An Unbiased Lipidomics Approach Identifies Early Second Trimester Lipids Predictive of Maternal Glycemic Traits and Gestational Diabetes Mellitus.
Diabetes care 2016 ; 39: 2232-2239.
Lu L, Koulman A, Petry CJ, Jenkins B, Matthews L, Hughes IA, Acerini CL, Ong KK, Dunger DB
DOI : 10.2337/dc16-0863
PubMed ID : 27703025
PMCID : PMC5123716
Abstract
To investigate the relationship between early second trimester serum lipidomic variation and maternal glycemic traits at 28 weeks and to identify predictive lipid biomarkers for gestational diabetes mellitus (GDM).
Prospective study of 817 pregnant women (discovery cohort, n = 200; validation cohort, n = 617) who provided an early second trimester serum sample and underwent an oral glucose tolerance test (OGTT) at 28 weeks. In the discovery cohort, lipids were measured using direct infusion mass spectrometry and correlated with OGTT results. Variable importance in projection (VIP) scores were used to identify candidate lipid biomarkers. Candidate biomarkers were measured in the validation cohort using liquid chromatography-mass spectrometry and tested for associations with OGTT results and GDM status.
Early second trimester lipidomic variation was associated with 1-h postload glucose levels but not with fasting plasma glucose levels. Of the 13 lipid species identified by VIP scores, 10 had nominally significant associations with postload glucose levels. In the validation cohort, 5 of these 10 lipids had significant associations with postload glucose levels that were independent of maternal age and BMI, i.e., TG(51.1), TG(48:1), PC(32:1), PCae(40:3), and PCae(40:4). All except the last were also associated with maternal GDM status. Together, these four lipid biomarkers had moderate ability to predict GDM (area under curve [AUC] = 0.71 ± 0.04, P = 4.85 × 10) and improved the prediction of GDM by age and BMI alone from AUC 0.69 to AUC 0.74.
Specific early second trimester lipid biomarkers can predict maternal GDM status independent of maternal age and BMI, potentially enhancing risk factor-based screening.