The combined effect of obesity and aging on human sperm DNA methylation signatures: inclusion of BMI in the paternal germ line age prediction model

Abstract Male aging and obesity have both been shown to contribute to declines in fertility in men. Recent work in aging has shown consistent epigenetic changes to sperm as a man ages. In fact, our lab has built a tool that utilizes DNA methylation signatures from sperm to effectively predict an individual’s age. Herein, we performed this preliminary cohort study to determine if increased BMI accelerates the epigenetic aging in sperm. A total of 96 participants were divided into four age groups (22–24, 30, 40–41, and > 48 years of age) and additionally parsed into two BMI sub-categories (normal and high/obese). We found no statistically significant epigenetic age acceleration. However, it is important to note that within each age category, high BMI individuals were predicted to be older on average than their actual age (~ 1.4 years), which was not observed in the normal BMI group. To further investigate this, we re-trained a model using only the present data with and without BMI as a feature. We found a modest but non-significant improvement in prediction with BMI [r2 = 0.8814, mean absolute error (MAE) = 3.2913] compared to prediction without BMI (r2 = 0.8739, MAE = 3.3567). Future studies with higher numbers of age-matched individuals are needed to definitively understand the impact of BMI on epigenetic aging in sperm.

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Dysfunctional epigenetic aging of the normal colon and colorectal cancer risk

Clinical Epigenetics ◽

10.1186/s13148-019-0801-3 ◽

2020 ◽

Vol 12 (1) ◽

Cited By ~ 11

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.

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Epigenetic age-predictions in mice using pyrosequencing, droplet digital PCR or barcoded bisulfite amplicon sequencing

10.1101/2020.07.30.228122 ◽

2020 ◽

Author(s):

Yang Han ◽

Miloš Nikolić ◽

Michael Gobs ◽

Julia Franzen ◽

Gerald de Haan ◽

...

Keyword(s):

Dna Methylation ◽

Model Organism ◽

Amplicon Sequencing ◽

Digital Pcr ◽

Droplet Digital Pcr ◽

Biological Aging ◽

Epigenetic Aging ◽

Epigenetic Age ◽

Trait Locus ◽

The Impact

AbstractAge-associated DNA methylation reflects aspects of biological aging - therefore epigenetic clocks for mice can help to elucidate the impact of treatments or genetic background on the aging process in this model organism. Initially, age-predictors for mice were trained on genome-wide DNA methylation profiles, whereas we have recently described a targeted assay based on pyrosequencing of DNA methylation at only three CG dinucleotides (CpGs). Here, we have re-evaluated pyrosequencing approaches in comparison to droplet digital PCR (ddPCR) and barcoded bisulfite amplicon sequencing (BBA-seq). At individual CpGs the correlation of DNA methylation with chronological age was slightly higher for pyrosequencing and ddPCR as compared to BBA-seq. On the other hand, BBA-seq revealed that neighboring CpGs tend to be stochastically modified in murine age-associated regions. Furthermore, the binary sequel of methylated and non-methylated CpGs in individual reads can be used for single-read predictions, which may reflect heterogeneity in epigenetic aging. In comparison to C57BL/6 mice the epigenetic age-predictions using BBA-seq were also accelerated in the shorter-lived DBA/2 mice, and in C57BL/6 mice with a lifespan quantitative trait locus of DBA/2 mice. Taken together, we describe further optimized and alternative targeted methods to determine epigenetic clocks in mice.

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

10.1101/2021.10.05.463222 ◽

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.

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Effects of Smoking Cessation on Epigenetic Aging

Innovation in Aging ◽

10.1093/geroni/igab046.2518 ◽

2021 ◽

Vol 5 (Supplement_1) ◽

pp. 673-673

Author(s):

Brian Chen ◽

Weiye Wang ◽

Nichole Rigby ◽

Randal Olson ◽

Steve Sabes

Keyword(s):

Dna Methylation ◽

Experimental Studies ◽

Biological Aging ◽

Multiple Time ◽

Epigenetic Aging ◽

Epigenetic Age ◽

Epigenetic Age Acceleration ◽

Aging Rates ◽

Multiple Time Points ◽

Over Time

Abstract “Epigenetic clocks” have become widely used to assess individual rates of biological aging. However, experimental data are limited in humans to identify potential confounding factors that may influence one’s rate of epigenetic aging and multiple health outcomes. We examined multiple epigenetic aging measures among regular smokers who quit smoking for two weeks. DNA methylation markers were assessed in both whole blood and saliva at multiple time points using a customized DNA methylation microarray. Generally, no changes in epigenetic aging rates were detected in the two week observation period with the exception of pronounced decreases over time in rate of Hannum’s clock and Extrinsic Epigenetic Age Acceleration in blood DNA. In saliva DNA, decreases over time were detected in the rates of the GrimAge and DNAmPhenoAge clocks, but we saw an increase in the rate of the Skin and Blood Clock. Additional experimental studies of other common exposures may be useful to better characterize factors that may affect the observed “rate” of epigenetic aging.

