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Metabolomic Identification of a Novel, Externally Validated Predictive Test for Gestational Diabetes Mellitus

CONTEXT: Undiagnosed gestational diabetes mellitus (GDM) is a major preventable cause of stillbirth. In the United Kingdom, women are selected for diagnostic testing for GDM based on risk factors, including body mass index (BMI) > 30 kg/m(2). OBJECTIVE: To improve the prediction of GDM using meta...

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Detalles Bibliográficos
Autores principales: Sovio, Ulla, Clayton, Gemma L, Cook, Emma, Gaccioli, Francesca, Charnock-Jones, D Stephen, Lawlor, Deborah A, Smith, Gordon C S
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9282248/
https://www.ncbi.nlm.nih.gov/pubmed/35436338
http://dx.doi.org/10.1210/clinem/dgac240
Descripción
Sumario:CONTEXT: Undiagnosed gestational diabetes mellitus (GDM) is a major preventable cause of stillbirth. In the United Kingdom, women are selected for diagnostic testing for GDM based on risk factors, including body mass index (BMI) > 30 kg/m(2). OBJECTIVE: To improve the prediction of GDM using metabolomics. METHODS: We performed metabolomics on maternal serum from the Pregnancy Outcome Prediction (POP) study at 12 and 20 weeks of gestational age (wkGA; 185 GDM cases and 314 noncases). Predictive metabolites were internally validated using the 28 wkGA POP study serum sample and externally validated using 24- to 28-wkGA fasting plasma from the Born in Bradford (BiB) cohort (349 GDM cases and 2347 noncases). The predictive ability of a model including the metabolites was compared with BMI > 30 kg/m(2) in the POP study. RESULTS: Forty-seven predictive metabolites were identified using the 12- and 20-wkGA samples. At 28 wkGA, 4 of these [mannose, 4-hydroxyglutamate, 1,5-anhydroglucitol, and lactosyl-N-palmitoyl-sphingosine (d18:1/16:0)] independently increased the bootstrapped area under the receiver operating characteristic curve (AUC) by >0.01. All 4 were externally validated in the BiB samples (P = 2.6 × 10(−12), 2.2 × 10(−13), 6.9 × 10(−28), and 2.6 × 10(−17), respectively). In the POP study, BMI > 30 kg/m(2) had a sensitivity of 28.7% (95% CI 22.3-36.0%) and a specificity of 85.4% whereas at the same level of specificity, a predictive model using age, BMI, and the 4 metabolites had a sensitivity of 60.2% (95% CI 52.6-67.4%) and an AUC of 0.82 (95% CI 0.78-0.86). CONCLUSIONS: We identified 4 strongly and independently predictive metabolites for GDM that could have clinical utility in screening for GDM.