Using Quantitative Medicine to Control Type 2 Diabetes

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Title:  Using Quantitative Medicine to Control Type 2 Diabetes

By:    Gerald C. Hsu,  eclaireMD Foundation

Date:   10/20/2016 – 3/25/2017; 7/5/2017 – 8/19/2017

Table of Contents

Abstract

Section 1: Introduction (2 figures)

Section 2: Methodology (3 figures)

Section 3: Weight and Diabetes (5 figures)

Section 4: Glucose – PPG (8 figures)

Section 5: Glucose – FPG (11 figures)

Section 6: Glucose and Food & Exercise (19 figures)

Section 7: Glucose and Others (8 figures)

Section 8: Hyperlipidemia and Hypertension (10 figures)

Section 9: Metabolism Index (MI) and General Health Status Unit (GHSU) (12 figures)

Section 10: Conclusion

Section 11: Acknowledgement

Total 78 figures

 

Abstract of paper

Title: “Using Quantitative Medicine to Control Type 2 Diabetes”

By:  Gerald C. Hsu, eclaireMD Foundation

Date: November 28, 2016

BACKGROUND

The author has had long-term chronic diseases and suffered from type 2 diabetes, hyperlipidemia, and hypertension for a period of 20 years.  His primary health data in 2010 are listed as follows:

Weight:  205 lbs.

Waistline:  44 inches

One-time snap check of PPG:  350 mg/dL

90-days of averaged glucose:  280 mg/dL

A1C:  10.0%

ACR:  116 mg/mmol

Triglycerides:  1161 mg/dL

AIMS

The author spent 7 years (2010-2017) conducting research to find an effective way to control his severe diabetic condition.  As a result, he developed three mathematical models and various tools to control his chronic diseases, with an emphasis on type 2 diabetes.  During this process, he collected approximately one million “clean” data regarding his health condition.  He applied multiple disciplines, including advanced mathematics, computer science (e.g. database, big data analytic, cloud computing, and mobile technology), nonlinear and dynamic digitized engineering modeling, and artificial intelligence or “AI” (automation and machine learning) to simulate the human organic metabolic system.

METHOD

The author created two prediction models of weight and glucose values in order to provide patients with an early warning to alter their lifestyle.  The glucose prediction model includes factors such as diabetes medication consumed, quality of food and meal, type of dining location (home or restaurant), exercise amount, stress and trauma, current residence location and weather condition, traveling category and frequency, decreasing internal organ function, and time delay impact on glucose measurement.  The weight prediction model includes factors such as quantity of food and meal, type of exercise, change in calories, sleep impact, water consumption, and other prominent factors.

The most significant achievement is that he successfully eliminated the use of all his diabetic medications within the past 2 years (2016-2017), while maintaining his A1C level within the range of 6.2% to 6.6%.  In addition, his hypertension and hyperlipidemia came under control.

His recent summarized health data is listed below:

Weight:  172 lbs.

Waistline:  32 inches

Averaged 90-days glucose:  115 mg/dL

A1C:  6.3 – 6.5% (without any medication)

ACR:  12.6 mg/mmol

RESULTS AND DISCUSSION

His entire research and development efforts have been based on lifestyle management as a part of preventive medicine, using collection, processing, and analysis of quantitative medical and health data.  The results are displayed in 75 figures and diagrams which have hundreds to thousands of data within each illustration.  This paper has indicated many conclusive correlations among 11 categories. These categories include four health outputs (weight and waistline, glucose, blood pressure, lipid), six health inputs (food and meal, exercise, stress, sleep, water drinking, life pattern regularity), and time effect.  All of these 11 categories are composed of approximately 500 elements. All of them are carefully monitored by special-designed computer software via smartphone or personal computer.  About 95% of these elements are managed by AI in which 20 to 25 elements are required by the patient’s daily input.  The metabolism indices for the past 5 years are highly consistent with the author’s health state found from various lab test results.  The accuracy of weight prediction and glucose prediction has reached 99.9% and 99.0%, respectively.

The author’s findings of correlations and conclusions are highly consistent with the commonly available understandings within the medical community.  There is no personal prejudice inside this study as it is based on experimental facts.  Therefore, the author hopes that the same existing medical conclusions can be further backed up and proven by a scientific big data analytics approach.

The phase 2 of this project will include the following:

(1) Collecting and presenting data from mass population of worldwide patients with type 2 diabetes and;

(2) Researching and developing more effective ways to influence or alter patients’ health behavior to adapt a better lifestyle management.

 

Title:   Using Quantitative Medicine to control Type 2 Diabetes

By:    Gerald C. Hsu, eclaireMD Foundation

Date:   10/20/2016 – 3/8/2017; 7/5/2017 -8/5/2017

Section 1:   Introduction

The 7-year research data contained in this paper are based on the work conducted by a long term diabetic patient, who is also the paper’s author.  From here on, I will use the first person to describe my conditions and findings.

