DNA Methylation-based Age Research Articles

Combining current knowledge on DNA methylation-based age estimation towards the development of a superior forensic DNA intelligence tool

The estimation of chronological age from biological fluids has been an important quest for forensic scientists worldwide, with recent approaches exploiting the variability of DNA methylation patterns with age in order to develop the next generation of forensic ‘DNA intelligence’ tools for this application. Drawing from the conclusions of previous work utilising massively parallel sequencing (MPS) for this analysis, this work introduces a DNA methylation-based age estimation method for blood that exhibits the best combination of prediction accuracy and sensitivity reported to date. Statistical evaluation of markers from 51 studies using microarray data from over 4000 individuals, followed by validation using in-house generated MPS data, revealed a final set of 11 markers with the greatest potential for accurate age estimation from minimal DNA material. Utilising an algorithm based on support vector machines, the proposed model achieved an average error (MAE) of 3.3 years, with this level of accuracy retained down to 5 ng of starting DNA input (~ 1 ng PCR input). The accuracy of the model was retained (MAE = 3.8 years) in a separate test set of 88 samples of Spanish origin, while predictions for donors of greater forensic interest (< 55 years of age) displayed even higher accuracy (MAE = 2.6 years). Finally, no sex-related bias was observed for this model, while there were also no signs of variation observed between control and disease-associated populations for schizophrenia, rheumatoid arthritis, frontal temporal dementia and progressive supranuclear palsy in microarray data relating to the 11 markers.

DNA Methylation-Based Age Prediction and Telomere Length Reveal an Accelerated Aging in Induced Sputum Cells Compared to Blood Leukocytes: A Pilot Study in COPD Patients.

Aging is the predominant risk factor for most degenerative diseases, including chronic obstructive pulmonary disease (COPD). This process is however very heterogeneous. Defining the biological aging of individual tissues may contribute to better assess this risky process. In this study, we examined the biological age of induced sputum (IS) cells, and peripheral blood leukocytes in the same subject, and compared these to assess whether biological aging of blood leukocytes mirrors that of IS cells. Biological aging was assessed in 18 COPD patients (72.4 ± 7.7 years; 50% males). We explored mitotic and non-mitotic aging pathways, using telomere length (TL) and DNA methylation-based age prediction (DNAmAge) and age acceleration (AgeAcc) (i.e., difference between DNAmAge and chronological age). Data on demographics, life style and occupational exposure, lung function, and clinical and blood parameters were collected. DNAmAge (67.4 ± 5.80 vs. 61.6 ± 5.40 years; p = 0.0003), AgeAcc (−4.5 ± 5.02 vs. −10.8 ± 3.50 years; p = 0.0003), and TL attrition (1.05 ± 0.35 vs. 1.48 ± 0.21 T/S; p = 0.0341) are higher in IS cells than in blood leukocytes in the same patients. Blood leukocytes DNAmAge (r = 0.927245; p = 0.0026) and AgeAcc (r = 0.916445; p = 0.0037), but not TL, highly correlate with that of IS cells. Multiple regression analysis shows that both blood leukocytes DNAmAge and AgeAcc decrease (i.e., younger) in patients with FEV1% enhancement (p = 0.0254 and p = 0.0296) and combined inhaled corticosteroid (ICS) therapy (p = 0.0494 and p = 0.0553). In conclusion, new findings from our work reveal a differential aging in the context of COPD, by a direct quantitative comparison of cell aging in the airway with that in the more accessible peripheral blood leukocytes, providing additional knowledge which could offer a potential translation into the disease management.

High social status males experience accelerated epigenetic aging in wild baboons

Aging, for virtually all life, is inescapable. However, within populations, biological aging rates vary. Understanding sources of variation in this process is central to understanding the biodemography of natural populations. We constructed a DNA methylation-based age predictor for an intensively studied wild baboon population in Kenya. Consistent with findings in humans, the resulting 'epigenetic clock' closely tracks chronological age, but individuals are predicted to be somewhat older or younger than their known ages. Surprisingly, these deviations are not explained by the strongest predictors of lifespan in this population, early adversity and social integration. Instead, they are best predicted by male dominance rank: high-ranking males are predicted to be older than their true ages, and epigenetic age tracks changes in rank over time. Our results argue that achieving high rank for male baboons - the best predictor of reproductive success - imposes costs consistent with a 'live fast, die young' life-history strategy.

Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

Regression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

DNA methylation-based age estimation in pediatric healthy tissues and brain tumors.

Several DNA methylation clocks have been developed to reflect chronological age of human tissues, but most clocks have been trained on adult samples. The rapid methylome changes in children and the role of epigenetics in pediatric tumors calls for tools accurately estimating methylation age in children. We aimed to evaluate seven methylation clocks in multiple tissues from healthy children to inform future studies on the optimal clock for pediatric cohorts, and analyzed the methylation age in brain tumors. We found that clocks trained on pediatric samples were the best in all tested tissues, highlighting the need for dedicated clocks. For blood samples, the Skin and blood clock had the best correlation with chronological age, while PedBE was the most accurate for saliva and buccal samples, and Horvath for brain tissue. Horvath methylation age was accelerated in pediatric brain tumors and the acceleration was subtype-specific for atypical teratoid rhabdoid tumor (ATRT), ependymoma, medulloblastoma and glioma. The subtypes with the highest acceleration corresponded to the worst prognostic categories in ATRT, ependymoma and glioma, whereas the relationship was reversed in medulloblastoma. This suggests that methylation age has potential as a prognostic biomarker in pediatric brain tumors and should be further explored.

Abstract 2332: Correlation of lifestyle factors with epigenetic aging unveiled in the practice of a DNA methylation-based age test

Abstract Background: DNA methylation clocks have been developed recently to estimate epigenetic age which is associated with chronological age in mouse and human. Changes in epigenetic age were reported in patients with health conditions including cancer. Different from genetic variations, epigenetic properties can be modified by environmental factors. Epigenetic age tests using blood samples have been offered directly to customers recently. We sought to analyze relationships between epigenetic aging and lifestyle factors, medication and aging interventions in the hope of assisting healthier living. Methods: Epigenetic age, called DNAge, is estimated using linear regression of DNA methylation levels at 206 loci containing 1344 CpGs and chronological age. Delta age is defined as the difference of epigenetic age minus chronological age. Correlations between lifestyle factors or medication and delta age of hundreds of whole blood samples were evaluated using biweight midcorrelation. Statistical significance is set at 0.05. Results: Our results showed positive correlations between accelerated epigenetic aging and BMI (bicor=0.148, p=0.009, n=311), current smoker (bicor=0.143, p=0.011, n=315), or stress (bicor=0.088, p=0.128, n=302). Factors with positive correlation but not reaching statistical significance included total cholesterol, alcohol consumption and Keto diet. Factors with negative correlation but not statistically significant included exercise, balanced diet, mostly meat, mostly vegetables, and education. Conclusions: The epigenetic age analysis of real customer data confirmed beneficial and adversarial health effects of certain lifestyle factors. The DNAge test offered proves to be useful as a biomarker for biological age and as a practical tool for monitoring and evaluation of health status which may be used in cancer epidemiology study and help cancer prevention. Citation Format: Wei Guo, Yap Ching Chew, Xiaojing Yang, Michiko Suwoto, Taikun Yamada, Xi Yu Jia. Correlation of lifestyle factors with epigenetic aging unveiled in the practice of a DNA methylation-based age test [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2332.

