Genetic and Environmental Causes of Variation in the Difference Between Biological Age Based on DNA Methylation and Chronological Age for Middle-Aged Women

2015 ◽  
Vol 18 (6) ◽  
pp. 720-726 ◽  
Author(s):  
Shuai Li ◽  
Ee Ming Wong ◽  
JiHoon E. Joo ◽  
Chol-Hee Jung ◽  
Jessica Chung ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Genetic Factors ◽  
Pair Correlation ◽  
Weighted Average ◽  
Biological Age ◽  
Sibling Pair ◽  
Chronological Age ◽  
Familial Correlation ◽  
Middle Aged ◽  
Pair Correlations

The disease- and mortality-related difference between biological age based on DNA methylation and chronological age (Δage) has been found to have approximately 40% heritability by assuming that the familial correlation is only explained by additive genetic factors. We calculated two different Δage measures for 132 middle-aged female twin pairs (66 monozygotic and 66 dizygotic twin pairs) and their 215 sisters using DNA methylation data measured by the Infinium HumanMethylation450 BeadChip arrays. For each Δage measure, and their combined measure, we estimated the familial correlation for MZ, DZ and sibling pairs using the multivariate normal model for pedigree analysis. We also pooled our estimates with those from a former study to estimate weighted average correlations. For both Δage measures, there was familial correlation that varied across different types of relatives. No evidence of a difference was found between the MZ and DZ pair correlations, or between the DZ and sibling pair correlations. The only difference was between the MZ and sibling pair correlations (p < .01), and there was marginal evidence that the MZ pair correlation was greater than twice the sibling pair correlation (p < .08). For weighted average correlation, there was evidence that the MZ pair correlation was greater than the DZ pair correlation (p < .03), and marginally greater than twice the sibling pair correlation (p < .08). The varied familial correlation of Δage is not explained by additive genetic factors alone, implying the existence of shared non-genetic factors explaining variation in Δage for middle-aged women.

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 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 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 P0070ARE BIOMARKERS OF AGEING IN THE FORM OF TELOMERE LENGTH AND/OR THE DNA METHYLATION STATUS ASSOCIATED WITH FRAILTY PHENOTYPE IN PATIENTS WITH END STAGE KIDNEY DISEASE ON DIALYSIS?

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Vasantha Muthuppalaniappan ◽  
Kieran McCafferty ◽  
Muhammad Yaqoob
Keyword(s):  
Dna Methylation ◽  
Kidney Disease ◽  
Telomere Length ◽  
Biological Age ◽  
Chronological Age ◽  
Dialysis Patients ◽  
End Stage Kidney Disease ◽  
Frailty Phenotype ◽  
Cpg Sites ◽  
End Stage

Abstract Background and Aims There appears to be an accelerated ageing process seen among patients with end stage kidney disease. They often exhibit prematurely aged phenotypes which include frailty, sarcopenia and protein energy wasting. These phenotypes are associated with increased morbidity and mortality. The exact reason for premature ageing in this cohort is poorly understood. We hypothesised that biomarkers of cellular senescence and biological age may be associated with phenotypes of ageing observed in dialysis patients; in particular the frailty phenotype. Both telomere length (TL) and DNA methylation (DNAm) status have been recognised as predictors of biological age. The aim of the study was to investigate the relationship between TL and DNAm status with frailty phenotype among patients on dialysis. Method This was a single centre prospective observational study among eligible haemodialysis (HD) and peritoneal dialysis (PD) patients as per the inclusion and exclusion criteria. All recruited patients had their DNA extracted from peripheral leucocytes and frailty was measured by using the Fried Frailty Phenotype criteria. Extracted DNA was used to measure TL by quantitate polymerase chain reaction according to the modified Cawthon protocol. DNAm status was measured by sodium bisulphite conversion with targeted sequencing of 48 CpG sites. All baseline demographic data were obtained from electronic patient record. Results Between the period of December 2015 to July 2018, 239 dialysis (125 HD, 114 PD) patients were recruited. The age range of the study recruits were 23 to 83 years of age with a mean age of 54.3 years. There were 44 and 43 females in the HD and PD group respectively. All patients had TL measured and 228 patients (118 in HD, 110 in PD) had DNAm status measured successfully. Frailty assessments at baseline were completed in 213 patients (110 in HD, 103 in PD). A decrease in mean TL (p&lt;0.001) and increased mean DNAm age (p&lt;0.001) was observed in the frail group. TL and DNAm status were significantly associated with frailty in a univariate analysis, p=0.010 and p=0.014 respectively but only TL remained significant in a multivariate analysis to predict frailty, p=0.018. Increased frailty was observed in dialysis patients with shorter TL which remained significant following multivariate logistic regression analysis. A receiver operating characteristic curve analysis demonstrated that TL was a significant predictor of frailty, p&lt;0.001 with an area under the curve of 0.64. A decrease of TL by one standard deviation was associated with a 52.2% increase risk of frailty when adjusted for age and gender in this dialysis cohort. Conclusion The study supports the hypothesis that TL; a biomarker of ageing is better associated with frailty, an ageing phenotype in comparison to DNAm status in dialysis patients. This is the first study to show a significant association between TL and frailty status in the dialysis patients. DNAm status is derived from an individual’s chronological age and may not be the best surrogate for chronological age. Therefore, to use this measure seems rather counter intuitive as the incorporation of chronological age might mask clinically significant associations that exist when the DNAm status is considered in isolation. These findings provide a preliminary proof of concept validation that TL is associated with frailty and may provide the basis of future research. The use of targeted methylation CpG sites may be a better predictor of frailty in this cohort which is an area that has sparked an interest.

