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      A methylation risk score for chronic kidney disease: a HyperGEN study

      research-article
      1 , 2 , , 3 , 3 , 2 , 3 , 4 , 2 , 5 , 6 , 7 , 8 , 9 , 9 , 10 , 11 , 26 , 12 , 13 , 13 , 14 , 15 , 15 , 16 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 20 , 2
      Scientific Reports
      Nature Publishing Group UK
      Chronic kidney disease, eGFR, Methylation risk score, Epigenetics, DNA methylation, Epidemiology, Chronic kidney disease

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          Abstract

          Chronic kidney disease (CKD) impacts about 1 in 7 adults in the United States, but African Americans (AAs) carry a disproportionately higher burden of disease. Epigenetic modifications, such as DNA methylation at cytosine-phosphate-guanine (CpG) sites, have been linked to kidney function and may have clinical utility in predicting the risk of CKD. Given the dynamic relationship between the epigenome, environment, and disease, AAs may be especially sensitive to environment-driven methylation alterations. Moreover, risk models incorporating CpG methylation have been shown to predict disease across multiple racial groups. In this study, we developed a methylation risk score (MRS) for CKD in cohorts of AAs. We selected nine CpG sites that were previously reported to be associated with estimated glomerular filtration rate (eGFR) in epigenome-wide association studies to construct a MRS in the Hypertension Genetic Epidemiology Network (HyperGEN). In logistic mixed models, the MRS was significantly associated with prevalent CKD and was robust to multiple sensitivity analyses, including CKD risk factors. There was modest replication in validation cohorts. In summary, we demonstrated that an eGFR-based CpG score is an independent predictor of prevalent CKD, suggesting that MRS should be further investigated for clinical utility in evaluating CKD risk and progression.

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          Most cited references53

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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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            New Creatinine- and Cystatin C–Based Equations to Estimate GFR without Race

            Current equations for estimated glomerular filtration rate (eGFR) that use serum creatinine or cystatin C incorporate age, sex, and race to estimate measured GFR. However, race in eGFR equations is a social and not a biologic construct.
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              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
                acjones@uab.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                1 August 2024
                1 August 2024
                2024
                : 14
                : 17757
                Affiliations
                [1 ]Medical Scientist Training Program, University of Alabama at Birmingham, ( https://ror.org/008s83205) 912 18th St S, Birmingham, AL 35233 USA
                [2 ]Department of Epidemiology, University of Alabama at Birmingham, ( https://ror.org/008s83205) 912 18th St S, Birmingham, AL 35233 USA
                [3 ]Department of Biostatistics, University of Alabama at Birmingham, ( https://ror.org/008s83205) Birmingham, AL USA
                [4 ]Department of Neurology, University of Alabama at Birmingham, ( https://ror.org/008s83205) Birmingham, AL USA
                [5 ]23andMe, ( https://ror.org/00q62jx03) South San Francisco, CA USA
                [6 ]Department of Biology, Florida State University-Panama City, ( https://ror.org/03k9hrc16) Panama City, FL USA
                [7 ]HudsonAlpha Institute for Biotechnology, ( https://ror.org/04nz0wq19) Huntsville, AL USA
                [8 ]Office of the Provost, University of South Carolina, ( https://ror.org/02b6qw903) Columbia, SC USA
                [9 ]GRID grid.430503.1, ISNI 0000 0001 0703 675X, Department of Biomedical Informatics, , University of Colorado-Anschutz, ; Aurora, CO USA
                [10 ]Division of Nephrology, University of Washington, ( https://ror.org/00cvxb145) Seattle, WA USA
                [11 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, Department of Medicine, , Duke University School of Medicine, ; Durham, NC USA
                [12 ]GRID grid.27755.32, ISNI 0000 0000 9136 933X, Department of Public Health Sciences, Center for Public Health Genomics, , University of Virginia, ; Charlottesville, VA USA
                [13 ]The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, ( https://ror.org/025j2nd68) Torrance, CA USA
                [14 ]Departments of Public Health Sciences and Medicine, Loyola University Medical Center, ( https://ror.org/05xcyt367) Taywood, IL USA
                [15 ]Department of Pathology and Laboratory Medicine, University of Vermont, ( https://ror.org/0155zta11) Colchester, VT USA
                [16 ]GRID grid.21729.3f, ISNI 0000000419368729, Department of Systems Biology, New York Genome Center, , Columbia University, ; New York, NY USA
                [17 ]Department of Medicine, Cardiology and Neurology, Duke University Medical Center, ( https://ror.org/03njmea73) Durham, NC USA
                [18 ]Department of Biostatistics, University of Washington, ( https://ror.org/00cvxb145) Seattle, WA USA
                [19 ]Department of Population and Public Health Sciences, Keck School of Medicine of USC, University of Southern California, ( https://ror.org/03taz7m60) Los Angeles, CA USA
                [20 ]Department of Epidemiology, Gillings School of Public Health, University of North Carolina at Chapel Hill, ( https://ror.org/0130frc33) Chapel Hill, NC USA
                [21 ]Department of Epidemiology, School of Public Health, Brown University, ( https://ror.org/05gq02987) Providence, RI USA
                [22 ]Department of Family Medicine, University of Texas Medical Branch Health, ( https://ror.org/016tfm930) Galveston, TX USA
                [23 ]Department of Medicine, School of Population and Public Health, University of British Columbia, ( https://ror.org/03rmrcq20) Vancouver, BC, CAN USA
                [24 ]GRID grid.19006.3e, ISNI 0000 0000 9632 6718, Department of Human Genetics, David Geffen School of Medicine, , Gonda Research Center, ; Los Angeles, CA USA
                [25 ]Altos Labs, ( https://ror.org/05467hx49) San Diego, CA USA
                [26 ]Department of Genome Sciences, University of Virginia, ( https://ror.org/0153tk833) Charlottesville, VA USA
                Article
                68470
                10.1038/s41598-024-68470-z
                11291488
                39085340
                72d0d3cd-c324-484e-a02d-6b3f090c1fe1
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

                History
                : 24 April 2024
                : 24 July 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000062, National Institute of Diabetes and Digestive and Kidney Diseases;
                Award ID: F31DK128990
                Award ID: T32DK116672
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000050, National Heart, Lung, and Blood Institute;
                Award ID: R01HL055673
                Award ID: R35HL155466
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000066, National Institute of Environmental Health Sciences;
                Award ID: R01ES020836
                Award Recipient :
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                © Springer Nature Limited 2024

                Uncategorized
                chronic kidney disease,egfr,methylation risk score,epigenetics,dna methylation,epidemiology

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