DNA methylation entropy is a biomarker for aging
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE288139 | GEO | 2025/01/27
REPOSITORIES: GEO
Similar Datasets
Project description:Chronological age prediction from DNA methylation sheds light on human aging, indicates poor health and predicts lifespan. Previous studies developed methylation clocks based on linear regression models on methylation array data. While accurate, these models are limited to fixed-rate changes in methylation levels across age. Moreover, the high cost of methylation arrays, compared to targeted-PCR sequencing, hinders widespread utility of such predictors. We present an AI-based alternative termed GP-age, which uses a non-parametric approach based on Gaussian Process Regression of a large cohort of ~12K blood methylomes. Given a new blood sample, methylation levels are compared to the cohort samples, which are then weighted to predict the query age. Using only 30 CpG sites, our approach outperforms state-of-the-art methylation clocks that use hundreds of sites, with a median error of 2.1 years (on held-out data). Our model was also applied to sequencing-based data yielding highly accurate predictions. Overall, we provide an accessible alternative to current array-based methylation clocks, with future applications in aging research, forensic profiling, and monitoring epigenetic processes in transplantation medicine and cancer.
2023-01-20 | GSE207605 | GEO
Project description:DNA methylation is a reaction that results in the formation of 5-methylcytosine when a methyl group is added to the cytosine’s C5 position. As organisms age, DNA methylation patterns change in a reproducible fashion. This phenomenon has established DNA methylation as a valuable biomarker in aging studies. Epigenetic clocks based on weighted combinations of methylation sites have been developed to accurately predict the age of an individual from their methylome. However, many epigenetic clocks, particularly those that utilize penalized regression, model the changes in methylation linearly with age. Moreover, these models, which use methylation levels as features, are not robust to missing data and do not account for the count-based nature of bisulfite sequence data. Additionally, the models are generally not interpretable. To overcome these challenges, we present BayesAge, an extension of the previously developed scAge approach that was developed for the analysis of single cell DNA methylation datasets. BayesAge utilizes maximum likelihood estimation (MLE) to infer ages, models count data using binomial distributions, and uses LOWESS smoothing to capture the non-linear dynamics between methylation and age. Our approach is designed for use with bulk bisulfite sequencing datasets. BayesAge outperforms scAge in several respects. Specifically, BayesAge’s age residuals are not age associated, thus providing a less biased representation of epigenetic age variation across populations. Moreover, BayesAge enables the estimation of error bounds on age inference and, when run on downsampled data, its coefficient of determination between predicted and actual ages surpasses both scAge and penalized regression.
2024-03-20 | GSE261769 | GEO
Project description:Epigenetic clocks are age predictors that use machine-learning models trained on DNA CpG methylation values to predict chronological or biological age. Increases in predicted epigenetic age relative to chronological age (epigenetic age acceleration) are connected to aging-associated pathologies, and changes in epigenetic age are linked to canonical aging hallmarks. However, epigenetic clocks rely on training data from bulk tissues whose cellular composition changes with age. We found that human naive CD8+ T cells, which decrease during aging, exhibit an epigenetic age 15–20 years younger than effector memory CD8+ T cells from the same individual. Importantly, homogenous naive T cells isolated from individuals of different ages show a progressive increase in epigenetic age, indicating that current epigenetic clocks measure two independent variables, aging and immune cell composition. To isolate the age-associated cell intrinsic changes, we created a new clock, the IntrinClock, that did not change among 10 immune cell types tested. IntrinClock showed a robust predicted epigenetic age increase in a model of replicative senescence in vitro and age reversal during OSKM-mediated reprogramming
2023-12-27 | GSE252045 | GEO
Project description:We developed two novel sperm epigenetic clocks by applying Super Learner, an ensemble machine learning algorithm, to predict age from sperm EPIC array DNA methylation data via individual CpG sites and differentially methylated regions (DMRs). Overall, our cox model showed that one-year increase in our developed sperm epigenetic age (SEA) was associated with up to 15% reduction in couples time-to-pregnancy (TTP).
2022-07-06 | GSE185445 | GEO
Project description:The establishment of highly accurate aging clocks based on DNA methylation highlighted the strong link between epigenetic alterations and aging. However, connecting methylation clocks to physiological changes is not straightforward. Transcriptomics and proteomics clocks on the other hand are directly connected to cellular function, yet they do not allow us to understand underlying epigenetic mechanisms. We hypothesize that chromatin accessibility, a readout that integrates many epigenetic mechanisms, may bridge epigenetic changes with their downstream effects in aging. To demonstrate that chromatin accessibility can predict age, we generated chromatin accessibility profiles from peripheral blood mononuclear cells (PBMCs) of 157 human donors, aged 20 to 74 and used them to construct a novel aging clock. Our clock predicted age with a root mean squared error of 7.69 years, a median absolute error of 5.69 years and a correlation of 0.83. Moreover, by comparing our chromatin accessibility data to matched transcriptomic profiles, we show that the genomic sites selected by our clock drive changes in transcription during aging. This chromatin accessibility clock could therefore be used to better understand the mechanisms driving the process of aging and evaluate anti-aging interventions.
