GH-METHODS

Math-Physical Medicine

NO. 383

A neural communication model between the brain and internal organs via postprandial plasma glucose waveforms study based on 165 liquid egg meals, 133 solid egg meals, and 30 tea only meals

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

Abstract
In this paper, the author presents a progressive research report on a 2+ year research project, from 5/5/2018 through 12/26/2020, to identify a neural communication model between the brain’s cerebral cortex and internal organs such as the stomach, liver, and pancreas.  In this report, he adds the results from 30 tea only meals.  He focused on using a two-hour long continuous glucose monitor (CGM) sensor collected postprandial plasma glucose (PPG) data from 328 meals to investigate the glucose production or release amount at different times and the PPG waveform differences among 165 liquid egg meals, 133 solid egg meals, and 30 tea only meals.  The data from the tea only meals serve as a reference baseline for this neuroscience study.

The significant PPG differences between these three meal types can be easily observed from the attached figures.  The PPG peak value differences are 24 mg/dL between solid egg and liquid egg, and 29 mg/dL between solid egg and tea only with almost identical inputs of carbs/sugar intake amounts of ~2.6 grams per meal for solid and liquid eggs, and post-meal walking of ~4,400 steps.  

The author conducted this special investigative experiment in five research phases.  All of the findings from these different phases are similar to each other, with minor deviations, but the experimental data size continuing to grow.

From a neuroscience viewpoint, he could utilize his developed GH-Method: math-physical medicine methodology (MPM) and his learned biomedical knowledge from this research work to trick” or trigger” the cerebral cortex of the brain into producing or releasing a lesser” amount of PPG via a liquified soup-based food, without altering or disturbing the required food nutritional balance.  If this idea is successful, just by changing the cooking method (meal preparation method), it can then help many type 2 diabetes (T2D) patients to lower their peak PPG values and average PPG levels without altering their food nutritional contents. Obviously, T2D patients must avoid overeating foods with high carbohydrates and sugar contents at all times.

By adding 30 experimental meals of tea only in the morning, similar to fasting, from the two conclusive PPG waveform diagrams, it is evident that the liquid eggs and tea only meals behave almost identical during the period of 0-minute to 120-minutes.  However, the tea only meals still have ~5-8 mg/dL of extra PPG than the liquid egg meals.  This shows the sensitivity of both cognition and judgement capabilities of human brain.  This observation further proves the author’s initial “gut feeling” or “hunch” that the brain considers liquid food “similar” to drinking tea or water.

Introduction
In this paper, the author presents a progressive research report on a 2+ year research project, from 5/5/2018 through 12/26/2020, to identify a neural communication model between the brain’s cerebral cortex and internal organs such as the stomach, liver, and pancreas.  In this report, he adds the results from 30 tea only meals.  He focused on using a two-hour long continuous glucose monitor (CGM) sensor collected postprandial plasma glucose (PPG) data from 328 meals to investigate the glucose production or release amount at different times and the PPG waveform differences among 165 liquid egg meals, 133 solid egg meals, and 30 tea only meals.  The data from the tea only meals serve as a reference baseline for this neuroscience study.

Method
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. Brief history of this study
Since 1/1/2012, the author developed a research-oriented software on his iPhone to collect all of his diabetes-related medical data and lifestyle details.  In addition, he started to collect his glucose data using a CGM sensor device from 5/5/2018.  He accumulated approximately 96 glucose data per day with 13 glucose data per meal over a 3-hour time span or 9 glucose data per meal over a 2-hour time span.

In mid-2018, he noticed some differences on his PPG results from his variety of meals.  Therefore, he started to use his own body to conduct the necessary experiments by eating certain meals with a simple food ingredient with low carbs/sugar amount and prepared them with different cooking methods.  For example, he started to eat “pan-fried egg” meal with one single large egg since 5/11/2018, “egg drop soup” meal with one single large egg since 12/30/2019, and “hard broiled egg” meal with one single large egg since 6/28/2020.  On 9/25/2019, he launched a special research project regarding various meal preparation methods or food cooking method using one large egg as the major food nutritional ingredient, resulting in quite different PPG results.  In the second half of 2020, he noticed the PPG waveform patterns and peak PPG values were fairly close to each other between the pan-fried egg meals and hard broiled egg meals; therefore, he combined these two types of meals into one group of “solid egg” meals.

