TY - JOUR
T1 - Early antenatal prediction of gestational diabetes in obese women
T2 - Development of prediction tools for targeted intervention
AU - White, Sara L.
AU - Lawlor, Debbie A.
AU - Briley, Annette L.
AU - Godfrey, Keith M.
AU - Nelson, Scott M.
AU - Oteng-Ntim, Eugene
AU - Robson, Stephen C.
AU - Sattar, Naveed
AU - Seed, Paul T.
AU - Vieira, Matias C.
AU - Welsh, Paul
AU - Whitworth, Melissa
AU - Poston, Lucilla
AU - Pasupathy, Dharmintra
AU - UPBEAT Consortium
AU - Shennan, Andrew
AU - Singh, Claire
AU - Sandall, Jane
AU - Sanders, Thomas
AU - Patel, Nashita
AU - Flynn, Angela
AU - Badger, Shirlene
AU - Barr, Suzanne
AU - Holmes, Bridget
AU - Goff, Louise
AU - Hunt, Clare
AU - Filmer, Judy
AU - Fetherstone, Jeni
AU - Scholtz, Laura
AU - Tarft, Hayley
AU - Lucas, Anna
AU - Tekletdadik, Tsigerada
AU - Ricketts, Deborah
AU - Gill, Carolyn
AU - Ignatian, Alex Seroge
AU - Boylen, Catherine
AU - Adegoke, Funso
AU - Lawley, Elodie
AU - Butler, James
AU - Maitland, Rahat
AU - Khazaezadeh, Nina
AU - Demilew, Jill
AU - O'Connor, Sile
AU - Evans, Yvonne
AU - O'Donnell, Susan
AU - De La Llera, Ari
AU - Gutzwiller, Georgina
AU - Hagg, Linda
AU - Bell, Ruth
AU - Hayes, Louise
AU - Kinnunen, Tarja
AU - McParlin, Catherine
AU - Miller, Nicola
AU - Kimber, Alison
AU - Riches, Jill
AU - Allen, Carly
AU - Boag, Claire
AU - Campbell, Fiona
AU - Fenn, Andrea
AU - Ritson, Sarah
AU - Rennie, Alison
AU - Durkin, Robin
AU - Gills, Gayle
AU - Carr, Roger
AU - McSorley, Therese
AU - Alba, Hilary
AU - Paterson, Kirsteen
AU - Johnston, Janet
AU - Clements, Suzanne
AU - Fernon, Maxine
AU - Bett, Savannah
AU - Rooney, Laura
AU - Miller, Sinead
AU - Cherry, Lynn
AU - Patterson, Natalie
AU - Lee, Sarah
AU - Grimshaw, Rachel
AU - Hughes, Christine
AU - Brown, Jay
AU - Hinshaw, Kim
AU - Campbell, Gillian
AU - Knight, Joanne
AU - Farrar, Diane
AU - Jones, Vicky
AU - Butterfield, Gillian
AU - Syson, Jennifer
AU - Eadle, Jennifer
AU - Wood, Dawn
AU - Todd, Merane
AU - Khalil, Asma
AU - Brown, Deborah
AU - Fernandez, Paola
AU - Cousins, Emma
AU - Smith, Melody
AU - Wardle, Jane
AU - Croker, Helen
AU - Broomfield, Laura
AU - Robinson, Sian
AU - Canadine, Sarah
AU - Greenwood, Lynne
AU - Nelson-Piercy, Catherine
AU - Amiel, Stephanie
AU - Goldberg, Gail
AU - Rajasingham, Daghni
AU - Jackson, Penny
AU - Kenyon, Sara
AU - Catalano, Patrick
PY - 2016/12/8
Y1 - 2016/12/8
N2 - 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 1518 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.
AB - 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 1518 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.
KW - Gestational Diabetes
KW - obese women
KW - Targeted interventions
KW - Randomised controlled trial
KW - anthropometric measures
UR - https://www.scopus.com/pages/publications/85002679571
U2 - 10.1371/journal.pone.0167846
DO - 10.1371/journal.pone.0167846
M3 - Article
C2 - 27930697
AN - SCOPUS:85002679571
SN - 1932-6203
VL - 11
JO - PLoS One
JF - PLoS One
IS - 12
M1 - e0167846
ER -