S14. ANALYSIS OF METHYLATION AGE AND BLOOD CELL COMPOSITION IN SUBJECTS WITH CURRENT SUICIDE IDEATION

BackgroundSuicidal Ideation (SI) remain an important and common risk factor affecting people with SCZ, who eventually attempt or complete suicide. Then the question is, what if factors (such as stressful life events and related molecular biomarkers) known to be involved in the aetiology of SCZ could help in predicting SI in this population? The accelerated aging hypothesis of SCZ posits that physiological changes associated with normal aging occur at an earlier age in individuals with SCZ than in the general population. Importantly, epigenetic changes may constitute an important component of aging process. Based on this, the chronological age can be predicted by the epigenetic clock in a highly consistent manner. The aims of this research were to determine the effect chronological and biological age on current SI and secondly, to determine the effect of the variation of cellular blood cell composition on current SI.MethodsA total of 103 participants with a DSM-IV diagnosis of schizophrenia spectrum and other psychotic disorders were recruited from the Center of Addiction and Mental Health. The SI was assessed by the Columbia-Suicide Severity Rating Scale. Genome-wide DNA methylation analysis was generated from whole blood cells. The DNA methylation was assessed using the Illumina Infinium HumanMethylation450 Bead Chip while the DNA methylation-based age prediction and white blood cell composition were performed using the statistical pipeline developed by Horvath.ResultsOut of 103 participants, 18 had current SI (17%) while 85 had NSI. The DNAm age correlated with chronological age in the overall sample (r=0.814, p<0.0001), NSI (r=0.823, p<0.0001) and SI subjects (r=0.734, p=0.001). The strong linear relationship between DNAm age and chronological age showed a high accuracy of the epigenetic clock. However, DNAm age acceleration residuals did not differ between NSI and SI groups (t=1.532, p= 0.129). Comparison of the cellular cell blood composition between the NSI and SI groups indicated no significant differences between the NSI and SI groups (lymphocytes (t= -0.338, p=0.736), monocytes (t=-1.405, p=0.163) and granulocytes (t=0.924, p=0.358)). Furthermore, there were no significant differences between the SI and NSI groups in the analysis of the plasmablast (t=0.138, p=0.890), CD4 naïve (t=0.010, p=0.992) and CD8 naïve (t=0.681, p=0.497)DiscussionStressful life events may change DNA methylation, which in turn can affect suicide ideation and suicidal behavior. Although SCZ is associated with age-related physiological factors, we were unable to find accelerated aging in our study. Nevertheless, we cannot rule out the possibility of other aging mechanism independent of epigenetic aging in SCZ patients.Conclusion: Further studies aimed at investigating the accelerated aging hypothesis in peripheral tissue are warranted to identify individuals with SCZ at risk for suicide. This will permit a tailored treatment and will prevent suicide in SCZ individuals.

DNA methylation-based age prediction using cell separation algorithm

The age of each individual can be predicted based on the alteration rule of DNA methylation with age. In this paper, an age prediction method is developed in order to solve multivariate regression problems from DNA methylation data, by optimizing the artificial neural network (ANN) model using a new proposed algorithm named the Cell Separation Algorithm (CSA). The CSA imitates cell separation action by using a differential centrifugation process involving multiple centrifugation steps and increasing the rotor speed in each step. The CSA performs similar to the centrifugal force in separating the solutions based on their objective function in different steps, with velocity increasing in each step. Firstly, 25 test functions are used to test the CSA. Secondly, the CSA is examined on three forms of age prediction problems from two body fluids (blood and saliva). The healthy blood samples, diseased blood samples and saliva samples are used to test the method's capability. The results of the CSA are compared not only with other methods proposed in previous studies, but also with the results from stochastic gradient descent (SGD), ADAM, and genetic algorithm (GA). The model results of CSA are extremely better than the four methods proposed in previous works that have not used ANN training process. The CSA also outperformed SGD, ADAM that employ the ANN model without ANN optimization by meta-heuristics. The CSA results are comparable (even superior) to the GA model which takes the advantages of both ANN and meta-heuristics.

Sperm Ribosomal DNA Promoter Methylation Levels Are Correlated With Paternal Aging and May Relate With in vitro Fertilization Outcomes.

The impact of aging on reproductive outcomes has received considerable critical attention; however, there is much less information available on the effects of paternal age compared to the effects of maternal age. In this study, methylation levels of sperm rDNA promoter regions and Long Interspersed Nucleotide Element 1 (LINE-1) were measured using pyrosequencing and fertilization, day 3 good-quality embryo, pregnancies, and implantation results were assessed. We observed significantly increasing levels of DNA methylation in the sperm rDNA promoter regions with age based on stratifying the samples by age alone (P = 0.0001) and performing linear regression analysis (P < 0.0001). Meanwhile, no statistically significant correlations were observed between global LINE-1 methylation with age. No statistically significant correlations were observed between sperm rDNA promoter methylation levels and either the day 3 good-quality embryo rate or clinical pregnancy rate. In contrast, the correlation between sperm rDNA promoter methylation levels and fertilization (2 pronuclei) rate was nearly significant (P = 0.0707), especially the methylation levels of some individual CpG units (CpG_10, P = 0.0176; CpG_11, P = 0.0438; CpG_14, P = 0.0232) and rDNA promoter methylation levels measured using primerS2 (P = 0.0513). No significant correlation was found between sperm rDNA promoter methylation levels and fertilization rates (2 pronuclei, 1 pronuclei, and 1 polypronuclei). Our results demonstrate that sperm are susceptible to age-associated alterations in methylation levels of rDNA promoter regions, suggesting that sperm rDNA promoter methylation levels can be applied to DNA methylation-based age prediction, and that the aberrant methylation of rDNA promoters may be partially responsible for enhanced disease susceptibility of offspring sired by older fathers. Methylation levels of sperm rDNA promoter regions may correlate with polypronuclei rates of IVF programs.

