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Early antenatal prediction of gestational diabetes in obese women: development of prediction tools for targeted intervention

Early antenatal prediction of gestational diabetes in obese women: development of prediction tools for targeted intervention
Early antenatal prediction of gestational diabetes in obese women: development of prediction tools for targeted intervention
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.
1932-6203
1-26
White, S.L.
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Lawlor, D.A.
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Briley, A.L.
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Godfrey, K.M.
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Nelson, S.M.
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Oteng-Ntim, E.
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Robson, S.C.
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Sattar, N.
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Seed, P.T.
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Vieria, M.C.
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Welsh, P.
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Whitworth, M.
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Poston, L.
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Pasupathy, D.
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UPBEAT Consortium
White, S.L.
47d71af0-e6f7-47f0-8504-92cf2d18442b
Lawlor, D.A.
666139b1-03b8-4d92-bee7-98b5913fcb31
Briley, A.L.
911d34e7-c314-47b5-8b67-d7a1ec10ca18
Godfrey, K.M.
0931701e-fe2c-44b5-8f0d-ec5c7477a6fd
Nelson, S.M.
607ef4e0-e87e-451f-8cb5-1bf4acaefbf6
Oteng-Ntim, E.
a65a5956-f0a2-44ba-bc32-8b083a689bf2
Robson, S.C.
34fbccfa-8f74-4e78-8d78-85e141f0ec2d
Sattar, N.
8f219920-7936-44a5-920c-0186d27d993e
Seed, P.T.
e65682d0-7ade-4de3-b8c9-16fd4bb434ba
Vieria, M.C.
3a652441-99ce-4ad0-81d8-1815df07dfee
Welsh, P.
9e3ecd34-6b37-43b1-815f-52772f2c6f4d
Whitworth, M.
96aa289a-050b-401a-a325-61861bbea367
Poston, L.
7503d11e-bc6a-4925-b622-bf2937ab1722
Pasupathy, D.
e9d043d3-ecb2-463b-8e42-c52a6dc66ac4

White, S.L., Lawlor, D.A., Briley, A.L., Godfrey, K.M., Nelson, S.M., Oteng-Ntim, E., Robson, S.C., Sattar, N., Seed, P.T., Vieria, M.C., Welsh, P., Whitworth, M., Poston, L. and Pasupathy, D. , UPBEAT Consortium (2016) Early antenatal prediction of gestational diabetes in obese women: development of prediction tools for targeted intervention. PLoS ONE, 11 (12), 1-26, [e0167846]. (doi:10.1371/journal.pone.0167846).

Record type: Article

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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Accepted/In Press date: 22 November 2016
Published date: 8 December 2016
Organisations: Faculty of Medicine

Identifiers

Local EPrints ID: 403433
URI: http://eprints.soton.ac.uk/id/eprint/403433
ISSN: 1932-6203
PURE UUID: f88fca1f-edb3-43e8-a37a-ed40d03713ac
ORCID for K.M. Godfrey: ORCID iD orcid.org/0000-0002-4643-0618

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Date deposited: 05 Dec 2016 09:25
Last modified: 07 Oct 2020 06:47

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Contributors

Author: S.L. White
Author: D.A. Lawlor
Author: A.L. Briley
Author: K.M. Godfrey ORCID iD
Author: S.M. Nelson
Author: E. Oteng-Ntim
Author: S.C. Robson
Author: N. Sattar
Author: P.T. Seed
Author: M.C. Vieria
Author: P. Welsh
Author: M. Whitworth
Author: L. Poston
Author: D. Pasupathy
Corporate Author: UPBEAT Consortium

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