Dataset Information

A Targeted Epigenetic Clock for the Prediction of Biological Age.

ABSTRACT: Epigenetic clocks were initially developed to track chronological age, but accumulating evidence indicates that they can also predict biological age. They are usually based on the analysis of DNA methylation by genome-wide methods, but targeted approaches, based on the assessment of a small number of CpG sites, are advisable in several settings. In this study, we developed a targeted epigenetic clock purposely optimized for the measurement of biological age. The clock includes six genomic regions mapping in ELOVL2, NHLRC1, AIM2, EDARADD, SIRT7 and TFAP2E genes, selected from a re-analysis of existing microarray data, whose DNA methylation is measured by EpiTYPER assay. In healthy subjects (n = 278), epigenetic age calculated using the targeted clock was highly correlated with chronological age (Spearman correlation = 0.89). Most importantly, and in agreement with previous results from genome-wide clocks, epigenetic age was significantly higher and lower than expected in models of increased (persons with Down syndrome, n = 62) and decreased (centenarians, n = 106; centenarians' offspring, n = 143; nutritional intervention in elderly, n = 233) biological age, respectively. These results support the potential of our targeted epigenetic clock as a new marker of biological age and open its evaluation in large cohorts to further promote the assessment of biological age in healthcare practice.

SUBMITTER: Gensous N

PROVIDER: S-EPMC9777448 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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A Targeted Epigenetic Clock for the Prediction of Biological Age.

Gensous Noémie N Sala Claudia C Pirazzini Chiara C Ravaioli Francesco F Milazzo Maddalena M Kwiatkowska Katarzyna Malgorzata KM Marasco Elena E De Fanti Sara S Giuliani Cristina C Pellegrini Camilla C Santoro Aurelia A Capri Miriam M Salvioli Stefano S Monti Daniela D Castellani Gastone G Franceschi Claudio C Bacalini Maria Giulia MG Garagnani Paolo P

Cells 20221214 24

Epigenetic clocks were initially developed to track chronological age, but accumulating evidence indicates that they can also predict biological age. They are usually based on the analysis of DNA methylation by genome-wide methods, but targeted approaches, based on the assessment of a small number of CpG sites, are advisable in several settings. In this study, we developed a targeted epigenetic clock purposely optimized for the measurement of biological age. The clock includes six genomic region ...[more]

PMID: 36552808

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Project description:BackgroundEpigenetic clocks can track both chronological age (cAge) and biological age (bAge). The latter is typically defined by physiological biomarkers and risk of adverse health outcomes, including all-cause mortality. As cohort sample sizes increase, estimates of cAge and bAge become more precise. Here, we aim to develop accurate epigenetic predictors of cAge and bAge, whilst improving our understanding of their epigenomic architecture.MethodsFirst, we perform large-scale (N = 18,413) epigenome-wide association studies (EWAS) of chronological age and all-cause mortality. Next, to create a cAge predictor, we use methylation data from 24,674 participants from the Generation Scotland study, the Lothian Birth Cohorts (LBC) of 1921 and 1936, and 8 other cohorts with publicly available data. In addition, we train a predictor of time to all-cause mortality as a proxy for bAge using the Generation Scotland cohort (1214 observed deaths). For this purpose, we use epigenetic surrogates (EpiScores) for 109 plasma proteins and the 8 component parts of GrimAge, one of the current best epigenetic predictors of survival. We test this bAge predictor in four external cohorts (LBC1921, LBC1936, the Framingham Heart Study and the Women's Health Initiative study).ResultsThrough the inclusion of linear and non-linear age-CpG associations from the EWAS, feature pre-selection in advance of elastic net regression, and a leave-one-cohort-out (LOCO) cross-validation framework, we obtain cAge prediction with a median absolute error equal to 2.3 years. Our bAge predictor was found to slightly outperform GrimAge in terms of the strength of its association to survival (HRGrimAge = 1.47 [1.40, 1.54] with p = 1.08 × 10-52, and HRbAge = 1.52 [1.44, 1.59] with p = 2.20 × 10-60). Finally, we introduce MethylBrowsR, an online tool to visualise epigenome-wide CpG-age associations.ConclusionsThe integration of multiple large datasets, EpiScores, non-linear DNAm effects, and new approaches to feature selection has facilitated improvements to the blood-based epigenetic prediction of biological and chronological age.

Project description:Human DNA methylation data have been used to develop biomarkers of ageing, referred to as 'epigenetic clocks', which have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks have been shown to be highly accurate in blood but are less precise when used in older samples or in tissue types not included in training the model, including brain. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life course (n = 1397, ages = 1 to 108 years). This dataset was split into 'training' and 'testing' samples (training: n = 1047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel independent human cortex dataset (n = 1221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination, optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically outperformed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts.

Project description:Immunoglobulin G is posttranslationally modified by the addition of complex N-glycans affecting its function and mediating inflammation at multiple levels. IgG glycome composition changes with age and health in a predictive pattern, presumably due to inflammaging. As a result, a novel biological aging biomarker, glycan clock of age, was developed. Glycan clock of age is the first of biological aging clocks for which multiple studies showed a possibility of clock reversal even with simple lifestyle interventions. However, none of the previous studies determined to which extent the glycan clock can be turned, and how much is fixed by genetic predisposition. To determine the contribution of genetic and environmental factors to phenotypic variation of the glycan clock, we performed heritability analysis on two TwinsUK female cohorts. IgG glycans from monozygotic and dizygotic twin pairs were analyzed by UHPLC and glycan age was calculated using the glycan clock. In order to determine additive genetic, shared, and unique environmental contributions, a classical twin design was applied. Heritability of the glycan clock was calculated for participants of one cross-sectional and one longitudinal cohort with three time points to assess the reliability of measurements. Heritability estimate for the glycan clock was 39% on average, suggesting a moderate contribution of additive genetic factors (A) to glycan clock variation. Remarkably, heritability estimates remained approximately the same in all time points of the longitudinal study, even though IgG glycome composition changed substantially. Most environmental contributions came from shared environmental factors (C), with unique environmental factors (E) having a minor role. Interestingly, heritability estimates nearly doubled, to an average of 71%, when we included age as a covariant. This intervention also inflated the estimates of unique environmental factors contributing to glycan clock variation. A complex interplay between genetic and environmental factors defines alternative IgG glycosylation during aging and, consequently, dictates the glycan clock's ticking. Apparently, environmental factors (including lifestyle choices) have a strong impact on the biological age measured with the glycan clock, which additionally clarifies why this aging clock is one of the most potent biomarkers of biological aging.

Dataset Information

A Targeted Epigenetic Clock for the Prediction of Biological Age.

Publications

A Targeted Epigenetic Clock for the Prediction of Biological Age.

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