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      Physical activity is associated with slower epigenetic ageing—Findings from the Rhineland study

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          Abstract

          Epigenetic ageing, i.e., age‐associated changes in DNA methylation patterns, is a sensitive marker of biological ageing, a major determinant of morbidity and functional decline. We examined the association of physical activity with epigenetic ageing and the role of immune function and cardiovascular risk factors in mediating this relation. Moreover, we aimed to identify novel molecular processes underlying the association between physical activity and epigenetic ageing. We analysed cross‐sectional data from 3567 eligible participants (mean age: 55.5 years, range: 30–94 years, 54.8% women) of the Rhineland Study, a community‐based cohort study in Bonn, Germany. Physical activity components (metabolic equivalent (MET)‐Hours, step counts, sedentary, light‐intensity and moderate‐to‐vigorous intensity activities) were recorded with accelerometers. DNA methylation was measured with the Illumina HumanMethylationEPIC BeadChip. Epigenetic age acceleration (Hannum's age, Horvath's age, PhenoAge and GrimAge) was calculated based on published algorithms. The relation between physical activity and epigenetic ageing was examined with multivariable regression, while structural equation modeling was used for mediation analysis. Moreover, we conducted an epigenome‐wide association study of physical activity across 850,000 CpG sites. After adjustment for age, sex, season, education, smoking, cell proportions and batch effects, physical activity (step counts, MET‐Hours and %time spend in moderate‐to‐vigorous activities) was non‐linearly associated with slower epigenetic ageing, in part through its beneficial effects on immune function and cardiovascular health. Additionally, we identified 12 and 7 CpGs associated with MET‐Hours and %time spent in moderate‐to‐vigorous activities, respectively ( p < 1 × 10 −5). Our findings suggest that regular physical activity slows epigenetic ageing by counteracting immunosenescence and lowering cardiovascular risk.

          Abstract

          In this study, we examined the relation between physical activity and epigenetic ageing in 3567 eligible participants of a large community‐based, prospective cohort study, the Rhineland Study. We show that physical activity is associated with slower epigenetic ageing (GrimAge acceleration and PhenoAge acceleration), which was in part mediated by cardiovascular health and immune function.

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          lavaan: AnRPackage for Structural Equation Modeling

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            clusterProfiler 4.0: A universal enrichment tool for interpreting omics data

            Summary Functional enrichment analysis is pivotal for interpreting high-throughput omics data in life science. It is crucial for this type of tool to use the latest annotation databases for as many organisms as possible. To meet these requirements, we present here an updated version of our popular Bioconductor package, clusterProfiler 4.0. This package has been enhanced considerably compared with its original version published 9 years ago. The new version provides a universal interface for functional enrichment analysis in thousands of organisms based on internally supported ontologies and pathways as well as annotation data provided by users or derived from online databases. It also extends the dplyr and ggplot2 packages to offer tidy interfaces for data operation and visualization. Other new features include gene set enrichment analysis and comparison of enrichment results from multiple gene lists. We anticipate that clusterProfiler 4.0 will be applied to a wide range of scenarios across diverse organisms.
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              General cardiovascular risk profile for use in primary care: the Framingham Heart Study.

              Separate multivariable risk algorithms are commonly used to assess risk of specific atherosclerotic cardiovascular disease (CVD) events, ie, coronary heart disease, cerebrovascular disease, peripheral vascular disease, and heart failure. The present report presents a single multivariable risk function that predicts risk of developing all CVD and of its constituents. We used Cox proportional-hazards regression to evaluate the risk of developing a first CVD event in 8491 Framingham study participants (mean age, 49 years; 4522 women) who attended a routine examination between 30 and 74 years of age and were free of CVD. Sex-specific multivariable risk functions ("general CVD" algorithms) were derived that incorporated age, total and high-density lipoprotein cholesterol, systolic blood pressure, treatment for hypertension, smoking, and diabetes status. We assessed the performance of the general CVD algorithms for predicting individual CVD events (coronary heart disease, stroke, peripheral artery disease, or heart failure). Over 12 years of follow-up, 1174 participants (456 women) developed a first CVD event. All traditional risk factors evaluated predicted CVD risk (multivariable-adjusted P<0.0001). The general CVD algorithm demonstrated good discrimination (C statistic, 0.763 [men] and 0.793 [women]) and calibration. Simple adjustments to the general CVD risk algorithms allowed estimation of the risks of each CVD component. Two simple risk scores are presented, 1 based on all traditional risk factors and the other based on non-laboratory-based predictors. A sex-specific multivariable risk factor algorithm can be conveniently used to assess general CVD risk and risk of individual CVD events (coronary, cerebrovascular, and peripheral arterial disease and heart failure). The estimated absolute CVD event rates can be used to quantify risk and to guide preventive care.
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                Author and article information

                Contributors
                ahmad.aziz@dzne.de
                Journal
                Aging Cell
                Aging Cell
                10.1111/(ISSN)1474-9726
                ACEL
                Aging Cell
                John Wiley and Sons Inc. (Hoboken )
                1474-9718
                1474-9726
                10 April 2023
                June 2023
                : 22
                : 6 ( doiID: 10.1111/acel.v22.6 )
                : e13828
                Affiliations
                [ 1 ] Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE) Bonn Germany
                [ 2 ] Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine University of Bonn Bonn Germany
                [ 3 ] Department of Neurology, Faculty of Medicine University of Bonn Bonn Germany
                Author notes
                [*] [* ] Correspondence

                Nasir Ahmad Aziz, Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Venusberg‐Campus 1/99, Bonn 53127, Germany.

                Email: ahmad.aziz@ 123456dzne.de

                Author information
                https://orcid.org/0000-0002-0656-219X
                https://orcid.org/0000-0002-6732-7433
                https://orcid.org/0000-0002-0626-9305
                https://orcid.org/0000-0001-6184-458X
                Article
                ACEL13828 ACE-22-0699.R1
                10.1111/acel.13828
                10265180
                37036021
                a39f74fc-ac36-4f68-861f-e2e9a30268dd
                © 2023 The Authors. Aging Cell published by Anatomical Society and John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 16 March 2023
                : 24 September 2022
                : 17 March 2023
                Page count
                Figures: 4, Tables: 4, Pages: 21, Words: 10241
                Funding
                Funded by: Alzheimer's Association , doi 10.13039/100000957;
                Award ID: AARG‐19‐616534
                Funded by: Bundesministerium für Bildung und Forschung , doi 10.13039/501100002347;
                Award ID: 01EA1410C
                Award ID: 01EA1809C
                Funded by: Deutsche Forschungsgemeinschaft , doi 10.13039/501100001659;
                Award ID: EXC 2151 ‐ 390873048
                Award ID: SFB1454 ‐ 432325352
                Funded by: European Research Council , doi 10.13039/100010663;
                Award ID: 101041677
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                June 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.9 mode:remove_FC converted:14.06.2023

                Cell biology
                biological age,cardiovascular diseases,cardiovascular risk factors,cohort studies,dna methylation,epidemiology,epigenetics,exercise,immune function,public health

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