GH-METHODS

Math-Physical Medicine

NO. 139

Guesstimate probable partial self-recovery of pancreatic beta cells using calculations of annualized glucose data using GH-Method: math-physical medicine

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

Introduction
In this paper, the author describes his hypothesis on the probable partial self-recovery of some insulin regeneration capability of pancreatic beta cells on a type 2 diabetes (T2D) patient via his collected data of both postprandial plasma glucose (PPG) and fasting plasma glucose (FPG) during the period of 1/1/2014 to 11/23/2019.

Methods
The author has had T2D for 25 years and took various diabetes medications to control his elevated glucose levels since 1998.  For the last 20 years, he has suffered many T2D complications, including 5 cardiac episodes and renal complications, except for having a stroke.  Starting from 2013, he reduced the dosages of his three prescribed diabetes medications.  On 12/8/2015, he discontinued taking his last remaining medication, the classical metformin HCL.  In other words, his body has been free of any chemical compound from medications or insulin injections for 4 years.

Since then, he has completely relied on a stringent lifestyle management program to control his diabetes conditions.  As a result, his T2D has been under control (HbA1C ~6.5%) since 2016.

He has kept nearly 2 million data of his own medical conditions and lifestyle details.  He also developed a sophisticated computer software by using artificial intelligence to analyze, process, and manage this massive health data.

To summarize prominent findings from the glucose data analysis based on his past 4 to 5 years’ experience, he has noticed two “opposite” phenomena.  For the first observation, his peak PPG value around 60-minutes after the first bite of his meal occasionally reaches to 200-300 mg/dL when he does not follow his stringent diet and exercise rules.  This shows the existing severity of his diabetes conditions in terms of insulin resistance or lack of insulin production supply.  For the second observation, from checking his massive data since 2014, his natural health state of pancreatic beta cells seems to be recovered somewhat, even though it might be on a small scale.

Recently, he read an article online,Diabetes: Can we teach the body to heal itself? on Medical News Today, which was published on January 8, 2019.  Here is an excerpt:

A new study by researchers from the University of Bergen in Norway, Maria Cohut, Ph.D. and Luiza Ghoul, suggests that, with just a small “push,” we may be able to train the body to start producing adequate levels of insulin once more, on its own.  The researchers were able, for the first time, to uncover some of the key mechanisms that allow cells to “switch” identity, looking specifically at pancreatic alpha- and beta-cells in a mouse model.  They found that alpha-cells respond to complex signals they receive from neighboring cells in the context of beta-cell loss. Approximately 2 percent of alpha-cells can thus “reprogram” themselves and start producing insulin.  By using a compound able to influence cell signaling in the pancreas, the researchers could boost the number of insulin-making cells by 5 percent.

The author’s research methodology is “math-physical medicine”, not “biochemical medicine” as used in the above article.  Math-physical medicine has three key steps of research methodology.  He starts with observing some prominent physical phenomena from his collected biomedical big data.  He then forms a reasonable hypothesis from his specific observations.  Finally, he derives a few mathematical equations, if possible, to verify his hypothesis.  Once verified, he can then use the same equations or formulas to reproduce (or predict) the results.

In his presented papers No.103, 108,  and 133, he described his hypothesis and math-physical models to guesstimate the pancreatic beta cells health state by using a data range including FPG (lower bound), pre-periods glucose (medium), and PPG baseline glucose (upper bound).  In this particular paper no.138, he will utilize the “baseline of annualized average PPG” over a 6-year period (2014 – 2019) to prove the probable partial recovery of insulin regeneration capability of pancreas beta cells.  functions either through converting alpha cells into beta cells (as the quoted article) or self-repairing some of damaged beta cells (as author’s own hypothesis).

Results
The author has collected a total of 8,646 Finger PPG data from 1/1/2012 through 11/23/2019.  Furthermore, he has collected 20,448 Sensor PPG data from 5/5/2018 through 11/23/2019.

First step, he calculated annualized Finger PPG value for 2014-2019 (Figure 1 & 2). It is obvious that the Finger PPG data are declining year after year.  This Finger PPG serves as one of medium levels of beta cells relative health state.  The other approach is to use the averaged value of both pre-meals and pre-bed glucose data as another medium level.  However, the author could not recreate his “pre-periods” data prior to 5/5/2018 because the continuous glucose monitoring devices (CGM) are only recently available to general public of diabetes patients.

