DNA methylation profile is a quantitative measure of biological aging in children

ELOVL2, PRKG2, and EDARADD DNA Methylation Strongly Estimate Indonesian Adolescents

Recently, there has been a growing interest in using DNA methylation analysis for age estimation. Despite this growing interest, there is a scarcity of research on the potential of DNA methylation as a biomarker for age estimation in Indonesia. This study aims to investigate the applicability of ELOVL2, PRKG2, and EDARADD genes for forensic identification in the 11-20 age group among Indonesians. This research utilizes 43 archived blood samples from healthy individuals who underwent blood tests at the Gatot Soebroto Army Hospital (RSPAD) in Central Jakarta, Indonesia. The methylation-specific PCR (MSP) technique assessed the DNA methylation level. The key findings of this study include (1) a strong positive correlation between methylation levels in the ELOVL2 gene and age; (2) a strong negative correlation between methylation levels in PRKG2 and EDARADD genes with age; (3) the development of three linear regression formulas for age prediction; and (4) mean absolute error (MAE) values derived from this research, which are ±0.48 for ELOVL2 gene regression formula, ±0.58 for PRKG2 gene regression formula, and ±0.72 for EDARADD gene regression formula. In summary, this study explores the potential of DNA methylation analysis for age estimation in Indonesia, focusing on ELOVL2, PRKG2, and EDARADD genes in the 11-20 age group. The findings underscore the applicability of DNA methylation analysis in forensic identification and age estimation, paving the way for future research in this field.

Prenatal and early-life air pollutant exposure and epigenetic aging acceleration

BACKGROUND

This study investigated the association of prenatal and early childhood exposure to air pollution with epigenetic age acceleration (EAA) at six years of age using the Environment and Development of Children Cohort (EDC Cohort) MATERIALS & METHODS: Air pollution, including particulate matter [< 2.5 µm (PM2.5) and < 10 µm (PM10) in an aerodynamic diameter], nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO), and sulfur dioxide (SO2) were estimated based on the residential address for two periods: 1) during the whole pregnancy, and 2) for one year before the follow-up in children at six years of age. The methylation levels in whole blood at six years of age were measured, and the methylation clocks, including Horvath's clock, Horvath's skin and blood clock, PedBE, and Wu's clock, were estimated. Multivariate linear regression models were constructed to analyze the association between EAA and air pollutants.

RESULTS

A total of 76 children in EDC cohort were enrolled in this study. During the whole pregnancy, interquartile range (IQR) increases in exposure to PM2.5 (4.56 μg/m3) and CO (0.156 ppm) were associated with 0.406 years and 0.799 years of EAA (Horvath's clock), respectively. An IQR increase in PM2.5 (4.76 μg/m3) for one year before the child was six years of age was associated with 0.509 years of EAA (Horvath's clock) and 0.289 years of EAA (Wu's clock). PM10 (4.30 μg/m3) and O3 (0.003 ppm) exposure in the period were also associated with EAA in Horvath's clock (0.280 years) and EAA in Horvath's skin and blood clock (0.163 years), respectively.

CONCLUSION

We found that prenatal and childhood exposure to ambient air pollutants is associated with EAA among children. The results suggest that air pollution could induce excess biological aging even in prenatal and early life.

Epistemic uncertainty challenges aging clock reliability in predicting rejuvenation effects

Epigenetic aging clocks have been widely used to validate rejuvenation effects during cellular reprogramming. However, these predictions are unverifiable because the true biological age of reprogrammed cells remains unknown. We present an analytical framework to consider rejuvenation predictions from the uncertainty perspective. Our analysis reveals that the DNA methylation profiles across reprogramming are poorly represented in the aging data used to train clock models, thus introducing high epistemic uncertainty in age estimations. Moreover, predictions of different published clocks are inconsistent, with some even suggesting zero or negative rejuvenation. While not questioning the possibility of age reversal, we show that the high clock uncertainty challenges the reliability of rejuvenation effects observed during in vitro reprogramming before pluripotency and throughout embryogenesis. Conversely, our method reveals a significant age increase after in vivo reprogramming. We recommend including uncertainty estimation in future aging clock models to avoid the risk of misinterpreting the results of biological age prediction.

Severe drought exposure in utero associates to children's epigenetic age acceleration in a global climate change hot spot

The goal of this study is to examine the association between in utero drought exposure and epigenetic age acceleration (EAA) in a global climate change hot spot. Calculations of EAA in adults using DNA methylation have been found to accurately predict chronic disease and longevity. However, fewer studies have examined EAA in children, and drought exposure in utero has not been investigated. Additionally, studies of EAA in low-income countries with diverse populations are rare. We assess EAA using epigenetic clocks and two DNAm-based pace-of-aging measurements from whole saliva samples in 104 drought-exposed children and 109 same-sex sibling controls in northern Kenya. We find a positive association between in utero drought exposure and EAA in two epigenetic clocks (Hannum's and GrimAge) and a negative association in the DNAm based telomere length (DNAmTL) clock. The combined impact of drought's multiple deleterious stressors may reduce overall life expectancy through accelerated epigenetic aging.

