DNA Methylation-based Age Research Articles

Abstract P413: Epigenetic Age Acceleration Measures Predict Cardiovascular Outcomes in Diabetic Kidney Disease

Introduction: Cardiovascular disease (CVD) remains the leading cause of mortality in the US, highlighting the need for novel approaches to its prediction, prevention, and management. Epigenetic age acceleration (EAA), or increased DNA methylation-based age relative to chronological age, is a promising biomarker of disease risk. While previous studies have prospectively linked EAA to CVD, recent work in patients with end-stage kidney disease showed no association between EAA and mortality from CVD, underscoring a need for more research across patient populations. Hypothesis: EAA measures, specifically intrinsic epigenetic age acceleration (IEAA), extrinsic epigenetic age acceleration (EEAA), phenotypic epigenetic age acceleration (PhenoAA), and Grim epigenetic age acceleration (GrimAA), are associated with incident CVD in diabetic kidney disease (DKD). Methods: In a subset of 496 participants from the Chronic Renal Insufficiency Cohort with DKD, selected based on kidney function decline over follow-up (large versus small), we examined four EAA measures: IEAA, EEAA, PhenoAA, and GrimAA. GrimAA is comprised of eight DNA methylation-based biomarkers, including plasminogen activator inhibitor-1 age acceleration (PAI-1 AA ) and tissue inhibitor of matrix metalloproteinase (TIMP-1 AA ), which were also assessed. Primary outcomes were incident CVD, atherosclerotic CVD, and CVD mortality; secondary outcomes included myocardial infarction, congestive heart failure (CHF), cerebrovascular accident, and peripheral artery disease (PAD). Cox proportional models tested associations between the EAA measures and CVD endpoints after adjusting for baseline demographics, clinical center, kidney function decline as a study design feature, lifestyle, and clinical risk factors. Results: Participants were predominantly male (63%), with a median age of 57 years, median eGFR of 44 mL/min/1.73m 2 , and mean hemoglobin A1c of 8.0%. After adjusting for multiple testing, each standard deviation increase in GrimAA was associated with incident CVD (HR=1.27; 95% confidence interval [CI], 1.08-1.50; p=0.012) and PAD (HR=1.65; 95% CI, 1.15-2.37; p=0.006). Likewise, PAI-1 AA was significantly associated with incident CVD (HR=1.24; 95% CI, 1.06-1.44; p=0.006) and PAD (HR=1.46; 95% CI, 1.17-1.81; p=0.001), while TIMP-1 AA was associated with CHF (HR=1.32; 95% CI, 1.09-1.60; p=0.005). Nominally significant associations between IEAA and atherosclerotic CVD were also identified (HR=1.24; 95% CI, 1.00-1.53; p=0.049). Similarly, EEAA was nominally associated with incident CVD (HR 1.22; 95% CI, 1.01-1.47, p=0.039). Conclusion: EAA measures, notably GrimAA, are promising CVD biomarkers in DKD patients. Associations of GrimAA components PAI-1 AA and TIMP-1 AA with incident CVD add to the accumulating evidence of their predictive and therapeutic potential.

Epigenetic age prediction using costal cartilage for the investigation of disaster victims and missing persons.

The DNA intelligence tool, DNA methylation-based age prediction, can help identify disaster victims and suspects in criminal investigations. In this study, we developed a costal cartilage-based age prediction tool that uses massive parallel sequencing (MPS) of age-associated DNA methylation markers. Costal cartilage samples were obtained from 85 deceased Koreans, aged between 26 and 89 years. An MPS library was prepared using two rounds of multiplex polymerase chain reaction of nine genes (TMEM51, MIR29B2CHG, EDARADD, FHL2, TRIM59, ELOVL2, KLF14, ASPA, and PDE4C). The DNA methylation status of 45 CpG sites was determined and used to train an age prediction model via stepwise regression analysis. Nine CpGs in MIR29B2CHG, FHL2, TRIM59, ELOVL2, KLF14, and ASPA were selected for regression model construction. A leave-one-out cross-validation analysis revealed the high performance of the age prediction model, with a mean absolute error (MAE) and root mean square error of 4.97 and 6.43 years, respectively. Additionally, our model showed good performance with a MAE of 6.06 years in the analysis of data of 181 costal cartilage samples collected from Europeans. Our model effectively estimates the age of deceased individuals using costal cartilage samples; therefore, it can be a valuable forensic tool for disaster victim and missing person investigation.

