GH-METHODS

Math-Physical Medicine

NO. 388

Investigating data of high-carbohydrate meals to study self-recovery of pancreatic beta cells using GH-Method: math-physical medicine

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

Abstract
The author studies the relationship changes between his postprandial plasma glucose (PPG) values for finger-pierced and CGM sensor collected and high carbohydrate meals (carbs/sugar intake amounts which are >30 grams or >40 grams per meal) between two time periods, from 5/5/2018 to 12/31/2019 of 1.5 years and from 1/1/2020 to 12/31/2020 of 1 year.

Based on his previous research notes, his damaged pancreatic beta cells have been self-repairing at an annual rate of 2.2% since 2014.  Therefore, under this concept, his pancreas has been repaired in an amount of 17% over the past 8 years.  This conclusion was derived via multiple angles or approaches based on fasting plasma glucose (FPG), PPG, along with triglycerides and glucose index (TyG), for verification of its accuracy.

In this particular research note, he utilized his collected PPG data and carbs/sugar intake amount to conduct a cross-check investigation for two time periods, an earlier period of 1.5 years and a later period of 1 year.  With almost constant input levels of both carbs/sugar intake amounts and post-meal walking steps, why does his PPG values have such a big difference between these two periods?  The only reasonable explanation of this improved situation is the direct results from his persistent lifestyle program after discontinuing his medications in 2015.  This research effort has proven the possibility of  “self-repair and recovery” of pancreatic beta cells regarding both quality and quantity of insulin production.  The author has written about 10 papers regarding this subject.  However, in this study, he singled out the “high-carbs” component to further validate his previous findings.  Hopefully, this series of papers regarding the self-repair and recovery of pancreatic beta cells can shed some light to type 2 diabetes patients worldwide.  At least, in his personal opinion, diabetes is no longer a “non-curable” disease!

Introduction
The author studies the relationship changes between his postprandial plasma glucose (PPG) values for finger-pierced and CGM sensor collected and high carbohydrate meals (carbs/sugar intake amounts which are >30 grams or >40 grams per meal) between two time periods, from 5/5/2018 to 12/31/2019 of 1.5 years and from 1/1/2020 to 12/31/2020 of 1 year.

Based on his previous research notes, his damaged pancreatic beta cells have been self-repairing at an annual rate of 2.2% since 2014.  Therefore, under this concept, his pancreas has been repaired in an amount of 17% over the past 8 years.  This conclusion was derived via multiple angles or approaches based on fasting plasma glucose (FPG), PPG, along with triglycerides and glucose index (TyG), for verification of its accuracy.

Methods
1. MPM Background:
To learn more about the author’s GH-Method: math-physical medicine (MPM) methodology, readers can refer to his articles to understand his developed MPM methodology in References 1 and 2.

2. Idea of this study:
When he was organizing the graphic diagrams for paper No. 386 covering the connecting biochemical medicine and math-physical medicine in controlling type 2 diabetes, he noticed some strange patterns from his December 2020 data of carbs/sugar in meals and associated PPG levels.  Within 91 meals, there were 27 meals (30%) having an average carbs/sugar intake amount of 59 grams (within a range of 40 grams to 100 grams) and their associated lower average finger PPG value of 115 mg/dL and lower average CGM Sensor PPG value of 126 mg/dL.  (The average sensor PPG is ~10% higher than the average finger PPG, which is reasonable according to his previous research).  As a matter of fact, during 2020, he noticed that his PPG values are acceptable even when he consumed starchy foods, such as pancakes, noodles, or brown rice.  This type of diet would have pushed his PPG level upwards of 180-200 mg/dL prior to 2020.

Based on his many years of health data observations and research conclusions, this strange pattern did not make sense to him.  Using his developed simple formula of predicted PPG, the predicted PPG value could be calculated using the following equation:

  • Predicted PPG
    = 0.89*FPG + (2*Carbs/sugar grams) – (5*post-meal walking steps/1000)
    = (0.89*93)+(2*59.1)-(5*5224/1000)
    = 82.77+118.2-26.12
    = 175 mg/dL

The above predicted PPG formula was developed based on his big data analytics from 2015-2019.