Evaluation of DNAmAge in paired fresh, frozen, and formalin-fixed paraffin-embedded heart tissues

The continued development in methylome analysis has enabled a more precise assessment of DNA methylation, but treatment of target tissue prior to analysis may affect DNA analysis. Prediction of age based on methylation levels in the genome (DNAmAge) has gained much interest in disease predisposition (biological age estimation), but also in chronological donor age estimation in crime case samples. Various epigenetic clocks were designed to predict the age. However, it remains unknown how the storage of the tissues affects the DNAmAge estimation. In this study, we investigated the storage method impact of DNAmAge by the comparing the DNAmAge of the two commonly used storage methods, freezing and formalin-fixation and paraffin-embedding (FFPE) to DNAmAge of fresh tissue. This was carried out by comparing paired heart tissue samples of fresh tissue, samples stored by freezing and FFPE to chronological age and whole blood samples from the same individuals. Illumina EPIC beadchip array was used for methylation analysis and the DNAmAge was evaluated with the following epigenetic clocks: Horvath, Hannum, Levine, Horvath skin+blood clock (Horvath2), PedBE, Wu, BLUP, EN, and TL. We observed differences in DNAmAge among the storage conditions. FFPE samples showed a lower DNAmAge compared to that of frozen and fresh samples. Additionally, the DNAmAge of the heart tissue was lower than that of the whole blood and the chronological age. This highlights caution when evaluating DNAmAge for FFPE samples as the results were underestimated compared with fresh and frozen tissue samples. Furthermore, the study also emphasizes the need for a DNAmAge model based on heart tissue samples for an accurate age estimation.

Associations of depression and anxiety symptoms in childhood and adolescence with epigenetic aging

BACKGROUND

Childhood anxiety and depression symptoms are potential risk factors for accelerated biological aging. In child and adolescent twins, we tested whether these symptoms were associated with DNA methylation (DNAm) aging, a measure of biological aging.

METHODS

276 twins (135 pairs, 6 singletons) had DNAm assayed from saliva in middle childhood (mean = 7.8 years). Residuals of five different DNAm age estimates regressed on chronological age were used to indicate accelerated aging. Anxiety and depression symptoms were assessed in middle childhood and early adolescence using the Child Behavior Checklist. Mixed effect regression was used to examine potential relationships between anxiety or depression symptoms, and accelerated DNAm age. MZ twin difference analysis was then utilized to determine if associations were environmentally-driven or due to genetic or shared-environment confounding.

RESULTS

Anxiety and depression symptoms were not associated with accelerated DNAm aging in middle childhood. In early adolescence, only the Wu clock was significant and indicated that each one symptom increase in anxiety symptoms had an associated age acceleration of 0.03 years (~0.4 months; p = 0.019). MZ twin difference analysis revealed non-significant within-pair effects, suggesting genetic and shared environmental influences.

LIMITATIONS

Sample is predominantly male and white. Generalizability to other populations may be limited.

CONCLUSION

Accelerated DNAm aging of the Wu clock in middle childhood is associated with anxiety, but not depression, symptoms in early adolescence. Further, this association may be the result of shared genetic and environmental influences. Accelerated DNAm aging may serve as an early risk factor or predictor of later anxiety symptoms.

Blood-Based Epigenetic Age Acceleration and Incident Colorectal Cancer Risk: Findings from a Population-Based Case-Control Study

This study investigates the association between epigenetic age acceleration (EAA) derived from DNA methylation and the risk of incident colorectal cancer (CRC). We utilized data from a random population sample of 9,360 individuals (men and women, aged 45-69) from the HAPIEE Study who had been followed up for 16 years. A nested case-control design yielded 35 incident CRC cases and 354 matched controls. Six baseline epigenetic age (EA) measures (Horvath, Hannum, PhenoAge, Skin and Blood (SB), BLUP, and Elastic Net (EN)) were calculated along with their respective EAAs. After adjustment, the odds ratios (ORs) for CRC risk per decile increase in EAA ranged from 1.20 (95% CI: 1.04-1.39) to 1.44 (95% CI: 1.21-1.76) for the Horvath, Hannum, PhenoAge, and BLUP measures. Conversely, the SB and EN EAA measures showed borderline inverse associations with ORs of 0.86-0.87 (95% CI: 0.76-0.99). Tertile analysis reinforced a positive association between CRC risk and four EAA measures (Horvath, Hannum, PhenoAge, and BLUP) and a modest inverse relationship with EN EAA. Our findings from a prospective population-based-case-control study indicate a direct association between incident CRC and four markers of accelerated baseline epigenetic age. In contrast, two markers showed a negative association or no association. These results warrant further exploration in larger cohorts and may have implications for CRC risk assessment and prevention.

Utilizing epigenetics to study the shared nature of development and biological aging across the lifespan

Recently, biological aging has been quantified in DNA-methylation samples of older adults and applied as so-called "methylation profile scores" (MPSs) in separate target samples, including samples of children. This nascent research indicates that (1) biological aging can be quantified early in the life course, decades before the onset of aging-related disease, (2) is affected by common environmental predictors of childhood development, and (3) shows overlap with "developmental processes" (e.g., puberty). Because the MPSs were computed using algorithms developed in adults, these studies indicate a molecular link between childhood environments, development, and adult biological aging. Yet, if MPSs can be used to connect development and aging, previous research has only traveled one way, deriving MPSs developed in adults and applying them to samples of children. Researchers have not yet quantified epigenetic measures that reflect the pace of child development, and tested whether resulting MPSs are associated with physical and psychological aging. In this perspective I posit that combining measures of biological aging with new quantifications of child development has the power to address fundamental questions about life span: How are development and experience in childhood related to biological aging in adulthood? And what is aging?