A cost-effective blood DNA methylation-based age estimation method in domestic cats, Tsushima leopard cats (Prionailurus bengalensis euptilurus) and Panthera species, using targeted bisulphite sequencing and machine learning models.

Individual age can be used to design more efficient and suitable management plans in both insitu and ex situ conservation programmes for targeted wildlife species. DNA methylation is a promising marker of epigenetic ageing that can accurately estimate age from small amounts of biological material, which can be collected in a minimally invasive manner. In this study, we sequenced five targeted genetic regions and used 8-23 selected CpG sites to build age estimation models using machine learning methods at only about $3-7 per sample. Blood samples of seven Felidae species were used, ranging from small to big, and domestic to endangered species: domestic cats (Felis catus, 139 samples), Tsushima leopard cats (Prionailurus bengalensis euptilurus, 84 samples) and five Panthera species (96 samples). The models achieved satisfactory accuracy, with the mean absolute error of the most accurate models recorded at 1.966, 1.348 and 1.552 years in domestic cats, Tsushima leopard cats and Panthera spp. respectively. We developed the models in domestic cats and Tsushima leopard cats, which were applicable to individuals regardless of health conditions; therefore, these models are applicable to samples collected from individuals with diverse characteristics, which is often the case in conservation. We also showed the possibility of developing universal age estimation models for the five Panthera spp. using only two of the five genetic regions. We do not recommend building a common age estimation model for all the target species using our markers, because of the degraded performance of models that included all species.

DNA methylation-based age estimation and quantification of the degradation levels of bisulfite-converted DNA

DNA methylation modifications are known to influence epigenetic phenomena and have been a focus of forensic science research for some time. Degraded DNA after bisulfite treatment is widely used in DNA methylation analysis. In this study, we analyzed methylation levels at 12 CpG sites of four selected genomic regions by pyrosequencing after bisulfite treatment. DNA was extracted from buccal swab samples collected from 102 Japanese individuals who were 21–77 years old. We also developed a simple method to quantify the degradation levels of bisulfite-converted DNA by real-time PCR, and evaluated the effect of DNA degradation on age estimation. We found that the methylation levels and chronological ages were highly correlated in the four selected regions, and the mean absolute deviation (MAD) between chronological and estimated ages was low at 3.88 years. These results indicated that pyrosequencing analysis at the 12 CpGs was useful for age estimation in the Japanese population. To develop a sensitive quantification method, we analyzed the amplification efficiency of short and long fragments from 10 regions by real-time PCR. The amplification efficiency was highest for CCDC102B, and the degradation levels of bisulfite-converted DNA for the 102 samples were categorized as moderately or heavily degraded. For the younger age groups (20–49 years), the MADs were lower for moderately degraded DNA than they were for heavily degraded DNA. This finding indicates that degradation levels affected the accuracy of age estimation in most of the samples; the exception was the samples from the 50–77 years age group.

Associations between cardiorespiratory fitness and lifestyle-related factors with DNA methylation-based ageing clocks in older men: WASEDA'S Health Study.

DNA methylation-based age estimators (DNAm ageing clocks) are currently one of the most promising biomarkers for predicting biological age. However, the relationships between cardiorespiratory fitness (CRF), measured directly by expiratory gas analysis, and DNAm ageing clocks are largely unknown. We investigated the relationships between CRF and the age-adjusted value from the residuals of the regression of DNAm ageing clock to chronological age (DNAmAgeAcceleration: DNAmAgeAccel) and attempted to determine the relative contribution of CRF to DNAmAgeAccel in the presence of other lifestyle factors. DNA samples from 144 Japanese men aged 65-72 years were used to appraise first- (i.e., DNAmHorvath and DNAmHannum) and second- (i.e., DNAmPhenoAge, DNAmGrimAge, and DNAmFitAge) generation DNAm ageing clocks. Various surveys and measurements were conducted, including physical fitness, body composition, blood biochemical parameters, nutrient intake, smoking, alcohol consumption, disease status, sleep status, and chronotype. Both oxygen uptake at ventilatory threshold (VO2 /kg at VT) and peak oxygen uptake (VO2 /kg at Peak) showed a significant negative correlation with GrimAgeAccel, even after adjustments for chronological age and smoking and drinking status. Notably, VO2 /kg at VT and VO2 /kg at Peak above the reference value were also associated with delayed GrimAgeAccel. Multiple regression analysis showed that calf circumference, serum triglyceride, carbohydrate intake, and smoking status, rather than CRF, contributed more to GrimAgeAccel and FitAgeAccel. In conclusion, although the contribution of CRF to GrimAgeAccel and FitAgeAccel is relatively low compared to lifestyle-related factors such as smoking, the results suggest that the maintenance of CRF is associated with delayed biological ageing in older men.

