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      Unstable Childhood, Adult Adversity, and Smoking Accelerate Biological Aging Among Middle-Age African Americans: Similar Findings for GrimAge and PoAm

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          Abstract

          Objectives: The recent biological clocks GrimAge and PoAm are robust predictors of morbidity and mortality. Little research, however, has investigated the factors that influence their ticking speed. No study has used multivariate analyses to examine whether childhood adversity, adult hardship, lifestyle practices, or some combination of these factors best explains acceleration of these indices. Methods: Using a sample of 506 middle-age African Americans, the present study investigated the extent to which childhood instability, adult adversity, and lifestyle predict accelerated GrimAge and PoAm. Results: The two clocks were highly correlated and the pattern of findings was very similar for the two measures. Childhood instability, adult financial hardship, and smoking were significant predictors of both clocks. Discussion: The findings support a life course perspective where both the long arm of childhood as well as later life conditions influence speed of aging. Similar results across the two clocks enhance confidence in the findings.

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          Most cited references51

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          DNA methylation age of human tissues and cell types

          Background It is not yet known whether DNA methylation levels can be used to accurately predict age across a broad spectrum of human tissues and cell types, nor whether the resulting age prediction is a biologically meaningful measure. Results I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance. Conclusions I propose that DNA methylation age measures the cumulative effect of an epigenetic maintenance system. This novel epigenetic clock can be used to address a host of questions in developmental biology, cancer and aging research.
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            Genome-wide methylation profiles reveal quantitative views of human aging rates.

            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. We also show that differences in aging rates help explain epigenetic drift and are reflected in the transcriptome. Moreover, we show how our aging model is upheld in other human tissues and reveals an advanced aging rate in tumor tissue. Our model highlights specific components of the aging process and provides a quantitative readout for studying the role of methylation in age-related disease. Copyright © 2013 Elsevier Inc. All rights reserved.
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              Is Open Access

              DNA methylation arrays as surrogate measures of cell mixture distribution

              Background There has been a long-standing need in biomedical research for a method that quantifies the normally mixed composition of leukocytes beyond what is possible by simple histological or flow cytometric assessments. The latter is restricted by the labile nature of protein epitopes, requirements for cell processing, and timely cell analysis. In a diverse array of diseases and following numerous immune-toxic exposures, leukocyte composition will critically inform the underlying immuno-biology to most chronic medical conditions. Emerging research demonstrates that DNA methylation is responsible for cellular differentiation, and when measured in whole peripheral blood, serves to distinguish cancer cases from controls. Results Here we present a method, similar to regression calibration, for inferring changes in the distribution of white blood cells between different subpopulations (e.g. cases and controls) using DNA methylation signatures, in combination with a previously obtained external validation set consisting of signatures from purified leukocyte samples. We validate the fundamental idea in a cell mixture reconstruction experiment, then demonstrate our method on DNA methylation data sets from several studies, including data from a Head and Neck Squamous Cell Carcinoma (HNSCC) study and an ovarian cancer study. Our method produces results consistent with prior biological findings, thereby validating the approach. Conclusions Our method, in combination with an appropriate external validation set, promises new opportunities for large-scale immunological studies of both disease states and noxious exposures.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Journal of Aging and Health
                J Aging Health
                SAGE Publications
                0898-2643
                1552-6887
                August 2022
                September 15 2021
                August 2022
                : 34
                : 4-5
                : 487-498
                Affiliations
                [1 ]University of Georgia, Athens, GA, USA
                [2 ]University of Southern California, California, CA, USA
                [3 ]University of Iowa, Iowa City, IA, USA
                Article
                10.1177/08982643211043668
                34525884
                837ee88e-bcdc-4c40-a84a-47d44903257e
                © 2022

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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