Observational Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Diabetes. Oct 15, 2023; 14(10): 1562-1572
Published online Oct 15, 2023. doi: 10.4239/wjd.v14.i10.1562
Characteristics of glucose change in diabetes mellitus generalized through continuous wavelet transform processing: A preliminary study
Yoichi Nakamura, Shinya Furukawa
Yoichi Nakamura, Cardiovascular Medicine & Diabetology, Specified Clinic of Soyokaze CardioVascular Medicine and Diabetes Care, Matsuyama 790-0026, Ehime, Japan
Shinya Furukawa, Health Services Center, Ehime University, Matsuyama 790-8577, Ehime, Japan
Author contributions: Nakamura Y was the guarantor, designed the study, performed the acquisition, analysis and interpretation of the data, and drafted the manuscript; Furukawa S participated in the study design, data interpretation, and manuscript drafting.
Institutional review board statement: The study was reviewed and approved by the ethics committee of Ehime University (Japan).
Informed consent statement: All study participants provided informed written consent prior to study enrollment.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Yoichi Nakamura, MD, PhD, Chairman, Chief Doctor, Instructor, Cardiovascular Medicine & Diabetology, Specified Clinic of Soyokaze CardioVascular Medicine and Diabetes Care, 73-1 Muromachi, Matsuyama 790-0026, Ehime, Japan. y13.nakamura@soyokaze-cvd.jp
Received: July 2, 2023
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
Abstract
BACKGROUND

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.

AIM

To investigate whether CWT can detect glucose fluctuations in T1DM.

METHODS

The 60-d and 296-d glucose fluctuation data of patients with T1DM (n = 5) and T2DM (n = 25) were evaluated respectively. Glucose data obtained every 15 min for 356 d were analyzed. Data were assessed by CWT with Morlet form (n = 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.

RESULTS

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 vs T2DM (median: 73 and 44 Hz; P = 0.006 and 0.005, respectively). The area of the > 100 Hz zone at midnight to forenoon was more frequent and larger in T1DM vs 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; P = 0.006).

CONCLUSION

CWT might be a novel tool for differentiate glucose fluctuation of each type of diabetes mellitus using CGM data.

Keywords: Continuous glucose monitoring; Pathophysiology; Fourier, Pseudo-frequency; Contour map; Scalogram matrix

Core Tip: In the present study, we hypothesized that continuous wavelet transform differentiates glucose fluctuation according to the type of diabetes mellitus. Type 1 diabetes mellitus (T1DM) was characterized by a rapid change (cycle of a 17-24-min interval at midnight). T2DM was characterized by a broad wave (cycle of a 41-96-min interval during a day). Plotting at the three-dimensional scattergram consisting of time, frequency, and an enclosed area of interest revealed that the data of T1DM on the high frequency zone (60-85 Hz) at midnight dispersed into the allocated box, although the glucose fluctuation of T2DM was aligned regularly.