GH-METHODS

Math-Physical Medicine

NO. 069

Using GH-Method: Math-Physical Medicine to Conduct Segmentation Analysis to Investigate the Impact of Low-Carb and High-Carb Diets on 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 4,347 meals of carbs/sugar intake amount and postprandial plasma glucose results for low-carb and high-carb diets.

Keywords
Type 2 diabetes, low-carb diet, high-carb diet, postprandial plasma glucose, artificial intelligence, and math-physical medicine.

Introduction
This paper is based on big data collected from a period of 1,449 days from 5/1/2015 to 4/19/2019 with 4,347 meals of carbs/sugar intake amount (grams per meal & per day) and measured postprandial plasma glucose (PPG) in mg/dL.  The dataset is provided by the author, who uses his own type 2 diabetes 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
In this analysis, the author selected the following carbs/sugar amount definition as his “low-carb diet”:

Less than 18 grams per meal and less than 6 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).  He further separated his data into two groups as low-carb diet vs. high-carb diet.

Results

  • Low-carb Diet:
    Carbs/sugar per meal:  9.6 grams
    Carbs/sugar per day:  32 grams
    Average PPG:  112 mg/dL
    Contribution ratio:  74%
  • High-carb Diet:
    Carbs/sugar per meal:  28.8 grams
    Carbs/sugar per day:  96 grams
    Average PPG:  134 mg/dL
    Contribution ratio:  26%
  • Overall mixed diet:
    Carbs/sugar per meal:  14.4 grams
    Carbs/sugar per day:  48 grams
    (still under “low-carb diet”)
    Average PPG:  118 mg/dL

PPG has a nonlinear and complicated inter-relationship with its 19 influential factors. Among them, the combined contribution from both carbs/sugar intake and post-meal exercise is ~80% (carbs/sugar ~39% and exercise ~41%).

Figure 1: Low-Carb Diet & High-Carb Diet
Figure 2: Daily & Average PPG Values
Table 1: Low-carbs, High-carbs, and Daily mixed diets

Conclusion
By using the GH-Method: math-physical medicine, the author investigated the impact from both low-carb and high-carb diets on PPG.  Through this quantitative analysis, it is obvious that low-carb diet is extremely effective and also important on diabetes control.  Although this patient is strong willed and disciplined, it is revealed that he still has cravings for high-carb diets from time to time (~27%), which some of them were under non-controllable environment such as airline in-flight food.

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