Continuous glucose monitoring (CGM) data has revolutionized the management of type 1 diabetes, particularly when integrated with insulin pumps to mitigate clinical events such as hypoglycemia. Recently, there has been growing interest in utilizing CGM devices in clinical studies involving healthy and diabetes populations. However, efficiently exploiting the high temporal resolution of CGM profiles remains a significant challenge. Numerous indices -- such as time-in-range metrics and glucose variability measures -- have been proposed, but evidence suggests these metrics overlook critical aspects of glucose dynamic homeostasis. As an alternative method, this paper explores the clinical value of glucodensity metrics in capturing glucose dynamics -- specifically the speed and acceleration of CGM time series -- as new biomarkers for predicting long-term glucose outcomes. Our results demonstrate significant information gains, exceeding 20\% in terms of adjusted $R^2$, in forecasting glycosylated hemoglobin (HbA1c) and fasting plasma glucose (FPG) at five and eight years from baseline AEGIS data, compared to traditional non-CGM and CGM glucose biomarkers. These findings underscore the importance of incorporating more complex CGM functional metrics, such as the glucodensity approach, to fully capture continuous glucose fluctuations across different time-scale resolutions.

翻译：连续血糖监测（CGM）数据革新了1型糖尿病的管理，尤其是在与胰岛素泵结合以减轻低血糖等临床事件方面。近来，在涉及健康人群和糖尿病人群的临床研究中，对使用CGM设备的兴趣日益增长。然而，如何有效利用CGM曲线的高时间分辨率仍然是一个重大挑战。尽管已提出众多指标——如时间在范围指标和血糖变异性测量——但有证据表明这些指标忽略了葡萄糖动态稳态的关键方面。作为一种替代方法，本文探讨了葡萄糖密度指标在捕捉葡萄糖动态——特别是CGM时间序列的速度和加速度——作为预测长期血糖结果的新型生物标志物的临床价值。我们的结果表明，与传统的非CGM和CGM血糖生物标志物相比，在利用基线AEGIS数据预测五年和八年后的糖化血红蛋白（HbA1c）和空腹血浆葡萄糖（FPG）时，信息增益显著，调整后$R^2$超过20%。这些发现强调了纳入更复杂的CGM功能指标（如葡萄糖密度方法）的重要性，以充分捕捉不同时间尺度分辨率下的连续血糖波动。