Towards optimizing diabetes management and diagnosis by personalizing HbA1c targets

血红蛋白A1 c（HbA 1c）被用作血糖监测的基础指标，以防止 糖尿病并发症和低血糖症。然而，HbA 1c具有局限性，包括无法解释的种族 HbA 1c和平均血糖（AG）之间的差异和临床显著性不匹配。背景下 其局限性，便利性的改进，成本和连续葡萄糖监测（CGM）的准确性 新的设备引起了对HbA 1c未来作用的重新审视。然而，CGM葡萄糖数据是1）a 解释的挑战，2）比现场血液样本更具侵入性，以及3）不确定的并发症风险。 Bergenstal，Beck及其同事最近提出了一种葡萄糖管理指标（GMI）， AG作为熟悉的“计算的”HbA 1c。然而，HbA 1c测量值与 GMI >0.5个百分点是GMI研究参考人群的28%。HbA 1c是唯一的 前瞻性评估糖尿病并发症风险的措施。挑战在于确定 HbA 1c-GMI不匹配的机制，以阐明GMI计算的A1 c是否是有效的AG替代物 与并发症风险密切相关。 我们假设AG和HbA 1c不匹配的主要原因是正常的 RBC寿命的变化。使用现代化的经典“金标准”稳定同位素（SI）方法，我们直接 在少数受试者中测量平均RBC年龄MRBC（si-MRBC），并证明了 MRBC足以导致HbA 1c出现1-2%的点差异（例如7 vs 6或8%）。然而，由于SI方法是一种 Higgins开发了半机械模型，从CGM和HbA 1c中推导出MRBC （cgm-MRBC）或RBC/网织红细胞体积和细胞Hb分布，从广泛可用的完整 血细胞计数（CBC）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完全解释了错配。对于此R 01应用，在目标1中，我们建议执行SI 70例受试者平均分布在非洲裔美国人和白人受试者之间。SI-MRBC将在 重复（n=10），以确认SI方法的重现性。将在整个过程中进行CGM。CGM和 将在初始评价后9 - 12个月对所有受试者重复HbA 1c，以在时间上确认 稳定的正常化。在与Bergenstal博士的合作中，我们将确定MRBC的变化是否完全解释了 HbA 1c和GMI之间的差异。在目标2中，我们将同时评估 希金斯半机械模型，以允许更广泛的应用。 我们希望明确证明MRBC占HbA 1c/AG和HbA 1c/GMI的80%以上 不匹配和容易获得的测量可以常规地校正MRBC变化。机械结果来自 这项研究将把几十年来对HbA 1c研究的风险预测转化为更坚实的基础， 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.