He described the results from Phase 1 of his research work, from 9/25/2019 to 2/11/2020, by utilizing the collected data from the 30 egg drop soup meals and 30 pan-fried egg meals (Reference 2).

For Phase 2 of his research work, from 9/25/2019 to 5/29/2020, he further collected an additional 39 liquid meals and 36 solid meals with identical food material and cooking method (Reference 6).  During this phase, he accumulated a total of 69 liquid meals (egg drop soup) and 66 solid meals (pan-fried egg).  He also enhanced his software program to present these collected glucose data using the Candlestick K-Line chart (References 3 and 4).  The chart clearly reflects five key PPG values at different time instants between liquid food and solid food.

For Phase 3, from 9/25/2019 through 8/13/2020, he accumulated additional data from a total of 95 liquid egg meals (egg drop soup) and 110 solid egg meals (68 pan-fried eggs and 42 hardboiled eggs).  In comparison to the Phase 2 data, he collected an additional 26 liquid meals and 44 solid meals over 76 days.

In Phase 4, from 8/14/2020 through 11/25/2020, he has accumulated additional data to reach a total of 159 liquid egg meals and 126 solid egg meals.  In comparison to the Phase 3 data, he collected an additional 64 liquid meals and 16 solid meals over 100 days.  During Phase 4, he started to add in 17 tea only meals to serve as the baseline of PPG comparison.

In Phase 5, from 11/26/2020 through 12/26/2020, he has accumulated additional data to reach a total of 165 liquid egg meals, 133 solid egg meals, and 30 tea only meals.  In comparison to the Phase 4 data, he collected an additional 6 liquid meals, 7 solid meals, and 13 tea only meals.

During these five phases of the food and glucose experiments, he has focused on investigating the relationships among different food inputs, such as meal nutritional contents, cooking methods, physical states (i.e., liquid vs. solid), peak and average PPG values, and PPG waveforms.  When he observed the different physical phenomenon of glucose waves from liquid and solid meals, he wondered why these two different cooking methods would end up with such large and obvious differences of the two PPG waveforms, regardless of having an identical food nutritional ingredients and exercise input amounts.  Most of his medical associates or colleagues, in the fields of internal medicine and food nutrition, have mentioned that the important relationship between PPG level and food nutritional components, particularly carbohydrates and sugar intake amounts along with the intensity and duration of exercise influence PPG values.  Therefore, he decided to conduct an experiment of eating the same food ingredients but with two different cooking or preparation methods.  It should be noted that he has kept the intensity and duration of his post-meal walking at the same level of over 4,000 steps.

By 2/11/2020 with ~30 meals in each liquid and solid category, he already discovered the vast differences in PPG values existing between these two types of meals.  At that moment, he came up with a preliminary neural communication hypothesis of his own regarding the brain and certain internal organs communication via the nervous system.  He then decided to extend his experiments in order to verify this neural communication model with the path of sending messages from the stomach to the brain and then forwarding the brains feedback message, or marching order, to the liver and pancreas.  This communication system carries the PPG production or release amount information at different time instance.  He also decided to use a larger experimental database with some mathematical tools for his follow-up analysis.

On May 27, 2020, David Templeton, a writer for the Pittsburgh Post-Gazette presented an excellent medical discovery report.  On May 29, 2020, the author read this report regarding this specific research work performed at the University of Pittsburgh (Reference 5).

Here is an excerpt:

Published May 18th of 2020 in the Proceedings of the National Academy of Sciences, an important world first, a study co-authored by Dr. Levinthal and Dr. Peter Strick, both from the Pitt School of Medicine, has explained what parts of the brains cerebral cortex influence stomach function and how it can impact health.  Dr. Peter Strick is a world leader in establishing evidence that internal organs are strongly modulated at the highest levels by the cerebral cortex.  Its been traditional in biology and medicine that the internal organs are self-regulatory through the autonomic nervous system, largely independent of higher brain regions. Dr. Stricks previous research, for instance, also showed that similar areas of the cerebral cortex also control kidney and adrenal function. That course of research now could extend to the heart, liver and pancreas to discover more about how the brain coordinates control of internal organs,” said Mr. Sterling who holds a Ph.D. in neuroscience. When it comes to trusting your gut, it already is well-established that the stomach and gut send ascending” signals to the brain in a way that influences brain function.  But the study has found that the central nervous system both influences and is influenced by the gastrointestinal system.”  What people havent understood to date, Dr. Strick said, is that the brain also has “descending influences on the stomach” with various parts of the brain involved in that signaling, including those areas that control movement and emotions. Those areas control the stomach “as directly as cortical control of movement.  These are not trivial influences.”

