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White SL, Lawlor DA, Briley AL, Godfrey KM, Nelson SM, Oteng-Ntim E, Robson SC, Sattar N, Seed PT, Vieira MC, Welsh P, Whitworth M, Poston L, Pasupathy D. Early Antenatal Prediction of Gestational Diabetes in Obese Women: Development of Prediction Tools for Targeted Intervention. PLoS One 2016; 11:e0167846. [PMID: 27930697 PMCID: PMC5145208 DOI: 10.1371/journal.pone.0167846] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Accepted: 11/16/2016] [Indexed: 12/16/2022] Open
Abstract
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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Affiliation(s)
- Sara L. White
- Division of Women’s Health, King’s College London, London, United Kingdom
| | - Debbie A. Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
| | - Annette L. Briley
- Division of Women’s Health, King’s College London, London, United Kingdom
- Guy’s & St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Keith M. Godfrey
- MRC Lifecourse Epidemiology Unit and NIHR Southampton Biomedical Research Centre, University of Southampton, Southampton, United Kingdom
- University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom
| | - Scott M. Nelson
- School of Medicine, University of Glasgow, Glasgow, United Kingdom
| | - Eugene Oteng-Ntim
- Division of Women’s Health, King’s College London, London, United Kingdom
- Guy’s & St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Stephen C. Robson
- Institute of Cellular Medicine, Uterine Cell Signalling Group, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Naveed Sattar
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Paul T. Seed
- Division of Women’s Health, King’s College London, London, United Kingdom
| | - Matias C. Vieira
- Division of Women’s Health, King’s College London, London, United Kingdom
| | - Paul Welsh
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Melissa Whitworth
- Maternity Services, Central Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom
- Maternal and Fetal Health Research Centre, University of Manchester, Manchester, United Kingdom
| | - Lucilla Poston
- Division of Women’s Health, King’s College London, London, United Kingdom
| | - Dharmintra Pasupathy
- Division of Women’s Health, King’s College London, London, United Kingdom
- Guy’s & St Thomas’ NHS Foundation Trust, London, United Kingdom
- * E-mail:
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