Centenarian clocks: epigenetic clocks for validating claims of exceptional longevity

Claims surrounding exceptional longevity are sometimes disputed or dismissed for lack of credible evidence. Here, we present three DNA methylation-based age estimators (epigenetic clocks) for verifying age claims of centenarians. The three centenarian clocks were developed based on n = 7039 blood and saliva samples from individuals older than 40, including n = 184 samples from centenarians, 122 samples from semi-supercentenarians (aged 105 +), and 25 samples from supercentenarians (aged 110 +). The oldest individual was 115 years old. Our most accurate centenarian clock resulted from applying a neural network model to a training set composed of individuals older than 40. An epigenome-wide association study of age in different age groups revealed that age effects in young individuals (age < 40) are correlated (r = 0.55) with age effects in old individuals (age > 90). We present a chromatin state analysis of age effects in centenarians. The centenarian clocks are expected to be useful for validating claims surrounding exceptional old age.

Forensic Age Estimation through a DNA Methylation-Based Age Prediction Model in the Italian Population: A Pilot Study

DNA methylation is one of the epigenetic marks which has been studied intensively in recent years for age predicting purposes in the forensic area. In order to integrate age prediction into routine forensic workflow, the purpose of this study was to standardize and optimize a DNA methylation-based protocol tailored to the Italian context. A previously published protocol and age-predictive method was implemented for the analysis of 84 blood samples originating from Central Italy. The study here presented is based on the Single Base Extension method, considering five genes: ELOVL2, FHL2, KLF14, C1orf132, now identified as MIR29B2C, and TRIM59. The precise and specific steps consist of DNA extraction and quantification, bisulfite conversion, amplification of converted DNA, first purification, single base extension, second purification, capillary electrophoresis, and analysis of the results to train and test the tool. The prediction error obtained, expressed as mean absolute deviation, showed a value of 3.12 years in the training set and 3.01 years in the test set. Given that population-based differences in DNA methylation patterns have been previously reported in the literature, it would be useful to further improve the study implementing additional samples representative of the entire Italian population.

DNA methylation-based age estimation for adults and minors: considering sex-specific differences and non-linear correlations

DNA methylation patterns change during human lifetime; thus, they can be used to estimate an individual’s age. It is known, however, that correlation between DNA methylation and aging might not be linear and that the sex might influence the methylation status. In this study, we conducted a comparative evaluation of linear and several non-linear regressions, as well as sex-specific versus unisex models. Buccal swab samples from 230 donors aged 1 to 88 years were analyzed using a minisequencing multiplex array. Samples were divided into a training set (n = 161) and a validation set (n = 69). The training set was used for a sequential replacement regression and a simultaneous 10-fold cross-validation. The resulting model was improved by including a cut-off of 20 years, dividing the younger individuals with non-linear from the older individuals with linear dependence between age and methylation status. Sex-specific models were developed and improved prediction accuracy in females but not in males, which might be explained by a small sample set. We finally established a non-linear, unisex model combining the markers EDARADD, KLF14, ELOVL2, FHL2, C1orf132, and TRIM59. While age- and sex-adjustments did not generally improve the performance of our model, we discuss how other models and large cohorts might benefit from such adjustments. Our model showed a cross-validated MAD and RMSE of 4.680 and 6.436 years in the training set and of 4.695 and 6.602 years in the validation set, respectively. We briefly explain how to apply the model for age prediction.

DNA methylation-based age prediction with bloodstains using pyrosequencing and random forest regression.

