## GH-METHODS

Math-Physical Medicine

### NO. 078

Using GH-Method: Math-Physical Medicine to study the relationship between Metabolism and Glucose

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

Introduction
This paper describes the investigation results of a strong relationship existing between the overall metabolic condition and glucose specifically.  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
The author applies the MPM approach to define a general mathematical model of metabolism.  He applied energy theory from physics to understand and interpret the so-called metabolism of the human body.  In general, energy has three processing portions, i.e. energy infusion (mathematical input), energy transmission (mathematical system of interaction and transfer of internal energy), and energy consumption (mathematical output).

Initially, he selected a total of 10 categories, including six inputs (food, water, sleep, stress, exercise, routine life pattern) and four outputs (weight, glucose, blood pressure, lipid) with a total of ~500 elements.  For example, the second metabolic category is “glucose” with a specific definition of:

M2=((1 FPG + 3 PPG)/4)/120 where FPG is fasting plasma glucose, PPG is postprandial plasma glucose, and 120 mg/dL of glucose level is the 100% perfect condition.

He then applied the “topology” concept from mathematics and “finite element method” from engineering to develop an approximated model of energy transmission system (internal energy interaction and transfer system), which covers the 10 categories above and 500 elements of metabolism.  When one element of a category changes its “state”, then a score will be applied, calculated, and entered into this metabolism model and a summarized Metabolism Index (MI) will be calculated to describe this person’s overall health status at that moment.  The 90-days moving average of MI is defined as General Health Status Unit (GHSU).  This metabolic mathematical system is an organic (material), dynamic (time dependent), and highly nonlinear system.  As a result, the author must apply some linearized approximation methods in order to develop a simpler yet accurate and rapid calculation for this complicated metabolic disorder for a patient’s real-life application.

It should be noted that 73.5% of MI is the dividing line between unhealthy and healthy states.  M2 (glucose) is just one of 10 categories of MI (metabolism) that uses 120 mg/dL as the dividing line between under control and out-of-control diabetes.

Both time-series analysis and spatial analysis of applied mathematics (statistics) were utilized in this investigation.

Results
The collected data covers a period of 7.5 yeas (1/1/2012-5/7/2019) with ~1.5 million data.  This period has the following three distinctive sub-periods with their respective average MI and M2 values from the time-series analysis:

• From 1/1/2012 to 7/1/2014 (unhealthy sub-period with 3 medications and some degree of lifestyle management):
MI is 92%, M2 is 1.09 (131 mg/dL)
• From 7/1/2012 to 9/1/2015 (improving sub-period due to knowledge acquired via MI model development in this sub-period):
MI is 67%, M2 dropped from 131 mg/dL to 118 mg/dL
• From 9/1/2015 to 5/7/2019 (healthy sub-period with stringent lifestyle management and without any medication):
MI is 58%, M2 is 0.98 (118 mg/dL)

Furthermore, a segmented spatial analysis results (i.e. MI versus M2 without time factor) show that these three data clouds of the sub-period are shaped as a “skewed cucumber”.  It means that when M2 is moving towards a higher value, then MI also moves similarly and vice versa.  This finding is quite interesting since M2 is only one of the four outputs of the metabolism system which are under the combined influences of all of those six inputs.  This numerical finding also provides another proof of what an important role the glucose (energy transporter) is in our human metabolism system.

Conclusion
This paper has further demonstrated the author’s belief that glucose plays a key role in metabolic disorders.  As a result, his hypertension and hyperlipidemia were greatly improved once his type 2 diabetes is under control via a stringent lifestyle management.

###### Figure 3: Time-series of Daily data and Spatial analysis of MI vs. M2

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