This published report has described exactly what the author, for almost two years, guessed and felt about the neural communication model between the brain and other internal organs.  Although the author, by training, is a mathematician, software expertise, physicist, and mechanical/structural engineer, he is not a medical doctor, neuroscientist, biologist, or chemist.  However, during his research work in this area since 9/15/2019, the author has discovered and proven his gut-feeling” or hunch” regarding the existence of these ascending” messages from the stomach to the brain regarding food entry, and descending” messages from the brain to the liver and pancreas regarding glucose production or release amount.  He also verified these physical observations through his careful developed mathematical models and numerical examinations. Specifically speaking, his biomedical interpretation of certain physical phenomena would be verified through a few carefully established mathematical models, and then confirmed his hypothesis or theory using artificial intelligence (AI) techniques, big data analytics, or a straight forward numerical analysis.

In 2019, he was cautious in using the words, such as hypotheses, guess, and might be, to describe his gut-feelings generated from his observed findings, but now he has found the supporting academic and biomedical proof from other neurosciences experts (Reference 5).

His friend, Dr. Nelson Hendler, a nerve pain specialist and a neuroscientist from Johns Hopkins University provided the following comments:

Embryologically, the brain and gut arise from the same neuroectodermal tissue. This is why the gut has many of the same neurosynaptic transmitters as the brain does. In fact, many pharmaceutical houses use gut preparation to predict how a drug may work in the brain. Chief differences among these receptors is various types of serotonin.”

Since the author has already published a few articles on this subject in early 2020, by using various food and glucose data, he will forgo the detailed explanations and similar conclusions based on this relatively “larger” size of data from egg meals experiments, including the data from drinking tea only. Although there are 30 tea only meals included in this experiment, its results have demonstrated that the tea only meals can serve as a useful baseline reference to support the relative positions of both solid and liquid eggs waveforms in his ~2 years long project of food and glucose neuroscience experiment.

The reason he chose tea as his baseline are twofold.  First, his hypothesis is that the stomach sends a food-arrival message and its associated physical state of either solid or liquid to the brain.  The brain then considers the liquid egg drop soup being “similar” to drinking water or tea.  Second, he has conducted some experiments on drinking coffee only, both regular and decaffeinated.  However, his exploration has found that the regular coffee produces an extremely high level of PPG which would damage his health.  The decaffeinated coffee provides somewhat better PPG results, but they are still much higher than liquid eggs maybe due to the added cream.

Starting from 11/8/2020, he initiated his experiments by adding in “tea only” and will be presented in the following Results section.

Results
Figure 1 shows that one large egg contains mainly proteins (6.3g) and fat (5g) with a small amount of carbohydrates (0.38g) and sugar (0.38g).  It should be noted that he occasionally takes two eggs or adds chopped spring onions in his pan-fried egg for flavor, or a small amount of seaweed in his egg drop soup for iodine intake.

Figure 2 depicts the data table used in this analysis.  It contains more useful numerical information compared to the thousand words description in this paper.  This figure also shows the PPG data difference between PPG at every 15 minutes time interval versus PPG at time instant of 0-minute (start PPG).  In this way, we can examine the “net” increase between PPG at the peak time (usually at 45-minutes or 60-minutes) and PPG at start time of 0-minute.

Figure 1: Nutrition ingredients of one large egg
Figure 2: Data table of this study

Figure 3 displays a bar chart of the averaged sensor collected PPG (within 120 minutes) versus Finger piercing PPG of solid eggs, liquid eggs, and tea only meals from 5/5/2018 to 12/26/2020.

Figure 3: Finger PPG versus Sensor PPG of three meal types

Figure 4 illustrates three Candlestick K-Line diagrams of solid eggs, liquid eggs, and tea only meals from 5/5/2018 to 12/26/2020. 

Figure 4: Summary information and Candlestick (K-Line) of meals

Figure 5 reveals the direct comparison of the PPG waveforms (upper diagram) and the PPG differences (lower diagram) of solid eggs, liquid eggs, and tea only meals.  The PPG difference is defined as the PPG values at each 15-minutes interval minus the PPG value at the starting moment of 0-minute. 

