Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates

By Gregory Hannum, Justin Guinney, Ling Zhao, Li Zhang, G. Hughes, SriniVas R. Sadda, Brandy Klotzle, M. Bibikova, Jian-Bing Fan, Yuan Gao and 6 more. Molecules and Cells, 2012

3,698 citations recorded by the source corpus.

Summary The ability to measure human aging from molecular profiles has practical implications in many fields, including disease prevention and treatment, forensics, and extension of life. Although chronological age has been linked to changes in DNA methylation, the methylome has not yet been used to measure and compare human aging rates. Here, we build a quantitative model of aging using measurements at more than 450,000 CpG markers from the whole blood of 656 human individuals, aged 19 to 101. This model measures the rate at which an individual’s methylome ages, which we show is impacted by gender and genetic variants. Furthermore, we show that differences in aging rates help explain epigenetic drift and are reflected in the transcriptome. Our model highlights specific components of the aging process and provides a quantitative read-out for studying the role of methylation in age-related disease.

Subfields: Molecular Biology, Pediatrics, Perinatology and Child Health

View Paper: https://www.semanticscholar.org/paper/a70d10aa35a2b953666c0b35e06ffe01d2672fd9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

In Pediatrics, Perinatology and Child Health

Citations in the corpus

DNA methylation-based measures of biological age: meta-analysis predicting time to death (2016, methodology): “DNA methylation age estimate by Hannum et al (2013) We also used an alternative measure of epigenetic age developed by Hannum et al (2013) [2].”
A computational solution for bolstering reliability of epigenetic clocks: Implications for clinical trials and longitudinal tracking (2021, background): “While the original clocks required 3–16 replicates for small-to-moderate effect sizes, the PC clocks only required 1–2 replicates for each condition.”
Obesity accelerates epigenetic aging of human liver (2014, background): “(17), who applied an alternative epigenetic biomarker of aging to a large (n = 656) blood methylation dataset.”
Measuring biological age using omics data (2022, methodology): “It has been widely hypothesized that this estimated age can serve as a measure of an individual’s biological age, and that the difference between estimated and actual chronological age, called ‘Δ age’ or ‘ age gap ’, reflects variation in their past rate of ageing 43,44 (Fig.”
Accounting for cellular heterogeneity is critical in epigenome-wide association studies (2014, methodology): “[10], which were determined using diagnostic plots across a range of k values.”
Aging of blood can be tracked by DNA methylation changes at just three CpG sites (2014, methodology): “We also used dataset GSE40279 [19], although this was generated with the HumanMethylation450 BeadChip (different assay design with type II bead type and only 99 of 102 AR-CpGs) [31] and we therefore trained an alternative model for use with this platform.”
Deep biomarkers of human aging: Application of deep neural networks to biomarker development (2016, background): “Methylation-based markers such as epigenetic aging clocks (Horvath [6] and Hannum [7]) are currently the most accurate, while transcriptomics [8,9] and metabolomics [10] have shown to be less so.”
DNA methylation age of human tissues and cell types (2013, background): “particular, cancer tissues were excluded from this first large data set since it is well known that cancer has a profound effect on DNA methylation levels [6,7,24-26].”
DNA methylation and healthy human aging (2015, methodology): “, 2014) 4: (Hannum et al., 2013) 5: (Horvath, 2013) 6: (Numata et al.”