DNA methylation levels and telomere length in human teeth: usefulness for age estimation.

In the last decade, increasing knowledge of epigenetics has led to the development of DNA methylation-based models to predict age, which have shown high predictive accuracy. However, despite the value of teeth as forensic samples, few studies have focused on this source of DNA. This study used bisulfite pyrosequencing to measure the methylation levels of specific CpG sites located in the ELOVL2, ASPA, and PDE4C genes, with the aim of selecting the most age-informative genes and determining their associations with age, in 65 tooth samples from individuals 15 to 85years old. As a second aim, methylation data and measurements of relative telomere length in the same set of samples were used to develop preliminary age prediction models to evaluate the accuracy of both biomarkers together and separately in estimating age from teeth for forensic purposes. In our sample, several CpG sites from ELOVL2 and PDE4C genes, as well as telomere length, were significantly associated with chronological age. We developed age prediction quantile regression models based on DNA methylation levels, with and without telomere length as an additional variable, and adjusted for type of tooth and sex. Our results suggest that telomere length may have limited usefulness as a supplementary marker for DNA methylation-based age estimation in tooth samples, given that it contributed little improvement in the prediction errors of the models. In addition, even at older ages, DNA methylation appeared to be more informative in predicting age than telomere length when both biomarkers were evaluated separately.

Psychosocial stress and epigenetic aging.

Aging is the single most important risk factor for diseases that are currently the leading causes of morbidity and mortality. However, there is considerable inter-individual variability in risk for aging-related disease, and studies suggest that biological age can be influenced by multiple factors, including exposure to psychosocial stress. Among markers of biological age that can be affected by stress, the present article focuses on the so-called measures of epigenetic aging: DNA methylation-based age predictors that are measured in a range of tissues, including the brain, and can predict lifespan and healthspan. We review evidence linking exposure to diverse types of psychosocial stress, including early-life stress, cumulative stressful experiences, and low socioeconomic status, with accelerated epigenetic aging as a putative mediator of the effects of psychosocial environment on health and disease. The chapter also discusses methodological differences that may contribute to discordant findings across studies to date and plausible mechanisms that may underlie the effects of stress on the aging epigenome. Future studies examining the effects of adversity on epigenetic and other indicators of biological weathering may provide important insights into the pathogenesis of aging-related disease states.

Novel multiplex strategy for DNA methylation-based age prediction from small amounts of DNA via Pyrosequencing

DNA methylation-based age estimation is a promising new tool for forensic molecular biology. There is growing understanding of the best predictive CpG loci and their performance in various sample types. Since forensic samples usually provide only small amounts of DNA, the sensitivity of the method is crucial. Pyrosequencing is one of the most sensitive methods but only capable to analyze different target regions separately. Thus, multiple input DNA samples are required for investigations of different target regions, which is required for all current age estimation models. To overcome this limitation, we developed a novel multiplex strategy for Pyrosequencing, which allows the investigation of different target regions from a single small amount of input DNA. A pre-amplification step was introduced to increase the amount of target-specific template for the subsequent sequencing PCR step. We tested this multiplex strategy for eight target regions including 15 age CpGs associated with the genes of ELOVL2, FHL2, CCDC102B, C1orf132, KLF14, EDARADD, PDE4C and SST. Except for FHL2, all target regions were successfully sequenced with the multiplex strategy and the precision in terms of reproducibility of the measurements was equal to the singleplex strategy. The measured methylation values at the age CpGs displayed borderline significant differences between both analytical strategies for six out of 14 CpG sites whereas both strategies delivered equal methylation values for the remaining eight age CpGs. In total, our results indicate that the multiplex strategy can act as a promising alternative for age estimation studies in cases when only limited amounts of DNA samples are available.

Sex differences in the associations of placental epigenetic aging with fetal growth.

