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      DNA Methylation-derived biological age and long-term mortality risk in subjects with type 2 diabetes

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          Abstract

          Background

          Individuals with type 2 diabetes (T2D) face an increased mortality risk, not fully captured by canonical risk factors. Biological age estimation through DNA methylation (DNAm), i.e. the epigenetic clocks, is emerging as a possible tool to improve risk stratification for multiple outcomes. However, whether these tools predict mortality independently of canonical risk factors in subjects with T2D is unknown.

          Methods

          Among a cohort of 568 T2D patients followed for 16.8 years, we selected a subgroup of 50 subjects, 27 survived and 23 deceased at present, passing the quality check and balanced for all risk factors after propensity score matching. We analyzed DNAm from peripheral blood leukocytes using the Infinium Human MethylationEPIC BeadChip (Illumina) to evaluate biological aging through previously validated epigenetic clocks and assess the DNAm-estimated levels of selected inflammatory proteins and blood cell counts. We tested the associations of these estimates with mortality using two-stage residual-outcome regression analysis, creating a reference model on data from the group of survived patients.

          Results

          Deceased subjects had higher median epigenetic age expressed with DNAmPhenoAge algorithm (57.49 [54.72; 60.58] years. vs. 53.40 [49.73; 56.75] years; p = 0.012), and accelerated DunedinPoAm pace of aging (1.05 [1.02; 1.11] vs. 1.02 [0.98; 1.06]; p = 0.012). DNAm PhenoAge (HR 1.16, 95% CI 1.05–1.28; p = 0.004) and DunedinPoAm (HR 3.65, 95% CI 1.43–9.35; p = 0.007) showed an association with mortality independently of canonical risk factors. The epigenetic predictors of 3 chronic inflammation-related proteins, i.e. CXCL10, CXCL11 and enRAGE, C-reactive protein methylation risk score and DNAm-based estimates of exhausted CD8 + T cell counts were higher in deceased subjects when compared to survived.

          Conclusions

          These findings suggest that biological aging, as estimated through existing epigenetic tools, is associated with mortality risk in individuals with T2D, independently of common risk factors and that increased DNAm-surrogates of inflammatory protein levels characterize deceased T2D patients. Replication in larger cohorts is needed to assess the potential of this approach to refine mortality risk in T2D.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12933-024-02351-7.

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          Most cited references74

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          KEGG: kyoto encyclopedia of genes and genomes.

          M Kanehisa (2000)
          KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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            Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

            Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.
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              Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool

              Background System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement. Results Here, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios. Conclusions Enrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr.
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                Author and article information

                Contributors
                angelica.giuliani@staff.univpm.it
                paolo.garagnani2@unibo.it
                Journal
                Cardiovasc Diabetol
                Cardiovasc Diabetol
                Cardiovascular Diabetology
                BioMed Central (London )
                1475-2840
                13 July 2024
                13 July 2024
                2024
                : 23
                : 250
                Affiliations
                [1 ]Department of Clinical and Molecular Sciences (DISCLIMO), Università Politecnica delle Marche, ( https://ror.org/00x69rs40) Ancona, Italy
                [2 ]Clinic of Laboratory and Precision Medicine, IRCCS INRCA, Ancona, Italy
                [3 ]Istituti Clinici Scientifici Maugeri IRCCS, Cardiac Rehabilitation Unit of Bari Institute, ( https://ror.org/00mc77d93) Bari, Italy
                [4 ]Department of Medical and Surgical Sciences (DIMEC), University of Bologna, ( https://ror.org/01111rn36) Bologna, Italy
                [5 ]GRID grid.420421.1, ISNI 0000 0004 1784 7240, IRCCS Multimedica, ; Milan, Italy
                [6 ]Advanced Technology Center for Aging Research, IRCCS INRCA, Ancona, Italy
                [7 ]Department of Metabolic Diseases and Diabetology, IRCCS INRCA, Ancona, Italy
                [8 ]Scientific Direction, IRCCS INRCA, Ancona, Italy
                [9 ]Department of Brain and Behavioral Sciences, Università di Pavia, ( https://ror.org/00s6t1f81) Pavia, Italy
                [10 ]Bioinformatics and Statistical Genomics Unit, Istituto Auxologico Italiano IRCCS, ( https://ror.org/033qpss18) Cusano Milanino, Milan, Italy
                Article
                2351
                10.1186/s12933-024-02351-7
                11245869
                39003492
                a42fd629-fb81-4a32-986c-b2363968c66a
                © The Author(s) 2024

                Open Access This 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
                : 22 April 2024
                : 6 July 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100003196, Ministero della Salute;
                Award ID: Ricerca corrente
                Award ID: Ricerca corrente
                Award ID: Ricerca corrente
                Award Recipient :
                Funded by: Fondazione di Medicina Molecolare e Terapia Cellulare
                Funded by: FundRef http://dx.doi.org/10.13039/501100000780, European Commission;
                Award ID: Age-It: “Ageing Well in an Ageing Society”
                Award ID: Age-It: “Ageing Well in an Ageing Society”
                Award Recipient :
                Categories
                Research
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                © BioMed Central Ltd., part of Springer Nature 2024

                Endocrinology & Diabetes
                type 2 diabetes,epigenetic clocks,dna methylation,phenoage,dunedinpoam
                Endocrinology & Diabetes
                type 2 diabetes, epigenetic clocks, dna methylation, phenoage, dunedinpoam

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