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Characteristics of glucose change in diabetes mellitus generalized through continuous wavelet transform processing: A
Yoichi Nakamura1, Shinya Furukawa2
1Cardiovascular Medicine & Diabetology, Specified Clinic of Soyokaze CardioVascular Medicine and Diabetes Care, Matsuyama 790-0026, Ehime, Japan. y13.nakamura@soyokaze-cvd.jp.
World Journal of Diabetes
|November 16, 2023
Summary
Continuous wavelet transform (CWT) can differentiate glucose fluctuations between type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM). High-frequency signals at midnight are characteristic of T1DM, offering a novel diagnostic tool for diabetes management.
Area of Science:
- Biomedical signal processing
- Diabetes mellitus research
- Endocrinology
Background:
- Continuous glucose monitoring (CGM) systems provide detailed glucose fluctuation data.
- Distinct etiologies of type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) may lead to unique glucose fluctuation patterns.
- Continuous Wavelet Transform (CWT) offers simultaneous time-frequency analysis, unlike Fourier transform.
Purpose of the Study:
- To investigate the utility of CWT in detecting and differentiating glucose fluctuations in T1DM.
- To explore if CWT can identify specific glucose fluctuation patterns distinguishing T1DM from T2DM.
Main Methods:
- Analyzed 356 days of CGM data from 5 T1DM and 25 T2DM patients.
- Applied CWT with a Morlet mother wavelet to analyze time and frequency domains of glucose fluctuations.
- Compared frequency and area of 18 emerged signals; used multiple regression for T1DM specificity.
Main Results:
- T1DM patients exhibited higher median frequencies at midnight (75 Hz) and noon (45.5 Hz) compared to T2DM (73 Hz and 44 Hz).
- The area of >100 Hz signals from midnight to forenoon was larger in T1DM, while T2DM showed larger areas in the 15-35 Hz zone.
- High-frequency signals at midnight with cycle times of 17-24 minutes were identified as characteristic of T1DM (OR: 1.33, P=0.006).
Conclusions:
- CWT is a potential novel tool for differentiating glucose fluctuations between T1DM and T2DM using CGM data.
- Specific high-frequency glucose fluctuation patterns at midnight can help characterize T1DM.
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