GH-METHODS

Math-Physical Medicine

NO. 122

Two case studies of the impacts on postprandial plasma glucose from plantar fasciitis and sensor device reliability using GH-Method: math-physical medicine

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

Introduction
In this paper, the author analyzed the impacts on postprandial plasma glucose (PPG) caused by plantar fasciitis for a period of six months, from 10/28/2018 to 5/1/2019.  In addition, he examined the results from the 14-day sensor device and verified its reliability for three months, from 6/30/2019 to 10/5/2019 using GH-Method: math-physical medicine.

Methods
In 2018, the patient (author) 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.  Toward the end of October 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 April of 2019, he noticed the increased values of both PPG and HbA1C.

During a period of 14 months, from 5/5/2018 to 6/29/2019, the author used a 10-day glucose sensor device (Sensor) that was applied to his upper left arm to collect 74 glucose data per day.  On 6/30/2019, he switched to a newer device with a 14-day lifespan to collect glucose measurements.  However, he immediately noticed some unusual high glucose values recorded with this new sensor device.  In order to keep the integrity of the medical research, during the entire period from 5/5/2018 to 10/5/2019, he has kept two parallel glucose measurements using both finger-piercing along with test strip (Finger) and Sensor.  To collect a meaningful size of data, he completed the three-month window before starting his sensor data analysis work.  The reason is that the average lifespan of red blood cells and the associated timespan of HbA1C are three months.

In order to better understand the “unusually high” sensor glucose activities, he began a study on PPG’s physical behaviors.  After completing this task, he can then derive applicable mathematical algorithms to insert them into his software program.  Initially, he generated more than 100 graphics which were based on ~160,000 glucose, carbs/sugar input, exercise, and weather temperature.  By using his two eyes and brain (natural intelligence) to visually study the patterns, he could extract certain odd phenomena, hidden evidences, and possible clues.  Eventually, he has developed a set of mathematical equations or formulas that are not only capable to describe but also predict those physical behaviors and characteristics of PPG.

Results
Figure 1 shows the total period of 17-months (519 days) from 5/5/2018 to 10/5/2019, including two special sub-periods of “plantar fasciitis” from 10/28/2018 to 5/1/2019 and “sensor device reliability” from 6/30/2019 to 10/5/2019.  This diagram includes glucoses (both Sensor and Finger), carbs/sugar intake amount, and post-meal walking steps.

Figure 2 depicts the sub-period of plantar fasciitis.  Figure 3 reflects the detailed information regarding his plantar fasciitis.  During this 6-month sub-period, the post-meal walking has reduced from 4,800 steps to 3,000 steps due to the sharp pain on his heel.  This 1,800 steps reduction increased his PPG by ~9 mg/dL.  His average daily carbs/sugars intake has slightly increased from 14 grams to 16 grams.  This mere 2 grams raised his average PPG by ~4 mg/dL.  It should be noted that, during this 6-month sub-period of winter and spring, the average weather temperature was around 69 degrees Fahrenheit.   Therefore, the “high” weather temperature influence on PPG is totally disregarded in this analysis.  In summary, the combined effect of both carbs intake and post-meal walking increased the daily Finger PPG by ~13 mg/dL.  From Figure 3, the actual recorded Finger PPG was elevated by 11 mg/dL (from 112 to 123) which is quite close to the above calculated 13 mg/dL via “two-parameters based linear equation”.  This linear equation approach yielded a PPG prediction accuracy of 85%.

Figure 1: Overview of two special case studies
Figure 2: Sub-period of Plantar Fasciitis
Figure 3: Impacts on Finger PPG via Carbs Intake and Post-meal Walking

Figure 4 reveals the comparison between Finger PPG and 14-day Sensor PPG.  However, in Figure 5, during this 3-month sub-period, both of the Finger PPG and Sensor PPG patterns are obviously following the combined effect of increased exercise and near-constant carbs intake.  However, the Sensor PPG curve’s plateau height is much higher than the Finger PPG curve.   This observation can also be seen in their comparison vs. HbA1C in Figure 5.  In other words, the gap between Sensor PPG vs. HbA1C is much wider than Finger PPG vs. HbA1C in the central region of this 3-month sub-period (~80% of total area).

Figure 4: Sub-period of 14-day Sensor Device
Figure 5: Finger PPG vs. HbA1C and Sensor PPG vs. HbA1C

Figure 6 further signifies a much more detailed pattern similarity between Finger PPG vs. HbA1C.  The declined Finger PPG matched with the declined pattern of HbA1C (from both daily mathematical A1C curve and lab-tested two discrete A1C data).

Figure 7 illustrates the detailed comparison between 10-day Sensor sub-period (5/5/2018 – 6/29/2019) and 14-day Sensor sub-period (6/30/2019 – 10/5/2018).  Both sub-periods have high correlations between Sensor glucose and Finger glucose (86% – 89%).  However, as the author suspected in early July of 2019, the average glucose difference between 14-days (higher at 23.1 mg/dL) and 10-days (lower at 16.8 mg/dL) is 6.3 mg/dL. 

Figure 6: Finger PPG vs. both Mathematical Daily HbA1C and Lab-tested HbA1C
Figure 7: Detailed analysis of Sensor PPG during senior period of Sensor Device Reliability

In summary, the author questions the reliability of the 14-day sensor data.  Although its general pattern is acceptable, the results are lacking the necessary precision.  If a diabetes patient uses this sensor device to determine the timing and dosage of the insulin injection, then it is important to pay close attention to the precision and reliability for these types of glucose measuring devices.  If the 14-day sensor device consistently shows higher glucose than its actual glucose state, then the incorrect administration of insulin may result in serious conditions.

Conclusions
From the plantar fasciitis case study, we can see that all parts and organs of the human body are interconnected.  In addition, certain lifestyle habits such as exercise are extremely important for diabetes control, but it may cause other discomforts or problems such as plantar fasciitis.  Detailed and thorough “local-view” research work of certain isolated organs, diseases, and medications are absolutely necessary.  An overall understanding and “global view” focus on the whole body instead of just certain body’s parts, especially those interconnectivity among organs, diseases, and all side-effects of medications are also extremely important to know.

From the 14-day sensor device case study, we observed that within the endocrinology domain and up to now, existing research mythologies and certain medical devices lack the necessary precision and accuracy.  The author has thorough training in both academic and professional work experiences in the areas of mathematics, physics, engineering, and computer science, excluding biology and chemistry.  In 2010, he entered the research field of diabetes and its complications in order to save his own life.  When comparing his previous design experiences in space shuttle and semiconductors, he could not help but wonder what would have happened to his previous assignments if he applied these “existing internal medicine’s research methodology” on those jobs?  His intentions are not to deliberately offend his medical research scientists in any way.  He is merely suggesting that a different but more precise approach could reveal additional hidden facts or discover more conclusions regarding these important but dangerous metabolic and endocrinological diseases.