Evidence of reduced gestational age in response to in utero arsenic exposure and implications for aging trajectories of the newborn

Arsenic exposure is associated with a plethora of age-related health outcomes of disparate etiology. However, evidence of the impact of arsenic on aging remains limited. Here, we investigated the utility of epigenetic clocks in two different populations and the impact of maternal arsenic exposure during pregnancy on epigenetic gestational age at birth. To do this, we examined publicly available DNA methylation data and estimated gestational age across five gestational clocks in two unrelated human populations. These populations also differ in the extent of arsenic exposure and the targeted tissue of analysis (cord blood and placental tissue). Our results indicate that same-tissue clocks produce gestational age estimates that are more highly correlated with clinical gestational age. Interestingly, our results also indicate that arsenic exposure is associated with gestational age, with higher arsenic exposures associated with decreased gestational age. We also applied two pediatric clocks to evaluate infant biological age in the same samples. The data is suggestive of higher pediatric age in infants exposed to higher arsenic levels during gestation. Taken altogether, our findings are consistent with past work indicating that that in utero arsenic exposure is associated with decreased gestational maturity as characterized by infant outcomes such as low birthweight and lung underdevelopment and dysfunction in arsenic exposed infants. The findings are also consistent with arsenic exposure setting infants on a trajectory of accelerated epigenetic aging that starts at birth.

Deciphering the role of immune cell composition in epigenetic age acceleration: Insights from cell-type deconvolution applied to human blood epigenetic clocks

Aging is a significant risk factor for various human disorders, and DNA methylation clocks have emerged as powerful tools for estimating biological age and predicting health-related outcomes. Methylation data from blood DNA has been a focus of more recently developed DNA methylation clocks. However, the impact of immune cell composition on epigenetic age acceleration (EAA) remains unclear as only some clocks incorporate partial cell type composition information when analyzing EAA. We investigated associations of 12 immune cell types measured by cell-type deconvolution with EAA predicted by six widely-used DNA methylation clocks in data from >10,000 blood samples. We observed significant associations of immune cell composition with EAA for all six clocks tested. Across the clocks, nine or more of the 12 cell types tested exhibited significant associations with EAA. Higher memory lymphocyte subtype proportions were associated with increased EAA, and naïve lymphocyte subtypes were associated with decreased EAA. To demonstrate the potential confounding of EAA by immune cell composition, we applied EAA in rheumatoid arthritis. Our research maps immune cell type contributions to EAA in human blood and offers opportunities to adjust for immune cell composition in EAA studies to a significantly more granular level. Understanding associations of EAA with immune profiles has implications for the interpretation of epigenetic age and its relevance in aging and disease research. Our detailed map of immune cell type contributions serves as a resource for studies utilizing epigenetic clocks across diverse research fields, including aging-related diseases, precision medicine, and therapeutic interventions.

Associations of prenatal one-carbon metabolism nutrients and metals with epigenetic aging biomarkers at birth and in childhood in a US cohort

Epigenetic gestational age acceleration (EGAA) at birth and epigenetic age acceleration (EAA) in childhood may be biomarkers of the intrauterine environment. We investigated the extent to which first-trimester folate, B12, 5 essential, and 7 non-essential metals in maternal circulation are associated with EGAA and EAA in early life. Bohlin EGAA and Horvath pan-tissue and skin and blood EAA were calculated using DNA methylation measured in cord blood (N=351) and mid-childhood blood (N=326; median age = 7.7 years) in the Project Viva pre-birth cohort. A one standard deviation increase in individual essential metals (copper, manganese, and zinc) was associated with 0.94-1.2 weeks lower Horvath EAA at birth, and patterns of exposures identified by exploratory factor analysis suggested that a common source of essential metals was associated with Horvath EAA. We also observed evidence nonlinear associations of zinc with Bohlin EGAA, magnesium and lead with Horvath EAA, and cesium with skin and blood EAA at birth. Overall, associations at birth did not persist in mid-childhood; however, arsenic was associated with greater EAA at birth and in childhood. Prenatal metals, including essential metals and arsenic, are associated with epigenetic aging in early life, which might be associated with future health.

Prenatal and childhood lead exposure is prospectively associated with biological markers of aging in adolescence

Few studies have related early life lead exposure to adolescent biological aging, a period characterized by marked increases in maturational tempo. We examined associations between prenatal and childhood lead exposure and adolescent biological age (mean 14.5 years) utilizing multiple epigenetic clocks including: intrinsic (IEAA), extrinsic (EEAA), Horvath, Hannum, PhenoAge, GrimAge, Skin-Blood, Wu, PedBE, as well as DNA methylation derived telomere length (DNAmTL). Epigenetic clocks and DNAmTL were calculated via adolescent blood DNA methylation measured by Infinium MethylationEPIC BeadChips. We constructed general linear models (GLMs) with individual lead measures predicting biological age. We additionally examined sex-stratified models and lead by sex interactions, adjusting for adolescent age and lead levels, maternal smoking and education, and proportion of cell types. We also estimated effects of lead exposure on biological age using generalized estimating equations (GEE). First trimester blood lead was positively associated with a 0.14 increase in EEAA age in the GLMs though not the GEE models (95%CI 0.03, 0.25). First and 2nd trimester blood lead levels were associated with a 0.02 year increase in PedBE age in GLM and GEE models (1st trimester, 95%CI 0.004, 0.03; 2nd trimester, 95%CI 0.01, 0.03). Third trimester and 24 month blood lead levels were associated with a -0.06 and -0.05 decrease in Skin-Blood age, respectively, in GLM models. Additionally, 3rd trimester blood lead levels were associated with a 0.08 year decrease in Hannum age in GLM and GEE models (95%CI -0.15, -0.01). There were multiple significant results in sex-stratified models and significant lead by sex interactions, where males experienced accelerated biological age, compared to females who saw a decelerated biological age, with respect to IEAA, EEAA, Horvath, Hannum, and PedBE clocks. Further research is needed to understand sex-specific relationships between lead exposure and measures of biological aging in adolescence and the trajectory of biological aging into young adulthood.

