Journal
PLOS ONE
Volume 11, Issue 12, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0167846
Keywords
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Categories
Funding
- UK's National Institute for Health Research [RP-PG-0407-10452]
- Chief Scientist Office Scottish Government Health Directorates (Edinburgh) [CZB/A/680]
- Guys and St Thomas' Charity [1060508]
- Tommy's Charity [SC039280]
- NIHR Biomedical Research Centre at Guy's and St Thomas' NHS Foundation Trust and King's College London
- Medical Research Council UK [MR/L002477/1]
- Diabetes UK Sir George Alberti Research Training Fellowship
- Chief Scientist Office [CZB/4/680] Funding Source: researchfish
- Medical Research Council [MR/L002477/1, MC_UU_12011/4, MC_UP_A620_1017] Funding Source: researchfish
- National Institute for Health Research [NF-SI-0512-10104, NF-SI-0515-10042, RP-PG-0407-10452, CL-2010-17-007] Funding Source: researchfish
- MRC [MC_UU_12011/4, MC_UP_A620_1017, MR/L002477/1] Funding Source: UKRI
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All obese women are categorised as being of equally high risk of gestational diabetes (GDM) whereas the majority do not develop the disorder. Lifestyle and pharmacological interventions in unselected obese pregnant women have been unsuccessful in preventing GDM. Our aim was to develop a prediction tool for early identification of obese women at high risk of GDM to facilitate targeted interventions in those most likely to benefit. Clinical and anthropometric data and non-fasting blood samples were obtained at 15(+0)-18(+6) weeks' gestation in 1303 obese pregnant women from UPBEAT, a randomised controlled trial of a behavioural intervention. Twenty one candidate biomarkers associated with insulin resistance, and a targeted nuclear magnetic resonance (NMR) metabolome were measured. Prediction models were constructed using stepwise logistic regression. Twenty six percent of women (n = 337) developed GDM (International Association of Diabetes and Pregnancy Study Groups criteria). A model based on clinical and anthropometric variables (age, previous GDM, family history of type 2 diabetes, systolic blood pressure, sum of skinfold thicknesses, waist: height and neck: thigh ratios) provided an area under the curve of 0.71 (95% CI 0.68-0.74). This increased to 0.77 (95% CI 0.73-0.80) with addition of candidate biomarkers (random glucose, haemoglobin A1c (HbA1c), fructosamine, adiponectin, sex hormone binding globulin, triglycerides), but was not improved by addition of NMR metabolites (0.77; 95% CI 0.74-0.81). Clinically translatable models for GDM prediction including readily measurable variables e.g. mid-arm circumference, age, systolic blood pressure, HbA1c and adiponectin are described. Using a >= 35% risk threshold, all models identified a group of high risk obese women of whom approximately 50% (positive predictive value) later developed GDM, with a negative predictive value of 80%. Tools for early pregnancy identification of obese women at risk of GDM are described which could enable targeted interventions for GDM prevention in women who will benefit the most.
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