Center for Diabetes Technology, School of Medicine, University of Virginia, Charlottesville, Virginia, USA.
Diabetes Technol Ther. 2022 Nov;24(11):797-804. doi: 10.1089/dia.2022.0104. Epub 2022 Jul 5.
With the proliferation of continuous glucose monitoring (CGM), a number of metrics were developed to assess quality of glycemic control. Many of them are highly correlated. Thus, we aim to identify the principal dimensions of glycemic control-a minimal set of metrics, necessary and sufficient for comprehensive assessment of diabetes management. Seventy-five thousand five hundred sixty-three 2-week CGM profiles recorded in six studies by 790 individuals with type 1 or type 2 diabetes were used to compute mean glucose (MG), percentage time >180 mg/dL (TAR), >250 mg/dL (TAR2), <70 mg/dL (TBR), <54 mg/dL (TBR2), and coefficient of variation (CV). The true dimensionality of the glycemic-metric space was identified in a training set (53,380 profiles) and validated in an independent test set (22,183 profiles). Correlation analysis identified two blocks of metrics-(MG, TAR, TAR2) and (TBR, TBR2, CV)-each with high internal correlation, but insignificant between-block correlation, suggesting that the true dimensionality of the glycemic-metric space is 2. Principal component analysis confirmed two essential metrics quantifying exposure to hyperglycemia (i.e., treatment efficacy) and risk for hypoglycemia (i.e., treatment safety), and explaining ∼90% of the variance in the training and test data. Two essential metrics, treatment efficacy and treatment safety, are necessary and sufficient to characterize glycemic control in diabetes. Thus, quantitatively, diabetes treatment optimization is reduced to a two-dimensional problem, meaning that minimizing both exposure to hyperglycemia and risk for hypoglycemia will lead to improvement in any other metric of glycemic control.
随着连续血糖监测 (CGM) 的普及,出现了许多评估血糖控制质量的指标。其中许多指标高度相关。因此,我们旨在确定血糖控制的主要维度——即全面评估糖尿病管理所需的最小指标集。
使用来自 6 项研究的 790 名 1 型或 2 型糖尿病患者的 75563 份为期 2 周的 CGM 图谱,计算平均血糖 (MG)、>180mg/dL 的时间百分比 (TAR)、>250mg/dL 的时间百分比 (TAR2)、<70mg/dL 的时间百分比 (TBR)、<54mg/dL 的时间百分比 (TBR2)和变异系数 (CV)。在训练集 (53380 个图谱) 中确定血糖指标空间的真实维度,并在独立测试集 (22183 个图谱) 中验证。相关性分析确定了两个指标块-(MG、TAR、TAR2)和 (TBR、TBR2、CV),每个块内相关性高,但块间相关性无统计学意义,表明血糖指标空间的真实维度为 2。主成分分析证实了两个基本指标,即暴露于高血糖的程度 (即治疗效果) 和低血糖的风险 (即治疗安全性),并解释了训练和测试数据中约 90%的变异。
两个基本指标,治疗效果和治疗安全性,是描述糖尿病患者血糖控制的必要和充分条件。因此,从数量上看,糖尿病治疗的优化可以简化为二维问题,即最大限度地减少高血糖暴露和低血糖风险将导致任何其他血糖控制指标的改善。