Maternal prenatal social experiences and offspring epigenetic age acceleration from birth to mid-childhood

PURPOSE

Investigate associations of maternal social experiences with offspring epigenetic age acceleration (EAA) from birth through mid-childhood among 205 mother-offspring dyads of minoritized racial and ethnic groups.

METHODS

We used linear regression to examine associations of maternal experiences of racial bias or discrimination (0 = none, 1-2 = intermediate, or 3+ = high), social support (tertile 1 = low, 2 = intermediate, 3 = high), and socioeconomic status index (tertile 1 = low, 2 = intermediate, 3 = high) during the prenatal period with offspring EAA according to Horvath's Pan-Tissue, Horvath's Skin and Blood, and Intrinsic EAA clocks at birth, 3 years, and 7 years.

RESULTS

In comparison to children of women who did not experience any racial bias or discrimination, those whose mothers reported highest levels of racial bias or discrimination had lower Pan-Tissue clock EAA in early (-0.50 years; 90% CI: -0.91, -0.09) and mid-childhood (-0.75 years; -1.41, -0.08). We observed similar associations for the Skin and Blood clock and Intrinsic EAA. Maternal experiences of discrimination were not associated with Pan-Tissue EAA at birth. Neither maternal social support nor socioeconomic status predicted offspring EAA.

CONCLUSIONS

Children whose mothers experienced higher racial bias or discrimination exhibited slower EAA. Future studies are warranted to confirm these findings and establish associations of early-life EAA with long-term health outcomes.

Supervised Learning of Physical Activity Features From Functional Accelerometer Data

OBJECTIVE

We propose a new health informatics framework to analyze physical activity (PA) from accelerometer devices. Accelerometry data enables scientists to extract personal digital features useful for precision health decision making. Existing methods in accelerometry data analysis typically begin with discretizing summary counts by certain fixed cutoffs into activity categories. One well-known limitation is that the chosen cutoffs are often validated under restricted settings, and cannot be generalizable across populations, devices, or studies.

METHODS

We develop a data-driven approach to overcome this bottleneck in PA data analysis, in which we holistically summarize a subject's activity profile using Occupation-Time curves (OTCs), which describe the percentage of time spent at or above a continuum of activity count levels. We develop multi-step adaptive learning algorithms to perform supervised learning via a scalar-on-function model that involves OTC as the functional predictor of interest as well as other scalar covariates. Our learning analytic first incorporates a hybrid approach of fused lasso for clustering and Hidden Markov Model for changepoint detection, then executes refinement procedures to determine activity windows of interest.

RESULTS

We evaluate and illustrate the performance of the proposed learning analytic through simulation experiments and real-world data analyses to assess the influence of PA on biological aging. Our findings indicate a different directional relationship between biological age and PA depending on the specific outcome of interest.

SIGNIFICANCE

Our bioinformatics methodology involves the biomedical outcome of interest to detect different critical points, and is thus adaptive to the specific data, study population, and health outcome under investigation.

Identification of sequence determinants for the ABHD14 enzymes

Over the course of evolution, enzymes have developed remarkable functional diversity in catalyzing important chemical reactions across various organisms, and understanding how new enzyme functions might have evolved remains an important question in modern enzymology. To systematically annotate functions, based on their protein sequences and available biochemical studies, enzymes with similar catalytic mechanisms have been clustered together into an enzyme superfamily. Typically, enzymes within a superfamily have similar overall three-dimensional structures, conserved catalytic residues, but large variations in substrate recognition sites and residues to accommodate the diverse biochemical reactions that are catalyzed within the superfamily. The serine hydrolases are an excellent example of such an enzyme superfamily. Based on known enzymatic activities and protein sequences, they are split almost equally into the serine proteases and metabolic serine hydrolases. Within the metabolic serine hydrolases, there are two outlying members, ABHD14A and ABHD14B, that have high sequence similarity, but their biological functions remained cryptic till recently. While ABHD14A still lacks any functional annotation to date, we recently showed that ABHD14B functions as a lysine deacetylase in mammals. Given their high sequence similarity, automated databases often wrongly assign ABHD14A and ABHD14B as the same enzyme, and therefore, annotating functions to them in various organisms has been problematic. In this article, we present a bioinformatics study coupled with biochemical experiments, which identifies key sequence determinants for both ABHD14A and ABHD14B, and enable better classification for them. In addition, we map these enzymes on an evolutionary timescale and provide a much-wanted resource for studying these interesting enzymes in different organisms.

Open access-enabled evaluation of epigenetic age acceleration in colorectal cancer and development of a classifier with diagnostic potential