The use of DNA methylation to predict chronological age has shown promising potential for obtaining additional information in forensic investigations. To date, several studies have reported age prediction models based on DNA methylation in body fluids with high DNA content. However, it is often difficult to apply these existing methods in practice due to the low amount of DNA present in stains of body fluids that are part of a trace material. In this study, we present a sensitive and rapid test for age prediction with bloodstains based on pyrosequencing and random forest regression. This assay requires only 0.1ng of genomic DNA and the entire procedure can be completed within 10h, making it practical for forensic investigations that require a short turnaround time. We examined the methylation levels of 46 CpG sites from six genes using bloodstain samples from 128 males and 113 females aged 10-79 years. A random forest regression model was then used to construct an age prediction model for males and females separately. The final age prediction models were developed with seven CpG sites (three for males and four for females) based on the performance of the random forest regression. The mean absolute deviation was less than 3 years for each model. Our results demonstrate that DNA methylation-based age prediction using pyrosequencing and random forest regression has potential applications in forensics to accurately predict the biological age of a bloodstain donor.

Childhood environment influences epigenetic age and methylation concordance of a CpG clock locus in British-Bangladeshi migrants

ABSTRACT Migration from one location to another often comes with a change in environmental conditions. Here, we analysed features of DNA methylation in young, adult British-Bangladeshi women who experienced different environments during their childhoods: a) migrants, who grew up in Bangladesh with exposure to comparatively higher pathogen loads and poorer health care, and b) second-generation British-Bangladeshis, born to Bangladeshi parents, who grew up in the UK. We used buccal DNA to estimate DNA methylation-based age (DNAm age) from 14 migrants and 11 second-generation migrants, aged 18–35 years. ‘AgeAccel,’ a measure of DNAm age, independent of chronological age, showed that the group of women who spent their childhood in Bangladesh had higher AgeAccel (P = 0.028), compared to their UK peers. Since epigenetic clocks have been proposed to be associated with maintenance processes of epigenetic systems, we evaluated the preference for concordant DNA methylation at the luteinizing hormone/choriogonadotropin receptor (LHCGR/LHR) locus, which harbours one of the CpGs contributing to Horvath’s epigenetic clock. Measurements on both strands of individual, double-stranded DNA molecules indicate higher stability of DNA methylation states at this LHCGR/LHR locus in samples of women who grew up in Bangladesh. Together, our two independent analytical approaches imply that childhood environments may induce subtle changes that are detectable long after exposure occurred, which might reflect altered activity of the epigenetic maintenance system or a difference in the proportion of cell types in buccal tissue. This exploratory work supports our earlier findings that adverse childhood environments lead to phenotypic life history trade-offs.

Centenarians consistently present a younger epigenetic age than their chronological age with four epigenetic clocks based on a small number of CpG sites.

Aging is a progressive time-dependent biological process affecting differentially individuals, who can sometimes present exceptional longevity. Epigenetic alterations are one of the hallmarks of aging, which comprise the epigenetic drift and clock at DNA methylation level. In the present study, we estimated the DNA methylation-based age (DNAmage) using four epigenetic clocks based on a small number of CpGs in French centenarians and semi-supercentenarians (CSSC, n=214) as well as nonagenarians' and centenarians' offspring (NCO, n=143) compared to individuals from the French general population (CG, n=149). DNA methylation analysis of the nine CpGs included in the epigenetic clocks showed high correlation with chronological age (-0.66>R>0.54) and also the presence of an epigenetic drift for four CpGs that was only visible in CSSC. DNAmage analysis showed that CSSC and to a lesser extend NCO present a younger DNAmage than their chronological age (15-28.5 years for CSSC, 4.4-11.5 years for NCO and 4.2-8.2 years for CG), which were strongly significant in CSSC compared to CG (p-values<2.2e-16). These differences suggest that epigenetic aging and potentially biological aging are slowed in exceptionally long-lived individuals and that epigenetic clocks based on a small number of CpGs are sufficient to reveal alterations of the global epigenetic clock.

Differences in DNA Methylation-Based Age Prediction Within Twin Pairs Discordant for Cancer.

