Vitamin D Supplementation is Associated with Slower Epigenetic Aging

Adverse effects of low vitamin D level on mortality and morbidity are controversially discussed. Especially older people are at risk for vitamin D deficiency and therefore exposed to its potentially harmful influence on the aging process. A way of measuring differences in the biological age is through DNA methylation age (DNAm age) and its deviation from chronological age, DNAm age acceleration (DNAmAA). We previously reported on an association between vitamin D deficiency and higher 7-CpG DNAmAA in participants of the Berlin Aging Study II (BASE-II).In this study, we employ a quasi-interventional study design to assess the relationship between DNAmAA of five epigenetic clocks and vitamin D supplementation. Longitudinal data were available for 1,036 participants of BASE-II that were reexamined on average 7.4 years later in the GendAge study (mean age at follow-up: 75.6 years, SD = 3.8 years, age range: 64.9 – 94.1 years, 51.9 % female). DNAmAA was estimated with the 7-CpG clock, Horvath’s clock, Hannum’s clock, PhenoAge and GrimAge. Methylation data were obtained through methylation-sensitive single nucleotide primer extension (MS-SNuPE) or Illumina’s Infinium “MethylationEPIC” array.Vitamin D deficient participants who chose to start vitamin D supplementation after baseline examination showed a 2.6 year lower 7-CpG DNAmAA (p=0.011) and 1.3-year lower Horvath DNAmAA (p=0.042) compared to untreated and vitamin D deficient participants. DNAmAA did not statistically differ between participants with successfully treated vitamin D deficiency and healthy controls (p>0.16).Therefore, we conclude that intake of vitamin D supplement is associated with lower DNAmAA in participants with vitamin D deficiency. Additionally, our findings suggest that sufficient vitamin D supplementation can compensate and potentially reverse the increase in 7-CpG DNAmAA that we found in in vitamin D deficient participants.

Universal DNA Methylation Age Across Mammalian Tissues

Aging is often perceived as a degenerative process caused by random accrual of cellular damage over time. In spite of this, age can be accurately estimated by epigenetic clocks based on DNA methylation profiles from almost any tissue of the body. Since such pan-tissue epigenetic clocks have been successfully developed for several different species, it is difficult to ignore the likelihood that a defined and shared mechanism instead, underlies the aging process. To address this, we generated over 10,000 methylation arrays, each profiling up to 37,000 cytosines in highly-conserved stretches of DNA, from over 59 tissue-types derived from 128 mammalian species. From these, we identified and characterized specific cytosines, whose methylation levels change with age across mammalian species. Genes associated with these cytosines are greatly enriched in mammalian developmental processes and implicated in age-associated diseases. From the methylation profiles of these age-related cytosines, we successfully constructed three highly accurate universal mammalian clocks for eutherians, and one universal clock for marsupials. The universal clocks for eutherians are similarly accurate for estimating ages (r>0.96) of any mammalian species and tissue with a single mathematical formula. Collectively, these new observations support the notion that aging is indeed evolutionarily conserved and coupled to developmental processes across all mammalian species - a notion that was long-debated without the benefit of this new and compelling evidence.

DNA Methylation Age Drift Is Associated with Poor Outcomes and De-Differentiation in Papillary and Follicular Thyroid Carcinomas

Alterations in global DNA methylation play a critical role in both aging and cancer, and DNA methylation (DNAm) age drift has been implicated in cancer risk and pathogenesis. In the present study, we analyzed the TCGA cohort of papillary and follicular thyroid carcinoma (PTC and FTC) for their DNAm age and association with clinic-pathological features. In 54 noncancerous thyroid (NT) samples, DNAm age was highly correlated with patient chronological age (R2 = 0.928, p = 2.6 × 10−31), but drifted to younger than chronological age in most specimens, especially those from patients >50 years old. DNAm age in 502 tumors was also correlated with patient chronological age, but to a much lesser extent (R2 = 0.403). Highly drifted DNAm age (HDDA) was identified in 161 tumors, among which were 101 with DNAm age acceleration while 60 with DNAm age deceleration. Tumors with HDDA were characterized by the robust aberrations in metabolic activities, extracellular microenvironment components and inflammation/immunology responses, and dedifferentiation. Importantly, HDDA in tumors independently predicted shorter disease-free survival of patients. Collectively, NT thyroids from TC patients have younger DNAm age, while HDDA frequently occurs in TCs, and contributes to the TC progression and poor patient outcomes. HDDA may serve as a new prognostic factor for TCs.

