Abstract
Background: Gestational diabetes (GDM) is usually diagnosed late in pregnancy, precluding early preventive interventions. This study aims to develop a predictive model based on clinical factors and selected biochemical markers for the early risk assessment of GDM.
Methods: Based on a prospective cohort of 7929 pregnant women from the Quebec City metropolitan area, a nested case-control study was performed including 264 women who developed GDM. Each woman who developed GDM was matched with two women with normal glycemic profile. Risk prediction models for GDM and GDM requiring insulin therapy were developed using multivariable logistic regression analyses, based on clinical characteristics and the measurement of three clinically validated biomarkers: glycated hemoglobin (HbA1c), sex hormone binding globulin (SHBG) and high-sensitivity C-reactive protein (hsCRP) measured between 14 and 17 weeks of gestation.
Results: HbA1c and hsCRP were higher and SHBG was lower in women who developed GDM (p<0.001). The selected model for the prediction of GDM, based on HbA1c, SHBG, BMI, past history of GDM, family history of diabetes and soft drink intake before pregnancy yielded an area under the ROC curve (AUC) of 0.79 (0.75–0.83). For the prediction of GDM requiring insulin therapy, the selected model including the same six variables yielded an AUC of 0.88 (0.84–0.92) and a sensitivity of 68.9% at a false-positive rate of 10%.
Conclusions: A simple model based on clinical characteristics and biomarkers available early in pregnancy could allow the identification of women at risk of developing GDM, especially GDM requiring insulin therapy.
Acknowledgments
The authors thank Nathalie Bernard, Mylène Badeau and Véronique Goulet for their professional assistance with the project, and the research nurses for the recruitment of participants and retrieval of data from the medical records.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission. ST assisted with the design of the study, interpreted the data and wrote the manuscript. YG assisted with the design of the study and writing and edition of the manuscript. JM analyzed the data and assisted with the interpretation of the data and writing and edition of the manuscript. JG contributed to the biomarkers measurements and assisted with writing and edition of the manuscript. JCF designed the study and assisted with the interpretation of the data and writing and edition of the manuscript.
Research funding: This work was supported by the Canadian Institutes of Health Research (CIHR, Healthy Pregnancy Initiative from the Institute for Human Development, Child and Youth Health, Grant number: NRF-HPG-78880). YG is a research scholar from the Fonds de la recherche du Québec – Santé (FRQ-S).
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
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