GH-METHODS

Math-Physical Medicine

NO. 137

The impact on postprandial plasma glucose due to plantar fasciitis (GH-Method: math-physical medicine)

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

Abstract
An analysis of the impact on postprandial plasma glucose caused by plantar fasciitis using the GH-Method: math-physical medicine has been performed for three sub-periods. By utilizing the author’s own data as a case study, it illustrates how body parts affect each other and the importance on managing diabetes control.

Key words
Type 2 diabetes, postprandial glucose, plantar fasciitis, GH-Method : math-physical medicine, exercise

Introduction
In this paper, the author analyzed the impact on postprandial plasma glucose (PPG) caused by plantar fasciitis using the GH-Method: math-physical medicine for three six-month sub-periods: pre-period, during-period, and post-period.  The author uses his own type 2 diabetes (T2D) metabolic conditions control as a case study for some detailed illustration and explanation of this methodology.

Math-physical medicine 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
In 2018, the patient walked 18,458 steps daily (~7.7 miles or ~12.3 km), where each heel took 9,229 impact force per day.  During the second half of 2018 while he was traveling, he wore a pair of leisure shoes instead of ones designed with protective insoles.  Around the end of October through the middle of November 2018, his plantar fasciitis condition returned.

As a result, he could not walk as much as usual due to the sharp pain in his heels.  By mid-May of 2019, he noticed his increased values of both PPG and HbA1C.

In order to better understand the above-mentioned “higher” glucose activities, he began with a study of PPG’s formation and its physical behaviors.  By examining many graphic diagrams generated from ~20,000 data of PPG, HbA1C, carbs/sugars intake, post-meal walking exercise, and weather temperature, he could extract certain hidden evidences and possible clues.  Eventually, he has developed a model that is not only capable to describe but also predict those unique physical behaviors and special characteristics of PPG and HbA1C resulting from plantar fasciitis.

Results
Figure 1 shows the total period of 18-months (555 days) from 5/15/2018 to 11/15/2019, including the following three sub-periods:

(1) pre-plantar fasciitis:  5/15/2018-11/15/2018
(2) during-plantar fasciitis:  11/15/2018-5/15/2019
(3) post-plantar fasciitis:  5/15/2019-11/15/2019

Figure 1: Overview of 18-month period (5/15/2018 - 11/15/2019)

Figure 2 displays three sub-periods, including a special six-month cycle, the middle during-period of “plantar fasciitis” from 11/15/2018 – 5/15/2019.  All of these diagrams include Finger PPG, carbs/sugar intake amount, and daily walking steps.

The tight relationships between PPG and walking steps are obvious. Summarized observations are listed below:

  • pre-period has constant walking steps
  • during-period has declining walking steps
  • post-period has increasing walking steps.
Figure 2: Finger PPG, Carbs/Sugars Intake, and Walking of 3 sub-periods

In Figure 4, during this 6-month sub-period of plantar fasciitis, his daily walking steps have been reduced from 20,353 steps to 12,227 steps (a 40% reduction or 8,126 steps) and his post-meal walking steps have been reduced from 4,808 steps to 3,076 steps (a 36% reduction or 1,732 steps).  These walking steps reductions were due to the sharp pain on his heels which directly contributed to his larger portion of PPG increased amount from 112 mg/dL to 123 mg/dL (a 10% increase or 11 mg/dL).  

Figure 4: Changes of PPG, carbs, walking during plantar fasciitis sub-period

His average daily carbs/sugar intake amount slightly rose from 15 grams to 16 grams (a 7% or 1.5 grams) which also contributed to the smaller portion of PPG increased amount.    

It should be noted here that, during this 6-month duration of winter and spring, the average mild weather temperature around 69 degrees Fahrenheit (Figure 3) has no direct contribution on his PPG variance.

Figure 3: Weather temperature during plantar fasciitis sub-period (11/15/2018 - 5/15/2019) with an average temperature of 69 degrees Fahrenheit

In Figure 4, both increased carbs/sugar intake (~20% contribution margin) and reduced walking steps (~80% contribution margin) have a combined contribution to his daily PPG increase amount of 11 mg/dL (from 112 mg/dL to 123 mg/dL). This 11 mg/dL increased PPG amount is also quite close to the calculated 10.7 mg/dL using a “two-parameters based linear equation” as shown below:

Incremental PPG =(1 gram * 2 PPG/gram +1.732 k-steps * 5 PPG/k-steps) =10.7 mg/dL

Conclusions
From this plantar fasciitis case study, it clearly demonstrates that all body parts and internal organs are highly influenced by each other.  In addition, certain lifestyle habits, such as exercise, are extremely important for diabetes control.  However, as illustrated, over-walking may cause discomfort like plantar fasciitis and certain medical conditions may cause elevated glucose.  A detailed “local-viewed” examination of certain organs, diseases, medications, and lifestyle are absolutely necessary. Therefore, the overall understanding and a “global-view” study on the whole body, not just certain body parts or organs, are equally significant.

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.
  2. Hsu, Gerald C. (2018). Using Signal Processing Techniques to Predict PPG for T2D. International Journal of Diabetes & Metabolic Disorders, 3(2),1-3.
  3. 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.
  4. 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.
  5. 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.

Acknowledgement
First and foremost, the author wishes to express his sincere appreciation to an especially important person in his life, Professor Norman Jones at MIT and University of Liverpool.  Not only did he give him the opportunity to study for his PhD at MIT, but he also trained him extensively on how to solve difficult problems and conduct any basic scientific research with a big vision, pure heart, and integrity.

The author would also like to thank Professor James Andrews at the University of Iowa.  He helped and supported him tremendously when he first came to the United States.  He believed in him and prepared him to build his solid engineering and computer science foundation.  He is forever grateful to his mentor, who has a kind heart and guided him during his undergraduate and master’s degree work at Iowa.