Environmental Influences Measured by Epigenetic Clock and Vulnerability Components at Birth Impact Clinical ASD Heterogeneity

Although Autism Spectrum Disorders (ASD) is recognized as being heavily influenced by genetic factors, the role of epigenetic and environmental factors is still being established. This study aimed to identify ASD vulnerability components based on familial history and intrauterine environmental stress exposure, explore possible vulnerability subgroups, access DNA methylation age acceleration (AA) as a proxy of stress exposure during life, and evaluate the association of ASD vulnerability components and AA to phenotypic severity measures. Principal Component Analysis (PCA) was used to search the vulnerability components from 67 mothers of autistic children. We found that PC1 had a higher correlation with psychosocial stress (maternal stress, maternal education, and social class), and PC2 had a higher correlation with biological factors (psychiatric family history and gestational complications). Comparing the methylome between above and below PC1 average subgroups we found 11,879 statistically significant differentially methylated probes (DMPs, p < 0.05). DMPs CpG sites were enriched in variably methylated regions (VMRs), most showing environmental and genetic influences. Hypermethylated probes presented higher rates in different regulatory regions associated with functional SNPs, indicating that the subgroups may have different affected regulatory regions and their liability to disease explained by common variations. Vulnerability components score moderated by epigenetic clock AA was associated with Vineland Total score (p = 0.0036, adjR2 = 0.31), suggesting risk factors with stress burden can influence ASD phenotype.

821Surrounding greenness is associated with slower biological ageing based on epigenetics

Background High surrounding greenness has many health benefits, but few studies have evaluated these from the perspective of epigenetics. Methods We derived Horvath’s DNA methylation age (DNAmAge), Hannum’s DNAmAge, PhenoAge, and GrimAge from DNA methylation data measured from peripheral blood samples of 479 Australian women in 130 families. The DNA methylation age acceleration (DNAmAgeAC) was calculated as the residuals after regressing DNAmAge on chronological age. Greenness was represented by the satellite-derived Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) within 300m, 500m, 1000m and 2000m surrounding participants’ home addresses. The greenness-DNAmAgeAC association was evaluated using a within-sibship design fitted by linear mixed effect models, adjusting for familial clustering and important covariates. Results Our analyses based on the whole sample suggested that the greenness-DNAmAgeAC association was only statistically significant for GrimAge acceleration. Each inter-quartile range (IQR) increase in NDVI within 1000m was associated with 0.59 (95% CI: 0.18, 1.01) year decrease in GrimAge acceleration. Greenness was also inversely associated with GrimAge’s three DNA methylation based components (cystatin-C, growth differentiation factor 15, and smoking pack-years). For participants at higher 50% area-level socioeconomic status (SES), each IQR increase in NDVI within 1000m was associated with 1.24 (95%CI: 0.39, 2.09) year and 0.79 (95%CI: 0.29, 1.30) year decrease in Hannum’s DNAmAgeAC and GrimAge acceleration, respectively. Conclusions Higher surrounding greenness was associated with slower biological aging measured by GrimAge acceleration and Hannun’s DNAmAgeAC in Australian women, particularly among those who lived in areas at higher SES. Key messages Epigenetics provides a potential mechanism for why surrounding greenness is an important health issue for community construction.

Seven-CpG DNA Methylation Age determined by Single Nucleotide Primer Extension and Illumina's Infinium MethylationEPIC array provide highly comparable results

DNA methylation age (DNAm age, epigenetic clock) is a novel and promising biomarker of aging. It is calculated from the methylation fraction of specific cytosine phosphate guanine sites (CpG sites) of genomic DNA. Several groups have proposed epigenetic clock algorithms and these differ mostly regarding the number and location of the CpG sites considered and the method used to assess the methylation status. Most epigenetic clocks are based on a large number of CpGs, e.g. as measured by DNAm microarrays. We have recently evaluated an epigenetic clock based on the methylation fraction of seven CpGs that were determined by methylation-sensitive single nucleotide primer extension (MS-SNuPE). This method is more cost-effective when compared to array-based technologies as only a few CpGs need to be examined. However, there is only little data on the correspondence in epigenetic age estimation using the 7-CpG clock and other algorithms. To bridge this gap, in this study we measured the 7-CpG DNAm age using two methods, via MS-SNuPE and via the MethylationEPIC array, in a sample of 1,058 participants of the Berlin Aging Study II (BASE-II), assessed as part of the GendAge study. On average, participants were 75.6 years old (SD: 3.7, age range: 64.9 - 90.0, 52.6% female). Agreement between methods was assessed by Bland-Altman plots. DNAm age was highly correlated between methods (Pearson's r=0.9) and Bland-Altman plots showed a difference of 3.1 years. DNAm age by the 7-CpG formula was 71.2 years (SD: 6.9 years, SNuPE) and 68.1 years (SD: 6.4 years, EPIC array). The mean of difference in methylation fraction between methods for the seven individual CpG sites was between 0.7 and 13 percent. To allow direct conversion between methods we developed an adjustment formula with a randomly selected training set of 529 participants using linear regression. After conversion of the Illumina data in a second and independent validation set, the adjusted DNAm age was 71.44 years (SD: 6.1 years, n=529). In summary, we found the results of DNAm clocks to be highly comparable. Furthermore, we developed an adjustment formula that allows for direct conversion of estimates between methods and enables one singular clock to be used in studies that employ either the Illumina or the SNuPE method.