Plasma Lipidomic Profiles in Relation to Incident Type 2 Diabetes among Women with a History of Gestational Diabetes Mellitus

Background: Dyslipidemia is a risk factor of type 2 diabetes (T2D). Women with a history of gestational diabetes (GDM) have a greater T2D risk; however, whether lipids patterns contribute to risk of progression remains unclear. Methods: We conducted a nested case-control study of incident T2D among women enrolled in the Nurses’ Health Study II reporting a history of GDM prior to the first blood collection (1995-1999) and followed-up through 2013. Plasma lipidomics containing 180 metabolites were measured in a subset of 175 incident cases and 176 age-matched controls by liquid chromatography-mass spectrometry. We constructed lipid networks using a weighted-correlation algorithm, and examined the association of individual lipids and sub-networks with T2D risk using logistic regression, adjusting for established T2D risk factors. Results: We identified 39 lipids species significantly associated with T2D risk (FDR<0.05), and observed different association patterns (in which associations were differed by numbers of carbon and double-bonds) across various lipid classes. We constructed 5 co-regulating lipid sub-networks, one of which (featuring the interactions between diacylglycerol, triacylglycerol, and phosphatidylethanolamine with mid-length carbon and less double-bonds) was significantly associated with incident T2D risk (odds ratio for increasing quartiles was 1 [reference], 2.4, 3.4, and 4.4; P-trend=0.0008). The addition of significant lipid species to a base model (13 traditional risk factors and medications) to predict incident T2D increased the c-statistics from 0.83 to 0.90 (P=0.001) with a corresponding categorical net reclassification index of 0.27 (P<0.001) in cross-validation. Conclusions: The dysregulation of certain lipid specie/class may contribute to a higher risk of incident T2D after a GDM pregnancy. Plasma lipid species and the network signatures may aid future clinical risk assessment and targeted interventions. Disclosure J. Li: None. L. Liang: None. F. Hu: None. C. Zhang: None. D. Tobias: None.