Liron Yoffe1, Avital Polsky1, Avital Gilam1, Chen Raff1, Federico Mecacci2, Agostino Ognibene3, Fatima Crispi4,5, Eduard Gratacós4,5, Hannah Kanety6, Shali Mazaki-Tovi1,7, Noam Shomron1, Moshe Hod1,8. 1. Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel. 2. Department of Health Sciences, University of Florence, Obstetrics and Gynecology, Careggi University Hospital, Florence, Italy. 3. Laboratory Department, Careggi University Hospital, Florence, Italy. 4. Fetal Medicine Research Center, BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), Institut Clínic de Ginecologia, Obstetricia i Neonatologia, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Universitat de Barcelona, Barcelona, Spain. 5. Center for Biomedical Research on Rare Diseases (CIBERER), Barcelona, Spain. 6. Institute of Endocrinology, Sheba Medical Center, Tel-Hashomer, Ramat-Gan, Israel. 7. Department of Obstetrics and Gynecology, Sheba Medical Center, Tel-Hashomer, Ramat-Gan, Israel. 8. Rabin Medical Center, Petah-Tikva, Israel.
Abstract
DESIGN: Gestational diabetes mellitus (GDM) is one of the most common pregnancy complications and its prevalence is constantly rising worldwide. Diagnosis is commonly in the late second or early third trimester of pregnancy, though the development of GDM starts early; hence, first-trimester diagnosis is feasible. OBJECTIVE: Our objective was to identify microRNAs that best distinguish GDM samples from those of healthy pregnant women and to evaluate the predictive value of microRNAs for GDM detection in the first trimester. METHODS: We investigated the abundance of circulating microRNAs in the plasma of pregnant women in their first trimester. Two populations were included in the study to enable population-specific as well as cross-population inspection of expression profiles. Each microRNA was tested for differential expression in GDM vs control samples, and their efficiency for GDM detection was evaluated using machine-learning models. RESULTS: Two upregulated microRNAs (miR-223 and miR-23a) were identified in GDM vs the control set, and validated on a new cohort of women. Using both microRNAs in a logistic-regression model, we achieved an AUC value of 0.91. We further demonstrated the overall predictive value of microRNAs using several types of multivariable machine-learning models that included the entire set of expressed microRNAs. All models achieved high accuracy when applied on the dataset (mean AUC = 0.77). The significance of the classification results was established via permutation tests. CONCLUSIONS: Our findings suggest that circulating microRNAs are potential biomarkers for GDM in the first trimester. This warrants further examination and lays the foundation for producing a novel early non-invasive diagnostic tool for GDM.
DESIGN: Gestational diabetes mellitus (GDM) is one of the most common pregnancy complications and its prevalence is constantly rising worldwide. Diagnosis is commonly in the late second or early third trimester of pregnancy, though the development of GDM starts early; hence, first-trimester diagnosis is feasible. OBJECTIVE: Our objective was to identify microRNAs that best distinguish GDM samples from those of healthy pregnant women and to evaluate the predictive value of microRNAs for GDM detection in the first trimester. METHODS: We investigated the abundance of circulating microRNAs in the plasma of pregnant women in their first trimester. Two populations were included in the study to enable population-specific as well as cross-population inspection of expression profiles. Each microRNA was tested for differential expression in GDM vs control samples, and their efficiency for GDM detection was evaluated using machine-learning models. RESULTS: Two upregulated microRNAs (miR-223 and miR-23a) were identified in GDM vs the control set, and validated on a new cohort of women. Using both microRNAs in a logistic-regression model, we achieved an AUC value of 0.91. We further demonstrated the overall predictive value of microRNAs using several types of multivariable machine-learning models that included the entire set of expressed microRNAs. All models achieved high accuracy when applied on the dataset (mean AUC = 0.77). The significance of the classification results was established via permutation tests. CONCLUSIONS: Our findings suggest that circulating microRNAs are potential biomarkers for GDM in the first trimester. This warrants further examination and lays the foundation for producing a novel early non-invasive diagnostic tool for GDM.
Authors: Mugdha V Joglekar; Wilson K M Wong; Fahmida K Ema; Harry M Georgiou; Alexis Shub; Anandwardhan A Hardikar; Martha Lappas Journal: Diabetologia Date: 2021-03-23 Impact factor: 10.122