Figure 1: Annualized Finger PPG (1/1/2014 - 11/23/2019)
Figure 2: Annualized PPG and changes (2014 - 2019)

Second step, as shown in Figure 3, he has synthesized all of his Sensor PPG data (~80 times per day and 12 times per meal) into one PPG waveform between 0-minute (open) and 180-minutes (close).  He named it the “OHCA – open, high, close, average” Model of PPG.  He then calculated to get a ratio of 1.13 (i.e. 13 % higher) between the higher PPG baseline connecting open PPG and close PPG vs. the lower Finger PPG.  He used this ratio to recreate his PPG baseline values based on his collected annual Finger PPG data (Figure 4).  These annual PPG baseline data are served as the upper bound of beta cells relative health state.

It should be noted that, as shown in Figure 5, the annualized Finger FPG data (in his paper no.133) are served as the lower bound of beta cells relative health state.

Figure 5 also shows the range of pancreas beta cells health state (the lower level the better health state, as “glucoses”).

It is the combination of these three sets of annualized data:

  • Finger FPG (lower bound),
  • Finger PPG (medium),
  • Sensor PPG baseline (upper bound)
Figure 3: Synthesized Sensor PPG waveform between 0-minute and 180-minutes (5/5/2018-11/23/2019)
Figure 4: Research conclusion of pancreatic beta cells partial self-recovery rate using PPG baseline data only (2014-2019)
Figure 5: Research conclusion of pancreatic beta cells partial self-recovery rate using FPG and PPG baseline data (2014-2019)

In conclusion, during the period of 2014 through 2019, the Finger FPG value decrease at a linear speed of 2.3% per year and the PPG baseline value decrease at a linear rate of 3.2% per year.  These two reduction rates of glucose values could be interpreted as the direct outcome of the pancreatic beta cells partial “self-recovery” of insulin generation capability.  Furthermore, these two percentages are closely related to the above quoted article result that 2% of alpha cells “reprogram” themselves and start producing insulin.

It is interesting to further examine the 2019 PPG data comparing against a “normal and heathy” person’s “standard” PPG waveform.  The author’s open (0-minute) and close (180-minutes) baseline level of  131 mg/dL is 48 mg/dL or 37% higher than the normal standard PPG baseline level of 83 mg/dL.  This higher amount is due to both his damaged beta cells condition and “left-over” effect from fruits/snacks intake between meals.  However, his peak PPG level (60-minutes) of 146 mg/dL is 12 mg/dL or ~8% lower than the standard normal PPG peak level.  This is due to his stringent lifestyle management, including both carbs/sugar intake and post-meal walking steps.

Conclusions
The author observed improvement in his diabetes conditions after following a stringent lifestyle management since 2014.  From examining his own glucose data in 2018 including the existing vulnerable conditions of his “damaged” beta cells due to his high carbs/sugar intake, he hypothesized that beta cells are still able to “repair” themselves to a certain degree.  This “dual-phenomena” can be observed with his higher open and close PPG values and his ultra-high PPG values when he violated his own strict control rules of diet and exercise during the same period of pancreatic beta cells partial recovery.

The author decided to work off his research and write a few articles to encourage other medical scientists to conduct similar work, even though they may use different research methods, to further explore this subject of “probable pancreatic beta cell’s self-recovery”.

Figure 6: Comparison between Synthesized Sensor PPG (blue curve) vs. Healthy-State PPG (orange curve) of 2019 data

References

  1. Hsu, Gerald C. (2018). Using Math-Physical Medicine to Control T2D via Metabolism Monitoring and Glucose Predictions. Journal of Endocrinology and Diabetes, 1(1), 1-6.
  2. Hsu, Gerald C. (2018). Using Signal Processing Techniques to Predict PPG for T2D. International Journal of Diabetes & Metabolic Disorders, 3(2),1-3. 
  3. Hsu, Gerald C. (2018). Using Math-Physical Medicine and Artificial Intelligence Technology to Manage Lifestyle and Control Metabolic Conditions of T2D. International Journal of Diabetes & Its Complications, 2(3),1-7.
  4. Hsu, Gerald C. (2018, June). Using Math-Physical Medicine to Analyze Metabolism and Improve Health Conditions. Video presented at the meeting of the 3rd International Conference on Endocrinology and Metabolic Syndrome 2018, Amsterdam, Netherlands.
  5. Hsu, Gerald C. (2018). Using Math-Physical Medicine to Study the Risk Probability of having a Heart Attack or Stroke Based on Three Approaches, Medical Conditions, Lifestyle Management Details, and Metabolic Index. EC Cardiology, 5(12), 1-9.