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Correlation between DNA methylation-predicted age based on the Horvath and the Hannum clocks, and chronological age in the HANDLS study. Abbreviation: AAs: African Americans
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Background:
African Americans (AAs) experience premature chronic health outcomes and longevity disparities consistent with an accelerated aging phenotype. DNA methylation (DNAm) levels at specific CpG positions are hallmarks of aging evidenced by the presence of age-associated differentially methylated CpG positions (aDMPs) that are the basis for...
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Context 1
... calculated DNA methylation age (DNAm age) for each of the participants using the Horvath [24] and Hannum algorithms [25] implemented in the online DNAm age calculator [24]. DNAm age predicted by both the Horvath and the Hannum clock was strongly correlated with chronological age (Pearson's r = 0.85) (Fig. 3). In subgroup correlation analysis stratified by sex, race, and poverty status, similar strong correlations between chronological age and DNAm age were observed (Pearson's r range 0.83-0.89) indicating that the epigenetic clocks are robust estimator of chronological age and that the prediction algorithms performed well in our ...
Context 2
... quantile-quantile plots, histogram and density plots of p values, and corresponding inflation measures before (AA inflation = 1.040 and whites inflation = 1.263) and after (AA inflation = 0.998 and whites inflation = 0.807) correction for inflation in both AAs and whites are shown in Additional file 10: Figure S3. In order to confirm the approximately 5000 differences found after correcting for genomic inflation were valid, we performed a sensitivity analyses by testing associations based on winsorized DNA methylation data. ...
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Citations
... Despite the fact that these reported epigenetic clocks are extensively used and studied, they show some limitations. First, the majority of them were developed by individuals of European or Hispanic ancestry [20,21,24,25,28], although ethnic differences in DNAm patterns have been reported [24, 40,41]. Apart from genetic confounding, environmental variations among populations also impact the physiological characteristics upon which these models are based. ...
Epigenetic modifications have been implicated in a number of complex diseases as well as being a hallmark of organismal aging. Several reports have indicated an involvement of these changes in Alzheimer’s disease (AD) risk and progression, most likely contributing to the dysregulation of AD-related gene expression measured by DNA methylation studies. Given that DNA methylation is tissue-specific and that AD is a brain disorder, the limitation of these studies is the ability to identify clinically useful biomarkers in a proxy tissue, reflective of the tissue of interest, that would be less invasive, more cost-effective, and easily obtainable. The age-related DNA methylation changes have also been used to develop different generations of epigenetic clocks devoted to measuring the aging in different tissues that sometimes suggests an age acceleration in AD patients. This review critically discusses epigenetic changes and aging measures as potential biomarkers for AD detection, prognosis, and progression. Given that epigenetic alterations are chemically reversible, treatments aiming at reversing these modifications will be also discussed as promising therapeutic strategies for AD.
... Interestingly, some of the listed genes are related to aging or associated with CpGs that are included in different epigenetic age models. For example, CpG sites near the genes GRM2, SCGN, and ZIK1 are used in region-based epigenetic clocks, or they are described as age-associated CpGs [25][26][27][28]. Lin28b has been found to delay vasculature aging, and ADRB1 beneficially impacts aging [29,30]. ...
Dynamic changes in genomic DNA methylation patterns govern the epigenetic developmental programs and accompany the organism's aging. Epigenetic clock (eAge) algorithms utilize DNA methylation to estimate the age and risk factors for diseases as well as analyze the impact of various interventions. High-throughput bisulfite sequencing methods, such as reduced-representation bisulfite sequencing (RRBS) or whole genome bisulfite sequencing (WGBS), provide an opportunity to identify the genomic regions of disordered or heterogeneous DNA methylation, which might be associated with cell-type heterogeneity, DNA methylation erosion, and allele-specific methylation. We systematically evaluated the applicability of five scores assessing the variability of methylation patterns by evaluating within-sample heterogeneity (WSH) to construct human blood epigenetic clock models using RRBS data. The best performance was demonstrated by the model based on a metric designed to assess DNA methylation erosion with an MAE of 3.686 years. We also trained a prediction model that uses the average methylation level over genomic regions. Although this region-based model was relatively more efficient than the WSH-based model, the latter required the analysis of just a few short genomic regions and, therefore, could be a useful tool to design a reduced epigenetic clock that is analyzed by targeted next-generation sequencing.
