GH-METHODS

Math-Physical Medicine

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

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

Abstract
In this paper, the author presents a progress report on a two-year research project, from 5/5/2018 through 11/25/2020, to identify a neural communication model between the brain’s cerebral cortex and certain internal organs such as the stomach, liver, and pancreas.  In this report, he added the results from 16 tea only meals and nothing else. 

He used a continuous glucose monitor (CGM) sensor to collect postprandial plasma glucose (PPG) data to investigate the glucose production or release amount at different times and waveform differences among 159 liquid egg meals, 126 solid egg meals, and 17 tea only meals, where the data from the tea meals serve as a reference baseline of this neuroscience study.

The significant PPG differences between these two food types can be easily observed (Figure 5).  The PPG peak value differences are 24 mg/dL between solid and liquid and 31 mg/dL between solid and tea with almost identical inputs of carbs/sugar intake amounts of ~2 grams for solid and liquid eggs or zero gram for tea, and post-meal walking of ~4,400 steps.  

The author conducted this special investigative experiment in four phases.  All of the findings from the research phases are similar to each other, with minor deviations, even though his collected experimental data size nearly doubled in each phase.

From a neuroscience viewpoint, he utilized his developed math-physical medicine methodology (MPM) and his learned biomedical knowledge 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, by changing the cooking method, it can then help many type 2 diabetes (T2D) patients to lower their peak PPG and average PPG levels without reducing or changing their food nutritional contents.  Obviously, T2D patients must avoid overeating foods with high carbohydrates and sugar contents at all times.

In this study, he added 17 more experimental meals by drinking tea only in the morning which can be served as a baseline reference if this experimental project.  Through those two diagrams of  “PPG minus FPG curve” and “Incremental PPG curve”, it is evident that the liquid eggs and tea only meals behave almost identical during the period of 0-minute to 120-minutes.  This observation further proves the author’s initial “gut feeling” or “hunch” that the brain considers liquid food similar to tea or water.

Introduction
In this paper, the author presents a progress report on a two-year research project, from 5/5/2018 through 11/25/2020, to identify a neural communication model between the brain’s cerebral cortex and certain internal organs such as the stomach, liver, and pancreas.  In this report, he added the results from 16 tea only meals and nothing else. 

He used a continuous glucose monitor (CGM) sensor to collect postprandial plasma glucose (PPG) data to investigate the glucose production or release amount at different times and waveform differences among 159 liquid egg meals, 126 solid egg meals, and 17 tea only meals, where the data from the tea meals serve as a reference baseline of 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.

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 (a subtotal of 74 meals) with one single large egg on 5/11/2018, egg drop soup meal (a total of 159 meals) with one single large egg on 12/30/2019, and hard broiled egg meal (a subtotal of 52 meals) with one single large egg on 6/28/2020.  On 9/25/2019, he launched a special research project regarding various food preparation or cooking method using one large egg as the major food nutritional ingredient, resulting in different PPG levels.  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).  Through the Candlestick chart, it 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 the four 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 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 food nutritional components, particularly carbohydrates and sugar intake amount, and 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 exercise, i.e. walking, at the same level.

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 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 over a year, 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 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 wrote him 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 he has already published quite a few articles on this subject in early 2020, by using various food and glucose data, he will forgo detailed explanations and those similar conclusions based on this relatively “larger” size of data from egg meals experiments, including the data from drinking tea only.  Although there are 17 tea only meals, its results have demonstrated that the tea o Ly meals result can indeed be served as an useful baseline reference to support the relative positions of solid and liquid eggs waveforms in his year-long food and glucose experiment.

The reason he chose tea as his baseline are twofold.  First, his hypothesis was the stomach sending 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 had conducted some experiments on drinking coffee only, both regular and decaffeinated coffee.  However, his exploration has found that the regular coffee produces extremely high level of PPG.  The decaffeinated coffee provides somewhat better PPG results, but they are still much higher than liquid eggs (maybe due to added cream).

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

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 1: Nutrition ingredients of one large egg

Figure 2 depicts the data table used in this analysis.  It contains more useful numerical information compared to the words described in this paper.

Figure 3 shows Weight, finger and sensor FPG, breakfast sensor PPG, and Candlestick K-line glucose values of days associated with these 17 tea only meals (from 11/8/2020 to 11/25/2020).

Figure 2: Data table of this study
Figure 3: PPG waveform, K-line chart, Weight, FPG of Tea meals

Figure 4 reveals the direct comparison of the PPG waveforms of solid eggs, liquid eggs, and tea. The following table lists a few key data of the PPG values in the form of (solid eggs, liquid eggs, and tea):  

  • Opening at 0-min:  (124, 106, 96)
  • Peak at 45-min: (135, 111, 104)
  • 120-min: (125, 110, 101)
  • 180-min: (129, 119, 95)
  • Average PPG: (129, 112, 100)

It appears that at 45-minutes after the first bite of meal, the PPG reaches to its peak and have differences of 24 mg/dL between solid and liquid; and 31 between solid and tea.  

