Glycemic Observation Using A1C for Gestational Diabetes Diagnosis

摘要 孕妇在怀孕24-28周时普遍接受妊娠期糖尿病(GDM)筛查，因为 关于高血糖和不良妊娠结局之间公认的联系。在过去的十年里， 血红蛋白A1C(A1C)是衡量糖化血红蛋白在红细胞(RBCs)中的百分比，具有 改变了怀孕期间糖尿病的诊断。虽然A1C已经使糖尿病诊断现代化 未怀孕的人，孕妇继续使用麻烦的口服被诊断为妊娠期糖尿病 葡萄糖耐量试验(OGTT)，需要禁食和多次定时抽血，但存在问题 具有个体内的可重复性。尽管A1C具有潜在的优势，但它尚未被用于筛查 妊娠期糖尿病是因为它受到与妊娠相关的RBC动力学变化的影响，使简单的A1C为基础 妊娠期血糖的推论不可靠。我们和其他人已经证明了怀孕是如何中断的 与妊娠相关的红细胞动力学变化导致的A1C与血糖之间的密切关系。在……里面 在之前的工作中，我们还成功地使用机械建模来调整非血糖变化的糖化血红蛋白 在怀孕之外。这项建议的目的是用糖化血红蛋白观察妊娠期糖尿病的血糖 诊断(GO A1C GDM)，是通过调整以下因素来优化A1C检测妊娠期高血糖的能力 妊娠期红细胞动力学变化影响糖化血红蛋白与血糖的关系。我们将利用精准的 连续血糖监测(CGM)的纵向血糖测量和严格确定 2150名孕妇服用降糖药后与高血糖相关的不良结局 母亲和后代的观察和代谢结果研究(GO MOMS)以实现这一目标。在……里面 Go妈妈们，参与者将在4点测量糖化血红蛋白，并接受为期10天的CGM监测 怀孕期间的时间点。我们的建议，Go A1C GDM，增加了一系列血液学测量(CBCs， 网织红细胞计数和铁蛋白)，并使用机械建模来改善基于A1C的 妊娠期血糖测定。在目标1中，我们将针对典型的RBC妊娠变化调整A1C 动力学(GA调整后的A1C)。在目标2中，我们将使用血液学测量对A1C进行个性化调整 捕捉个体特有的RBC动力学的妊娠变化(CBC调整的A1C)。在《目标3》中，我们将 测试经红细胞动力学调整的糖化血红蛋白预测高血糖相关不良结局的能力。我们 我将把这种预测能力与传统的基于OGTT的GDM诊断进行比较。建议数 研究有可能极大地简化我们诊断妊娠期糖尿病的方法，在 对整个产科人群进行精准的糖尿病筛查。

Abstract Pregnant women are universally screened for gestational diabetes (GDM) at 24-28 weeks gestation because of the well-established link between hyperglycemia and adverse pregnancy outcomes. In the past decade, hemoglobin A1c (A1C), which measures the percentage of glycated hemoglobin in red blood cells (RBCs), has transformed the diagnosis of diabetes outside of pregnancy. While A1C has modernized diabetes diagnosis in non-pregnant individuals, pregnant women continue to be diagnosed with GDM using cumbersome oral glucose tolerance tests (OGTTs), which require fasting and multiple timed blood draws, and have problems with intra-individual reproducibility. Despite its potential advantages, A1C has not been adopted to screen for GDM because it is affected by pregnancy-related changes in RBC kinetics, rendering simple A1C-based inferences of glycemia during gestation unreliable. We and others have demonstrated how pregnancy disrupts the strong relationship between A1C and glycemia due to pregnancy-related changes in RBC kinetics. In previous work, we have also successfully used mechanistic modeling to adjust A1Cs for non-glycemic variation outside of pregnancy. The goal of this proposal, Glycemic Observation Using A1C for Gestational Diabetes Diagnosis (GO A1C GDM), is to optimize A1C’s ability to detect hyperglycemia in pregnancy by adjusting for gestational changes in RBC kinetics that affect A1C’s relationship with glycemia. We will leverage accurate longitudinal glycemic measurements from continuous glucose monitoring (CGM) and rigorous ascertainment of hyperglycemia-associated adverse outcomes in 2150 pregnant women participating in the Glycemic Observation and Metabolic Outcomes in Mothers and Offspring study (GO MOMs) to accomplish this goal. In GO MOMs, participants will have A1Cs measured and undergo serial 10-day periods of CGM monitoring at 4 time points across pregnancy. Our proposal, GO A1C GDM, adds serial hematologic measurements (CBCs, reticulocyte counts, and ferritin) across gestation and employs mechanistic modeling to improve A1C-based glycemia estimation during pregnancy. In Aim 1, we will adjust A1C for typical gestational changes in RBC kinetics (GA-adjusted A1C). In Aim 2, we will personalize A1C adjustments, using hematologic measurements to capture gestational changes in RBC kinetics specific to individuals (CBC-adjusted A1C). In Aim 3, we will test the ability of A1Cs adjusted for RBC kinetics to predict hyperglycemia-associated adverse outcomes. We will compare this predictive ability to that of traditional OGTT-based GDM diagnosis. The proposed investigations have potential to greatly simplify the method by which we diagnose GDM, delivering advances in precision diabetes screening to the entire obstetric population.