Nakamura Yoichi, Furukawa Shinya
Cardiovascular Medicine & Diabetology, Specified Clinic of Soyokaze CardioVascular Medicine and Diabetes Care, Matsuyama 790-0026, Ehime, Japan.
Health Services Center, Ehime University, Matsuyama 790-8577, Ehime, Japan.
World J Diabetes. 2023 Oct 15;14(10):1562-1572. doi: 10.4239/wjd.v14.i10.1562.
The continuous glucose monitoring (CGM) system has become a popular evaluation tool for glucose fluctuation, providing a detailed description of glucose change patterns. We hypothesized that glucose fluctuations may contain specific information on differences in glucose change between type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM), despite similarities in change patterns, because of different etiologies. Unlike Fourier transform, continuous wavelet transform (CWT) is able to simultaneously analyze the time and fre-quency domains of oscillating data.
To investigate whether CWT can detect glucose fluctuations in T1DM.
The 60-d and 296-d glucose fluctuation data of patients with T1DM ( = 5) and T2DM ( = 25) were evaluated respectively. Glucose data obtained every 15 min for 356 d were analyzed. Data were assessed by CWT with Morlet form ( = 7) as the mother wavelet. This methodology was employed to search for limited frequency glucose fluctuation in the daily glucose change. The frequency and enclosed area (0.02625 scalogram value) of 18 emerged signals were compared. The specificity for T1DM was evaluated through multiple regression analysis using items that demonstrated significant differences between them as explanatory variables.
The high frequency at midnight (median: 75 Hz, cycle time: 19 min) and middle frequency at noon (median: 45.5 Hz, cycle time: 32 min) were higher in T1DM T2DM (median: 73 and 44 Hz; = 0.006 and 0.005, respectively). The area of the > 100 Hz zone at midnight to forenoon was more frequent and larger in T1DM T2DM. In a day, the lower frequency zone (15-35 Hz) was more frequent and the area was larger in T2DM than in T1DM. The three-dimensional scatter diagrams, which consist of the time of day, frequency, and area of each signal after CWT, revealed that high frequency signals belonging to T1DM at midnight had a loose distribution of wave cycles that were 17-24 min. Multivariate analysis revealed that the high frequency signal at midnight could characterize T1DM (odds ratio: 1.33, 95% confidence interval: 1.08-1.62; = 0.006).
CWT might be a novel tool for differentiate glucose fluctuation of each type of diabetes mellitus using CGM data.
连续血糖监测（CGM）系统已成为评估血糖波动的常用工具，能详细描述血糖变化模式。我们推测，尽管1型糖尿病（T1DM）和2型糖尿病（T2DM）的血糖变化模式相似，但由于病因不同，血糖波动可能包含有关两者血糖变化差异的特定信息。与傅里叶变换不同，连续小波变换（CWT）能够同时分析振荡数据的时域和频域。
研究CWT能否检测T1DM患者的血糖波动。
分别评估了T1DM患者（n = 5）和T2DM患者（n = 25）60天和296天的血糖波动数据。分析了356天内每15分钟获取的血糖数据。采用以Morlet形式（n = 7）作为母小波的CWT对数据进行评估。该方法用于在每日血糖变化中寻找有限频率的血糖波动。比较了18个出现信号的频率和封闭面积（0.02625尺度图值）。通过多元回归分析，以两者之间显示出显著差异的项目作为解释变量，评估T1DM的特异性。
T1DM患者午夜高频（中位数：75 Hz，周期时间：19分钟）和中午中频（中位数：45.5 Hz，周期时间：32分钟）高于T2DM患者（中位数：73和44 Hz；P分别为0.006和0.005）。T1DM患者午夜至上午>100 Hz区域的面积更频繁且更大。在一天中，T2DM患者低频区（15 - 35 Hz）比T1DM患者更频繁且面积更大。由CWT后每个信号的时间、频率和面积组成的三维散点图显示，午夜属于T1DM的高频信号的波周期分布松散，为17 - 24分钟。多变量分析显示，午夜高频信号可表征T1DM（优势比：1.33，95%置信区间：1.08 - 1.62；P = 0.006）。
CWT可能是一种利用CGM数据区分各型糖尿病血糖波动的新工具。