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Paternal germ line aging: DNA methylation age prediction from human sperm

10.1101/220764 ◽

2017 ◽

Cited By ~ 1

Author(s):

Timothy G Jenkins ◽

Kenneth I Aston ◽

Andrew Smith ◽

Douglas T Carrell

Keyword(s):

Dna Methylation ◽

Germ Line ◽

Human Sperm ◽

Chronological Age ◽

Methylation Array ◽

Data Set ◽

Sperm Dna ◽

High Degree ◽

Age Prediction ◽

Sperm Dna Methylation

AbstractBackgroundThe relationship between aging and epigenetic profiles has been highlighted in many recent studies. Models using somatic cell methylomes to predict age have been successfully constructed. However, gamete aging is quite distinct and as such age prediction using sperm methylomes is ineffective with current techniques.ResultsWe have produced a model that utilizes human sperm DNA methylation signatures to predict chronological age by utilizing methylation array data from a total of 329 samples. The dataset used for model construction includes infertile patients, sperm donors, and individuals from the general population. Our model is capable of accurately predicting age with an R2 of 0.928 in our test data set. We additionally investigated the repeatability of prediction by processing the same sample on 6 different arrays and found very robust age prediction with an average standard deviation of only 0.877 years. Additionally, we found that smokers have approximately 5% increased age profiles compared to ‘never smokers.’ConclusionsThe predictive model described herein was built to offer researchers the ability to assess “germ line age” by accessing sperm DNA methylation signatures at genomic regions affected by age. Our data suggest that this model can predict an individual’s chronological age with a high degree of accuracy regardless of fertility status and with a high degree of repeatability. Additionally, our data appear to show age acceleration patterns as a result of smoking suggesting that the aging process in sperm may be impacted by environmental factors, though this effect appears to be quite subtle.

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Accelerated Epigenetic Aging in Peripheral Blood Does not Predict Dementia Risk

Current Alzheimer Research ◽

10.2174/1567205018666210823100721 ◽

2021 ◽

Vol 18 ◽

Author(s):

P.D. Fransquet ◽

P. Lacaze ◽

R. Saffery ◽

R.C. Shah ◽

R. Vryer ◽

...

Keyword(s):

Dna Methylation ◽

Strong Evidence ◽

Peripheral Blood ◽

Later Life ◽

Epigenetic Biomarkers ◽

Dementia Risk ◽

Epigenetic Aging ◽

Epigenetic Age ◽

Increased Risk ◽

Epigenetic Age Acceleration

Background: There is strong evidence that epigenetic age acceleration is associated with increased risk of later-life diseases and all-cause mortality. However, there is currently limited evidence that suggests accelerated epigenetic age is associated with dementia risk. Objective: This study aims to clarify whether epigenetic biomarkers of accelerated aging can pre- dict dementia risk, which is an important consideration as aging is the greatest risk factor for the disease. Methods: DNA methylation was measured in peripheral blood samples provided by 160 partici- pants from the ASPirin in Reducing Events in the Elderly study, including 73 pre-symptomatic de- mentia cases and 87 controls matched for age, sex, and smoking and education status. Epigenetic age was calculated using Horvath, Hannum, GrimAge and PhenoAge DNA methylation clocks, and age acceleration (the disparity between chronological age and epigenetic age) was determined. Results: There was no difference in age acceleration between dementia cases and controls. In males, only Hannum’s intrinsic epigenetic age acceleration was increased in pre-symptomatic de- mentia cases compared to controls (Δ +1.8 years, p = 0.03). Conclusion: These findings provide no strong evidence that accelerated epigenetic aging measured in peripheral blood can predict dementia risk.

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

Circulation ◽

10.1161/circ.135.suppl_1.p106 ◽

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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Age-associated sperm DNA methylation patterns do not directly persist trans-generationally

Epigenetics & Chromatin ◽

10.1186/s13072-019-0323-4 ◽

2019 ◽

Vol 12 (1) ◽

Cited By ~ 4

Author(s):

Timothy G. Jenkins ◽

Emma R. James ◽

Kenneth I. Aston ◽

Albert Salas-Huetos ◽

Alexander W. Pastuszak ◽

...

Keyword(s):