Aberrant DNA methylation (DNAm) is known to be associated with the aetiology of cancer, including colorectal cancer (CRC). In the past, the availability of open access data has been the main driver of innovative method development and research training. However, this is increasingly being eroded by the move to controlled access, particularly of medical data, including cancer DNAm data. To rejuvenate this valuable tradition, we leveraged DNAm data from 1,845 samples (535 CRC tumours, 522 normal colon tissues adjacent to tumours, 72 colorectal adenomas, and 716 normal colon tissues from healthy individuals) from 14 open access studies deposited in NCBI GEO and ArrayExpress. We calculated each sample's epigenetic age (EA) using eleven epigenetic clock models and derived the corresponding epigenetic age acceleration (EAA). For EA, we observed that most first- and second-generation epigenetic clocks reflect the chronological age in normal tissues adjacent to tumours and healthy individuals [e.g., Horvath (r = 0.77 and 0.79), Zhang elastic net (EN) (r = 0.70 and 0.73)] unlike the epigenetic mitotic clocks (EpiTOC, HypoClock, MiAge) (r < 0.3). For EAA, we used PhenoAge, Wu, and the above mitotic clocks and found them to have distinct distributions in different tissue types, particularly between normal colon tissues adjacent to tumours and cancerous tumours, as well as between normal colon tissues adjacent to tumours and normal colon tissue from healthy individuals. Finally, we harnessed these associations to develop a classifier using elastic net regression (with lasso and ridge regularisations) that predicts CRC diagnosis based on a patient's sex and EAAs calculated from histologically normal controls (i.e., normal colon tissues adjacent to tumours and normal colon tissue from healthy individuals). The classifier demonstrated good diagnostic potential with ROC-AUC = 0.886, which suggests that an EAA-based classifier trained on relevant data could become a tool to support diagnostic/prognostic decisions in CRC for clinical professionals. Our study also reemphasises the importance of open access clinical data for method development and training of young scientists. Obtaining the required approvals for controlled access data would not have been possible in the timeframe of this study.

Exploring perinatal biopsychosocial factors and epigenetic age in 1-year-old offspring

Background: Little is known about the determinants of epigenetic aging in pediatric populations. Methods: Epigenetic age was estimated from 258 1-year-olds, using pediatric buccal epigenetic and Horvath clocks. We explored associations between epigenetic age and maternal indicators of mental and relational health, substance use and general physical health assessed during trimester three. Results: Higher anxiety and stress, BMI and higher parent-parent relationship quality were associated with pediatric buccal epigenetic clock differences. High blood pressure during pregnancy was associated with Horvath age acceleration. Third-trimester smoking and pre-pregnancy weight were associated with acceleration and deceleration respectively, and concordant across clocks. Conclusion: A broad range of maternal factors may shape epigenetic age in infancy; further research is needed to explore the possible effects on health and development.

MRI-based brain age prediction model for children under 3 years old using deep residual network

Early identification and intervention of abnormal brain development individual subjects are of great significance, especially during the earliest and most active stage of brain development in children aged under 3. Neuroimage-based brain's biological age has been associated with health, ability, and remaining life. However, the existing brain age prediction models based on neuroimage are predominantly adult-oriented. Here, we collected 658 T1-weighted MRI scans from 0 to 3 years old healthy controls and developed an accurate brain age prediction model for young children using deep learning techniques with high accuracy in capturing age-related changes. The performance of the deep learning-based model is comparable to that of the SVR-based model, showcasing remarkable precision and yielding a noteworthy correlation of 91% between the predicted brain age and the chronological age. Our results demonstrate the accuracy of convolutional neural network (CNN) brain-predicted age using raw T1-weighted MRI data with minimum preprocessing necessary. We also applied our model to children with low birth weight, premature delivery history, autism, and ADHD, and discovered that the brain age was delayed in children with extremely low birth weight (less than 1000 g) while ADHD may cause accelerated aging of the brain. Our child-specific brain age prediction model can be a valuable quantitative tool to detect abnormal brain development and can be helpful in the early identification and intervention of age-related brain disorders.

Impact of Plasmodium falciparum infection on DNA methylation of circulating immune cells

The regulation of immune cell responses to infection is a complex process that involves various molecular mechanisms, including epigenetic regulation. DNA methylation has been shown to play central roles in regulating gene expression and modulating cell response during infection. However, the nature and extent to which DNA methylation is involved in the host immune response in human malaria remains largely unknown. Here, we present a longitudinal study investigating the temporal dynamics of genome-wide in vivo DNA methylation profiles using 189 MethylationEPIC 850 K profiles from 66 children in Burkina Faso, West Africa, sampled three times: before infection, during symptomatic parasitemia, and after malaria treatment. The results revealed major changes in the DNA methylation profiles of children in response to both Plasmodium falciparum infection and malaria treatment, with widespread hypomethylation of CpGs upon infection (82% of 6.8 K differentially methylated regions). We document a remarkable reversal of CpG methylation profiles upon treatment to pre-infection states. These changes implicate divergence in core immune processes, including the regulation of lymphocyte, neutrophil, and myeloid leukocyte function. Integrative DNA methylation-mRNA analysis of a top differentially methylated region overlapping the pro-inflammatory gene TNF implicates DNA methylation of TNF cis regulatory elements in the molecular mechanisms of TNF regulation in human malaria. Our results highlight a central role of epigenetic regulation in mounting the host immune response to P. falciparum infection and in response to malaria treatment.

Epigenetic age deacceleration in youth at familial risk for schizophrenia and bipolar disorder

Epigenetic modifications occur sequentially during the lifespan, but their pace can be altered by external stimuli. The onset of schizophrenia and bipolar disorder is critically modulated by stressors that may alter the epigenetic pattern, a putative signature marker of exposure to environmental risk factors. In this study, we estimated the age-related epigenetic modifications to assess the differences between young individuals at familial high risk (FHR) and controls and their association with environmental stressors. The sample included 117 individuals (6-17 years) at FHR (45%) and a control group (55%). Blood and saliva samples were used estimate the epigenetic age with six epigenetic clocks through methylation data. Environmental risk was measured with obstetric complications, socioeconomic statuses and recent stressful life events data. Epigenetic age was correlated with chronological age. FHR individuals showed epigenetic age deacceleration of Horvath and Hannum epigenetic clocks compared to controls. No effect of the environmental risk factors on the epigenetic age acceleration could be detected. Epigenetic age acceleration adjusted by cell counts showed that the FHR group was deaccelerated also with the PedBE epigenetic clock. Epigenetic age asynchronicities were found in the young at high risk, suggesting that offspring of affected parents follow a slower pace of biological aging than the control group. It still remains unclear which environmental stressors orchestrate the changes in the methylation pattern. Further studies are needed to better characterize the molecular impact of environmental stressors before illness onset, which could be critical in the development of tools for personalized psychiatry.

