Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China.
Hunan Provincial Key Laboratory of Clinical Epidemiology, Xiangya School of Public Health, Central South University, Changsha, China.
Front Immunol. 2022 Feb 8;13:705852. doi: 10.3389/fimmu.2022.705852. eCollection 2022.
Studies investigating chemokines in gestational diabetes mellitus (GDM) have yielded mixed results. The purpose of this meta-analysis was to explore whether concentrations of chemokines in patients with GDM differed from that of the controls.
Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically searched Web of Science, Embase, Cochrane Library, and PubMed databases for articles, published in any language, on chemokines and GDM through August 1st, 2021. The difference in concentrations of chemokines between patients with GDM and controls was determined by a standardized mean difference (SMD) with a 95% confidence interval (CI), calculated in the meta-analysis of the eligible studies using a random-effects model with restricted maximum-likelihood estimator.
Seventeen studies met the inclusion criteria for the meta-analysis. Altogether, they included nine different chemokines comparisons involving 5,158 participants (1,934 GDM patients and 3,224 controls). Results showed a significant increase of these chemokines (CCL2, CXCL1, CXCL8, CXCL9, and CXCL12) in the GDM patients compared with the controls. However, there was a significant decrease of the chemokines, CCL4, CCL11 and CXCL10, in the GDM patients compared with the controls. Moreover, subgroup analysis revealed a potential role of chemokines as biomarkers in relation to laboratory detection (different sample type and assay methods) and clinical characteristics of GDM patients (ethnicity and body mass index).
GDM is associated with several chemokines (CCL2, CCL4, CCL11, CXCL1, CXCL8, CXCL9, CXCL10 and CXCL12). Therefore, consideration of these chemokines as potential targets or biomarkers in the pathophysiology of GDM development is necessary. Notably, the information of subgroup analysis underscores the importance of exploring putative mechanisms underlying this association, in order to develop new individualized clinical and therapeutic strategies.
研究趋化因子在妊娠期糖尿病(GDM)中的作用,结果不一。本研究旨在探讨 GDM 患者与对照组趋化因子浓度是否存在差异。
按照系统评价和荟萃分析首选报告项目(PRISMA)指南,我们系统地检索了 Web of Science、Embase、Cochrane Library 和 PubMed 数据库,检索时间截至 2021 年 8 月 1 日,以获取关于趋化因子和 GDM 的所有语言发表的文章。采用随机效应模型和最大似然估计的限制,用标准化均数差(SMD)和 95%置信区间(CI)对纳入研究进行荟萃分析,以确定 GDM 患者与对照组趋化因子浓度的差异。
17 项研究符合荟萃分析的纳入标准。共有 9 种不同的趋化因子比较纳入研究,共纳入 5158 名参与者(1934 名 GDM 患者和 3224 名对照组)。结果显示,与对照组相比,GDM 患者的这些趋化因子(CCL2、CXCL1、CXCL8、CXCL9 和 CXCL12)显著增加。然而,与对照组相比,GDM 患者的趋化因子 CCL4、CCL11 和 CXCL10 显著减少。此外,亚组分析显示,趋化因子可能作为与实验室检测(不同样本类型和检测方法)和 GDM 患者临床特征(种族和体重指数)相关的生物标志物发挥作用。
GDM 与多种趋化因子(CCL2、CCL4、CCL11、CXCL1、CXCL8、CXCL9、CXCL10 和 CXCL12)相关。因此,有必要考虑将这些趋化因子作为 GDM 发病机制潜在的治疗靶点或生物标志物。值得注意的是,亚组分析的信息强调了探讨这种关联潜在机制的重要性,以便制定新的个体化临床和治疗策略。