Advancement in predictive biomarkers for gestational diabetes mellitus diagnosis and related outcomes: a scoping review - Fabricio Da Silva Costa; Hasini Rathnayake; Luhao Han; Cristiane Paganoti; Brett Dyer; Avinash Kundur; Indu Singh; Olivia J Holland (BMJ Open)
Objective 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.Design The Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for scoping reviews was used to guide the study.Data sources 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.Eligibility criteria 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.Data extraction and synthesis 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.Results 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.Conclusion 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.