Identifying factors that influence fetal growth in a sex-specific manner can help unravel mechanisms that explain sex differences in adverse neonatal outcomes and in-utero origins of cardiovascular disease disparities. Premature aging of the placenta, a tissue that supports fetal growth and exhibits sex-specific epigenetic changes, is associated with pregnancy complications. Using DNA methylation-based age estimator, we investigated the sex-specific relationship of placental epigenetic aging with fetal growth across 13-40 weeks gestation, neonatal size, and risk of low birth weight. Placental epigenetic age acceleration (PAA), the difference between DNA methylation age and gestational age, was associated with reduced fetal weight among males but with increased fetal weight among females. PAA was inversely associated with fetal weight, abdominal circumference, and biparietal diameter at 32-40 weeks among males but was positively associated with all growth measures among females across 13-40 weeks. A 1-week increase in PAA was associated with 2-fold (95% CI 1.2, 3.2) increased odds for low birth weight and 1.5-fold (95% CI 1.1, 2.0) increased odds for small-for-gestational age among males. In all, fetal growth was significantly reduced in males but not females exposed to a rapidly aging placenta. Epigenetic aging of the placenta may underlie sex differences in neonatal outcomes.

DNA methylation-based age prediction and telomere length in white blood cells and cumulus cells of infertile women with normal or poor response to ovarian stimulation.

An algorithm assessing the methylation levels of 353 informative CpG sites in the human genome permits accurate prediction of the chronologic age of a subject. Interestingly, when there is discrepancy between the predicted age and chronologic age (age acceleration or “AgeAccel”), patients are at risk for morbidity and mortality. Identification of infertile patients at risk for accelerated reproductive senescence may permit preventative action. This study aimed to assess the accuracy of the “epigenetic clock” concept in reproductive age women undergoing fertility treatment by applying the age prediction algorithm in peripheral (white blood cells [WBCs]) and follicular somatic cells (cumulus cells [CCs]), and to identify whether women with premature reproductive aging (diminished ovarian reserve) were at risk of AgeAccel in their age prediction. Results indicated that the epigenetic algorithm accurately predicts age when applied to WBCs but not to CCs. The age prediction of CCs was substantially younger than chronologic age regardless of the patient’s age or response to stimulation. In addition, telomeres of CCs were significantly longer than that of WBCs. Our findings suggest that CCs do not demonstrate changes in methylome-predicted age or telomere-length in association with increasing female age or ovarian response to stimulation.

DNA methylation-based age prediction using massively parallel sequencing data and multiple machine learning models

The field of DNA intelligence focuses on retrieving information from DNA evidence that can help narrow down large groups of suspects or define target groups of interest. With recent breakthroughs on the estimation of geographical ancestry and physical appearance, the estimation of chronological age comes to complete this circle of information. Recent studies have identified methylation sites in the human genome that correlate strongly with age and can be used for the development of age-estimation algorithms. In this study, 110 whole blood samples from individuals aged 11–93 years were analysed using a DNA methylation quantification assay based on bisulphite conversion and massively parallel sequencing (Illumina MiSeq) of 12 CpG sites. Using this data, 17 different statistical modelling approaches were compared based on root mean square error (RMSE) and a Support Vector Machine with polynomial function (SVMp) model was selected for further testing. For the selected model (RMSE = 4.9 years) the mean average error (MAE) of the blind test (n = 33) was calculated at 4.1 years, with 52% of the samples predicting with less than 4 years of error and 86% with less than 7 years. Furthermore, the sensitivity of the method was assessed both in terms of methylation quantification accuracy and prediction accuracy in the first validation of this kind. The described method retained its accuracy down to 10 ng of initial DNA input or ∼2 ng bisulphite PCR input. Finally, 34 saliva samples were analysed and following basic normalisation, the chronological age of the donors was predicted with less than 4 years of error for 50% of the samples and with less than 7 years of error for 70%.

DNA methylation and the epigenetic clock in relation to physical frailty in older people: the Lothian Birth Cohort 1936

BackgroundThe biological mechanisms underlying frailty in older people are poorly understood. There is some evidence to suggest that DNA methylation patterns may be altered in frail individuals.MethodsParticipants were 791 people aged 70 years from the Lothian Birth Cohort 1936. DNA methylation was measured in whole blood. Biological age was estimated using two measures of DNA methylation-based age acceleration-extrinsic and intrinsic epigenetic age acceleration. We carried out an epigenome-wide association study of physical frailty, as defined by the Fried phenotype. Multinomial logistic regression was used to calculate relative risk ratios for being physically frail or pre-frail according to epigenetic age acceleration.ResultsThere was a single significant (P = 1.16 × 10–7) association in the epigenome-wide association study comparing frail versus not frail. The same CpG was not significant when comparing pre-frail versus not frail. Greater extrinsic epigenetic age acceleration was associated with an increased risk of being physically frail, but not of being pre-frail. For a year increase in extrinsic epigenetic age acceleration, age- and sex-adjusted relative risk ratios (95% CI) for being physically frail or pre-frail were 1.06 (1.02, 1.10) and 1.02 (1.00, 1.04), respectively. After further adjustment for smoking and chronic disease, the association with physical frailty remained significant. Intrinsic epigenetic age acceleration was not associated with physical frailty status.ConclusionsPeople who are biologically older, as indexed by greater extrinsic epigenetic age acceleration, are more likely to be physically frail. Future research will need to investigate whether epigenetic age acceleration plays a causal role in the onset of physical frailty.

