Glycemic Observation Using A1C for Gestational Diabetes Diagnosis

摘要 孕妇普遍筛查妊娠糖尿病（GDM）在24-28周妊娠，因为 高血糖症和不良妊娠结局之间的明确联系。在过去的十年里， 血红蛋白A1 c（A1 C），测量红细胞（RBC）中糖化血红蛋白的百分比， 改变了妊娠期以外的糖尿病诊断。虽然A1 C使糖尿病诊断现代化， 对于非妊娠个体，孕妇继续使用繁琐的口服给药方法诊断为GDM。 葡萄糖耐量试验（OGTT），需要空腹和多次定时抽血， 个体内的可重复性。尽管A1 C具有潜在的优势，但它尚未被用于筛查 GDM，因为它受妊娠相关的红细胞动力学变化的影响， 妊娠期流产的推断不可靠。我们和其他人已经证明了怀孕是如何破坏 A1 C和RBC动力学中妊娠相关变化导致的A1 C和RBC之间的强相关性。在 在以前的工作中，我们也成功地使用了机制模型来调整A1 C的非血糖变化 除了怀孕。本提案的目的是，使用糖化血红蛋白观察妊娠期糖尿病 诊断（GO A1 C GDM）是通过调整以下因素来优化A1 C检测妊娠高血糖症的能力： 妊娠期红细胞动力学的变化影响A1 C与血小板的关系。我们将利用准确的 通过连续血糖监测（CGM）进行纵向血糖测量，并严格确定 2150例参与Glycoprotein的孕妇中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 GO MOM，参与者将在4时测量A1 C并接受连续10天的CGM监测 怀孕期间的时间点。我们的建议，GO A1 C GDM，增加了系列血液学测量（CBC， 网织红细胞计数和铁蛋白），并采用机制建模来改善基于A1 C的 怀孕期间的自我评估。在目标1中，我们将根据RBC的典型妊娠变化调整A1 C 动力学（GA调整的A1 C）。在目标2中，我们将使用血液学测量进行个性化A1 C调整 以捕获个体特定RBC动力学的妊娠变化（CBC调整的A1 C）。在目标3中，我们 检测经RBC动力学校正的A1 C预测高血糖相关不良结局的能力。我们 将这种预测能力与传统的基于OGTT的GDM诊断进行比较。拟议 研究有可能大大简化我们诊断GDM的方法， 对整个产科人口进行精确的糖尿病筛查。

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.