DNA methylation-based age acceleration (DNAmAA) is associated with cancer, with both cancer tissue and blood showing increased DNAmAA. We aimed to investigate whether DNAmAA is associated with cancer risk within twin pairs discordant for cancer, and whether DNAmAA has the potential to serve as a biomarker for such. The study included 47 monozygotic and 48 same-sex-dizygotic cancer-discordant twin pairs from the Finnish Twin Cohort study with blood samples available between 17 and 31 years after the cancer diagnosis. We studied all cancers (95 pairs), then separately breast cancer (24 pairs) and all sites other than breast cancer (71 pairs). DNAmAA was calculated for seven models: Horvath, Horvath intrinsic epigenetic age acceleration, Hannum, Hannum intrinsic epigenetic age acceleration, Hannum extrinsic epigenetic age acceleration, PhenoAge and GrimAge. Within-pair differences in DNAmAA were analyzed by paired t tests and linear regression. Twin pairs sampled before cancer diagnosis did not differ significantly in DNAmAA. However, the within-pair differences in DNAmAA before cancer diagnosis increased significantly the closer the cancer diagnosis was, and this acceleration extended for years after the diagnosis. Pairs sampled after the diagnosis differed for DNAmAA with the Horvath models capturing cancer diagnosis-associated DNAmAA across all three cancer groupings. The results suggest that DNAmAA in blood is associated with cancer diagnosis. This may be due to epigenetic alterations in relation to cancer, its treatment or associated lifestyle changes. Based on the current study, the biomarker potential of DNAmAA in blood appears to be limited.

Markers of ovarian reserve are associated with reproductive age acceleration in granulosa cells from IVF patients.

Is reproductive aging in granulosa cells associated with markers of ovarian reserve? Age acceleration was associated with anti-Mullerian hormone (AMH) levels, antral follicle count (AFC), oocyte yield and maturity, and the number of successfully fertilized embryos. The rate of reproductive aging varies among women of the same age. DNA methylation can be used to predict epigenetic age in a variety of tissues. This was a cross-sectional study of 70 women at the time of oocyte retrieval. The 70 participants were recruited for this study at an academic medical center and they provided follicular fluid samples at the time of oocyte retrieval. Granulosa cells were isolated and assessed on the MethylationEPIC array. Linear regression was used to evaluate the associations between DNA methylation-based age predictions from granulosa cells and chronological age. Age acceleration was calculated as the residual of regressing DNA methylation-based age on chronological age. Linear regressions were used to determine the associations between age acceleration and markers of ovarian reserve and IVF cycle outcomes. Participants were a mean of 36.7 ± 3.9 years old. In regards to race, 54% were white, 19% were African American and 27% were of another background. Age acceleration was normally distributed and not associated with chronological age. Age acceleration was negatively associated with AMH levels (t = -3.1, P = 0.003) and AFC (t = -4.0, P = 0.0001), such that women with a higher age acceleration had a lower ovarian reserve. Age acceleration was also negatively correlated with the total number of oocytes retrieved (t = -3.9, P = 0.0002), the number of mature oocytes (t = -3.8, P = 0.0003) and the number of fertilized oocytes or two-pronuclear oocytes (t = -2.8, P = 0.008) in the main analysis. This study used pooled follicular fluid, which does not allow for the investigation of individual follicles. Infertility patients may also be different from the general population, but, as we used granulosa cells, the participants had to be from an IVF population. This study demonstrated that epigenetic age and age acceleration can be calculated from granulosa cells collected at the time of oocyte retrieval. GrimAge most strongly predicted chronological age, and GrimAge acceleration was associated with baseline and cycle characteristics as well as cycle outcomes, which indicates its potential clinical relevance in evaluating both oocyte quantity and quality. This study was supported by the National Institutes of Health (UL1TR002378) and the Building Interdisciplinary Research Careers in Women's Health Program (K12HD085850) to A.K.K. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funding source had no role in any aspect of this study. J.B.S. serves as Vice Chair for the American Society for Reproductive Medicine Education Committee, is a Medical Committee Advisor for the Jewish Fertility Foundation and works with Jscreen. J.B.S. has received funding from Georgia Clinical Translational Research Alliance. H.S.H., J.B.S. and A.K.S. have received NIH funding for other projects. A.K.K., S.A.G., S.G., Q.S.K., L.J.M. and W.S. have no conflicts of interest. N/A.

Evaluation of different computational methods for DNA methylation-based biological age.

In recent years there has been a widespread interest in researching biomarkers of aging that could predict physiological vulnerability better than chronological age. Aging, in fact, is one of the most relevant risk factors for a wide range of maladies, and molecular surrogates of this phenotype could enable better patients stratification. Among the most promising of such biomarkers is DNA methylation-based biological age. Given the potential and variety of computational implementations (epigenetic clocks), we here present a systematic review of such clocks. Furthermore, we provide a large-scale performance comparison across different tissues and diseases in terms of age prediction accuracy and age acceleration, a measure of deviance from physiology. Our analysis offers both a state-of-the-art overview of the computational techniques developed so far and a heterogeneous picture of performances, which can be helpful in orienting future research.