Dna Methylation ◽

Large Scale ◽

Human Sperm ◽

Paternal Age ◽

Sperm Dna ◽

Two Generations ◽

Methylation Patterns ◽

The Impact ◽

Sperm Dna Methylation

Abstract Background The impact of aging on the sperm methylome is well understood. However, the direct, subsequent impact on offspring and the role of altered sperm DNA methylation alterations in this process remain poorly understood. The well-defined impact of aging on sperm DNA methylation represents an excellent opportunity to trace the direct, transgenerational transmission of these signals. Results We utilized the Illumina MethylationEPIC array to analyze the sperm of 16 patients with older (> 40 years of age) paternal grandfathers (‘old grand paternal age’ patients; OGPA) and 16 patients with younger (< 25 years of age) grandfathers (‘young grand paternal age’ patients; YGPA) identified through the Subfertility Health Assisted Reproduction and the Environment (SHARE) cohort to investigate differences in DNA methylation. No differentially methylated regions were identified between the OGPA and YGPA groups. Further, when assessing only the sites previously shown to be altered by age, no statistically significant differences between OGPA and YGPA were identified. This was true even despite the lower bar for significance after removing multiple comparison correction in a targeted approach. Interestingly though, in an analysis of the 140 loci known to have decreased methylation with age, the majority (~ 72%) had lower methylation in OGPA compared to YGPA though the differences were extremely small (~ 1.5%). Conclusions This study suggests that the robust and consistent age-associated methylation alterations seen in human sperm are ‘reset’ during large-scale epigenetic reprograming processes and are not directly inherited trans-generationally (over two generations). An extremely small trend was present between the YGPA and OGPA groups that resemble the aging pattern in older sperm. However, this trend was not significant and was so small that, if real, is almost certainly biologically inert.

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Male obesity impacts DNA methylation reprogramming in sperm

Clinical Epigenetics ◽

10.1186/s13148-020-00997-0 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Sanaz Keyhan ◽

Emily Burke ◽

Rose Schrott ◽

Zhiqing Huang ◽

Carole Grenier ◽

...

Keyword(s):

Dna Methylation ◽

System Development ◽

Human Sperm ◽

Nervous System Development ◽

Cpg Sites ◽

Epididymal Maturation ◽

Sperm Dna ◽

Stem Cell Pluripotency ◽

The Impact ◽

Sperm Dna Methylation

Abstract Background Male obesity has profound effects on morbidity and mortality, but relatively little is known about the impact of obesity on gametes and the potential for adverse effects of male obesity to be passed to the next generation. DNA methylation contributes to gene regulation and is erased and re-established during gametogenesis. Throughout post-pubertal spermatogenesis, there are continual needs to both maintain established methylation and complete DNA methylation programming, even during epididymal maturation. This dynamic epigenetic landscape may confer increased vulnerability to environmental influences, including the obesogenic environment, that could disrupt reprogramming fidelity. Here we conducted an exploratory analysis that showed that overweight/obesity (n = 20) is associated with differences in mature spermatozoa DNA methylation profiles relative to controls with normal BMI (n = 47). Results We identified 3264 CpG sites in human sperm that are significantly associated with BMI (p < 0.05) using Infinium HumanMethylation450 BeadChips. These CpG sites were significantly overrepresented among genes involved in transcriptional regulation and misregulation in cancer, nervous system development, and stem cell pluripotency. Analysis of individual sperm using bisulfite sequencing of cloned alleles revealed that the methylation differences are present in a subset of sperm rather than being randomly distributed across all sperm. Conclusions Male obesity is associated with altered sperm DNA methylation profiles that appear to affect reprogramming fidelity in a subset of sperm, suggestive of an influence on the spermatogonia. Further work is required to determine the potential heritability of these DNA methylation alterations. If heritable, these changes have the potential to impede normal development.

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Association of cardiovascular health and epigenetic age acceleration

Clinical Epigenetics ◽

10.1186/s13148-021-01028-2 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Tess D. Pottinger ◽

Sadiya S. Khan ◽

Yinan Zheng ◽

Wei Zhang ◽

Hilary A. Tindle ◽

...

Keyword(s):

Dna Methylation ◽

Cardiovascular Health ◽

Heart Association ◽

The United States ◽

Cross Sectional ◽

Epigenetic Age ◽

Younger Age ◽

Cardiovascular Morbidity And Mortality ◽

Epigenetic Age Acceleration ◽

Linear Regression Modeling

Abstract Background Cardiovascular health (CVH) has been defined by the American Heart Association (AHA) as the presence of the “Life’s Simple 7” ideal lifestyle and clinical factors. CVH is known to predict longevity and freedom from cardiovascular disease, the leading cause of death for women in the United States. DNA methylation markers of aging have been aggregated into a composite epigenetic age score, which is associated with cardiovascular morbidity and mortality. However, it is unknown whether poor CVH is associated with acceleration of aging as measured by DNA methylation markers in epigenetic age. Methods and results We performed a cross-sectional analysis of racially/ethnically diverse post-menopausal women enrolled in the Women’s Health Initiative cohort recruited between 1993 and 1998. Epigenetic age acceleration (EAA) was calculated using DNA methylation data on a subset of participants and the published Horvath and Hannum methods for intrinsic and extrinsic EAA. CVH was calculated using the AHA measures of CVH contributing to a 7-point score. We examined the association between CVH score and EAA using linear regression modeling adjusting for self-reported race/ethnicity and education. Among the 2,170 participants analyzed, 50% were white and mean age was 64 (7 SD) years. Higher or more favorable CVH scores were associated with lower extrinsic EAA (~ 6 months younger age per 1 point higher CVH score, p < 0.0001), and lower intrinsic EAA (3 months younger age per 1 point higher CVH score, p < 0.028). Conclusions These cross-sectional observations suggest a possible mechanism by which ideal CVH is associated with greater longevity.

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