White Sara L, Lawlor Debbie A, Briley Annette L, Godfrey Keith M, Nelson Scott M, Oteng-Ntim Eugene, Robson Stephen C, Sattar Naveed, Seed Paul T, Vieira Matias C, Welsh Paul, Whitworth Melissa, Poston Lucilla, Pasupathy Dharmintra
Division of Women's Health, King's College London, London, United Kingdom.
MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom.
PLoS One. 2016 Dec 8;11(12):e0167846. doi: 10.1371/journal.pone.0167846. eCollection 2016.
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.
所有肥胖女性都被归类为患妊娠期糖尿病（GDM）风险同样高，然而大多数人并未患这种疾病。对未经过挑选的肥胖孕妇进行生活方式和药物干预，在预防GDM方面并未成功。我们的目标是开发一种预测工具，用于早期识别患GDM风险高的肥胖女性，以便对最有可能受益的女性进行有针对性的干预。在一项行为干预的随机对照试验UPBEAT中，对1303名肥胖孕妇在妊娠15 + 0至18 + 6周时获取了临床和人体测量数据以及非空腹血样。测量了21种与胰岛素抵抗相关的候选生物标志物以及靶向核磁共振（NMR）代谢组。使用逐步逻辑回归构建预测模型。26%的女性（n = 337）患了GDM（国际糖尿病与妊娠研究组标准）。一个基于临床和人体测量变量（年龄、既往GDM、2型糖尿病家族史、收缩压、皮褶厚度总和、腰高比和颈股比）的模型曲线下面积为0.71（95%CI 0.68 - 0.74）。加入候选生物标志物（随机血糖、糖化血红蛋白（HbA1c）、果糖胺、脂联素、性激素结合球蛋白、甘油三酯）后，该面积增加到0.77（95%CI 0.73 - 0.80），但加入NMR代谢物后并未改善（0.77；95%CI 0.74 - 0.81）。描述了用于GDM预测的临床可转化模型，包括易于测量的变量，如臂围、年龄、收缩压、HbA1c和脂联素。使用≥35%的风险阈值，所有模型都识别出一组高风险肥胖女性，其中约50%（阳性预测值）后来患了GDM，阴性预测值为80%。描述了用于早期妊娠识别有GDM风险的肥胖女性的工具，这可以对最有可能受益的女性进行预防GDM的有针对性干预。