A validation study of DNA methylation-based age prediction using semen in forensic casework samples.

Previously, an age-predictive method based on DNA-methylation patterns in semen was developed, using three CpG sites (cg06304190 in the TTC7B gene, cg12837463, and cg06979108 in the NOX4 gene). Before considering the routine use of a new method in forensics, validation studies such as concordance and sensitivity tests are essential for obtaining expanded and more reliable forensic information. Here, we evaluated a previously described age-predictive method for semen for routine forensic use. Concordance testing showed a high correlation between the predicted and chronological age, with a mean absolute deviation from the chronological age of 4.8 years. Sensitivity testing suggested that age prediction with reliable accuracy and consistency was possible with >5 ng of bisulfite-converted DNA. We also confirmed the applicability of the age-predictive method in forensic casework, using forensic samples. Thus, the proposed method could serve as a very valuable forensics tool for accurate age prediction with semen samples.

The epigenetic clock and objectively measured sedentary and walking behavior in older adults: the Lothian Birth Cohort 1936

BackgroundEstimates of biological age derived from DNA-methylation patterns—known as the epigenetic clock—are associated with mortality, physical and cognitive function, and frailty, but little is known about their relationship with sedentary behavior or physical activity. We investigated the cross-sectional relationship between two such estimates of biological age and objectively measured sedentary and walking behavior in older people.MethodsParticipants were 248 members of the Lothian Birth Cohort 1936. At age 79 years, sedentary behavior and physical activity were measured over 7 days using an activPAL activity monitor. Biological age was estimated using two measures of DNA methylation-based age acceleration—i.e., extrinsic and intrinsic epigenetic age acceleration. We used linear regression to assess the relationship between these two estimates of biological age and average daily time spent sedentary, number of sit-to-stand transitions, and step count.ResultsOf the six associations examined, only two were statistically significant in initial models adjusted for age and sex alone. Greater extrinsic age acceleration was associated with taking fewer steps (regression coefficient (95% CI) − 0.100 (− 0.008, − 0.001), and greater intrinsic age acceleration was associated with making more sit-to-stand transitions (regression coefficient (95% CI) 0.006 (0.0001, 0.012). When we controlled for multiple statistical testing, neither of these associations survived correction (both P ≥ 0.17).ConclusionIn this cross-sectional study of 79-year-olds, we found no convincing evidence that biological age, as indexed by extrinsic or intrinsic epigenetic age acceleration, was associated with objectively measured sedentary or walking behavior.

DNA methylation-based age prediction from various tissues and body fluids.

Aging is a natural and gradual process in human life. It is influenced by heredity, environment, lifestyle, and disease. DNA methylation varies with age, and the ability to predict the age of donor using DNA from evidence materials at a crime scene is of considerable value in forensic investigations. Recently, many studies have reported age prediction models based on DNA methylation from various tissues and body fluids. Those models seem to be very promising because of their high prediction accuracies. In this review, the changes of age-associated DNA methylation and the age prediction models for various tissues and body fluids were examined, and then the applicability of the DNA methylation-based age prediction method to the forensic investigations was discussed. This will improve the understandings about DNA methylation markers and their potential to be used as biomarkers in the forensic field, as well as the clinical field.

Evaluation of DNA methylation-based age prediction on blood

DNA methylation has been increasingly recognised for its potential use in forensic age prediction. In this study, our prediction model based on three CpG sites in three methylation markers was able to predict age for liquid blood samples with a mean absolute deviation of 5 years. The overall prediction accuracy of our model was similar to a previously published model, reanalyzed in our study. Additionally, the present study examined age prediction from bloodstains recovered at Day 0 and Day 21 using reduced quantities of DNA.