GH-METHODS

Math-Physical Medicine

NO. 368

The differences in characteristics and energy levels associated with different glucose frequency components of 5-minute and 15-minute measurement time intervals from the continuous glucose monitor device using GH-Method: math-physical medicine

Corresponding Author: Gerald C. Hsu, eclaireMD Foundation, USA.

Abstract
This paper describes the observed characteristic differences between glucose results by using 5-minute (5-min) and 15-minute (15-min) time intervals of measuring glucoses with a continuous glucose monitor (CGM) sensor device during a period of 280 days, from 2/19/2020 to 11/25/2020, based on GH-Method: math-physical medicine.

The comparison includes glucose waveforms of fasting plasma glucose (FPG), postprandial plasma glucose (PPG), and daily glucose, average glucose values and deviations, energy level associated with both lower frequency glucose components and higher frequency glucose components.

By observing the glucose values, all below 120 mg/dL without medication intervention, listed on the data table (Figure 1), the author’s diabetes conditions have been well controlled during the 9-month period (280 days) regardless of different sensor measurement methods.  It should be noted that his measured glucoses in the same period using finger-piercing and test-strip are 99 mg/dL for FPG, 107 mg/dL for PPG, and 105 mg/dL for finger daily glucoses.  This finger daily glucose of 105 mg/dL is ~9% lower than the CGM sensor collected glucoses of 115 mg/dL.

The glucose differences between 5-min and 15-min using simple arithmetic and statistical methods are around 1% which is an insignificant difference; therefore, it is not worth the effort to collect 3x more data.  In other words, the 15-min time interval is sufficient for glucose collection and evaluation.

The author continues his research to pursue this investigation on energy level associated with different frequency glucose components in order to determine the glucose energy’s impact or damage on the human organs (i.e., diabetes complications).

The author is a 25-years veteran of type 2 diabetes and has read many medical papers about diabetes.  The majority of them are related to the medication effects on glucose symptoms control with minimal investigation and understanding of “glucose” itself.  This situation is similar to taming and training a horse without a good understanding of the temperament and behaviors of the animal.  Of course, medications are important for treating many diseases, including diabetes.  However, a deep and true understanding of glucose behaviors can also help us to control the root cause of diabetes instead of only managing the symptoms of hyperglycemia.

Introduction
This paper describes the observed characteristic differences between glucose results by using 5-minute (5-min) and 15-minute (15-min) time intervals of measuring glucoses with a continuous glucose monitor (CGM) sensor device during a period of 280 days, from 2/19/2020 to 11/25/2020, based on GH-Method: math-physical medicine.

The comparison includes glucose waveforms of fasting plasma glucose (FPG), postprandial plasma glucose (PPG), and daily glucose, average glucose values and deviations, energy level associated with both lower frequency glucose components and higher frequency glucose components.

Methods
1. Background
To learn more about the author’s GH-Method: math-physical medicine (MPM) methodology, readers can refer to his article to understand his developed MPM analysis method in Reference 1.

2. Case description
Since 1/1/2012, the author measured his glucose values using the finger-piercing method: once for FPG and three times for PPG each day.  On 5/5/2018, he applied the Libre Freestyle CGM sensor device on his upper arm and checked his glucose measurements every 15 minutes, a total of 91.4 times each day.  During these 280 days from 2/19/2020 to 11/25/2020, he has collected 25,292 glucose data of 15-min data.

Furthermore, starting from 2/19/2020, he used an electronic device based on Bluetooth technology and embedded with a customized application software to automatically transmit all of his CGM collected glucose data from the Libre sensor directly into his customized research software program known as the eclaireMD system, with a shorter time interval of every 5 minutes.  Therefore, he is able to collect 258.4 glucose data within 24 hours.  During these 280 days from 2/19/2020 to 11/25/2020, he has collected 72,352 glucose data of 5-min data.

In total, he has collected a total of 97,644 glucose data during these 280 days from 2/19/2020 to 11/25/2020. This big dataset of glucoses allow him not only to conduct some traditional mathematical analysis, but also to explore more and delve deeper regarding the collected glucose data and waveforms.  He has chosen the past 9-months from 2/19/2020 to 11/25/2020, as his investigation period for analyzing his glucose situation.  The comparison study included the average glucose, and glucose waveforms (glucose curves), and correlation coefficients which are the similarity of curve patterns.

The author was intrigued with the existence of “high frequency glucose components” versus “low frequency glucose components”. They are defined as the higher glucose values (higher amplitude) but occurring less frequently (lower frequency) versus lower glucose values (lower amplitude) but occurring more frequently (higher frequency).  For example, there are 13 data-points for the 15-minute PPG waveforms, while there are 37 data-points for the 5-minute PPG waveforms.  These 24 additional data points could provide more hidden information about the higher frequency PPG components.  Normally, around 60-minutes after the first bite of meal, where PPG would reach to its highest level which is a typical low-frequency with high-amplitude glucose component.  His main purpose is to investigate the different degrees of damage on the human organs by the varying energy levels associated with low-frequency but high-amplitude glucose components which damage the heart, kidney, retina, and more.

He has applied wave theory from physics and Fourier transform from mathematics to convert his time domain data and curve, e.g. glucose vs. time, into frequency domain data and curve, e.g. energy vs. frequency to conduct his above-mentioned energy study.