Stressful life events and accelerated biological aging over time in youths

Experiencing adversity in childhood and adolescence, including stressful life events (SLEs), may accelerate the pace of development, leading to adverse mental and physical health. However, most research on adverse early experiences and biological aging (BA) in youths relies on cross-sectional designs. In 171 youths followed for approximately 2 years, we examined if SLEs over follow-up predicted rate of change in two BA metrics: epigenetic age and Tanner stage. We also investigated if rate of change in BA was associated with changes in depressive symptoms over time. Youths aged 8-16 years at baseline self-reported Tanner stage and depressive symptoms at baseline and follow-up and provided saliva samples for DNA at both assessments. Horvath epigenetic age estimates were derived from DNA methylation data measured with the Illumina EPIC array. At follow-up, contextual threat interviews were administered to youths and caregivers to assess youths' experiences of past-year SLEs. Interviews were objectively coded by an independent rating team to generate a SLE impact score, reflecting the severity of all SLEs occurring over the prior year. Rate of change in BA metrics was operationalized as change in epigenetic age or Tanner stage as a function of time between assessments. Higher objective SLE impact scores over follow-up were related to a greater rate of change in epigenetic age (β = 0.21, p = .043). Additionally, among youths with lower-but not higher-Tanner stage at baseline, there was a positive association of SLE impact scores with rate of change in Tanner stage (Baseline Tanner Stage × SLE Impact Score interaction: β = - 0.21, p = .011). A greater rate of change in epigenetic age was also associated with higher depressive symptom levels at follow-up, adjusting for baseline symptoms (β = 0.15, p = .043). Associations with epigenetic age were similar, although slightly attenuated, when adjusting for epithelial (buccal) cell proportions. Whereas much research in youths has focused on severe experiences of early adversity, we demonstrate that more commonly experienced SLEs during adolescence may also contribute to accelerated BA. Further research is needed to understand the long-term consequences of changes in BA metrics for health.

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.

A new blood based epigenetic age predictor for adolescents and young adults

Children have special rights for protection compared to adults in our society. However, more than 1/4 of children globally have no documentation of their date of birth. Hence, there is a pressing need to develop biological methods for chronological age prediction, robust to differences in genetics, psychosocial events and physical living conditions. At present, DNA methylation is the most promising biological biomarker applied for age assessment. The human genome contains around 28 million DNA methylation sites, many of which change with age. Several epigenetic clocks accurately predict chronological age using methylation levels at age associated GpG-sites. However, variation in DNA methylation increases with age, and there is no epigenetic clock specifically designed for adolescents and young adults. Here we present a novel age Predictor for Adolescents and Young Adults (PAYA), using 267 CpG methylation sites to assess the chronological age of adolescents and young adults. We compared different preprocessing approaches and investigated the effect on prediction performance of the epigenetic clock. We evaluated performance using an independent validation data set consisting of 18-year-old individuals, where we obtained a median absolute deviation of just below 0.7 years. This tool may be helpful in age assessment of adolescents and young adults. However, there is a need to investigate the robustness of the age predictor across geographical and disease populations as well as environmental effects.

Single-base methylome analysis reveals dynamic changes of genome-wide DNA methylation associated with rapid stem growth of woody bamboos

CG and CHG methylation levels in the rapid shoot growth stages (ST2-ST4) of woody bamboos were obviously decreased, which might regulate the internode elongation during rapid shoot growth, while CHH methylation was strongly associated with shoot developmental time or age. DNA methylation plays a critical role in the regulation of plant growth and development. Woody bamboos have a unique trait of rapid stem growth resulted from internode elongation at the shooting period. However, it is still unclear whether DNA methylation significantly controls the bamboo rapid stem growth. Here we present whole-genome DNA methylation profiles of the paleotropical woody bamboo Bonia amplexicaulis at five newly defined stages of shoot growth, named ST1-ST5. We found that CG and CHG methylation levels in the rapid shoot growth stages (ST2-ST4) were significantly lower than in the incubation (ST1) and plateau stages (ST5). The changes in methylation levels mainly occurred in flanking regions of genes and gene body regions, and 23647 differentially methylated regions (DMRs) were identified between ST1 and rapid shoot growth stages (ST2-ST4). Combined with transcriptome analysis, we found that DMR-related genes enriched in the auxin and jasmonic acid (JA) signal transduction, and other pathways closely related to plant growth. Intriguingly, CHH methylation was not involved in the rapid shoot growth, but strongly associated with shoot developmental time by gradually accumulating in transposable elements (TEs) regions. Overall, our results reveal the importance of DNA methylation in regulating the bamboo rapid shoot growth and suggest a role of DNA methylation associated with development time or age in woody bamboos.

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.

Altered DNA methylation at age-associated CpG sites in children with growth disorders: impact on age estimation?

