4.2 Article

The pre-pregnancy fasting blood glucose, glycated hemoglobin and lipid profiles as blood biomarkers for gestational diabetes mellitus: evidence from a multigenerational cohort study

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Publisher

TAYLOR & FRANCIS LTD
DOI: 10.1080/14767058.2023.2195524

Keywords

Gestational diabetes mellitus; blood glucose; lipid profile; insulin resistance

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The study aimed to investigate the relationship between blood biomarkers before pregnancy and the risk of gestational diabetes mellitus (GDM). The results showed that pre-pregnancy fasting blood glucose, insulin, and insulin resistance were independent predictors of GDM, which could be used as early markers for predicting the incidence of GDM.
Background Early prevention of gestational diabetes mellitus (GDM) is important to reduce the risk of adverse pregnancy outcomes and post-pregnancy cardiometabolic risk in women and offspring over the life course. This study aimed to investigate some blood biomarkers before pregnancy as GDM predictors. Methods We investigated the prospective association of blood biomarkers before pregnancy and GDM risk among women from the Mater-University of Queensland Study of Pregnancy (MUSP) cohort. A multiple logistic regression model was applied to estimate the odds of experiencing GDM by blood biomarkers. Results Out of 525 women included in this study, the prevalence of GDM was 7.43%. There was an increased risk of experiencing GDM among women who experienced obesity (Odds ratio = OR 2.4; 95% confidence interval = CI 1.6-3.7), had high fasting blood glucose (OR = 2.2; 95% CI = 1.3-3.8), high insulin (OR = 1.1; 95% CI = 1.0-1.2), high insulin resistance (OR = 1.2; 95% CI = 1.0-1.3) and low high-density lipoprotein (OR = 0.2; 95% CI = 0.1-0.7) before pregnancy. Adjustment for potential confounders, such as age, marital status, and BMI did not attenuate these associations substantially. Conclusion The pre-pregnancy fasting blood glucose, insulin, and insulin resistance were independent predictors of GDM. They may be used as early markers for predicting the incidence of GDM.

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