Published online Oct 15, 2023. doi: 10.4239/wjd.v14.i10.1562
Peer-review started: July 2, 2023
First decision: August 4, 2023
Revised: August 16, 2023
Accepted: September 8, 2023
Article in press: September 8, 2023
Published online: October 15, 2023
Processing time: 98 Days and 23 Hours
Recently, the continuous glucose monitoring (CGM) system was readily accepted in the clinical setting. Although that system provides details of the glucose fluctuation that occur during a day, occasionally, a lot of vague data might confuse the interpretation of a glucose shift. Continuous wavelet transform (CWT) is a novel approach for analyzing oscillating data in the case of clinical field. That methodology is able to analyze time domain and frequency domain simultaneously, although Fourier transforms are limited to the analysis of frequency domain.
When the glucose change during a day can replace a waveform, the glucose fluctuation includes some waveform in the glucose change. We hypothesized the specific waveform of type 1 diabetes mellitus (T1DM) might be present because glucose change pattern might be different from T2DM due to a different etiology. The CWT is an available method to explore the target substance into the objects through analyzing oscillating data.
The present study evaluated 60-d glucose fluctuation data obtained from T1DM patients (n = 5) and 296-d data from T2DM patients (n = 25).
The data obtained every 15 min from a flash glucose monitoring system during 14 d were converted through the CWT process. In the present study, Morlet form (n = 7) was employed as the mother wavelet. The produced scalogram matrix by CWT was converted to the contour diagram. Through this process, the waveform obtained from CGM divided 18 segment signals, that is, 3 super-high frequency (> 100 Hz) zones, 3 high frequency (60-85 Hz) zones, 5 middle frequency (35-55 Hz) zones, and 7 low frequency (15-35 Hz) zones. The frequency and an enclosed area at 0.02625 scalogram value obtained from those emerged signals were compared between the T1DM and T2DM groups at 18 segments. To identify the specificity of T1DM, a statistical approach was applied. The explanatory variables of a logistic regression analysis model were the nominated items, which were significantly different between groups.
In the T1DM group, super-high frequency signals at midnight and forenoon emerged more frequently. On the other hand, the prevalence rate of low frequency signals in a day in the T2DM group was increased. The high frequency signal at night and middle frequency signal also emerged frequently in the T2DM group. The frequency of the high frequency signal at midnight and the middle frequency signal at noon in the T1DM group were higher than those of the T2DM group. The areas of low frequent zone in a day in the T2DM group were significantly higher than those of the T1DM group. In multivariate analysis, some data were excluded because of the variance inflation factor and a large 95% confidence interval (CI). Finally, the fine waveform presented in the high frequency signal zone at midnight showed the characteristic wave pattern of T1DM (odds ratio = 1.33, 95%CI: 1.08-1.62; P = 0.006).
Through the contour diagram after CWT processing, the fine waveform indicating a time cycle of 17-24 min at midnight had characterized the glucose fluctuation of T1DM. However, the low frequency signals emerged frequently in T2DM in 1 d.
Confirming the accuracy of present study required a lot of data to be obtained from both groups. If an artificial intelligence including deep learning is available in this analyzing system, it will obtain the results rapidly and correctly because this manual process takes a lot of time, even though it is a 1 d data calculation. Furthermore, this novel approach will be available to research the relationship between the diabetic complications and any specific waveform and might select medications according to the patients’ conditions to decease any diabetic complications.