... It is commonly observed that an individual's biological age may not correspond to their chronological age [14,17,18] due to various factors such as environmental exposures, lifestyle habits, and diseases [19][20][21][22][23][24][25][26]. Moreover, factors like ancestry [27][28][29] and biological sex [13,24,30,31] could further contribute to this discrepancy. ...
Age estimation is a critical aspect of reconstructing a biological profile in forensic sciences. Diverse biochemical processes have been studied in their correlation with age, and the results have driven DNA methylation to the forefront as a promising biomarker. DNA methylation, an epigenetic modification, has been extensively studied in recent years for developing age estimation models in criminalistics and forensic anthropology. Epigenetic clocks, which analyze DNA sites undergoing hypermethylation or hypomethylation as individuals age, have paved the way for improved prediction models. A wide range of biomarkers and methods for DNA methylation analysis have been proposed, achieving different accuracies across samples and cell types. This review extensively explores literature from the past 5 years, showing scientific efforts toward the ultimate goal: applying age prediction models to assist in human identification.
... A previous investigations demonstrated that over 90% of age-associated differentially methylated CpG positions (aDMPs) were significantly identified in African Americans, whereas only 5% of aDMPs were shared between the two racial populations (African Americans vs. whites). Additionally, it was observed that only 3% of hypermethylated aDMPs overlapped, while the remaining methylated aDMPs were unique to each racial population (African Americans vs. whites) [23]. ...
... 14,24,26,28,[53][54][55] Black Americans also tend to have a higher prevalence of hypertension and cardiovascular disease. 2,21,[56][57][58] By investigating ethnicity-specific risk at the level of genes and their networks, we aim to uncover the mechanisms potentially involved in ethnicity-specific risk, as it contributes to differential manifestations with varying etiology, in our future work. ...
INTRODUCTION : Here we evaluate frequencies of the top 10 Alzheimer's disease (AD) risk alleles for late‐onset AD in Mexican American (MA) and non‐Hispanic White (NHW) American participants enrolled in the Health and Aging Brain Study–Health Disparities Study cohort.
METHODS : Using DNA extracted from this community‐based diverse population, we calculated the genotype frequencies in each population to determine whether a significant difference is detected between the different ethnicities. DNA genotyping was performed per manufacturers’ protocols.
RESULTS : Allele and genotype frequencies for 9 of the 11 single nucleotide polymorphisms (two apolipoprotein E variants , CR1, BIN1, DRB1, NYAP1, PTK2B, FERMT2 , and ABCA7 ) differed significantly between MAs and NHWs.
DISCUSSION : The significant differences in frequencies of top AD risk alleles observed here across MAs and NHWs suggest that ethnicity‐specific genetic risks for AD exist. Given our results, we are advancing additional projects to further elucidate ethnicity‐specific differences in AD.
... Recent studies have investigated cross-sectional changes in DNA methylation correlated with age using data derived from the EPIC850k array 14,26,27 . To our knowledge, the only longitudinal study on age using the EPIC850k array is Pérez et al. 28 , where changes in DNA methylation in young individuals (birth-10 years old) were investigated. ...
DNA methylation, a pivotal epigenetic modification, plays a crucial role in regulating gene expression and is known to undergo dynamic changes with age. The present study investigated epigenome-wide methylation profiles in 64 individuals over two time points, 15 years apart, using the Illumina EPIC850k arrays. A mixed-effects model identified 2821 age-associated differentially methylated CpG positions (aDMPs) with a median rate of change of 0.18% per year, consistent with a 10–15% change during a human lifespan. Significant variation in the baseline DNA methylation levels between individuals of similar ages as well as inconsistent direction of change with time across individuals were observed for all the aDMPs. Twenty-three of the 2821 aDMPs were previously incorporated into forensic age prediction models. These markers displayed larger changes in DNA methylation with age compared to all the aDMPs and less variation among individuals. Nevertheless, the forensic aDMPs also showed inter-individual variations in the direction of DNA methylation changes. Only cg16867657 in ELOVL2 exhibited a uniform direction of the age-related change among the investigated individuals, which supports the current knowledge that CpG sites in ELOVL2 are the best markers for age prediction.