I am an entrepreneur and, starting in 1995, was the CEO of a high-tech publicly traded company. I had lived a continuous high-stress life due to the nature of my work.  During a routine blood test in 1998, my glucose level measured 350 mg/dL.  Between 1998 and 2000, I suffered several bouts of severe low blood sugar known as insulin shock.  Attached Figure 1-1: Comparison of health data, is a table which summarizes my health examination data from 2000 to 2010.  Note that my highest triglycerides level was 1,161 mg/dL, A1C 10.0%, and ACR 116 mg/mmol.  On August 3, 2010, my physician advised me that I needed to begin insulin immediately and would most likely end up on dialysis within 3 years.  The following 3 months, I moved to a new city with less stressors, shut down all of my business enterprises, and started the journey to save my own life.  After 7 years of doing research on six types of chronic diseases, I have completely changed my lifestyle and have my diseases under control.  During this time, I lost 26 lbs. (going from 198 lbs. to 172 lbs.), and reduced my waistline by 12 inches (going from 44 inches to 32 inches).  On September 1, 2016, my health examination data indicated the following much improved results: ACR 12.6 mg/mmol, A1C 6.6% (WITHOUT taking any diabetic medications for almost 2 years), triglycerides level 67 mg/dL, HDL 48 mg/dL, LDL 103 mg/dL, total cholesterol 156 mg/dL, and BMI 25.

My overall diabetic condition for the past 15 years can be seen in Figure 1-2: A1C over 17 years (2000-2017).  Prior to 2012, my lab tested A1C values were out of control, “all over the map”.  However, after 2012, my lab tested A1C values were around 6.6% and they have been enveloped and confined by my mathematical simulated A1C curve.  It should also be noted that, during this same period, I have gradually and then completely eliminated all kinds of diabetes medications.

Fig 1.1

 

 

 

 

 

 

 

 

 

Figure 1-1:  Comparison of Health Data

Fig 1.2

 

 

 

 

 

 

 

 

Figure 1-2:  A1C Over 17 Years (2000 – 2017)

 

Date:   10/21/2016 17:00

Section 2:   Methodology

During the late part of 2010, I realized that I had been totally ignorant in the area of chronic diseases, even though I was very well educated in other areas (having studied 7 different fields at various colleges over 17 years).  Therefore, I decided to dedicate my efforts on acquiring the needed “knowledge” to control and improve my health conditions.  During the initial 2-year period (2010-2011), I studied internal medicine, with a special interest in 6 chronic diseases which were diabetes, hypertension, hyperlipidemia, heart disease, stroke, and obesity. For the next 2 years (2012-2013), I focused on food science and nutrition.

After finishing those 4 years of self-studying and preparation, I was ready to fully address my health problems.  I thought about starting with traditional medical research methodology, i.e. basic research, starting from the “cellular” level.  However, I did not have sufficient financial resources and professional knowledge to go down that route.  I also believed the most difficult hurdle was my age and severity of diseases.  I might not have enough time to go through that route.  Therefore, I took stock of my strengths, which were mathematics, computer science, physics, and various engineering disciplines.  I have never received any formal training in the biomedical area.  I also decided to use the nonlinear dynamic structural engineering modeling and Finite Element Method concept of digitized engineering, both with inorganic materials, to simulate the human body’s organic metabolism system.  And then, I applied advanced mathematics to conceive its hypothesis and develop the governing equation.  I defined 4 inter-connected body output categories of weight, glucose, lipid, blood pressure; and 6 inter-connected body input categories of food, exercise, stress, sleep, water hydration, and regular life pattern for longevity.  These 10 categories contain around 500 detailed elements.  For example, just “Stress” category contains 33 different stressors for both “normal” persons (their stress comes from inter-personal social relationships) and “abnormal” persons (e.g. “personality disorders,” their stress are mostly self-induced).  I also included “Time” as the 11th category since human body conditions evolve over time, i.e. “Dynamic”.  The human body’s organic characteristics must be dealt with using Artificial Intelligence (AI), through trial-and-error and other computational techniques.  In the modeling process, I excluded all environmental factors such as pollution, radiation, toxic chemicals, poison, hormonal therapy, viral infection, and others due to their complexity and the difficulty of data collection.  These factors are important for cancer which is also a chronic disease.  It should be noted that my research is focused on preventive medicine; therefore, drugs used for treatment is only included as a part of the glucose prediction tool since it does contribute to glucose readings.

Given the average 3-4 months lifespan of blood cells, which carry glucose and lipids throughout human body, I defined the data collected during the first 3-month period as the initial conditions for solving these mathematical governing equations.  Therefore, it is important for any patient to use my tool to collect his/her initial 3-months data as complete as possible.  After applying these initial conditions on the mathematical system, I can then start to “solve the equation” (in a mathematical sense), and afterwards, the system starts to learn by itself through Artificial Intelligence (AI).  In 2014 and 2015, I began building this “organic” biomedical mathematical model and two practical prediction models for both weight (after one night of sleep) and post-meal glucose levels (2 hours after a meal) by using AI.  On January 1, 2012, I started collecting my own body health data and used that data to continuously test and improve my mathematical system. To date, I have collected and processed near 1 million “clean” data on myself.  Without including AI in system capabilities, the human mind would not be able to deal with such a large and complicated database.  As shown in Figures 2-1 and 2-2, I have reached a 99% accuracy on body weight prediction (4/11/2015-7/25/2017) and a 97% to 98% accuracy on glucose prediction (6/1/2015-7/25/2017).