However, the fasting plasma glucose (FPG) values can be conveniently and accurately utilized to guesstimate the follow-on PPG’s baseline position.  Therefore, Figure 5 illustrates the three curves of PPG minus FPG during the 180-minute timespan.  In Figure 5, once again, the following table lists a few key data of (PPG -FPG) in the form of (solid eggs, liquid eggs, and tea): 

  • Opening at 0-min: (15, 7, 6)
  • Peak at 45-min: (25, 15, 11)
  • 120-min: (16, 11, 11)
  • 180-min: (9, 19, 6)

From the above table, it is clear that the solid peak is still 14 mg/dL higher than the liquid peak and 10 mg/dL higher than the tea peak; however, when we examine the waveforms, these two curves of liquid eggs and tea only are almost identical from 0-minute to 120-minutes.  The reason that the liquid eggs have a tilting upward portion in the region of 120-min to 180-min is due to its “excessive energy” or “unburned energy” after he stops his walking exercise around 120 minutes.  On the contrary, water would have no excessive energy left behind; therefore, its curve is tilting downward after 120-min.  

Figure 6 displays three curves of the Incremental PPG during the 180-minute timespan.  This Incremental PPG is an expression of the increased PPG amount influenced by both FPG and exercise which is defined as follows:

  • Incremental PPG = PPG – (0.97 * FPG) + (walking k-steps * 5)

Again, The following table lists a few key data of (Incremental PPG) in the form of (solid eggs, liquid eggs, tea).  

  • Opening at 0-min:  (38, 27, 29)
  • Peak at 45-min: (49, 32, 37)
  • 120-min: (39, 32, 33)
  • 180-min: (33, 40, 28)

From the above table, it is clear that the solid peak is still 13 mg/dL higher than the liquid peak and 17 mg/dL higher than the tea peak.  Once more, similar to Figure 5, these two curves of liquid eggs and tea only in Figure 6 are almost identical from 0-minute to 120-minute.

Figures 4, 5, and 6 have further demonstrated his earlier intuition that the brain may consider liquid egg soup as water or tea.  In physics, all of them belong to the liquid state.  Therefore, the tea only meals almost can be considered as the lower bound for this study regarding glucose amount, physical state of food, and function of brain.  

Figure 4: Comparison of three (PPG waveforms) for 126 Solid egg meals, 159 Liquid egg meals, and 17 Tea only meals
Figure 5: Comparison of three (PPG minus FPG curves) for 126 Solid egg meals, 159 Liquid egg meals, and 17 Tea only meals
Figure 6: Comparison of three (Incremental PPG curves) for 126 Solid egg meals, 159 Liquid egg meals, and 17 Tea only meals

Discussion

With the same food ingredient, why do they have different PPG values?
Both food’s physical appearances have the same nutritional ingredient inputs; however, their different cooking or preparation methods result into different physical states, liquid or solid.  Maybe the message (or signal) ascending from the stomach to the cerebral cortex is not detailed food ingredients, but rather the messages 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 for producing or releasing a lesser amount of glucose.  In case of hyperglycemia, the brain then instruct the pancreas to produce or release insulin to regulate the glucose amount. 

Another point is, during the period of 5/5/2018 to 11/25/2020, his diabetes conditions were already under control without taking any medication.  This means that these real body results are strictly the internal biological outcomes by his stringent lifestyle management program and without any external medication’s chemical intervention.  When medication arrives into our body, its strong chemical power would create a series of biological chain reactions and then alter the symptoms appeared on our body.

When the author could not locate a satisfactory explanation from academic knowledge and professional experience of either food nutrition or clinical internal medicine, he started to delve deeper into the source 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 “energy” to function.

As a result, he came up with his first hypothesis that the glucose difference is probably due to the physical state of consumed food, such as liquid or solid, that is decided by the brain.

Furthermore, the author has learned three basic facts from his past 10+ years of biomedical 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 arrival of food and its physical state.  After receiving this input signal from the stomach, the brain will then start to process 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 to 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 reading.  The magnitude of FPG is a pretty reliable source to indicate a patient’s relative health state of pancreatic beta cells.  That was why the author calculated the values of (PPG-FPG) for comparison.

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 regarding the PPG production (during the 180-minute period) after the first bite of meal.

Perhaps someone could argue that the difference in PPG readings may also be affected by the absorption factors and 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 shape 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 including tea only.  Therefore, the gastrointestinal system’s absorption speed cannot support his observed physical phenomena.

Conclusions
The significant PPG differences between these two food types can be easily observed (Figure 5).  The PPG peak value differences are 24 mg/dL between solid and liquid and 31 mg/dL between solid and tea with almost identical inputs of carbs/sugar intake amounts of ~2 grams for solid and liquid eggs or zero gram for tea, and post-meal walking of ~4,400 steps.  

The author conducted this special investigative experiment in four phases.  All of the findings from the research phases are similar to each other, with minor deviations, even though his collected experimental data size nearly doubled in each phase.

From a neuroscience viewpoint, he utilized his developed math-physical medicine methodology (MPM) and his learned biomedical knowledge 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, by changing the cooking method, it can then help many type 2 diabetes (T2D) patients to lower their peak PPG and average PPG levels without reducing or changing their food nutritional contents.  Obviously, T2D patients must avoid overeating foods with high carbohydrates and sugar contents at all times.

In this study, he added 17 more experimental meals by drinking tea only in the morning which can be served as a baseline reference if this experimental project.  Through those two diagrams of  “PPG minus FPG curve” and “Incremental PPG curve”, it is evident that the liquid eggs and tea only meals behave almost identical during the period of 0-minute to 120-minutes.  This observation further proves the author’s initial “gut feeling” or “hunch” that the brain considers liquid food similar to 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”