Epigenetic Predictor of Age

By S. Bocklandt, Wen Lin, M. Sehl, Francisco J. Sánchez, Janet S. Sinsheimer, Steve Horvath, E. Vilain. PLoS ONE, 2011

937 citations recorded by the source corpus.

From the moment of conception, we begin to age. A decay of cellular structures, gene regulation, and DNA sequence ages cells and organisms. DNA methylation patterns change with increasing age and contribute to age related disease. Here we identify 88 sites in or near 80 genes for which the degree of cytosine methylation is significantly correlated with age in saliva of 34 male identical twin pairs between 21 and 55 years of age. Furthermore, we validated sites in the promoters of three genes and replicated our results in a general population sample of 31 males and 29 females between 18 and 70 years of age. The methylation of three sites—in the promoters of the EDARADD, TOM1L1, and NPTX2 genes—is linear with age over a range of five decades. Using just two cytosines from these loci, we built a regression model that explained 73% of the variance in age, and is able to predict the age of an individual with an average accuracy of 5.2 years. In forensic science, such a model could estimate the age of a person, based on a biological sample alone. Furthermore, a measurement of relevant sites in the genome could be a tool in routine medical screening to predict the risk of age-related diseases and to tailor interventions based on the epigenetic bio-age instead of the chronological age.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9b7b06b23da53116687b1fe89238d2f039ca108a

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Citations in the corpus

DNA methylation age of human tissues and cell types (2013, background): “While several recent articles describe age predictors based on DNAm levels in specific tissues (for example, saliva or blood [23,24]), it is not yet known whether age can be predicted irrespective of tissue type using a single predictor.”
Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016, background): “…Yang, Korn, Burwinkel, & Brenner, 2011; Shenker et al.; Sun, 2014; Sun, Smith, et al., 2013), poor nutrition(Waterland & Jirtle, 2003), genetic factors (Sun, 2014; D. Zhang et al., 2010), and age – all well known risk factors human diseases (Bocklandt et al., 2011; Teschendorff et al., 2010).”
DNA methylation and healthy human aging (2015, methodology): “it has been repeatedly shown that DNA methylation levels at specific sites in the genome are so highly associated with age and that in some cases, they have been used to accurately predict chronological age (Bocklandt et al., 2011; Horvath et al., 2012; Hannum et al., 2013; Florath et al., 2014; Wei”
A Six Months Exercise Intervention Influences the Genome-wide DNA Methylation Pattern in Human Adipose Tissue (2013, neutral): “5%) with three studies reporting agingdifferentially methylated regions (a-DMRs) in a total of 597 unique positions [28–30].”
An epigenetic biomarker of aging for lifespan and healthspan (2018, background): “Chronological time has been shown to elicit predictable hypo- and hyper-methylation changes at many regions across the genome [3-7], and as a result, the first generation of DNAm based biomarkers of aging were developed to predict chronological chronological age [8-13].”
DNA methylation arrays as surrogate measures of cell mixture distribution (2012, background): “At certain CpG loci, DNA methylation is known to change with age [43], especially in T cells [44].”
Aging of blood can be tracked by DNA methylation changes at just three CpG sites (2014, methodology): “These CpG sites are associated with EDAR-associated death domain (EDARADD), which has been associated with AR-DNAm changes before [20]; integrin, alpha 2b (ITGA2B); RAB36, a member of the RAS oncogene family (RAB36); phosphodiesterase 4C, cAMP specific (PDE4C); and aspartoacylase (ASPA) (Figure S7 i”
Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates (2012, neutral): “In particular, DNA methylation associates with chronological age over long time scales (Alisch et al., 2012; Christensen et al., 2009; Bollati et al., 2009; Boks et al., 2009; Rakyan et al., 2010; Bocklandt et al., 2011; Bell et al., 2012), and changes in methylation have been linked to complex age-”
Epigenetic clock analysis of diet, exercise, education, and lifestyle factors (2017, neutral): “As a result, several highly accurate epigenetic biomarkers of chronological age have been proposed [35-39].”
Epigenome-Wide Scans Identify Differentially Methylated Regions for Age and Age-Related Phenotypes in a Healthy Ageing Population (2012, result): “Furthermore, 36 a-DMRs from our study replicated with the same direction of effect as 88 a-DMRs identified in saliva samples in male twins [11], and 3 a-DMRs were also in the top 10 reported a-DMRs from multiple brain tissues [13].”
Measuring biological age using omics data (2022, methodology): “The first DNA methylation clock was an elastic net model (Box 1) built in 2011 by Bocklandt et al. 53 , who demonstrated highly accurate chronological age prediction (within ~5 years) using only about 100 saliva samples.”