GH-METHODS

Math-Physical Medicine

NO. 230

Hypothesis on glucose production communication model between the brain and other internal organs, especially the stomach and liver

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

Introduction
In this paper, the author described his 5-month research study, from September of 2019 through February of 2020, to identify a specific nervous system’s communication model between the brain and certain internal organs, i.e. the stomach and liver regarding postprandial plasma glucose (PPG) production.

Method
The author has used a continuous glucose monitor (CGM) device to collect 48,740 glucose data during the past 647 days (from 5/5/2018 through 2/11/2020 at ~75 glucose measurements per day).  After the first bite of his meal, he measured his PPG data every 15 minutes for a period of 3 hours (180 minutes).  He focused on investigating the relationships between different food inputs, i.e. meal nutrition contents, cooking methods, physical phases, and different glucose outputs, i.e. PPG waveforms (post-meal glucose curves).  Based on his observation of physical phenomenon differences of glucose results, he developed a hypothesis for a communication model between the brain and certain internal organs via our nervous system.  He tried to verify his hypothesis of this nervous system communication model regarding glucose production by using his experimental data and various mathematical analysis tools.

In this particular study, he focused on the following six different breakfast categories.

First, he ate 233 McDonald’s breakfasts consisting of one egg and one sausage patty (protein and fat only).  Also, he occasionally ate half of a muffin (15g of carbohydrates) or one hash brown (16g of carbohydrates).  His average carbs/sugar amount was 10 grams.

Second, he ate 80 McDonald’s breakfasts with eggs, without any muffin or hash brown, but sometimes mixed with other ordered food.  His average carbs/sugar amount was 7.9 grams.

Third, he ate 30 pan-fried egg “solid shape” breakfasts at home without any other contents with carbs/sugar ingredients.  His average carbs/sugar amount was 0.7 gram.

Fourth, he ate 30 egg drop soup (pouring mixed eggs into boiling hot water slowly to make thin-layered egg “clouds or sheets” in the soup).  This “liquid shape” of egg drop soup has the exact same nutritional ingredients and amounts as the pan-fried solid egg breakfast at home, which contains only protein and fat without any carbs/sugar.  His averaged carbs/sugar amount was 0.8 gram.

Fifth, he ate finely diced cabbage and tomatoes boiled in hot water until it became a liquified vegetable soup. His averaged carbs/sugar amount was 10.7 grams. (It should be noted that, thus far, he has only collected 3 cabbage & tomato soup breakfasts data which is an insufficient quantity but it still can be used as a reference).

Finally, the sixth meal category of a total of 643 breakfasts consisted many different types of breakfasts (both home cook and dine-out) in more than 20 different international cities.  The averaged carbs/sugar contents of this category of these 636 breakfasts was 10.9 grams.

It should be noted that the average post-breakfast walking steps for 5 out of 6 meal categories are between 4,463 steps to 4,843 steps except for the cabbage tomato soup which has 3,933 steps.  His target post-meal walking exercise is 4,000 steps.  Since his exercise amounts are almost equal, he can mainly focus on the food’s influence on post-breakfast PPG.

Results
Figure 1 shows the detailed data of this particular analysis.  The highlight of the data is the PPG difference between “peak” PPG (45-75 minutes after the first bite) and “start” PPG (first bite of meal at 0-minute).  The reason he focuses on PPG difference is that the breakfast’s starting PPG data at 0-minute is largely dependent on the morning time’s fasting plasma glucose (FPG) which has about five primary influential factors.

Figure 1: Detailed PPG data of 6 breakfast categories

The first phenomenon is that the PPG difference for two liquid phase foods, both egg drop soup and cabbage tomato soup, are in the range of 4 to 9 mg/dL, while the other four solid phase foods are in the range of 21 to 26 mg/dL.  In addition to PPG difference between peak and start time instants, we should also pay attention to the second phenomenon which is the averaged peak PPG for two liquid phase foods that are in the range of 120 to 127 mg/dL, while the remaining four solid phase foods are in the range of 145 to 152 mg/dL.  So, what would be the reasonable explanation for these two different observed physical phenomena?

