Rathnayake Hasini, Han Luhao, da Silva Costa Fabrício, Paganoti Cristiane, Dyer Brett, Kundur Avinash, Singh Indu, Holland Olivia J
Griffith University School of Pharmacy and Medical Sciences, Gold Coast, Queensland, Australia.
Department of Medical Laboratory Science, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka.
BMJ Open. 2024 Dec 15;14(12):e089937. doi: 10.1136/bmjopen-2024-089937.
Gestational diabetes mellitus (GDM) is a metabolic disorder associated with adverse maternal and neonatal outcomes. While GDM is diagnosed by oral glucose tolerance testing between 24-28 weeks, earlier prediction of risk of developing GDM via circulating biomarkers has the potential to risk-stratify women and implement targeted risk reduction before adverse obstetric outcomes. This scoping review aims to collate biomarkers associated with GDM development, associated perinatal outcome and medication requirement in GDM.
The Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for scoping reviews was used to guide the study.
This review searched for articles on PubMed, Embase, Scopus, Cochrane Central Register of Controlled Trials, the Cumulative Index to Nursing and Allied Health Literature and the Web of Science from January 2013 to February 2023.
The eligibility criteria included analytical observational studies published in English, focusing on pregnant women with maternal plasma or serum biomarkers collected between 6 and 24 weeks of gestation. Studies were excluded if they evaluated drug effects, non-GDM diabetes types or involved twin pregnancies, microbiota, genetic analyses or non-English publications.
Two independent reviewers extracted data. One reviewer extracted data from papers included in the scoping review using Covidence. From the 8837 retrieved records, 137 studies were included.
A total of 278 biomarkers with significant changes in individuals with GDM compared with controls were identified. The univariate predictive biomarkers exhibited insufficient clinical sensitivity and specificity for predicting GDM, perinatal outcomes, and the necessity of medication. Multivariable models combining maternal risk factors with biomarkers provided more accurate detection but required validation for use in clinical settings.
This review recommends further research integrating novel omics technology for building accurate models for predicting GDM, perinatal outcome, and the necessity of medication while considering the optimal testing time.
妊娠期糖尿病（GDM）是一种与不良母婴结局相关的代谢紊乱疾病。虽然GDM通过在孕24 - 28周进行口服葡萄糖耐量试验来诊断，但通过循环生物标志物更早预测发生GDM的风险有可能对女性进行风险分层，并在不良产科结局出现之前实施有针对性的风险降低措施。本综述旨在整理与GDM发生、相关围产期结局及GDM用药需求相关的生物标志物。
采用系统评价和Meta分析扩展版的首选报告项目（PRISMA-ScR）来指导本研究。
本综述检索了2013年1月至2023年2月期间发表在PubMed、Embase、Scopus、Cochrane对照试验中心注册库、护理及相关健康文献累积索引以及科学网的文章。
纳入标准包括以英文发表且聚焦于在妊娠6至24周期间收集孕妇母血血浆或血清生物标志物的分析性观察性研究。如果研究评估药物效果、非GDM糖尿病类型或涉及双胎妊娠、微生物群、基因分析或非英文出版物，则将其排除。
两名独立评审员提取数据。一名评审员使用Covidence从纳入范围综述的论文中提取数据。在检索到的8837条记录中，共纳入137项研究。
共鉴定出278种与对照组相比在GDM个体中有显著变化的生物标志物。单变量预测生物标志物在预测GDM、围产期结局和用药必要性方面表现出不足的临床敏感性和特异性。将母亲风险因素与生物标志物相结合的多变量模型提供了更准确的检测，但需要在临床环境中进行验证方可使用。
本综述建议进一步开展研究，整合新型组学技术，以构建在考虑最佳检测时间的情况下准确预测GDM、围产期结局和用药必要性的模型。
