GH-METHODS

Math-Physical Medicine

NO. 278

Analyzing CGM sensor glucoses at 5-minute intervals using GH-Method: math-physical medicine

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

Introduction
This paper describes the research results by comparing the data from a continuous glucose monitor (CGM) sensor device collecting glucose at 5-minute and 15-minute intervals using the GH-Method: math-physical medicine approach.  The purposes of this study are to compare the measurement differences and to uncover any possible useful information due to the different time intervals of the glucose collection.

Methods
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 a CGM sensor device (Libre) on his upper arm and checked his glucose measurements ~80 times each day.  After the first bite of his meal, he measured the glucose level every 15 minutes for a total of 3-hours or 180 minutes.  He maintained the same measurement pattern during all of his awakening hours.  However, during his sleeping hours (00:00-07:00), he measured his FPG in one-hour intervals.

With his academic background in mathematics, physics, computer science, and engineering plus his working experience in the semiconductor high-tech industry, he was intrigued with the existence of high frequency glucose components.  In addition, he was interested in identifying energies associated with higher frequency glucoses which would contribute to various diabetes complications.  For example, there are 13 data-points for 15-minute PPG waveforms, while there are 37 data-points for 5-minute PPG waveforms. These additional data points (~3x) would provide more information about the higher frequency PPG components which are lower leveled PPG but occurring frequently.

Starting from 2/19/2020, he utilized hardware device based on Bluetooth technology and embedded with a customized application software to automatically transmit all of his CGM sensor glucose Libre data directly into his customized research software on his iPhone (eclaireMD software), but at a much shorter time period for each data transmission.  He decided to transmit his data at 5-minute intervals continuously throughout the day; therefore, he is able to collect ~240 glucose data per day.

He chose the past 4-months from 2/19/2020 to 6/19/2020, as the study period for his glucose situation.  The comparison study included average glucose, high glucose, low glucose, waveforms (i.e. curves), correlation coefficients (similarity of curve patterns), and ADA-defined TAR/TIR/TBR analyses.  This is his first research report on the 5-minute glucose data, where he initially focused on the most rudimentary comparisons.

Results
The top diagram of Figure 1 shows that he has an average of 259 glucose measurements per day using 5-minute intervals and an average of 85 measurements per day using 15-minute intervals.  The fact of 5-minutes’ 259 data per day (greater than 240 data per day) is due to signal transmission stability of Bluetooth technology.

The middle diagram of Figure 1 illustrates that the correlation coefficient between these two daily average glucose curves, 5-minutes and 15-minutes, is 95.4% which is extremely high.  This means that these two glucose data patterns are highly similar to each other.

The bottom diagram of Figure 1 depicts three daily average glucose curves over 121 days (4-months), 5-minute sensor, 15-minute sensor, and finger glucose readings during the recent 4 months which are listed below:

  • 5-min sensor: 118 mg/dL (108%)
  • 15-min sensor: 120 mg/dL (110%)
  • Finger glucose: 109 mg/dL (100%)

In average, the CGM sensor daily averaged glucoses are about 8% to 10% higher than the finger glucose for this 4-month period.

Figure 1: glucose counts, 5-minutes she.15-minutes, finger vs. sensor

Figure 2 shows the comparison of daily average glucose over a 24-hour period between 5-minutes and 15-minutes.  These two curves are remarkably similar, except for the 5-minutes curve has a zig-zag part in the early morning period (00:30-6:30).

Listed below are the values in mg/dL for peak glucose and average glucose of two synthesized daily glucose waveforms, which are almost equal. “Synthesized” is defined as the averaged data of 121 days.

  • 5-minutes:
    Peak daily glucose144 mg/dL and average daily glucose 119 mg/dL
  • 15-minutes:
    Peak daily glucose 143 mg/dL and average daily glucose 117 mg/dL

Figure 3 reveals the comparison of PPG over a 3-hour period between 0-minute and 180-minutes.  These two curves are also quite compatible; however, if we examine these curves closely, we will see the PPG rising portions are quite similar except for the 5-minutes’ peak glucose occurring at 45 minutes, while the 15-minutes occurring between 45-60 minutes.  It also displays the 5-minutes’ PPG descending portion have some zig-zag parts which are the synthesized results from different big glucose data.  Listed below are the mg/dL values for peak glucose and average glucose of these two synthesized PPG waveforms, which are almost equal.

  • 5-minutes:
    Peak PPG 128 mg/dL and average PPG 116 mg/dL
  • 15-minutes:</u
    Peak PPG 128 mg/dL and average PPG 117 mg/dL
Figure 2: Synthesized daily average glucose over 24-hours
(5-minutes sensor vs. 15-minutes sensor)
Figure 3: Synthesized PPG over 180-minutes
(5-minutes sensor vs. 15-minutes sensor)

Figure 4 signifies the comparison of FPG over a 7-hour sleeping period between 00:00 and 07:00.  These two curves are in compatible patterns in the form of a salad bowl; however, if we examine them closely, we will see the FPG for the 5-minutes trending down to the bottom (at 99 mg/dL) around 4:00 to 5:00 am, while the 15-minutes move downward to the bottom at 4:00 am (at 95 mg/dL).  Moreover, their starting FPG values at 00:00 midnight are different due to the 5-minute method captures more of the biochemical changes of the pre-bed glucose prior to sleep.  Listed below are the mg/dL values for the “lowest” glucose, in order to prevent the possibility of insulin shock, and the averaged glucose of two synthesized FPG waveforms, which have noticeable FPG differences between the 5-minutes and 15-minutes.

  • 5-minutes:
    Lowest FPG 99 mg/dL and average FPG 105 mg/dL
  • 15-minutes:
    Lowest FPG 95 mg/dL and average FPG 102 mg/dL
Figure 4: Synthesized FPG over 7-hours (5-minutes sensor vs. 15-minutes sensor)

Figure 5 shows two “synthesized glucose patterns comparison” between 5-min vs. 15-min for daily glucose, PPG, and FPG.  The similarity between these two glucose patterns and some of the wave characteristics mentioned above, including the zig-zag effects, are evident from this figure.  

Figure 5: Two curves comparison between 5-min vs. 15-min (daily glucose, PPG, FPG)

Conclusion
The author has read many medical papers about diabetes.  The majority of them are related to the medication effects on glucose symptoms control, not so much on investigating and understanding “glucose” itself.  This situation is similar to taming and training a horse without a good understanding of the temperament and behaviors of the animal.  Medication is like giving the horse a tranquilizer to calm it down.  Without a deep understanding of glucose behaviors, how can we truly control the root cause of diabetes disease, but only trying to manage the symptoms of hyperglycemia?

References

  1. 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).”
  2. 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).”
  3. 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).”