Age estimation based on DNA methylation (DNAm) can be applied to children, adolescents and adults, but many CG dinucleotides (CpGs) exhibit different kinetics of age-associated DNAm across these age ranges. Furthermore, it is still unclear how growth disorders impact epigenetic age predictions, and this may be particularly relevant for a forensic application. In this study, we analyzed buccal mucosa samples from 95 healthy children and 104 children with different growth disorders. DNAm was analysed by pyrosequencing for 22 CpGs in the genes PDE4C, ELOVL2, RPA2, EDARADD and DDO. The relationship between DNAm and age in healthy children was tested by Spearman's rank correlation. Differences in DNAm between the groups "healthy children" and the (sub-)groups of children with growth disorders were tested by ANCOVA. Models for age estimation were trained (1) based on the data from 11 CpGs with a close correlation between DNAm and age (R ≥ 0.75) and (2) on five CpGs that also did not present significant differences in DNAm between healthy and diseased children. Statistical analysis revealed significant differences between the healthy group and the group with growth disorders (11 CpGs), the subgroup with a short stature (12 CpGs) and the non-short stature subgroup (three CpGs). The results are in line with the assumption of an epigenetic regulation of height-influencing genes. Age predictors trained on 11 CpGs with high correlations between DNAm and age revealed higher mean absolute errors (MAEs) in the group of growth disorders (mean MAE 2.21 years versus MAE 1.79 in the healthy group) as well as in the short stature (sub-)groups; furthermore, there was a clear tendency for overestimation of ages in all growth disorder groups (mean age deviations: total growth disorder group 1.85 years, short stature group 1.99 years). Age estimates on samples from children with growth disorders were more precise when using a model containing only the five CpGs that did not present significant differences in DNAm between healthy and diseased children (mean age deviations: total growth disorder group 1.45 years, short stature group 1.66 years). The results suggest that CpGs in genes involved in processes relevant for growth and development should be avoided in age prediction models for children since they may be sensitive for alterations in the DNAm pattern in cases of growth disorders.

eClock: An ensemble-based method to accurately predict ages with a biased distribution from DNA methylation data

DNA methylation is closely related to senescence, so it has been used to develop statistical models, called clock models, to predict chronological ages accurately. However, because the training data always have a biased age distribution, the model performance becomes weak for the samples with a small age distribution density. To solve this problem, we developed the R package eClock, which uses a bagging-SMOTE method to adjust the biased distribution and predict age with an ensemble model. Moreover, it also provides a bootstrapped model based on bagging only and a traditional clock model. The performance on three datasets showed that the bagging-SMOTE model significantly improved rare sample age prediction. In addition to model construction, the package also provides other functions such as data visualization and methylation feature conversion to facilitate the research in relevant areas.

Evaluation of epigenetic age calculators between preeclampsia and normotensive pregnancies in an Australian cohort

Advanced biological aging, as assessed through DNA methylation markers, is associated with several complex diseases. The associations between maternal DNA methylation age and preeclampsia (PE) have not been fully assessed. The aim of this study was to examine if increased maternal DNA methylation age (DNAmAge) was shown to be accelerated in women with PE when compared to women who had normotensive pregnancies. The case/control cohort available for study consisted of 166 women (89 with normotensive pregnancy, 77 with PE) recruited previously at the Royal Women’s Hospital in Melbourne, Australia. DNA methylation profiles were obtained using the Illumina EPIC Infinium array for analysis of genomic DNA isolated from whole blood. These profiles were used to calculate seven estimates of DNAmAge and included (1) Horvath, (2) Hannum, (3) Horvath Skin and Blood, (4) Wu, (5) PhenoAge, (6) telomere length and (7) GrimAge and its surrogate measures. Three measures of DNA methylation age acceleration were calculated for all seven measures using linear regression. Pearson's correlation was performed to investigate associations between chronological age and DNAmAge. Differences between chronological age and DNAmAge and epigenetic age acceleration were investigated using t-tests. No significant difference was observed for chronological age between women with PE (age = 30.53 ± 5.68) and women who had normotensive pregnancies (age = 31.76 ± 4.76). All seven DNAmAge measures were significantly correlated (p < 0.001) with chronological age. After accounting for multiple testing and investigating differences in DNAmAge between normotensive women and women with PE, only Wu DNAmAge was significant (p = 0.001). When examining differences for epigenetic age acceleration between PE and normotensive women Hannum, Wu, and PhenoAge DNAmAge estimates (p < 0.001) were significant for both epigenetic age acceleration and intrinsic acceleration models. We found that accelerated maternal DNAmAge is increased in women with PE in some models of epigenetic aging. This research underlines the importance for further investigation into the potential changes of differential DNA methylation in PE.

Epigenetic age acceleration and cardiovascular outcomes in school-age children: The Generation R Study

BACKGROUND

Hypertension and atherosclerosis may partly originate in early life. Altered epigenetic aging may be a mechanism underlying associations of early-life exposures and the development of cardiovascular risk factors in childhood. A discrepancy between chronological age and age predicted from neonatal DNA methylation data is referred to as age acceleration. It may either be positive, if DNA methylation age is older than clinical age, or negative, if DNA methylation age is younger than chronological age. We examined associations of age acceleration at birth ('gestational age acceleration'), and of age acceleration at school-age, with blood pressure and with intima-media thickness and distensibility of the common carotid artery, as markers of vascular structure and function, respectively, measured at age 10 years.

RESULTS

This study was embedded in the Generation R Study, a population-based prospective cohort study. We included 1115 children with information on cord blood DNA methylation and blood pressure, carotid intima-media thickness or carotid distensibility. Gestational age acceleration was calculated using the Bohlin epigenetic clock, which was developed specifically for cord blood DNA methylation data. It predicts gestational age based on methylation levels of 96 CpGs from HumanMethylation450 BeadChip. We observed no associations of gestational age acceleration with blood pressure, carotid intima-media thickness or carotid distensibility at age 10 years. In analyses among children with peripheral blood DNA methylation measured at age 6 (n = 470) and 10 (n = 449) years, we also observed no associations of age acceleration at these ages with the same cardiovascular outcomes, using the 'skin and blood clock,' which predicts age based on methylation levels at 391 CpGs from HumanMethylation450 BeadChip.