... Other studies noting significant effects of genetic ancestry on epigenetic age analyzed multiple cohorts from different genetic backgrounds. 49,50 Additionally, aging was assessed here in a SCD case-only cohort. To better understand the effects of background genetic ancestry, epigenetic ages could be calculated in control populations of healthy age and sex-matched Black or African-American individuals with methylation data and compared to those of SCD individuals. ...
Sickle cell disease (SCD) affects approximately 100,000 predominantly African-American individuals in the United States, causing significant cellular damage, increased disease complications, and premature death. The contribution of epigenetic factors to SCD pathophysiology is relatively unexplored. DNA methylation (DNAm), a primary epigenetic mechanism for regulating gene expression in response to the environment, is an important driver of normal cellular aging. Several DNAm epigenetic clocks have been developed to serve as a proxy for cellular aging. We calculated the epigenetic ages of 89 adults with SCD (mean age: 30.64 years; 60.64% female) using five published epigenetic clocks: Horvath, Hannum, PhenoAge, GrimAge, and DunedinPACE. We hypothesized that in a chronic disease like SCD, individuals would demonstrate epigenetic age acceleration, but results differed depending on the clock used. Recently developed clocks more consistently demonstrated acceleration (GrimAge, DunedinPACE). Additional demographic and clinical phenotypes were analyzed to explore their associations with epigenetic age estimates. Chronological age was significantly correlated with epigenetic age in all clocks (Horvath, r = 0.88; Hannum, r = 0.89; PhenoAge, r = 0.85; GrimAge, r = 0.88; DunedinPACE, r=0.34). SCD genotype was associated with two clocks (PhenoAge, p = 0.02; DunedinPACE, p < 0.001). Genetic ancestry, biological sex, beta-globin haplotypes, BCL11A rs11886868 and SCD disease severity were not associated. These findings, among the first to interrogate epigenetic aging in adults with SCD, demonstrate epigenetic age acceleration with recently developed epigenetic clocks but not older generation clocks. Further development of epigenetic clocks may improve predictive ability and utility in chronic diseases like SCD. -
... These estimators have been applied broadly to study centenarians [12], socioeconomic inequalities, education [13], nutritional status and lifetime stress [14], conditions including Down's syndrome [15], Alzheimer's diseases and cognitive decline [16]. Furthermore, some studies have suggested variations in epigenetic age estimations across different human populations and ethnic groups [17][18][19]. These differences have been largely attributed to social and environmental health disparities, but in many contexts, these two factors covary with genetic ancestry. ...
Epigenetic estimators based on DNA methylation levels have emerged as promising biomarkers of human aging. These estimators exhibit natural variations across human groups, but data about indigenous populations remain still underrepresented in research. This study aims to investigate differences in epigenetic estimators between two distinct human populations, both residing in the Gran Chaco region of Argentina: Native-American Wichí and admixed Criollos, who are descendants of intermarriages between Native Americans and the first European colonizers, using a population genetic approach.
We analyzed 24 Wichí (mean age: 39.2 ± 12.9 yo) and 24 Criollos (mean age: 41.1 ± 14.0 yo) for DNA methylation levels using the Infinium MethylationEPIC (Illumina) to calculate 16 epigenetic estimators. Additionally, we examined genome-wide genetic variation using the HumanOmniExpress BeadChip (Illumina) to gain insights into the genetic history of these populations.
