Early prediction of gestational diabetes mellitus based on systematically selected multi-panel biomarkers and clinical accessibility-a longitudinal study of a multi-racial pregnant cohort.

Abstract

Background: Early identification of high-risk women is critical for preventing gestational diabetes mellitus (GDM). We aimed to improve early prediction of GDM using multiple panels of cardiometabolic biomarkers assessed in early and mid-pregnancy, considering clinical accessibility.

Methods: In a US study of 2802 pregnant individuals, we assessed 91 cardiometabolic biomarkers at 10-14 (random blood) and 15-26 (fasting) gestational weeks (GW) in 107 GDM cases and 214 controls. Candidate biomarkers were categorized by clinical accessibility from high to low: group I (clinically accessible tests like HbA1c, lipids), group II (clinically accessible biomarkers upon request like insulin-like growth factor (IGF) axis markers, adipokines), and group III (specialty lab-required targeted metabolomics: amino acids (AAs) and phospholipid fatty acids (FAs)). At each visit, we constructed a full model incorporating all candidate biomarkers and conventional predictors. We built alternative models utilizing different groups of biomarkers considering clinical accessibility. Variable selection was performed to retain variables with a p value < 0.10 for a parsimonious model. Model performance was evaluated by area under receiver operating characteristics curve (AUC), proportion of cases followed (PCF, %) and proportion needed to follow (PNF, %), and decision curve analysis.

Results: A full model comprising conventional predictors, clinical and non-clinical cardiometabolic biomarkers, and metabolomic markers achieved the highest discriminative accuracy (AUC: 0.842 at 10-14 GW, 0.829 at 15-26 GW). The addition of novel biomarkers increased PCF and decreased PNF, suggesting increased clinical utility. For example, at 10-14 GW, 69.5% of GDM cases are expected to be detected from women whose risk is above the 80% percentile estimated by the full model vs. 49.1% by the conventional model. Additionally, 46.1% of women identified as being at the highest risk by the full model are expected to account for 90.0% of GDM cases vs. 71.1% by the conventional model. Decision curve analysis showed that models incorporating novel biomarkers performed better than the conventional model including glucose, and the full model at 10-14 GW had the highest net benefit, overall.

Conclusions: This study suggested that a selected panel of cardiometabolic biomarkers using early-pregnancy random plasma samples predicted GDM comparably to those using mid-pregnancy fasting samples.