A Comparison Study on the Postprandial Plasma Glucose Waves and Fluctuations for 65 Fasting Days Versus 65 Non-Fasting Days Applying Time Domain and Frequency Domain Analyses Along with wave Theory and Energy Theory of GH-Method: Math-Physical Medicine (No. 408)

Abstract

Gerald C Hsu

This particular investigation includes two parts utilizing the collected data from the period from 10/19/2020 to 3/1/2021. The collected data are segregated into 65 non-fasting breakfasts and 65 intermittent fasting days (~16 fasting hours in a day). The purpose is to study the effect on the author’s diabetes control due to the ongoing intermittent fasting. The first part investigates the sensor-collected postprandial plasma glucose (PPG) values and their associated relative energies. The second part focuses on PPG wave fluctuations or glycemic variability (GV), which is the maximum PPG value minus the minimum PPG value of the glucose waves. At first, the author applies the wave theory to study the mean values of PPG waves i.e., the Y-amplitude of a curve in a time domain (TD). He then utilizes the signal processing techniques and Fast Fourier Transform (FFT) software program to convert these PPG waves into a frequency domain (FD) representation. In his previous research, he has proven that the Y-axis magnitude (Y-amplitude) in FD is directly proportional to the square of the Y-axis magnitude (glucose value) in TD. In this way, he can quickly estimate the “relative” energy levels associated with his glucose levels. The relative energy are generated by glucose and carried by red blood cells circulating in the blood system. Furthermore, in order to have a better understanding of the different degree of organ impact via glucose energies, he then segregated them into the low-frequency energy segment versus the high-frequency energy segment, where he conducted their corresponding analyses. The author has drawn five major conclusions from this study: 1. Within a relative shorter intermittent fasting period, the fasting effort has no significant impacts on his weight change. Weight impact can only be observed over a longer period of time. 2. For the strength of PPG, both of its value and energy of non-fasting are 8% higher than fasting. This means that fasting effort offers a better benefit for his diabetes control. 3. For the strength of PPG value fluctuations with the maximum minus the minimum, the non-fasting’s amount of fluctuation value is about 1/3 higher than the fasting amount. However, the non-fasting’s fluctuation energy is about 2x higher than the fasting’s fluctuation energy. This means that the PPG fluctuation has a bigger impact and it also reveals more information about his diabetes control than the average glucose level, such as HbA1C. 4. Regarding the PPG fluctuation energy, non-fasting (125) is 30% higher than the PPG level’s energy of fasting (96). Furthermore, the PPG fluctuation’s energy of non-fasting (125) is more than 2x higher than the PPG fluctuation’s energy of fasting (58). These phenomena infer that glucose fluctuation is an important factor for diabetes patients to consider. 5. In terms of comparison between high-frequency (higher frequency with lower amplitude, selecting 80% of frequency components), and low-frequency (lower frequency with higher amplitude, selecting 20% of frequency components), for the PPG level, the high-frequency has about 2x more energy than the low-frequency; while for the PPG fluctuation, thehigh-frequency has about 3-4x more energy than the low-frequency. This indicates that diabetes patients must be careful with their glucose wave fluctuation.

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