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      Epigenetic age and pregnancy outcomes: GrimAge acceleration is associated with shorter gestational length and lower birthweight

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          Abstract

          Background

          Advanced biological aging, as measured by epigenetic aging indices, is associated with early mortality and morbidity. Associations between maternal epigenetic aging indices in pregnancy and pregnancy outcomes, namely gestational length and birthweight, have not been assessed. The purpose of this study was to examine whether epigenetic age during pregnancy was associated with gestational length and birthweight.

          Results

          The sample consisted of 77 women from the Los Angeles, CA, area enrolled in the Healthy Babies Before Birth study. Whole blood samples for DNA methylation assay were obtained during the second trimester (15.6 ± 2.15 weeks gestation). Epigenetic age indices GrimAge acceleration (GrimAgeAccel), DNAm PAI-1, DNAm ADM, and DNAm cystatin C were calculated. Gestational length and birthweight were obtained from medical chart review. Covariates were maternal sociodemographic variables, gestational age at blood sample collection, and pre-pregnancy body mass index. In separate covariate-adjusted linear regression models, higher early second trimester GrimAgeAccel, b(SE) = − .171 (.056), p = .004; DNAm PAI-1, b(SE) = − 1.95 × 10 −4 (8.5 × 10 −5), p = .004; DNAm ADM, b(SE) = − .033 (.011), p = .003; and DNAm cystatin C, b(SE) = 2.10 × 10 −5 (8.0 × 10 −5), p = .012, were each associated with shorter gestational length. Higher GrimAgeAccel, b(SE) = − 75.2 (19.7), p < .001; DNAm PAI-1, b(SE) = − .079(.031), p = .013; DNAm ADM, b(SE) = − 13.8 (3.87), p = .001; and DNAm cystatin C, b(SE) = − .010 (.003), p = .001, were also associated with lower birthweight, independent of gestational length.

          Discussion

          Higher maternal prenatal GrimAgeAccel, DNAm PAI-1, DNAm ADM, and DNAm cystatin C were associated with shorter gestational length and lower birthweight. These findings suggest that biological age, as measured by these epigenetic indices, could indicate risk for adverse pregnancy outcomes.

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          Most cited references37

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          An epigenetic biomarker of aging for lifespan and healthspan

          Identifying reliable biomarkers of aging is a major goal in geroscience. While the first generation of epigenetic biomarkers of aging were developed using chronological age as a surrogate for biological age, we hypothesized that incorporation of composite clinical measures of phenotypic age that capture differences in lifespan and healthspan may identify novel CpGs and facilitate the development of a more powerful epigenetic biomarker of aging. Using an innovative two-step process, we develop a new epigenetic biomarker of aging, DNAm PhenoAge, that strongly outperforms previous measures in regards to predictions for a variety of aging outcomes, including all-cause mortality, cancers, healthspan, physical functioning, and Alzheimer's disease. While this biomarker was developed using data from whole blood, it correlates strongly with age in every tissue and cell tested. Based on an in-depth transcriptional analysis in sorted cells, we find that increased epigenetic, relative to chronological age, is associated with increased activation of pro-inflammatory and interferon pathways, and decreased activation of transcriptional/translational machinery, DNA damage response, and mitochondrial signatures. Overall, this single epigenetic biomarker of aging is able to capture risks for an array of diverse outcomes across multiple tissues and cells, and provide insight into important pathways in aging.
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            DNA methylation age of blood predicts all-cause mortality in later life

            Background DNA methylation levels change with age. Recent studies have identified biomarkers of chronological age based on DNA methylation levels. It is not yet known whether DNA methylation age captures aspects of biological age. Results Here we test whether differences between people’s chronological ages and estimated ages, DNA methylation age, predict all-cause mortality in later life. The difference between DNA methylation age and chronological age (Δage) was calculated in four longitudinal cohorts of older people. Meta-analysis of proportional hazards models from the four cohorts was used to determine the association between Δage and mortality. A 5-year higher Δage is associated with a 21% higher mortality risk, adjusting for age and sex. After further adjustments for childhood IQ, education, social class, hypertension, diabetes, cardiovascular disease, and APOE e4 status, there is a 16% increased mortality risk for those with a 5-year higher Δage. A pedigree-based heritability analysis of Δage was conducted in a separate cohort. The heritability of Δage was 0.43. Conclusions DNA methylation-derived measures of accelerated aging are heritable traits that predict mortality independently of health status, lifestyle factors, and known genetic factors. Electronic supplementary material The online version of this article (doi:10.1186/s13059-015-0584-6) contains supplementary material, which is available to authorized users.
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              Pre-eclampsia part 1: current understanding of its pathophysiology.

