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      Chemokines in Prediabetes and Type 2 Diabetes: A Meta-Analysis


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          A growing number of studies found inconsistent results on the role of chemokines in the progression of type 2 diabetes (T2DM) and prediabetes (PDM). The purpose of this meta-analysis was to summarize the results of previous studies on the association between the chemokines system and T2DM/PDM.


          We searched in the databases, PubMed, Web of Science, Embase and Cochrane Library, for eligible studies published not later than March 1, 2020. Data extraction was performed independently by 2 reviewers, on a standardized, prepiloted form. Group differences in chemokines concentrations were summarized using the standardized mean difference (SMD) with a 95% confidence interval (CI), calculated by performing a meta-analysis using the random-effects model.


          We identified 98 relevant studies that investigated the association between 32 different chemokines and T2DM/PDM. Altogether, these studies involved 14,708 patients and 14,574 controls. Results showed that the concentrations of CCL1, CCL2, CCL4, CCL5, CCL11, CXCL8, CXCL10 and CX3CL1 in the T2DM patients were significantly higher than that in the controls, while no difference in these concentrations was found between the PDM patients and controls.


          Progression of T2DM may be associated with elevated concentrations of chemokines.

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          PROSPERO, identifier CRD42019148305.

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          Most cited references163

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          Measuring inconsistency in meta-analyses.

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            Bias in meta-analysis detected by a simple, graphical test

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              Quantifying heterogeneity in a meta-analysis.

              The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.

                Author and article information

                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                13 May 2021
                : 12
                : 622438
                [1] 1 Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University , Changsha, China
                [2] 2 Hunan Provincial Key Laboratory of Clinical Epidemiology, Xiangya School of Public Health, Central South University , Changsha, China
                [3] 3 Department of Mathematics and Statistics, Mzuzu University , Mzuzu, Malawi
                [4] 4 OMNI Research Group, Ottawa Hospital Research Institute , Ottawa, ON, Canada
                [5] 5 Department of Obstetrics and Gynaecology and School of Epidemiology and Public Health, University of Ottawa Faculty of Medicine , Ottawa, ON, Canada
                Author notes

                Edited by: Remo Castro Russo, Federal University of Minas Gerais, Brazil

                Reviewed by: Silvia Martina Ferrari, University of Pisa, Italy; Alessandro Antonelli, University of Pisa, Italy

                *Correspondence: Aizhong Liu, lazroy@ 123456live.cn

                This article was submitted to Cytokines and Soluble Mediators in Immunity, a section of the journal Frontiers in Immunology

                Copyright © 2021 Pan, Kaminga, Wen and Liu

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                : 28 October 2020
                : 09 March 2021
                Page count
                Figures: 3, Tables: 3, Equations: 0, References: 163, Pages: 17, Words: 7374
                Original Research

                chemokines,type 2 diabetes,prediabetes,inflammation,meta-analysis
                chemokines, type 2 diabetes, prediabetes, inflammation, meta-analysis


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