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 Epigenetic clocks are not accelerated in COVID-19 patients

2020 ◽  
Author(s):  
Julia Franzen ◽  
Selina Nüchtern ◽  
Vithurithra Tharmapalan ◽  
Margherita Vieri ◽  
Miloš Nikolić ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Surrogate Marker ◽  
Amplicon Sequencing ◽  
Biological Age ◽  
Chronological Age ◽  
Donor Age ◽  
Blood Samples ◽  
Telomere Attrition ◽  
Epigenetic Age ◽  
Genomic Regions

AbstractAge is a major risk factor for severe outcome of coronavirus disease 2019 (COVID-19), but it remains unclear if this is rather due to increased chronological age or biological age. During lifetime, specific DNA methylation changes are acquired in our genome that act as “epigenetic clocks” allowing to estimate donor age and to provide a surrogate marker for biological age. In this study, we followed the hypothesis that particularly patients with accelerated epigenetic age are affected by severe outcomes of COVID-19. Using four different age predictors, we did not observe accelerated age in global DNA methylation profiles of blood samples of nine COVID-19 patients. Alternatively, we used targeted bisulfite amplicon sequencing of three age-associated genomic regions to estimate donor-age of blood samples of 95 controls and seventeen COVID-19 patients. The predictions correlated well with chronological age, while COVID-19 patients even tended to be predicted younger than expected. Furthermore, lymphocytes in nineteen COVID-19 patients did not reveal significantly accelerated telomere attrition. Our results demonstrate that these biomarkers of biological age are therefore not suitable to predict a higher risk for severe COVID-19 infection in elderly patients.

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 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 Association of Facial Aging with DNA Methylation and Epigenetic Age Predictions

2018 ◽  
Author(s):  
Riccardo E Marioni ◽  
Daniel W Belsky ◽  
Ian J Deary ◽  
Wolfgang Wagner
Keyword(s):  
Dna Methylation ◽  
Healthy Aging ◽  
Later Life ◽  
Biological Age ◽  
Chronological Age ◽  
Biological Aging ◽  
Facial Aging ◽  
Epigenetic Age ◽  
All Cause Mortality ◽  
Facial Images

AbstractEvaluation of biological age, as opposed to chronological age, is of high relevance for interventions to increase healthy aging. Highly reproducible age-associated DNA methylation (DNAm) changes can be integrated into algorithms for epigenetic age predictions. These predictors have mostly been trained to correlate with chronological age, but they are also indicative for biological aging. For example accelerated epigenetic age of blood is associated with higher risk of all-cause mortality in later life. The perceived age of facial images (face-age) is also associated with all-cause mortality and other aging-associated traits. In this study, we therefore tested the hypothesis that an epigenetic predictor for biological age might be trained on face-age as surrogate for biological age, rather than on chronological age. Our data demonstrate that facial aging and DNAm changes in blood provide two independent measures for biological aging.

Sign in / Sign up

Export Citation Format

Share Document