pregnancy.ObjectivesThe purpose of this exploratory study was to examine associations between age acceleration, preeclampsia status, and race across pregnancy.Study designThis was a longitudinal, observational, case-control study of 56 pregnant individuals who developed preeclampsia (n=28) or were normotensive controls (n=28). Peripheral blood samples were collected at trimester-specific time points and genome-wide DNA methylation data were generated using the Infinium MethylationEPIC Beadchip. DNA methylation age was estimated using the Elastic Net ‘Improved Precision’ clock and age acceleration was computed as Δage, the difference between DNA methylation age and chronological age. DNA methylation age was compared with chronological age using scatterplots and Pearson correlations, while considering preeclampsia status and race. The relationships between preeclampsia status, race, and Δage were formally tested using multiple linear regression, while adjusting for pre-pregnancy body mass index, chronological age, and (chronological age)2. Regressions were performed both with and without consideration of cell-type heterogeneity.ResultsWe observed strong correlations between chronological age and DNA methylation age in all trimesters, ranging from R=0.91-0.95 in cases and R=0.86-0.90 in controls. We observed significantly stronger correlations between chronological age and DNA methylation age in White versus Black participants ranging from R=0.89-0.98 in White participants and R=0.77-0.83 in Black participants. We observed no association between Δage and preeclampsia status within trimesters. However, even while controlling for covariates, Δage was higher in trimester 1 in participants with higher pre-pregnancy BMI (β=0.12, 95% CI=0.02 to 0.22, p=0.02) and lower in Black participants relative to White participants in trimesters 2 (β=−2.68, 95% CI=−4.43 to −0.94, p=0.003) and 3 (β=−2.10, 95% CI=−4.03 to −0.17, p=0.03). When controlling for cell-type heterogeneity, the observations with BMI in trimester 1 and race in trimester 2 persisted.ConclusionsWe report no association between Δage and preeclampsia status, although there were associations with pre-pregnancy BMI and race. In particular, our findings in a small sample demonstrate the need for additional studies to not only investigate the complex pathophysiology of preeclampsia, but also the relationship between race and biological aging, which could provide further insight into racial disparities in pregnancy and birth. Future efforts to confirm these findings in larger samples, including exploration and applications of other epigenetic clocks, is needed.

scholarly journals Epigenetic clock and DNA methylation studies of roe deer in the wild

2020 ◽  
Author(s):  
Jean-François Lemaître ◽  
Benjamin Rey ◽  
Jean-Michel Gaillard ◽  
Corinne Régis ◽  
Emmanuelle Gilot ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Evolutionary Biology ◽  
Roe Deer ◽  
Chronological Age ◽  
Epigenetic Clock ◽  
Aging Effects ◽  
Methylation Array ◽  
Biomarkers Of Aging ◽  
Epigenetic Age ◽  
The Relationship

AbstractDNA methylation-based biomarkers of aging (epigenetic clocks) promise to lead to new insights in the evolutionary biology of ageing. Relatively little is known about how the natural environment affects epigenetic aging 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=90 blood samples 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 through the use of sex-specific epigenetic clocks. Our results highlight that both populations and sex influence the epigenetic age, with the bias toward a stronger male average age acceleration (i.e. differences between epigenetic age and chronological ages) particularly pronounced in the population facing harsh environmental conditions. Further, we identify the main sites of epigenetic alteration that have distinct aging patterns across the two sexes. These findings open the door to promising avenues of research at the crossroad of evolutionary biology and biogerontology.

Abstract 160: DNA-Methylation and Aging. Contribution of Biological Age to Recovery After Ischemic Stroke

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Jordi Jimenez-Conde ◽  
Carolina Soriano-Tarraga ◽  
Eva Giralt-Steinhauer ◽  
Marina Mola ◽  
Rosa Vivanco-Hidalgo ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Ischemic Stroke ◽  
Functional Status ◽  
Biological Age ◽  
Chronological Age ◽  
Bivariate Analysis ◽  
Stroke Severity ◽  
Age Related ◽  
Recovery Capacity ◽  
The Impact

Background: Stroke has a great impact in functional status of patients, although there are substantial interindividual differences in recovery capacity. Apart from stroke severity, age is considered an important predictor of outcome after stroke, but aging is not only due to chronological age. There are age-related DNA-methylation changes in multiple CpG sites across the genome that can be used to estimate the biological age (b-Age), and we seek to analyze the impact of this b-Age in recovery after an ischemic stroke. Methods: We include 600 individuals with acute ischemic stroke assessed in Hospital del Mar (Barcelona). Demographic and clinical data such as chronological age (c-Age), vascular risk factors, NIHSS at admission, recanalization treatment (rtPA or endovascular treatment), previous modified Rankin scale (p-mRS) and 3 months post stroke functional status (3-mRS) were registered. Biological age (b-Age) was estimated with Hannumm algorithm, based on DNA methylation in 71 CpGs. Results: The bivariate analyses for association with 3-mRS showed a significant results of NIHSS, c-Age, b-Age, p-mRS, and current smoking (all with p<0.001). Recanalization treatment showed no significant differences in bivariate analysis. In multivariate ordinal models, b-Age kept its significance (p=0.025) nullifying c-Age (p=0.84). Initial NIHSS, p-mRS and recanalization treatment kept also significant results (p<0.001). Conclusions: Biological Age, estimated by DNA methylation, is an independent predictor of stroke prognosis, irrespective to chronological age. "Healthy aging” affects the capacity of recovering after an ischemic stroke.

scholarly journals Exploring Epigenetic Age in Response to Intensive Relaxing Training: A Pilot Study to Slow Down Biological Age

2019 ◽  
Vol 16 (17) ◽  
pp. 3074 ◽  
Author(s):  
Pavanello ◽  
Campisi ◽  
Tona ◽  
Lin ◽  
Iliceto
Keyword(s):  
Myocardial Infarction ◽  
Age Estimation ◽  
Healthy Subjects ◽  
Molecular Mechanisms ◽  
Biological Age ◽  
Chronological Age ◽  
Biological Aging ◽  
Epigenetic Clock ◽  
Age Related ◽  
Epigenetic Age

DNA methylation (DNAm) is an emerging estimator of biological aging, i.e., the often-defined “epigenetic clock”, with a unique accuracy for chronological age estimation (DNAmAge). In this pilot longitudinal study, we examine the hypothesis that intensive relaxing training of 60 days in patients after myocardial infarction and in healthy subjects may influence leucocyte DNAmAge by turning back the epigenetic clock. Moreover, we compare DNAmAge with another mechanism of biological age, leucocyte telomere length (LTL) and telomerase. DNAmAge is reduced after training in healthy subjects (p = 0.053), but not in patients. LTL is preserved after intervention in healthy subjects, while it continues to decrease in patients (p = 0.051). The conventional negative correlation between LTL and chronological age becomes positive after training in both patients (p < 0.01) and healthy subjects (p < 0.05). In our subjects, DNAmAge is not associated with LTL. Our findings would suggest that intensive relaxing practices influence different aging molecular mechanisms, i.e., DNAmAge and LTL, with a rejuvenating effect. Our study reveals that DNAmAge may represent an accurate tool to measure the effectiveness of lifestyle-based interventions in the prevention of age-related diseases.

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