DNA methylation-based age acceleration observed in IDH wild-type glioblastoma is associated with better outcome\u2014including in elderly patients

Elderly patients represent a growing proportion of individuals with glioblastoma, who however, are often excluded from clinical trials owing to poor expected prognosis. We aimed at identifying age-related molecular differences that would justify and guide distinct treatment decisions in elderly glioblastoma patients. The combined DNA methylome (450 k) of four IDH wild-type glioblastoma datasets, comprising two clinical trial cohorts, was interrogated for differences based on the patients’ age, DNA methylation (DNAm) age acceleration (DNAm age “Horvath-clock” minus patient age), DNA methylation-based tumor classification (Heidelberg), entropy, and functional methylation of DNA damage response (DDR) genes. Age dependent methylation included 19 CpGs (p-value ≤ 0.1, Bonferroni corrected), comprising a CpG located in the ELOVL2 gene that is part of a 13-gene forensic age predictor. Most of the age related CpGs (n = 16) were also associated with age acceleration that itself was associated with a large number of CpGs (n = 50,551). Over 70% age acceleration-associated CpGs (n = 36,348) overlapped with those associated with the DNA methylation based tumor classification (n = 170,759). Gene set enrichment analysis identified associated pathways, providing insights into the biology of DNAm age acceleration and respective commonalities with glioblastoma classification. Functional methylation of several DDR genes, defined as correlation of methylation with gene expression (r ≤ −0.3), was associated with age acceleration (n = 8), tumor classification (n = 12), or both (n = 4), the latter including MGMT. DNAm age acceleration was significantly associated with better outcome in both clinical trial cohorts, whereof one comprised only elderly patients. Multivariate analysis included treatment (RT, RT/TMZ→TMZ; TMZ, RT), MGMT promoter methylation status, and interaction with treatment. In conclusion, DNA methylation features of age acceleration are an integrative part of the methylation-based tumor classification (RTK I, RTK II, MES), while patient age seems hardly reflected in the glioblastoma DNA methylome. We found no molecular evidence justifying other treatments in elderly patients, not owing to frailty or co-morbidities.

A high-throughput real-time PCR tissue-of-origin test to distinguish blood from lymphoblastoid cell line DNA for (epi)genomic studies

Lymphoblastoid cell lines (LCLs) derive from blood infected in vitro by Epstein–Barr virus and were used in several genetic, transcriptomic and epigenomic studies. Although few changes were shown between LCL and blood genotypes (SNPs) validating their use in genetics, more were highlighted for other genomic features and/or in their transcriptome and epigenome. This could render them less appropriate for these studies, notably when blood DNA could still be available. Here we developed a simple, high-throughput and cost-effective real-time PCR approach allowing to distinguish blood from LCL DNA samples based on the presence of EBV relative load and rearranged T-cell receptors γ and β. Our approach was able to achieve 98.5% sensitivity and 100% specificity on DNA of known origin (458 blood and 316 LCL DNA). It was further applied to 1957 DNA samples from the CEPH Aging cohort comprising DNA of uncertain origin, identifying 784 blood and 1016 LCL DNA. A subset of these DNA was further analyzed with an epigenetic clock indicating that DNA extracted from blood should be preferred to LCL for DNA methylation-based age prediction analysis. Our approach could thereby be a powerful tool to ascertain the origin of DNA in old collections prior to (epi)genomic studies.

Abstract P180: Relative Contributions Of Six Lifestyle Components To Epigenetic Aging In The Coronary Artery Risk Development In Young Adults (CARDIA) Study