The following table lists a few key data of the PPG values in the form of solid eggs, liquid eggs, and tea:  

  • Starting at 0-min: (124, 106, 99)
  • Peak at 45/60 min: (135, 111, 106)
  • PPG at 120-min: (125, 111, 103)
  • Average CGM PPG: (129, 110, 103)
  • Finger pierced PPG: (112, 105, 101)

At 45-minutes or 60-minutes after the first bite of meal, it appears that the PPG reaches its peak.  At the peak PPG position, they have differences of 24 mg/dL between solid and liquid and 29 mg/dL between solid and tea.  Even when using the PPG data differences, they have differences of 6 mg/dL between solid and liquid and 5 mg/dL between solid and tea.

Figure 5: Comparison of sensor measured PPG and PPG difference versus time at 0-min (start PPG)

Discussion
With the same food ingredient, why do they have different PPG values?
The food’s physical appearances have the same nutritional ingredient inputs; however, their different cooking methods or meal preparation methods result in different physical states, liquid or solid.  Maybe the message (or signal) ascending from the stomach to the cerebral cortex is not the detailed food ingredients, but rather the message of food arrival and its physical state.  Therefore, the brain misinterprets soup as an equivalent to a cup of tea or water and then the brain descends a message to the liver to produce or release a lesser amount of glucose. However, in this report, the author still notices the “smaller” difference between liquid eggs waveform and tea only waveform due to brain’s strong sensitivity.  In case of hyperglycemia, the brain then instructs the pancreas to produce or release insulin to regulate the glucose amount.  

Another point is, during the period of 5/5/2018 to 12/26/2020, his diabetes conditions were already under control without taking any medication.  This means that the reactions from the organ results are strictly internal biological outcomes by his stringent lifestyle management program and without any external medication chemical intervention. When the medication arrives in the body, its strong chemical power would create a series of biological chain reactions and then alter the symptoms appearing in the body.

The author applied an approach of “decision making via elimination” which he learned from quantitative analysis of business situations in his MBA studies.  When the author could not locate a satisfactory explanation from his academic sources and professional experience of either food nutrition or clinical internal medicine, he started to focus on the only remaining choice, the “unknown reason”.  He then delve deeper into the cause of this problem: “the creation of glucose”.  He realized that glucose is not directly converted from food nutritional ingredients. Instead, the glucose was directly produced by the liver or released from liver or muscle.  Of course, the human body and all of its internal organs, including the stomach, liver, and pancreas are dependent on the food nutrition supply for their needed “nutrition and energy” to function.

After eliminating the possibility of influences from both food nutrition and internal medicine, his puzzle has ended up with the only remaining choice of the “unknown reason”.  He then thought about his very first physics course at the first year of his junior high school, “the three states of matter are the three distinct physical forms that matter can take in most environments: solid, liquid, and gas.”  That was how he came up with his first hypothesis that the glucose difference is probably due to the input of this physical state of his consumed food, such as liquid or solid, that is decided by his brain.

Furthermore, the author has learned three basic facts from his past 10+ years of medical research work.  First, 70% of our daily energy intake are consumed by our brain and nervous system.  Second, the brain is the only internal organ which has the power of cognition, judgement, information processing, decision making, and marching order issuance.  Third, all of the internal organs work closely together but under the orders from a single command center, which is the brain.

The brain is indeed similar to a CPU of a computer.

Based on the above acquired biomedical knowledge, the author further developed his second hypothesis.  When one particular food type enters the gastrointestinal system, the stomach will immediately send a message (or a signal) to inform the brain about the food arrival and its physical state.  After receiving this input signal from the stomach, the brain will then start to process the incoming information, make proper judgements, and then issues its feedback message (descending marching order) to the liver regarding how much glucose amount should be produced or released at what time instant, as well as within what time frame to reach the peak of glucose.  At the same time, the brain will also inform the pancreas regarding how much insulin should be produced or released when an excessive amount of glucose has been produced or released by the liver.  However, for severe diabetes patients whose pancreatic beta cells were damaged to a certain degree, each patient’s insulin capabilities and qualities (i.e., production quantity and insulin resistance) will not be the same to influence the final PPG output reading. The magnitude of FPG is a reliable source to indicate a patient’s relative health state of pancreatic beta cells since it lacks both food (energy infuse) and exercise (energy consumption).

These two particular hypotheses support the author’s view on how his neural communication model between the cerebral cortex of the brain and internal organs, specifically the stomach, liver, and pancreas affect the PPG production after the first bite of meal.