2022-12-01 | GSE193141 | GEO
Project description:The establishment of highly accurate aging clocks based on DNA methylation highlighted the strong link between epigenetic alterations and aging. However, connecting methylation clocks to physiological changes is not straightforward. Transcriptomics and proteomics clocks on the other hand are directly connected to cellular function, yet they do not allow us to understand underlying epigenetic mechanisms. We hypothesize that chromatin accessibility, a readout that integrates many epigenetic mechanisms, may bridge epigenetic changes with their downstream effects in aging. To demonstrate that chromatin accessibility can predict age, we generated chromatin accessibility profiles from peripheral blood mononuclear cells (PBMCs) of 157 human donors, aged 20 to 74 and used them to construct a novel aging clock. Our clock predicted age with a root mean squared error of 7.69 years, a median absolute error of 5.69 years and a correlation of 0.83. Moreover, by comparing our chromatin accessibility data to matched transcriptomic profiles, we show that the genomic sites selected by our clock drive changes in transcription during aging. This chromatin accessibility clock could therefore be used to better understand the mechanisms driving the process of aging and evaluate anti-aging interventions.
2022-12-01 | GSE193140 | GEO
Project description:We aim to improve anti-ageing drug discovery, currently achieved through laborious and lengthy longevity analysis. Recent studies demonstrated that the most accurate molecular method to measure human age is based on CpG methylation profiles, as exemplified by several epigenetics clocks that can accurately predict an individual’s age. Here, we developed CellAge, a new epigenetic clock that measures subtle ageing changes in primary human cells in vitro. As such it provides a unique tool to measure effects of relatively short pharmacological treatments on ageing. We validated our CellAge clock against known longevity drugs such as rapamycin and trametinib. Moreover, we uncovered novel anti-ageing drugs, torin2 and Dactolisib (BEZ-235), demonstrating the value of our approach as a screening and discovery platform for anti-ageing strategies. CellAge outperforms other epigenetic clocks in measuring subtle ageing changes in primary human cells in culture. The tested drug treatments reduced senescence and other ageing markers, further consolidating our approach as a screening platform. Finally, we showed that the novel anti-ageing drugs we uncovered in vitro, indeed increased longevity in vivo. Our method expands the scope of CpG methylation profiling from measuring human chronological and biological age from human samples in years, to accurately and rapidly detecting anti-ageing potential of drugs using human cells in vitro, providing a novel accelerated discovery platform to test sought after geroprotectors.
2021-10-01 | E-MTAB-8327 | biostudies-arrayexpress
Project description:Human DNA-methylation data have been used to develop highly accurate biomarkers of aging ("epigenetic clocks"). Recent studies demonstrate that similar epigenetic clocks for mice (Mus Musculus) can be slowed by gold standard anti-aging interventions such as calorie restriction and growth hormone receptor knock-outs. Using DNA methylation data from previous publications with data collected in house for a total 1189 samples spanning 193,651 CpG sites, we developed 4 novel epigenetic clocks by choosing different regression models (elastic net- versus ridge regression) and by considering different sets of CpGs (all CpGs vs highly conserved CpGs). We demonstrate that accurate age estimators can be built on the basis of highly conserved CpGs. However, the most accurate clock results from applying elastic net regression to all CpGs. While the anti-aging effect of calorie restriction could be detected with all types of epigenetic clocks, only ridge regression based clocks replicated the finding of slow epigenetic aging effects in dwarf mice. Overall, this study demonstrates that there are trade-offs when it comes to epigenetic clocks in mice. Highly accurate clocks might not be optimal for detecting the beneficial effects of anti-aging interventions.
2018-11-29 | GSE120136 | GEO
Project description:Human DNA-methylation data have been used to develop highly accurate biomarkers of aging ("epigenetic clocks"). Recent studies demonstrate that similar epigenetic clocks for mice (Mus Musculus) can be slowed by gold standard anti-aging interventions such as calorie restriction and growth hormone receptor knock-outs. Using DNA methylation data from previous publications with data collected in house for a total 1189 samples spanning 193,651 CpG sites, we developed 4 novel epigenetic clocks by choosing different regression models (elastic net- versus ridge regression) and by considering different sets of CpGs (all CpGs vs highly conserved CpGs). We demonstrate that accurate age estimators can be built on the basis of highly conserved CpGs. However, the most accurate clock results from applying elastic net regression to all CpGs. While the anti-aging effect of calorie restriction could be detected with all types of epigenetic clocks, only ridge regression based clocks replicated the finding of slow epigenetic aging effects in dwarf mice. Overall, this study demonstrates that there are trade-offs when it comes to epigenetic clocks in mice. Highly accurate clocks might not be optimal for detecting the beneficial effects of anti-aging interventions.
2018-11-29 | GSE120132 | GEO
Project description:Ageing is the accumulation of changes and overall decline of the function of cells, organs and organisms over time. At the molecular and cellular level, the concept of biological age has been established and novel biomarkers of biological age have been identified, notably epigenetic DNA-methylation based clocks. With the emergence of single-cell DNA methylation profiling methods, the possibility to study biological age of individual cells has been proposed, and a first proof-of-concept study, based on limited single cell datasets mostly from early developmental origin, indicated the feasibility and relevance of this approach to better understand organismal changes and cellular ageing heterogeneity. Here, we generated a large single-cell DNA methylation and matched transcriptome dataset from mouse peripheral blood samples, spanning a broad range of ages (10-101 weeks of age), and developed a robust single-cell DNA methylation age prediction model (scEpiAge-blood and also scEpiAge-liver). We find that our new scEpiAge can accurately predict age in a broad range of publicly available datasets, including very sparse data and it also predicts age in single cells. Interestingly, the epigenetic age distribution is wider than technically expected in 19% of single cells, suggesting that epigenetic age heterogeneity is present in vivo and may relate to functional differences between cells. In addition, we observe differences in epigenetic ageing between the major blood cell types. Our work provides a foundation for better single-cell and sparse data epigenetic age predictors and highlights the significance of cellular heterogeneity during ageing.
2024-06-03 | GSE225172 | GEO