The measured Sensor PPG level of 126 mg/dL in December 2020 is at 72% level of the predicted PPG value of 175 mg/dL.  In other words, if the author included an “insulin resistance modification factor”, or an “IR factor” of 0.72, then the predicted PPG output becomes:

  • Predicted PPG
    = (0.89*FPG + (2*Carbs/sugar grams) – (5*post-meal walking steps/1000)) * IR Factor of 0.72 = 126 mg/dL

As an alternative, he could replace the “baseline PPG” of “0.89*FPG” by a much lower multiplier than 0.89 to achieve the same outcome.  Either way, the improved IR factor of a lower value can help to reduce the predicted PPG value.

Originally, he thought about the possibilities that he might have some input data error (more likely) or a software programming bug (less likely).  After some data checking and software code examination, he did not find any input data errors or program bugs in the software.  When he was still struggling to find the possible source of the problems, he suddenly remembered his previous research work on the self-repair of the pancreatic beta cells.  Actually, during the entire year of the COVID-19 quarantine lifestyle in 2020, he reached his optimal health conditions compared to his past 25 years.  For example, his key diabetes biomedical data in 2020 are: FPG at 101 mg/dL, PPG at 108 mg/dL, daily glucose at 106 mg/dL, CGM Sensor daily glucose at 116 mg/dL, and HbA1C at 6.2%.  By all known medical standards, his above-listed data showed that he is no longer a T2D patient anymore, at least during 2020, but he knows his body best.  The excellent data (in comparison against his 2010 data of average daily glucose at 279 mg/dL and HbA1C at 10%) was a result of his acquired medical knowledge from his medical research work and his persistent lifestyle management program.  Although his research data have shown that his pancreatic beta cells have been repaired by ~17% (in comparison with his data from December 2020, 28% of self-repair work has been achieved), but he knows that he is still a T2D patient.  When and if he loosens the tight control on both carbs/sugar intake and post-meal exercise, his PPG will rise again, although it would not be as severe as in previous years.

3. This particular study:
The above accidental discovery and hypothesis regarding pancreatic beta cells self-recovery have implored him to conduct additional detailed and thorough study of this specific subject on “high-carbohydrates meals and PPG”.  This article will discuss this research project.

The first task is to select data for two separated timespan, the first is from 5/5/2018 to 12/31/2019 and the second is from 1/1/2020 to 12/31/2020.  During these time periods, he would have measured data of carbs/sugar intake amount, post-meal walking steps, finger-piercing PPG, and CGM sensor PPG.  The reason to cut the entire CGM Sensor glucose period into two segments is due to his observation of improved glucose control along with a much less hectic and stressful traveling lifestyle in 2020 (Note: he only made one trip in early January of 2020).

The second task is to select the appropriate ranges for his “high carbohydrates” meals.  He has selected two ranges for this study, 30g to 100g and 40g to 100g from his data sets.

The third task is to compare the average PPG values and 3-hour PPG waveforms associated with the average high-carbs values within these two different time periods.

Results
Figure 1 shows the data table of this study which is followed by two different diagrams including both line-chart for PPG waveforms and bar-chart for the average values of PPG to demonstrate his conclusive results.

Figure 1: Data tables

Figure 2 displays the results of using high-carbs range of 40g to 100g.  The first analysis, using a higher-carbs amount of 40g-100g, yields the average carbs/sugar amounts of 54.7g for the first time period of 2018-2019 and 52.8g for the second period of 2020.  These two carbs/sugar values are remarkably close to each other along with post-meal walking steps above 4,000 steps each.  This means that their PPG’s inputs (influential factors) are quite similar.  However, the outputs of PPG values are quite different as shown below in the format of first period PPG, second period PPG, PPG difference, and % of PPG difference:  

  • Avg. Finger PPG: (151, 125, 26, 18%)
  • Avg. Sensor PPG: (168, 134, 34, 20%)
  • Peak Sensor PPG: (181, 140, 41, 23%)
  • Max. K-line PPG: (205, 164, 41, 20%)

It is quite obvious that the PPG differences for peak sensor PPG and maximum K-line sensor PPG are 41 mg/dL (but with different 20% and 23%); and the differences in the average finger PPG and average sensor PPG are 26 mg/dL (18%) and 34 mg/dL (20%) respectively.  