Perhaps someone could argue that the difference in PPG readings may also be affected by the absorption factors e.g., the different absorption rates of chyme.  Chyme is a semiliquid digested food that passes from the stomach to the small intestine, consisting of gastric juices and some leftover food.  In theory, chyme from solid meals is relatively dense and may take more time passing through the absorptive surface area of the small intestine, while chyme from liquid meals is mostly liquid form and may pass through the absorptive surface more quickly.  However, the author is not convinced about the absorption speed of chyme affecting the timing and magnitude of the peak PPG.  In his findings during these 4 phases of experiments, he found that all of these peak PPG values occurred approximately 45 to 60 minutes for liquid meals, solid meals, and even for tea only meals.  Besides, the chyme interpretation cannot explain why their peak PPG magnitudes have such a significant difference.  Therefore, the gastrointestinal system’s absorption speed cannot support his observed physical phenomena.

Conclusions
The significant PPG differences between these three meal types can be easily observed from the attached figures.  The PPG peak value differences are 24 mg/dL between solid egg and liquid egg, and 29 mg/dL between solid egg and tea only with almost identical inputs of carbs/sugar intake amounts of ~2.6 grams per meal for solid and liquid eggs, and post-meal walking of ~4,400 steps.  

The author conducted this special investigative experiment in five research phases.  All of the findings from these different phases are similar to each other, with minor deviations, but the experimental data size continuing to grow.

From a neuroscience viewpoint, he could utilize his developed GH-Method: math-physical medicine methodology (MPM) and his learned biomedical knowledge from this research work to trick” or trigger” the cerebral cortex of the brain into producing or releasing a lesser” amount of PPG via a liquified soup-based food, without altering or disturbing the required food nutritional balance.  If this idea is successful, just by changing the cooking method (meal preparation method), it can then help many type 2 diabetes (T2D) patients to lower their peak PPG values and average PPG levels without altering their food nutritional contents. Obviously, T2D patients must avoid overeating foods with high carbohydrates and sugar contents at all times.

By adding 30 experimental meals of tea only in the morning, similar to fasting, from the two conclusive PPG waveform diagrams, it is evident that the liquid eggs and tea only meals behave almost identical during the period of 0-minute to 120-minutes.  However, the tea only meals still have ~5-8 mg/dL of extra PPG than the liquid egg meals.  This shows the sensitivity of both cognition and judgement capabilities of human brain.  This observation further proves the author’s initial “gut feeling” or “hunch” that the brain considers liquid food “similar” to drinking tea or water.

References

  1. Hsu, Gerald C., eclaireMD Foundation, USA, No. 310: “Biomedical research methodology based on GH-Method: math-physical medicine”
  2. Hsu, Gerald C., eclaireMD Foundation, USA, 9/25/2019 – 2/11/2020, No. 229: “Hypothesis on glucose production communication model between the brain and internal organs via investigating the PPG values of pan-fried solid egg meal vs. egg drop liquid soup meal”
  3. Hsu, Gerald C., eclaireMD Foundation, USA, No. 76: “Using Candlestick Charting Techniques to Investigate Glucose Behaviors (GH-Method: Math-Physical Medicine)”
  4. Hsu, Gerald C., eclaireMD Foundation, USA, No. 261: “Comparison study of PPG characteristics from candlestick model using GH-Method: Math-Physical Medicine”
  5. Templeton, David, Pittsburgh Post-Gazette, May 27, 2020: “Pitt study shows brain and stomach connections are a two-way street” dtempleton@post-gazette.com https://www.post-gazette.com/news/health/2020/05/27/Peter-Strick-David-Levinthal-Pittsburgh-School-of-Medicine-PNAS-cerebral-cortex-microbiome-stomach/stories/202005190088.
  6. Hsu, Gerald C., eclaireMD Foundation, USA, No. 266: “Physical evidence of neural communication among brain, stomach, and liver via PPG waveform differences between liquid food and solid food”
  7. Hsu, Gerald C., eclaireMD Foundation, USA, No. 311: neural communication model between brain and internal organs via postprandial plasma glucose waveforms study based on 95 liquid egg meals and 110 solid egg meals”
  8. Hsu, Gerald C., eclaireMD Foundation, USA, No. 366: “A neural communication model between the brain and internal organs via postprandial plasma glucose waveforms study based on 159 liquid egg meals, 126 solid egg meals, and 17 tea only meals”