葡萄糖密度：使用分布数据分析的葡萄糖曲线的新表示
Glucodensities: a new representation of glucose profiles using distributional data analysis
摘要：
生物传感器数据具有改进疾病控制和检测的潜在能力。然而，在自由生活条件下对这些数据进行分析，以目前的统计技术是不可行的。为了应对这一挑战，我们引入了一种新的生物传感器数据的功能表示，称为葡萄糖密度，以及基于它们之间的距离的数据分析框架。新的数据分析程序说明了通过应用在糖尿病与连续时间葡萄糖监测（CGM）数据。在这一领域，我们表现出显着的改进与国家的最先进的分析方法。特别地，我们的研究结果表明：i）葡萄糖密度具有非凡的临床敏感性，以捕获在糖尿病的标准临床实践中使用的典型生物标志物，ii）先前的生物标志物不能准确地预测葡萄糖密度，使得后者是更丰富的信息源，以及iii）葡萄糖密度是范围度量中的时间的自然概括，这是处理CGM数据的黄金标准。此外，新方法克服了时间范围度量的许多缺点，并提供了对评估葡萄糖代谢的更深入的了解。
英文摘要：
Biosensor data has the potential ability to improve disease control and detection. However, the analysis of these data under free-living conditions is not feasible with current statistical techniques. To address this challenge, we introduce a new functional representation of biosensor data, termed the glucodensity, together with a data analysis framework based on distances between them. The new data analysis procedure is illustrated through an application in diabetes with continuous-time glucose monitoring (CGM) data. In this domain, we show marked improvement with respect to state of the art analysis methods. In particular, our findings demonstrate that i) the glucodensity possesses an extraordinary clinical sensitivity to capture the typical biomarkers used in the standard clinical practice in diabetes, ii) previous biomarkers cannot accurately predict glucodensity, so that the latter is a richer source of information, and iii) the glucodensity is a natural generalization of the time in range metric, this being the gold standard in the handling of CGM data. Furthermore, the new method overcomes many of the drawbacks of time in range metrics, and provides deeper insight into assessing glucose metabolism.
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