The obvious PPG difference range is within 26 mg/dL to 41 mg/dL (18% to 23%) which is significant to reveal the importance of self-recovery of pancreatic beta cells on PPG values.  It should be noted that the PPG differences are not only occurring in the average PPG values but also throughout the entire 180-minute PPG waveforms.  

Figure 2: Case of >40g of carbs/sugar

Figure 3 reflects the results in using a high-carbs range of 30g to 100g.  This second analysis, using the high-carbs as 30g-100g, yields the average carbs/sugar amounts at 43.7g for the first time period of 2018-2019 and 42.8g for the second period of 2020.  These two carbs/sugar values are again extremely close to each other along with post-meal walking steps above 4,000 steps each.  This means that their PPG’s inputs (influential factors) are again quite similar.  However, the outputs of PPG values are still quite different as shown below in the format of first period PPG, second period PPG, PPG difference, and % of PPG difference:  

  • Avg. Finger PPG: (140, 119, 21, 15%)
  • Avg. Sensor PPG: (159, 133, 26, 16%)
  • Peak Sensor PPG: (168, 141, 27, 17%)
  • Max. K-line PPG: (192, 163, 29, 15%)

It is quite obvious that the PPG differences in the peak sensor PPG is 27 mg/dL (17%) and maximum K-line sensor PPG is 29 mg/dL (15%); and the differences of average finger PPG and average sensor PPG are 21 mg/dL (15%) and 26 mg/dL (16%) respectively; and the differences of average finger PPG and average sensor PPG are 21 mg/dL (15%) and 26 mg/dL (16%), respectively.  

The PPG difference range for a high-carbs range of 30g-100g is within 21 mg/dL to 29 mg/dL (15% to 17%), which is slightly lower than the high-carbs range of 40g-100g within 26 mg/dL to 41 mg/dL (18% to 23%).  However, it is still significant to reveal the importance of self-recovery of pancreatic beta cells on PPG values.  

Figure 3: Case of >40g of carbs/sugar

Graphically, both Figures 2 and 3 clearly demonstrate the PPG differences using carbs/sugar ranges of either 40g-100g or 30g-100g.

It should be noted that the PPG differences not only occur in average PPG values, but also throughout the entire 180-minute PPG waveforms.

4. Summary:
Both analyses, using >40g carbs/sugar or >30g carbs/sugar, illustrate fairly consistent carbs and exercise inputs during 2018-2019 and 2020 time periods.  The only logical and rational explanation for the occurrence of the PPG differences is due to the “Insulin Resistance IR” factor.  Only when patients with the improved overall health state of pancreas and insulin, then their PPG values can be observed with large improvements.

The author also did a quick check of insulin resistance status by using the biomarker of TyG between these two time periods.  His average TyG value was 4.69 based on 7 input components during 2018-2019, while his TyG value in 2020 is based on a single lab-tested input of 4.52 on 10/21/2020.  Unlike the analysis using big PPG data at a collection rate of 96 input glucoses per day, he could only have his triglycerides tested once each quarter, as the best-case scenario.  Therefore, this rather smaller difference of TyG values of 4% is a result from the small triglycerides data collected from a laboratory or a hospital.

Conclusions
In this particular research note, he utilized his collected PPG data and carbs/sugar intake amount to conduct a cross-check investigation for two time periods, an earlier period of 1.5 years and a later period of 1 year.  With almost constant input levels of both carbs/sugar intake amounts and post-meal walking steps, why does his PPG values have such a big difference between these two periods?  The only reasonable explanation of this improved situation is the direct results from his persistent lifestyle program after discontinuing his medications in 2015.  This research effort has proven the possibility of  “self-repair and recovery” of pancreatic beta cells regarding both quality and quantity of insulin production.  The author has written about 10 papers regarding this subject.  However, in this study, he singled out the “high-carbs” component to further validate his previous findings.  Hopefully, this series of papers regarding the self-repair and recovery of pancreatic beta cells can shed some light to type 2 diabetes patients worldwide.  At least, in his personal opinion, diabetes is no longer a “non-curable” disease!