              Pre-eclampsia is characterized by new-onset hypertension and proteinuria at ≥20 weeks of gestation. In the absence of proteinuria, hypertension together with evidence of systemic disease (such as thrombocytopenia or elevated levels of liver transaminases) is required for diagnosis. This multisystemic disorder targets several organs, including the kidneys, liver and brain, and is a leading cause of maternal and perinatal morbidity and mortality. Glomeruloendotheliosis is considered to be a characteristic lesion of pre-eclampsia, but can also occur in healthy pregnant women. The placenta has an essential role in development of this disorder. Pathogenetic mechanisms implicated in pre-eclampsia include defective deep placentation, oxidative and endoplasmic reticulum stress, autoantibodies to type-1 angiotensin II receptor, platelet and thrombin activation, intravascular inflammation, endothelial dysfunction and the presence of an antiangiogenic state, among which an imbalance of angiogenesis has emerged as one of the most important factors. However, this imbalance is not specific to pre-eclampsia, as it also occurs in intrauterine growth restriction, fetal death, spontaneous preterm labour and maternal floor infarction (massive perivillous fibrin deposition). The severity and timing of the angiogenic imbalance, together with maternal susceptibility, might determine the clinical presentation of pre-eclampsia. This Review discusses the diagnosis, classification, clinical manifestations and putative pathogenetic mechanisms of pre-eclampsia.
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                Author and article information

                Contributors
                kharahr@athabascau.ca
                jcarroll@mednet.ucla.edu
                shorvath@mednet.ucla.edu
                calvin.hobel@cshs.org
                mcousson@uccs.edu
                dunkel@psych.ucla.edu
                Journal
                Clin Epigenetics
                Clin Epigenetics
                Clinical Epigenetics
                BioMed Central (London )
                1868-7075
                1868-7083
                6 August 2020
                6 August 2020
                2020
                : 12
                : 120
                Affiliations
                [1 ]GRID grid.36110.35, ISNI 0000 0001 0725 2874, Centre for Social Sciences, , Athabasca University, ; 1 University Drive, Athabasca, AB T9S 3A3 Canada
                [2 ]GRID grid.22072.35, ISNI 0000 0004 1936 7697, Department of Psychology, , University of Calgary, ; Calgary, AB Canada
                [3 ]GRID grid.19006.3e, ISNI 0000 0000 9632 6718, Cousins Center for Psychoneuroimmunology, David Geffen School of Medicine, Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, , University of California – Los Angeles, ; Los Angeles, CA USA
                [4 ]GRID grid.19006.3e, ISNI 0000 0000 9632 6718, Department of Biostatistics, , University of California – Los Angeles, ; Los Angeles, CA USA
                [5 ]GRID grid.50956.3f, ISNI 0000 0001 2152 9905, Department of Obstetrics and Gynecology, , Cedars-Sinai Medical Center, ; Los Angeles, CA USA
                [6 ]GRID grid.266186.d, ISNI 0000 0001 0684 1394, Psychology Department, , University of Colorado – Colorado Springs, ; Colorado Springs, CO USA
                [7 ]GRID grid.19006.3e, ISNI 0000 0000 9632 6718, Department of Psychology, , University of California – Los Angeles, ; Los Angeles, CA USA
                Author information
                http://orcid.org/0000-0002-1472-5630
                Article
                909
                10.1186/s13148-020-00909-2
                7409637
                32762768
                4d87b9a9-213b-422c-84e8-b7041d0ef12a
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 changes were made. 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/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 27 February 2020
                : 20 July 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01 HD073491
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100006108, National Center for Advancing Translational Sciences;
                Award ID: UL1TR001881
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2020

                Genetics
                pregnancy,grimageaccel,epigenetic age,gestational length,birthweight
                Genetics
                pregnancy, grimageaccel, epigenetic age, gestational length, birthweight

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