CONCLUSIONS

Our findings do not provide support for the hypothesis that altered epigenetic aging during the earliest phase of life is involved in the development of cardiovascular risk factors in childhood.

The early-life exposome and epigenetic age acceleration in children

The early-life exposome influences future health and accelerated biological aging has been proposed as one of the underlying biological mechanisms. We investigated the association between more than 100 exposures assessed during pregnancy and in childhood (including indoor and outdoor air pollutants, built environment, green environments, tobacco smoking, lifestyle exposures, and biomarkers of chemical pollutants), and epigenetic age acceleration in 1,173 children aged 7 years old from the Human Early-Life Exposome project. Age acceleration was calculated based on Horvath's Skin and Blood clock using child blood DNA methylation measured by Infinium HumanMethylation450 BeadChips. We performed an exposure-wide association study between prenatal and childhood exposome and age acceleration. Maternal tobacco smoking during pregnancy was nominally associated with increased age acceleration. For childhood exposures, indoor particulate matter absorbance (PM_abs) and parental smoking were nominally associated with an increase in age acceleration. Exposure to the organic pesticide dimethyl dithiophosphate and the persistent pollutant polychlorinated biphenyl-138 (inversely associated with child body mass index) were protective for age acceleration. None of the associations remained significant after multiple-testing correction. Pregnancy and childhood exposure to tobacco smoke and childhood exposure to indoor PM_abs may accelerate epigenetic aging from an early age.

Epigenetic clocks in the pediatric population: when and why they tick?

ABSTRACT

Recent research efforts have provided compelling evidence of genome-wide DNA methylation alterations in pediatrics. It is currently well established that epigenetic clocks, composed of DNA methylation sites, can estimate the gestational and chronological age of cells and tissues from different ages. Also, extensive research is aimed at their correlation with early life exposure and pediatric diseases. This review aimed to systematically summarize the epigenetic clocks in the pediatric population. Publications were collected from PubMed and Web of Science databases up to Apr 2021. Epigenetic clocks, DNA methylation clocks, epigenetic age acceleration or deceleration, pediatric and the pediatric population were used as search criteria. Here, we first review the currently applicative pediatric epigenetic clocks. We then highlight the interpretation for epigenetic age deviations in the pediatric population and their association with external factors, developmental trajectories, and pediatric diseases. Considering the remaining unknown of pediatric clocks, research strategies into them are also discussed. In all, pediatric epigenetic clocks may act as potent tools to understand development, growth and diseases in early life.

Epigenetic Age Acceleration Was Delayed in Schizophrenia

Schizophrenia is a serious neuropsychiatric disorder with abnormal age-related neurodevelopmental (or neurodegenerative) trajectories. Although an accelerated aging hypothesis of schizophrenia has been proposed, the quantitative study of the disruption of the physiological trajectory caused by schizophrenia is inconclusive. In this study, we employed 3 "epigenetic clock" methods to quantify the epigenetic age of a large sample size of whole blood (1069 samples from patients with schizophrenia vs 1264 samples from unaffected controls) and brain tissues (500 samples from patients with schizophrenia vs 711 samples from unaffected controls). We observed significant positive correlations between epigenetic age and chronological age in both blood and brain tissues from unaffected controls and patients with schizophrenia, as estimated by 3 methods. Furthermore, we observed that epigenetic age acceleration was significantly delayed in schizophrenia from the whole blood samples (aged 20-90 years) and brain frontal cortex tissues (aged 20-39 years). Intriguingly, the genes regulated by the epigenetic clock also contained schizophrenia-associated genes, displaying differential expression and methylation in patients with schizophrenia and involving in the regulation of cell activation and development. These findings were further supported by the dysregulated leukocyte composition in patients with schizophrenia. Our study presents quantitative evidence for a neurodevelopmental model of schizophrenia from the perspective of a skewed "epigenetic clock." Moreover, landmark changes in an easily accessible biological sample, blood, reveal the value of these epigenetic clock genes as peripheral biomarkers for schizophrenia.

The use of DNA methylation clock in aging research

One of the key characteristics of aging is a progressive loss of physiological integrity, which weakens bodily functions and increases the risk of death. A robust biomarker is important for the assessment of biological age, the rate of aging, and a person's health status. DNA methylation clocks, novel biomarkers of aging, are composed of a group of cytosine-phosphate-guanine dinucleotides, the DNA methylation status of which can be used to accurately measure subjective age. These clocks are considered accurate biomarkers of chronological age for humans and other vertebrates. Numerous studies have demonstrated these clocks to quantify the rate of biological aging and the effects of longevity and anti-aging interventions. In this review, we describe the purpose and use of DNA methylation clocks in aging research.

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.

Current perspectives on the cellular and molecular features of epigenetic ageing

IMPACT STATEMENT

The field of epigenetic ageing is relatively new, and the speed of its expansion presents a challenge in keeping abreast with new discoveries and their implications. Several reviews have already addressed the great number of pathologies, health conditions, life-style, and external stressors that are associated with changes to the rate of epigenetic ageing. While these associations highlight and affirm the ability of epigenetic clock to capture biologically meaningful changes associated with age, they do not inform us about the underlying mechanisms. In this very early period since the development of the clock, there have been rather limited experimental research that are aimed at uncovering the mechanism. Hence, the perspective that we proffer is derived from available but nevertheless limited lines of evidence that together provide a seemingly coherent narrative that can be tested. This, we believe would be helpful towards uncovering the workings of the epigenetic clock.