Gestational diabetes (GD) represents the main metabolic alteration during pregnancy affecting 17% of pregnancies in Mexico. The clinical relevance of this pathology is the short and long-term complications such as obesity, diabetes, and hypertension; that affect mother and child. GD is often diagnosed between weeks 24 to 28 of gestation through an oral glucose tolerance test. The aim of the present study was to identify early pregnancy metabolites (under 18 weeks of gestation) present on women who were diagnosed with GD; in order, to generate an early prediction model to identify those women who will later be diagnosed with GD. We used targeted metabolomics (11 amino acids and 39 acylcarnitines) to select early serum biomarkers with the most predictive power to identify women who will later be diagnosed with GD on a Mexican cohort of pregnant women. We assessed 79 pregnant women, which 66 underwent a normal pregnancy and 13 were diagnosed with GD under the IADPSG criteria. Random forest analyses were performed to assess the contribution and association of the metabolites identified to the development of GD. We developed a model that included two short-chain acylcarnitines (namely AC4 and AC5:1) that allowed an AUC of 0.904 (0.843-0.965, 95 CI) for classification of gestational diabetes. This model allows the identification of women on early stages of pregnancy that will be diagnosed with gestational diabetes between weeks 24 to 28 of gestation. However, additional studies in a large sample of pregnant women are required.


M. Razo-Azamar: None. R. Nambo-Venegas: None. J. Delgadillo-Velazquez: None. B. Palacios-Gonzalez: None.

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