Results
Figure 1 shows the collected glucose data and the ratio analysis of the associated energy level comparison.  From the average daily glucose comparison, it is clear that there are not many differences (~1% of difference) between the 15-min and 5-min intervals, though the amount of collected data from 5-min is almost 3x more than the 15-min.

Figure 2 depicts the time domain glucose data for both 15-min and 5-min.  Their waveform patterns are very similar to each other and the average glucose values are very close to each other as well.

Figure 1: data table and ratio analysis
Figure 2: Daily glucose, PPG, and FPG from both 15-minutes sensor
and 5-minutes sensor

Figure 3 illustrates the waveform comparison between the 15-min and 5-min of the all-day glucose curve (24 hours), FPG (7 hours), and PPG (3 hours duration for 3 meals per day).  The different sets of the two curves are not only have similar wave patterns, but they also have extremely high correlation coefficients, within a range from 96% to 99%.  The only noticeable slightly different curve pattern is the 15-min FPG curve which has some zigzags, while in comparison with the 5-min FPG curve which is smoother due to having more data in between.    

Figure 4 reflects the energy distribution between lower-frequency with higher-glucose amplitude components and higher-frequency with lower-glucose amplitude components.  

The following table lists the summarized data ranges in Figure 4.  It is in the form of (15-min low-frequency energy & high-frequency energy; and 5-min low-frequency energy & high-frequency energy):

  • 10% low-frequency & 90% high-frequency:  (32% & 68%; 27% & 73%)
  • 20% low-frequency & 80% high-frequency:  (47% & 53%; 43% & 57%)
  • 30% low-frequency & 70% high-frequency:  (59% & 41%; 55% & 45%)

Although this table looks complex, but the main message is that a smaller group of lower-frequency glucose components, occurring less frequently but with higher magnitude of glucose value, carry a relatively larger portion of the associated energy.  This extra energy level indicates the contribution and degree of damage on human organs due to glucoses which are “complications resulted from diabetes”.  The differences of energy level between the 15-min and 5-min are bigger and more noticeable then the pure numerical comparison of average glucoses.  Therefore, the 5-min CGM sensor data are informative and useful in estimating the associated energy levels.  

Figure 3: 24-hours All-day glucose urge, 7-hours FPG curve, and 3-hours PPG curve of both 15-minutes and 5-minutes sensor
Figure 4: Energy ratio associated with low-frequency and high-frequency glucose components

Conclusion
By observing the glucose values, all below 120 mg/dL without medication intervention, listed on the data table (Figure 1), the author’s diabetes conditions have been well controlled during the 9-month period (280 days) regardless of different sensor measurement methods.  It should be noted that his measured glucoses in the same period using finger-piercing and test-strip are 99 mg/dL for FPG, 107 mg/dL for PPG, and 105 mg/dL for finger daily glucoses.  This finger daily glucose of 105 mg/dL is ~9% lower than the CGM sensor collected glucoses of 115 mg/dL.

The glucose differences between 5-min and 15-min using simple arithmetic and statistical methods are around 1% which is an insignificant difference; therefore, it is not worth the effort to collect 3x more data.  In other words, the 15-min time interval is sufficient for glucose collection and evaluation.

The author continues his research to pursue this investigation on energy level associated with different frequency glucose components in order to determine the glucose energy’s impact or damage on the human organs (i.e., diabetes complications).

The author is a 25-years veteran of type 2 diabetes and has read many medical papers about diabetes.  The majority of them are related to the medication effects on glucose symptoms control with minimal investigation and understanding of “glucose” itself.  This situation is similar to taming and training a horse without a good understanding of the temperament and behaviors of the animal.  Of course, medications are important for treating many diseases, including diabetes.  However, a deep and true understanding of glucose behaviors can also help us to control the root cause of diabetes instead of only managing the symptoms of hyperglycemia.

References

  1. Hsu, Gerald C., eclaireMD Foundation, USA, No. 310: “Biomedical research methodology based on GH-Method: math-physical medicine”
  2. Gerald C. Hsu, eclaireMD Foundation, USA. April 2020. No. 278: “Analyzing CGM sensor glucoses at 5-minute intervals using GH-Method: math-physical medicine”
  3. Gerald C. Hsu, eclaireMD Foundation, USA. April 2020. No.249: ”Predicting Finger PPG by using Sensor PPG waveform and data via regression analysis with three different methods  (GH-Method: math-physical medicine).”
  4. Gerald C. Hsu, eclaireMD Foundation, USA. December 2019. No.150: ”Applying segmentation pattern analysis to investigate postprandial plasma glucose characteristics and behaviors of the carbs/sugar intake amounts in different eating places (GH Method: math-physical medicine).”
  5. Gerald C. Hsu, eclaireMD Foundation, USA. October 2019. No. 124:  ”A case study of the impact on glucose, particularly postprandial plasma glucose based on the 14-day sensor device reliability (using GH-Method: math-physical medicine).”
  6. Hsu, Gerald C., eclaireMD Foundation, USA, No. 261: “Comparison study of PPG characteristics from candlestick model using GH-Method: Math-Physical Medicine”
  7. Hsu, Gerald C., eclaireMD Foundation, USA, No. 281: “Differences between 5-minute and 15-minute measurement time intervals of the CGM sensor glucoses device using GH-Method: math-physical medicine”
  8. Hsu, Gerald C., eclaireMD Foundation, USA, No. 240: “The analysis of the ADA defined TIR, TAR, and TBR based on 5-minute measurement intervals of the CGM sensor glucose data using GH-Method: Math-physical medicine”