Continuous glucose monitors (CGM) record interstitial glucose levels ‘continuously’, producing a sequence of measurements for each participant (e.g. the average glucose level every 5 min over several days, both day and night). To analyse these data, researchers tend to derive summary variables such as the area under the curve (AUC), to then use in subsequent analyses. To date, a lack of consistency and transparency of precise definitions used for these summary variables has hindered interpretation, replication and comparison of results across studies. We present GLU, an open-source software package for deriving a consistent set of summary variables from CGM data. GLU performs quality control of each CGM sample (e.g. addressing missing data), derives a diverse set of summary variables (e.g. AUC and proportion of time spent in hypo-, normo- and hyper- glycaemic levels) covering six broad domains, and outputs these (with quality control information) to the user. GLU is implemented in R and is available on GitHub at https://github.com/MRCIEU/GLU. Git tag v0.2 corresponds to the version presented here.

中文翻译：

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GLU：用于分析流行病学中连续测量的葡萄糖水平的软件包。

连续血糖监测仪（CGM）“连续”记录间质葡萄糖水平，为每个参与者产生一系列测量值（例如，在几天内每5分钟平均葡萄糖水平，白天和晚上）。为了分析这些数据，研究人员倾向于得出曲线下面积 (AUC) 等汇总变量，然后用于后续分析。迄今为止，用于这些汇总变量的精确定义缺乏一致性和透明度，阻碍了研究结果的解释、复制和比较。我们提出了 GLU，这是一个开源软件包，用于从 CGM 数据中导出一组一致的汇总变量。GLU 对每个 CGM 样本进行质量控制（例如解决缺失数据），导出一组不同的汇总变量（例如 AUC 和在低血糖中花费的时间比例，正常和高血糖水平）涵盖六个广泛的领域，并将这些（带有质量控制信息）输出给用户。GLU 在 R 中实现，可在 GitHub 上的 https://github.com/MRCIEU/GLU 上获得。Git 标签 v0.2 对应于此处显示的版本。