妊娠期糖尿病风险评分：一种实用的工具，可用于预测坦桑尼亚的妊娠期糖尿病风险。
Gestational diabetes mellitus risk score: A practical tool to predict gestational diabetes mellitus risk in Tanzania.
机构信息
Sokoine University of Agriculture, Department of Food Technology, Nutrition and Consumer Sciences, P.O. Box 3006, Morogoro, Tanzania.
Sokoine University of Agriculture, Department of Food Technology, Nutrition and Consumer Sciences, P.O. Box 3006, Morogoro, Tanzania.
出版信息
Diabetes Res Clin Pract. 2018 Nov;145:130-137. doi: 10.1016/j.diabres.2018.05.001. Epub 2018 May 28.
BACKGROUND
Universal screening for hyperglycemia during pregnancy may be in-practical in resource constrained countries. Therefore, the aim of this study was to develop a simple, non-invasive practical tool to predict undiagnosed Gestational diabetes mellitus (GDM) in Tanzania.
METHODS
We used cross-sectional data of 609 pregnant women, without known diabetes, collected in six health facilities from Dar es Salaam city (urban). Women underwent screening for GDM during ante-natal clinics visit. Smoking habit, alcohol consumption, pre-existing hypertension, birth weight of the previous child, high parity, gravida, previous caesarean section, age, MUAC ≥ 28 cm, previous stillbirth, haemoglobin level, gestational age (weeks), family history of type 2 diabetes, intake of sweetened drinks (soda), physical activity, vegetables and fruits consumption were considered as important predictors for GDM. Multivariate logistic regression modelling was used to create the prediction model, using a cut-off value of 2.5 to minimise the number of undiagnosed GDM (false negatives).
RESULTS
Mid-upper arm circumference (MUAC) ≥ 28 cm, previous stillbirth, and family history of type 2 diabetes were identified as significant risk factors of GDM with a sensitivity, specificity, positive predictive value, and negative predictive value of 69%, 53%, 12% and 95%, respectively. Moreover, the inclusion of these three predictors resulted in an area under the curve (AUC) of 0.64 (0.56-0.72), indicating that the current tool correctly classifies 64% of high risk individuals.
CONCLUSION
The findings of this study indicate that MUAC, previous stillbirth, and family history of type 2 diabetes significantly predict GDM development in this Tanzanian population. However, the developed non-invasive practical tool to predict undiagnosed GDM only identified 6 out of 10 individuals at risk of developing GDM. Thus, further development of the tool is warranted, for instance by testing the impact of other known risk factors such as maternal age, pre-pregnancy BMI, hypertension during or before pregnancy and pregnancy weight gain.
背景
在资源有限的国家，进行普遍的孕期高血糖筛查可能不切实际。因此，本研究旨在开发一种简单、无创的实用工具，以预测坦桑尼亚未诊断的妊娠期糖尿病（GDM）。
方法
我们使用了来自达累斯萨拉姆市（城市）的 6 个卫生机构的 609 名无已知糖尿病的孕妇的横断面数据。孕妇在产前诊所就诊时进行 GDM 筛查。吸烟习惯、饮酒、既往高血压、前次孩子的出生体重、高产次、孕周、臂围（MUAC）≥28cm、前次死产、血红蛋白水平、年龄、有 2 型糖尿病家族史、摄入含糖饮料（苏打水）、体力活动、蔬菜和水果摄入被认为是 GDM 的重要预测因素。使用多元逻辑回归模型创建预测模型，使用 2.5 的截断值来最小化未诊断 GDM（假阴性）的数量。
结果
臂围（MUAC）≥28cm、前次死产和 2 型糖尿病家族史被确定为 GDM 的显著危险因素，其灵敏度、特异性、阳性预测值和阴性预测值分别为 69%、53%、12%和 95%。此外，这三个预测因素的纳入导致曲线下面积（AUC）为 0.64（0.56-0.72），表明当前工具正确分类了 64%的高危个体。
结论
本研究结果表明，MUAC、前次死产和 2 型糖尿病家族史显著预测了坦桑尼亚人群 GDM 的发生。然而，开发的用于预测未诊断 GDM 的无创实用工具仅识别出 10 名高危个体中的 6 名。因此，需要进一步开发该工具，例如测试其他已知危险因素（如产妇年龄、孕前 BMI、孕期或孕前高血压和孕期体重增加）的影响。
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