GH-METHODS

Math-Physical Medicine

NO. 071

Using GH-Method: Math-Physical Medicine to develop a simple yet practical guideline for carbs/sugar intake amount and post-meal walking steps in order to control Postprandial Plasma Glucose

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

Abstract
The author developed his GH-Method: math-physical medicine (MPM) by applying mathematics, physics, engineering modeling, and computer science such as big data analytics and artificial intelligence to derive the mathematical metabolism model and three prediction tools for weight, FPG, and PPG with >30 input elements. This research paper is based on a collection of big data of 1,449 days reflecting the 4,398 meals of carbs/sugar intake amount and post-meal walking steps in order to control postprandial plasma glucose (PPG).

Keywords
Type 2 diabetes, carbs/sugar intake, post-meal walking, postprandial plasma glucose, artificial intelligence, and math-physical medicine.

Introduction
This paper is based on big data collected for a period of 1,449 days from 5/1/2015 to 4/19/2019 with 4,398 meals of carbs/sugar intake amount (grams per meal & per day), post/meal walking steps, and measured postprandial plasma glucose (PPG) in mg/dL.  The dataset is provided by the author, who uses his own type 2 diabetes (T2D) metabolic conditions control, as a case study via the “math-physical medicine” approach of a non-traditional methodology in medical research.

Math-physical medicine (MPM) starts with the observation of the human body’s physical phenomena (not biological or chemical characteristics), collecting elements of the disease related data (preferring big data), utilizing applicable engineering modeling techniques, developing appropriate mathematical equations (not just statistical analysis), and finally predicting the direction of the development and control mechanism of the disease.

Method
The author selected the following carbs/sugar amount definition as his “low-carb diet”:

Less than 17 grams per meal and less than 9 grams for his daily snacks or fruits intake in between meals, so that his daily upper limit of carbs and sugar intake amount is 60 grams.

The chosen target is consistent with the general consensus of low-carb diet definition (<60 grams of carbs/sugar intake per day).  By using 60 grams as the dividing line, he further separated his data into two groups as low-carb diet vs. high-carb diet.

Furthermore, he made the following two definitions for his post-meal walking exercise:

  1. 1,000 to 2,000 steps as his minimum requirement
  2. Above 4,000 steps as his optimal exercise target

He then divided his data into the following five levels of walking intensity:

  • 0 – 1,000 steps
  • 1,000 – 2,000 steps
  • 2,000 – 3,000 steps
  • 3,000 – 4,000 steps
  • 4,000 – 10,000 steps
Figure: 1 A “pseudo-linear” relationship between Walking steps and PPG level

Results

(A) Diet:
(A1) Low-carbs Diet (74%)
Carbs/sugar per meal:  9.6 grams
Carbs/sugar per day:  32 grams
Average PPG:  112 mg/dL

(A2) High-carbs Diet (26%)
Carbs/sugar per meal:  28.8 grams
Carbs/sugar per day:  96 grams
Average PPG:  134 mg/dL

(A3) Overall mixed diet (100%)
Carbs/sugar per meal:  14.4 grams
Carbs/sugar per day:  48 grams
(still under “low-carbs diet” limit)
Average PPG:  118 mg/dL

(B) Exercise:
(B1) Inactivity (10%:  0-2,000 steps):
Average PPG:  133 mg/dL

(B2) Low-intensity (16%:  2000-3,000 steps):
Average PPG:  124 mg/dL

(B3) Medium-intensity (27%:  3,000-4,000 steps):
Average PPG:  121 mg/dL

(B4) High-intensity (76%:  4,000-10,000 steps):
Contribution:  76%
Average PPG:  116 mg/dL

(B5) Overall Averaged Results (100%:  Averaged 4,214 steps)
Average PPG:  118 mg/dL

Table 1: Simple yet practical formulas to control PPG level

The relationship between PPG and diet/exercise is highly nonlinear, dynamic, and complicated.  However, these data also showed a “pseudo-linear” mathematical connection existing between both diet/exercise amounts and PPG values.  Therefore, the author attempts to develop two “linear” mathematical formulas as follows to provide a simple yet practical guideline for T2D patients to control their PPG levels.

  • Every gram of carbs/sugar intake generates ~2.5 mg/dL of PPG. Therefore, 15 grams of carbs/sugar per meal could generate 37.5 mg/dL of PPG value.
  • Every block of thousand steps of post-meal walking reduces ~5 mg/dL of PPG value.  Therefore, 4,000 steps post-meal walking could reduce ~20 mg/dL of PPG value.

These two simple guidelines helped the author to maintain his PPG level around 117 mg/dL.

Conclusion
By using the GH-Method: math-physical medicine, the author conducted segmentation analysis to investigate the impact on PPG from two different diets (low-carb diet and high-carb diet) and four different walking intensity levels.  He further developed two simple yet practical formulas for T2D patients to control their PPG levels.

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. Retrieved from http://www.kosmospublishers.com/wp-content/uploads/ 2018/06/JEAD-101-1.pdf
  2. 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.
  3. Hsu, Gerald C. (2018). Using Signal Processing Techniques to Predict PPG for T2D. International Journal of Diabetes & Metabolic Disorders, 3(2),1-3. Retrieved from https://www.opastonline.com/wp-content/uploads/2018/06/using-signal-processing-techniques-to-predict-ppg-for-t2d-ijdmd-18.pdf
  4. 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. Retrieved from http://cmepub.com/pdfs/using-mathphysical-medicine-and-artificial-intelligence-technology-to-manage-lifestyle-and-control-metabolic-conditions-of-t2d-412.pdf
  5. 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. Retrieved from https://www.opastonline.com/wp-content/uploads/2018/07/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-jcrc-2018.pdf