Plasma mitochondrial DNA is elevated in maternal serum at first trimester and may serve as a biomarker for prediction of gestational diabetes mellitus.

Abstract

Background: We evaluated whether an abundance of first-trimester plasma mitochondrial DNA (mtDNA) fragments could predict the risk for the development of gestational diabetes mellitus (GDM) by the late second or early third trimester.

Methods: It was a prospective study wherein we enrolled 150 women in their first trimester of gestation. Oral glucose tolerance test (OGTT) was administered both in the first and second trimesters to diagnose GDM.

Results: Among our cohort, 23 women were diagnosed with GDM in the first trimester and excluded from the study. Of the remaining 127, 29 women were diagnosed with GDM in the second trimester, and 98 women who did not develop GDM served as controls. We amplified blood drawn from each participant during the first trimester for three distinct mtDNA gene sequences: COX, ND4, and D-loop. An abundance of each mtDNA sequence, estimated by the ΔCt method between mtDNA and 18S rRNA, correlated with GDM occurrence in the late second or early third trimester. There was a significant difference in ΔCt COX between controls and those with GDM occurrence in the second trimester (p = .006). These levels were not associated with age or fasting plasma glucose levels in the first trimester. ΔCt COX could predict GDM with a sensitivity of 90% and a specificity of 40%. Though ΔCt ND4 was higher in the GDM-positive group, the levels did not reach statistical significance. ΔCt D-loop was similar in GDM-positive cases and controls who did not develop GDM during pregnancy.

Conclusions: These results were in plasma samples collected 3 to 4 months before overt hyperglycemia diagnosis suggestive of GDM. The abundance of plasma mtDNA fragments represents a promising cost-effective, convenient early-stage biomarker for predicting GDM development. Importantly, it can be administered irrespective of the fasting status of the subject. Further assessment of the predictive capacity of these biomarkers within large, diverse populations is needed for effective clinical utility.