GH-METHODS

Math-Physical Medicine

NO. 087

Using GH-Method: Math-Physical Medicine to calculate individual metabolism category’s score for maintaining general health from the endocrinology viewpoint

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 (big data analytics and AI) to derive the mathematical metabolism. This study provides quantitative details of 10 metabolic categories for the metabolism index to achieve a better score for the 90-days moving average from the endocrinology viewpoint.

Key Words
Type 2 diabetes, metabolism, metabolic conditions, chronic diseases, lifestyle data, artificial intelligence, and math-physical medicine.

Introduction
This paper provides quantitative details of the 10 metabolic categories of 4 outputs and 6 inputs for the metabolism index (MI) model to achieve a better score on the general health status unit: GHSU: 90-days moving average of MI.  The results provide insightful knowledge on maintaining general health from the endocrinology viewpoint.  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.

Methods
The data collection for this analysis started from 2012 to 2015 and completed on 5/31/2019.  Approximately 1.5 million data was collected from a severe type 2 diabetes patient, who is the author himself.  The GH-Method: MPM methodology has been described in many of his previous publications.

His initial health conditions from 2010-2012 were:

1. Weight/ Waistline/ BMI: 220 lbs./ 44in./ 32 (obese)
2. Glucose/ A1C: 280mg/dL/ 10%
3. Triglycerides/ ACR: 1,161mg/dL/ 116mg/dL
4. Cardiac episodes: Five times
5. Other complications: Renal, retinal, foot ulcer, and thyroid

Results
As shown in Table 1 and Figures 2-8, here are his performance scores for the metabolism categories:

• (1) Energy infusion:
(1a) Water drinking: 5.56 bottles or 2,780 cc per day
(1b) Food & Meal Score/ Quantity/ Quality: 83%/ 86% of normal food portion/ 97% quality
(1c) Sleep score/ Sleep hours/ Wake up times: 86%/ 7.5 hours/ 1.56 times
• (2) Energy consumption
(2a) Walking steps: 16,200 per day and 4,200 per meal
(2b) Stress: Satisfaction level 97%
(2c) Daily Routine: Satisfaction level 96%

As depicted in Figure 1, due to his stringent and disciplined lifestyle management, his health conditions (metabolism outputs) are:

1. MI & GHSU: From >100% (unhealthy) down to ~60% (healthy)
2. Weight/ Waistline/ BMI: 170lbs./ 32 in./ 24.7
3. Glucose/ A1C: ~116mg/dL/ ~6.6%
4. Hypertension & Hyperlipidemia: Both are under control
5. ACR: from 116 to 8mg/g
6. Cardiac episodes & other complications: None
Figure 11: Daily Routine

Conclusion
This paper presents the summarized results of the author’s 8.5 year’s effort to control his metabolic disorders via a scientific and quantitative lifestyle management program by using the GH Method: math-physical medicine.  The comparison of health conditions at 2012 and 2019 shows significant improvements.  Using this kind of health maintenance program is extremely beneficial for controlling many endocrine diseases.

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