Towards optimizing diabetes management and diagnosis by personalizing HbA1c targets

糖化血红蛋白(HbA1c)被用作血糖监测的基石指标，以防止 糖尿病并发症和低血糖。然而，HbA1c有一些限制，包括无法解释的种族 糖化血红蛋白(HbA1c)和平均血糖(AG)之间的差异和临床显著不匹配。在…的背景下 连续血糖监测(CGM)的局限性、方便性、成本和准确性的改进 设备引发了对HbA1c未来角色的重新审视。然而，CGM的血糖数据是1)a 2)比现场血样更具侵入性，3)不确定的并发症风险。 Bergenstal、Beck和他的同事最近提出了一个葡萄糖管理指标(GMI)，它代表了 AG作为一种熟悉的“计算”的HbA1c。然而，测量的HbA1c和HbA1c之间的差异的发生率。 GMI&>0.5个百分点是GMI研究参考人群的28%。根据测量，HbA1c是唯一 用于评估糖尿病并发症风险的前瞻性评估措施。所面临的挑战是确定 糖化血红蛋白-糖化血红蛋白失配的机制以澄清糖化血红蛋白计算的糖化血红蛋白是否为有效的AG替代物 与并发症风险密切相关。 我们假设AG和HbA1c不匹配的主要原因是正常的 红细胞寿命的变化。使用现代化的经典“黄金标准”稳定同位素(SI)方法，我们直接 在少数受试者中测量的平均RBC年龄MRBC(si-MRBC)和在 MRBC足以引起HbA1c 1-2%的点差(例如，7vs6%或8%)。但是，由于SI方法是 研究方法，希金斯开发了半机械模型，从CGM和HbA1c推导出MRBC (CGM-MRBC)或RBC/网织红细胞体积和细胞Hb分布 血细胞计数分析仪(CBC-MRBC)。在4组独立的受试者(年龄范围8-90岁)中使用CGM-MRBC， CGM-MRBC完全考虑了错配。对于此R01应用程序，在目标1中，我们建议执行SI 方法将70名受试者平均分为非裔美国人和高加索人两组。SI-MRBC将是 重复(n=10)以确认SI方法的重复性。CGM将贯穿始终。CGM和 所有受试者在初始评估后9至12个月重复HbA1c，以暂时确认 稳定的正常化。在与Bergenstal博士的合作下，我们将确定MRBC的变异是否充分考虑到 了解糖化血红蛋白和糖化血红蛋白在个体间的差异。在目标2中，我们将同时评估 希金斯的半机械化模型允许更广泛的应用。 我们希望最终证明，MRBC占HbA1c/AG和HbA1c/GMI的80% 不匹配和现成的测量方法可以对MRBC变异进行常规校正。机械性结果来自 这项研究将把几十年来与HbA1c合作的风险预测翻译成更坚实的基础 CGM提供的快速发展的临床信息的新时代。

Hemoglobin A1c (HbA1c) is used as the cornerstone indicator of blood sugar monitoring to prevent diabetic complications and hypoglycemia. However, HbA1c has limitations including unexplained racial differences, and clinically significant mismatches between HbA1c and average glucose (AG). In the context of its limitations, improvements in convenience, cost and accuracy of continuous glucose monitoring (CGM) devices have caused a reexamination of the future role for HbA1c. However, CGM glucose data is 1) a challenge to interpret, 2) more invasive than a spot blood sample, and 3) of uncertain complications risk. Bergenstal, Beck and colleagues recently proposed a Glucose Management Indicator (GMI) that represents AG as a familiar “calculated” HbA1c. However, the prevalence of discrepancies between measured HbA1c vs. GMI >0.5 percentage points is 28% of the GMI study reference population. As measured, HbA1c is the only prospectively evaluated measure for assessing diabetes complications risk. The challenge is to determine the mechanism of HbA1c-GMI mismatch in order to clarify whether GMI-calculated A1c is a valid AG surrogate linked faithfully to complication risk. We hypothesize that the predominant cause for mismatch between AG and HbA1c is normal variation in RBC lifespan. Using a modernized classic “gold standard” stable isotope (SI) method, we directly measured mean RBC age MRBC (si-MRBC) in a small number of subjects and demonstrated heterogeneity in MRBC sufficient to cause 1-2% point differences (e.g. 7 vs 6 or 8%) in HbA1c. However, as the SI method is a research technique, Higgins developed semi-mechanistic models to derive MRBC from either CGM and HbA1c (cgm-MRBC) or RBC/ reticulocyte cell volume and cell Hb distributions obtained from widely available complete blood count (CBC) analyzers (cbc-MRBC). Using cgm-MRBC in 4 independent sets of subjects (age range 8-90), cgm-MRBC fully accounted for mismatches. For this R01 application, in Aim 1, we propose to perform the SI method on 70 subjects distributed equally between African-American and Caucasian subjects. si-MRBC will be repeated (n=10) to confirm reproducibility of the SI method. CGM will be performed throughout. CGM and HbA1c will be repeated in all subjects nine to twelve months after the initial evaluation to confirm temporally stable normalization. In collaboration with Dr. Bergenstal, we will determine if variations in MRBC fully account for differences between HbA1c and GMI within individuals. In Aim 2, we will concurrently assess the validity of the Higgins semi-mechanistic models to allow more widespread application. We expect to demonstrate definitively that MRBC accounts for >80% of HbA1c/AG and HbA1c/GMI mismatches and readily available measures can routinely correct for MRBC variation. Mechanistic results from this study will put translation of risk prediction from decades of work with HbA1c on a more solid footing to this new era with rapidly evolving clinical information provided by CGM.