We have learned from high school physics that the three fundamental phases of matter are solid, liquid, and gas (vapor or steam).  This breakfast study involves only two phases, liquid phase for both egg drop soup and cabbage tomato soup, and solid phase for the pan-fried eggs at home, McDonald’s egg breakfasts, and all of McDonald’s breakfasts.  Of course, the category of All breakfasts include both liquid phase and solid phase.  The author’s first hypothesis is that the glucose difference is due to his food material’s physical phase, liquid or solid, which is a direct result of his cooking method.

The author has learned three basic facts from his past 9-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 our internal organs are working closely together but under the commands issued by our brain.

Based on these knowledge, the author develops his second hypothesis.  When one particular food type enters into our gastrointestinal system, our stomach will immediately send a signal to inform our brain about the food entry and its physical phase.  After receiving the input signal from the stomach, our brain will then process information, make judgements and decisions, and then issue the appropriate marching orders to our liver regarding how much glucose amount should be produced and within what time frame to reach to the glucose peak.  At the same time, the brain will also inform our pancreas regarding how much insulin should be produced when an excessive amount of glucose has been produced by our liver.  For example, the author has observed from his 8,886 food and glucose experiments during the past 8-years that our body takes about 10-15 minutes to reach the glucose peak from high sugar content liquid food intake, about 45-60 minutes to reach the glucose peak from liquid food intake, and about 60-75 minutes to reach the glucose peak from solid food intake.  However, for severe diabetes patients whose pancreatic beta cells are damaged, their insulin production capabilities will not be accurately or properly functioning as a normal person.  This particular hypothesis explains the author’s view on how the brain communicates with both the stomach and liver via our nervous system regarding PPG production during the 180 minutes period after the first bite of our food.

Figure 2 illustrates the upper three curves of 30 home pan-fried eggs, 80 McDonald’s egg breakfasts, and 233 McDonald’s breakfasts, that are clustered around the green curve of 643 All breakfasts.  However, the liquid phase of food, both 30 egg drop soup and three cabbage tomato soup, are located far below the upper glucose curves.  The reason there is a lower PPG curve associated with the liquid phase food may be due to this specific food phase “tricking” the brain into thinking a similar food entry as coffee, tea, or water, and then not raising any alert to our liver.

Figure 2: Sensor PPG curves comparison of 6 breakfasts

Figure 3 shows the detailed glucose data on two specific curves with the identical food nutritional ingredients but prepared with totally different cooking methods for eggs via liquid egg drop soup and pan-fried solid egg.  For this particular finding, the author delved deeply about our brain and nervous system’s functions using multiple categories of food and associated PPG values for this comparison study.  

Of course, the author will continue to experiment on eating more different materials soup-based meals.  He has also urged other two type-2 diabetes (T2D) patients, who are using the CGM device to conduct similar experiments in order to collect more glucose data from human beings with different DNA and diabetes disease conditions.

Figure 3: Comparison of pan-fried egg (solid) vs. egg drop soup (liquid)

Conclusions
The author was trying to find a scientific method by utilizing his math-physical medicine (MPM) and exploring from a brain and nervous system’s scientific viewpoint, to “trick” our brain into producing “less” amount of glucose after food entry without altering or suffering on our needed food nutritional ingredients and balance.  If this works, by just changing the cooking method, it can help many T2D patients to lower their peak and averaged PPG levels without reducing or altering their food nutritional contents.  Of course, T2D patients must avoid overeating food with high carbs/sugar contents all of the time.

By sharing his research findings with other fellow medical research scientists, he hopes that they can provide some explanations or further justifications to medical research community by using a different or traditional research methodology, such as biochemical medicine (BCM) approach.

References

  1. 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.
  2. 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.
  3. Hsu, Gerald C. (2018). Using Signal Processing Techniques to Predict PPG for T2D. International Journal of Diabetes & Metabolic Disorders, 3(2),1-3.
  4. Hsu, Gerald C. (2018). A Clinic Case of Using Math-Physical Medicine to Study the Probability of Having a Heart Attack or Stroke Based on Combination of Metabolic Conditions, Lifestyle, and Metabolism Index. Journal of Clinical Review & Case Reports, 3(5), 1-2.
  5. Hsu, Gerald C. (2019). Using Wave and Energy Theories on Quantitative Control of Postprandial Plasma Glucose via Optimized Combination of Food and Exercise (Math-Physical Medicine). International Journal of Research Studies in Medical and Health Sciences, 5(4), 1-7.