Our results indicate that Native-American Wichí are epigenetically older compared to Criollos according to 5 epigenetic estimators. Analyses within the Criollos population reveal that global ancestry does not influence the differences observed, while local (chromosomal) ancestry shows positive associations between specific SNPs located in genomic regions over-represented by Native-American ancestry and measures of epigenetic age acceleration (AgeAccelHannum). Furthermore, we demonstrate that differences in population ecologies also contribute to observed epigenetic differences. Overall, our study suggests that while the genomic history may partially account for the observed epigenetic differences, non-genetic factors, such as lifestyle and ecological factors, play a substantial role in the variability of epigenetic estimators, thereby contributing to variations in human epigenetic aging.
... In addition, all DNAm ageing clocks were mainly developed by European, African, or Hispanic individuals, and studies using data from Asians are extremely limited (Lin, 2023). Previous studies have suggested that DNAm age differs among ethnic groups (Crimmins et al., 2021;Horvath et al., 2016;Tajuddin et al., 2019), but it remains unclear whether it reflects the same phenotype in Mongoloids as in other ethnic groups. ...
... Furthermore, racial and sex differences in DNAmAgeAccel should be considered (Horvath et al., 2016;Tajuddin et al., 2019). ...
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.
... Black and White infants at birth, 15 and a higher number of age-associated differentially methylated CpG 53 sites have been found in Black adults compared to White adults, 16 which could play a role in age-related 54 diseases. DNAm is also partially genetically regulated with the average heritability of blood-based DNAm 55 levels at CpG sites across the genome estimated to be 0.09 ± 0.02 (mean ± standard deviation) with >9% 56 of CpG sites exhibiting heritability >0.3. ...
DNA methylation studies of incident type 2 diabetes in US populations are limited, and to our knowledge none included individuals of African descent living in the US. We performed an epigenome-wide association analysis of blood-based methylation levels at CpG sites with incident type 2 diabetes using Cox regression in 2,091 Black and 1,029 White individuals from the Atherosclerosis Risk in Communities study. At an epigenome-wide significance threshold of 1e-7, we detected 7 novel diabetes-associated CpG sites in C1orf151 (cg05380846: HR=0.89, p=8.4e-12), ZNF2 (cg01585592: HR=0.88, p=1.6e-9), JPH3 (cg16696007: HR=0.87, p=7.8e-9), GPX6 (cg02793507: HR=0.85, p=2.7e-8 and cg00647063: HR=1.20, p=2.5e-8), chr17q25 (cg16865890: HR=0.8, p=6.9e-8), and chr11p15 (cg13738793: HR=1.11, p=7.7e-8). The CpG sites at C1orf151, ZNF2, JPH3 and GPX6, were identified in Black adults, chr17q25 was identified in White adults, and chr11p15 was identified upon meta-analyzing the two groups. The CpG sites at JPH3 and GPX6 were likely associated with incident type 2 diabetes independent of BMI. All the CpG sites, except at JPH3, were likely consequences of elevated glucose at baseline. We additionally replicated known type 2 diabetes-associated CpG sites including cg19693031 at TXNIP, cg00574958 at CPT1A, cg16567056 at PLBC2, cg11024682 at SREBF1, cg08857797 at VPS25, and cg06500161 at ABCG1, 3 of which were replicated in Black adults at the epigenome-wide threshold. We observed modest increase in type 2 diabetes variance explained upon addition of the significantly associated CpG sites to a Cox model that included traditional type 2 diabetes risk factors and fasting glucose (increase from 26.2% to 30.5% in Black adults; increase from 36.9% to 39.4% in White adults). We examined if groups of proximal CpG sites were associated with incident type 2 diabetes using a gene-region specific and a gene-region agnostic differentially methylated region (DMR) analysis. Our DMR analyses revealed several clusters of significant CpG sites, including a DMR consisting of a previously discovered CpG site at ADCY7 and promoter regions of TP63 which were differentially methylated across all race groups. This study illustrates improved discovery of CpG sites/regions by leveraging both individual CpG site and DMR analyses in an unexplored population. Our findings include genes linked to diabetes in experimental studies (e.g., GPX6, JPH3, and TP63), and future gene-specific methylation studies could elucidate the link between genes, environment, and methylation in the pathogenesis of type 2 diabetes.
