Marling Cynthia R, Shubrook Jay H, Vernier Stanley J, Wiley Matthew T, Schwartz Frank L
School of Electrical Engineering and Computer Science, Russ College of Engineering and Technology, Ohio University, Athens, Ohio 45701, USA.
J Diabetes Sci Technol. 2011 Jul 1;5(4):871-8. doi: 10.1177/193229681100500408.
Glycemic variability contributes to oxidative stress, which has been linked to the pathogenesis of the long-term complications of diabetes. Currently, the best metric for assessing glycemic variability is mean amplitude of glycemic excursion (MAGE); however, MAGE is not in routine clinical use. A glycemic variability metric in routine clinical use could potentially be an important measure of overall glucose control and a predictor of diabetes complication risk not detected by glycosylated hemoglobin (A1C) levels. This study aimed to develop and evaluate new automated metrics of glycemic variability that could be routinely applied to continuous glucose monitoring (CGM) data to assess and enhance glucose control.
Individual 24 h CGM tracings from our clinical diabetes research database were scored for MAGE and two additional metrics designed to compensate for aspects of variability not captured by MAGE: (1) number of daily glucose fluctuations >75 mg/dl that leave the normal range (70-175 mg/dl), or excursion frequency, and (2) total daily fluctuation, or distance traveled. These scores were used to train machine learning algorithms to recognize excessive variability based on physician ratings of daily CGM charts, producing a third metric of glycemic variability: perceived variability. Finger stick A1C (average) and serum 1,5-anhydroglucitol (postprandial) levels were used as clinical markers of overall glucose control for comparison.
Mean amplitude of glycemic excursion, excursion frequency, and distance traveled did not adequately quantify the glycemic variability visualized by physicians who evaluated the daily CGM plots. A naive Bayes classifier was developed that characterizes CGM tracings based on physician interpretations of tracings. Preliminary results suggest that the number of excessively variable days, as determined by this naive Bayes classifier, may be an effective way to automatically assess glycemic variability of CGM data. This metric more closely reflects 90-day changes in serum 1,5-anhydroglucitol levels than does MAGE.
We have developed a new automated metric to assess overall glycemic variability in people with diabetes using CGM, which could easily be incorporated into commercially available CGM software. Additional work to validate and refine this metric is underway. Future studies are planned to correlate the metric with both urinary 8-iso-prostaglandin F2 alpha excretion and serum 1,5-anhydroglucitol levels to see how well it identifies patients with high glycemic variability and increased markers of oxidative stress to assess risk for long-term complications of diabetes.
血糖变异性会导致氧化应激，而氧化应激与糖尿病长期并发症的发病机制有关。目前，评估血糖变异性的最佳指标是血糖波动幅度均值（MAGE）；然而，MAGE并未在常规临床中使用。一种常规临床使用的血糖变异性指标可能是评估总体血糖控制的重要指标，也是糖化血红蛋白（A1C）水平未检测到的糖尿病并发症风险的预测指标。本研究旨在开发并评估新的血糖变异性自动指标，这些指标可常规应用于连续血糖监测（CGM）数据，以评估和加强血糖控制。
从我们的临床糖尿病研究数据库中获取个体24小时的CGM记录，对其进行MAGE评分以及另外两个指标的评分，这两个指标旨在弥补MAGE未涵盖的变异性方面：（1）每日血糖波动幅度>75mg/dl且超出正常范围（70 - 175mg/dl）的次数，即波动频率，以及（2）每日总波动幅度，即波动距离。这些分数用于训练机器学习算法，以根据医生对每日CGM图表的评级来识别过度变异性，从而产生血糖变异性的第三个指标：感知变异性。将指尖血糖A1C（平均值）和血清1,5 - 脱水葡萄糖醇（餐后）水平用作总体血糖控制的临床标志物进行比较。
血糖波动幅度均值、波动频率和波动距离未能充分量化评估每日CGM图表的医生所观察到的血糖变异性。开发了一种朴素贝叶斯分类器，该分类器根据医生对记录的解读来表征CGM记录。初步结果表明，由该朴素贝叶斯分类器确定的过度变异性天数可能是自动评估CGM数据血糖变异性的有效方法。该指标比MAGE更能准确反映血清1,5 - 脱水葡萄糖醇水平在90天内的变化。
我们开发了一种新的自动指标，用于使用CGM评估糖尿病患者的总体血糖变异性，该指标可轻松整合到市售的CGM软件中。正在进行进一步的工作以验证和完善该指标。计划开展未来研究，将该指标与尿8 - 异前列腺素F2α排泄量和血清1,5 - 脱水葡萄糖醇水平进行关联，以了解其在识别高血糖变异性患者以及氧化应激标志物增加的患者方面的效果，从而评估糖尿病长期并发症的风险。