Background: DNA methylation-based epigenetic age acceleration (EAA) is associated with age-related health outcomes including cardiovascular disease. Since DNA methylation is modifiable by external stimuli, including lifestyle components, interventions for healthier lifestyles could contribute to decreased risk of health conditions related to epigenetic changes including EAA. In consideration of public health intervention, it is also important to identify which lifestyle components have the greatest impact on EAA. Here, we assessed the relative contributions of lifestyle components to EAA, as well as their collective association. Methods: We included 744 participants (391 men and 353 women) from the Coronary Artery Risk Development in Young Adults (CARDIA) study with DNA methylation information at Y20 (2005-2006, mean age 45.9). Six lifestyle components at Y20 were included in the analysis: cumulative smoking, cumulative alcohol consumption, education, diet, sleep, and physical activity. We used quantile-based g-computation (QGC) and Bayesian kernel machine regression (BKMR) methods to assess the relative contribution of each lifestyle component to EAA. We also assessed overall association of the six lifestyle components as a combination with EAA. Finally, we performed stratified analyses by participants’ smoking history, race, and sex. Results: Cumulative smoking showed the greatest positive contribution to EAA in total participants. Healthy diet and education years showed inverse contributions to EAA. We observed a non-linear pattern in the contribution of cumulative alcohol consumption to EAA. We observed positive associations between the collective lifestyle components and greater EAA. While cumulative smoking was the greatest contributor across sex and race subgroups, the relative contributions of other components varied by subgroups. Conclusion: Cumulative smoking, cumulative alcohol consumption, diet, and education showed the highest contributions in our study. Higher amounts of smoking and alcohol consumption were likely to contribute to greater EAA, whereas healthier diet and achieved education were likely to contribute to lesser EAA. Identifying pertinent lifestyle components in a context of collective lifestyle components can provide a direction for intervention strategies, and suggests which components should be the primary focus for promoting younger EAA.

Adaptive feature selection framework for DNA methylation-based age prediction

Adaptive feature selection framework for DNA methylation-based age prediction

Application of droplet digital PCR method for DNA methylation-based age prediction from saliva.

The recent studies reported that DNA methylation markers show changes with age, and expected that the DNA methylation markers can be effectively used for estimation of age in forensic genetics. In this study, we applied droplet digital PCR (ddPCR) method to investigate the DNA methylation pattern in the CpG sites, and we constructed an age prediction model based on the ddPCR method. The ddPCR is capable of highly sensitive quantitation of nucleic acid and detection of sequence variations in gene by separating the sample into large number of partitions and clonally amplifying nucleic acids in each partition. We extracted DNA from saliva samples collected from several age groups. The DNA was bisulfite converted and subjected to ddPCR using specifically designed primers and probes. The methylation ratio of each sample was calculated and correlation between the methylation ratio and the chronological age was analyzed. In the results, methylated DNA ratio at the 4 CpG sites (cg14361627, cg14361627, cg08928145 and cg07547549) showed strong correlation with chronological age. Percent-methylation values at 4 CpG markers and chronological ages of the 76 individuals were analyzed by multiple regression analysis, and we constructed an age prediction model. We observed a strong correlation (Spearman's rho=0.922) between predicted and chronological ages of 76 individuals with a MAD from chronological age of 3.3years. Collectively, the result in this study showed the potential applicability of ddPCR to predict age from saliva.

A collaborative exercise on DNA methylation-based age prediction and body fluid typing

DNA methylation has become one of the most useful biomarkers for age prediction and body fluid identification in the forensic field. Therefore, several assays have been developed to detect age-associated and body fluid-specific DNA methylation changes. Among the many methods developed, SNaPshot-based assays should be particularly useful in forensic laboratories, as they permit multiplex analysis and use the same capillary electrophoresis instrumentation as STR analysis. However, technical validation of any developed assays is crucial for their proper integration into routine forensic workflow. In the present collaborative exercise, two SNaPshot multiplex assays for age prediction and a SNaPshot multiplex for body fluid identification were tested in twelve laboratories. The experimental set-up of the exercise was designed to reflect the entire workflow of SNaPshot-based methylation analysis and involved four increasingly complex tasks designed to detect potential factors influencing methylation measurements. The results of body fluid identification from each laboratory provided sufficient information to determine appropriate age prediction methods in subsequent analysis. In age prediction, systematic measurement differences resulting from the type of genetic analyzer used were identified as the biggest cause of DNA methylation variation between laboratories. Also, the use of a buffer that ensures a high ratio of specific to non-specific primer binding resulted in changes in DNA methylation measurement, especially when using degenerate primers in the PCR reaction. In addition, high input volumes of bisulfite-converted DNA often caused PCR failure, presumably due to carry-over of PCR inhibitors from the bisulfite conversion reaction. The proficiency of the analysts and experimental conditions for efficient SNaPshot reactions were also important for consistent DNA methylation measurement. Several bisulfite conversion kits were used for this study, but differences resulting from the use of any specific kit were not clearly discerned. Even when different experimental settings were used in each laboratory, a positive outcome of the study was a mean absolute age prediction error amongst participant’s data of only 2.7 years for semen, 5.0 years for blood and 3.8 years for saliva.