Using my iPhone APP “Tool”, I can easily manage the massive health data over the cloud, predict my vital signs such as weight and glucose, and to monitor my overall health status via the Metabolism Index (MI) and General Health Status Unit (GHSU), which is defined as a three-month running average of the MI value.  From the chart, by mid-2014 (both MI and GHSU had dropped below the dividing health level of 73.5% for my specific case), it was clear that my overall health conditions has improved significantly (as of now, my MI and GHSU are at around 55%) through a better lifestyle management.  My “break-even” line at 73.5% is due to the set of my personal goals of weight, glucose, blood pressure, exercise, meal, sleep, etc.

Fig 2.1

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 2-1:  Predicted and Actual Body Weight (4/11/2015 – 7/20/2017)

 

Fig 2.2

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 2-2:   Predicted and Actual Daily Average Glucose (6/1/2015 – 7/25/2017)

 

Fig 2.3

 

 

 

 

 

 

 

 

Figure 2-3:   Metabolism Index (MI) and General Health Status Unit (GHSU)

Date:   10/22/2016 – 10/23/2016

Section 3:   Weight & Diabetes

It is well known how difficult it is to reduce body weight and maintain for an extended period of time.  In 2000, my average weight was greater than 200 lbs., in 2010 it was 194 lbs., and in 2017 it was 171 lbs.  From 2013 to 2014, my weight fluctuated around 180 lbs. due to my busy travel schedule and not managing my lifestyle and health well.  After developing the metabolism model in 2014, I started to use this tool to manage my overall lifestyle along with reducing the amount of long-distance flying trips.  Combined with my newly developed weight prediction model on April 11, 2015, I have achieved significant weight reduction.  See Figure 3-1 of weight reduction.  Reducing my waistline was a much tougher problem to address than weight reduction.  From 2000 to 2014, my waistline ranged from 42 to 44 inches.  Only until mid-2015, when I started to watch my overall metabolism and use the weight prediction tool, I finally achieved my waistline reduction goal of 32 inches.  See Figure 3-2 for waistline reduction.

There are many factors that affect body weight; however, diet and exercise are the two primary components that people can control.  While types and “quality” of food have a strong correlation with types of chronic diseases (to be discussed in future sections), the “quantity” of food has a strong correlation with weight as indicated in Figures 3-3.  Please note that during the period of June 1, 2015 through July 21, 2017, my average weight has been around 174 lbs. while my food and meal quantity was around 89% of my normal portions.

Now, let us examine the correlation between weight and glucose.  Other than genetic factors, being overweight or obese is the main cause of Type 2 Diabetes (T2D). During my analysis of weight vs. glucose, when I plot out my weight >180 lbs. and daily average glucose >160 mg/dL, I have identified a strong correlation between these two factors.  See Figure 3-4 below.  My tool’s capability on predicting post-meal glucose value before my first bite of the meal is crucial for controlling my PPG (Postprandial Plasma Glucose) level.

A person’s weight is constantly changing throughout the day and evening due to food and exercise as the most important factors.  During the day time, food consumption increases weight and exercise burns off calories. Throughout the night, the processes of vaporization, urination, and bowel movement affect body weight reduction.  During the past two years, my average weight gain between bedtime and the morning of the same day is between 2.5 to 2.8 lbs.  My weight reduction between bedtime and the next morning is also between 2.5 to 2.8 lbs.  That is why I can maintain a constant weight around 173 lbs. (+/- 5 lbs.) over the past 2 years.  My tool’s functionality of predicting the next morning’s weight is a crucial component of controlling my weight; in turn, my weight control is a crucial part of controlling my diabetic condition.  Figures 3-5 shows two kinds of weight changes.

Fig 3.1

 

 

 

 

 

 

 

 

Figure 3-1:   Weight (1/1/2012 – 7/20/2017)

 

Fig 3.2

 

 

 

 

 

 

 

 

Figure 3-2:   Waistline (1/1/2012 – 7/21/2017)

 

Fig 3.3

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 3-3:  Weight and Food & Meal Quantity (4/11/2015 – 7/21/2017)

 

Fig 3.4

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 3-4:  Weight Over 180 lbs. and Daily Average Glucose Over 160 mg/dL

Fig 3.5

 

 

 

 

 

 

 

 

Figure 3-5:  Weight Gain between morning and bedtime of the same day                                                                                                                         Weight Loss between previous day’s bedtime and this day’s morning

 

Date:   10/24/2016  13:00

Section 4:  Glucose – PPG

Approximately 9 to 10% of the populations in Eastern Asia, Europe, and USA have diabetes, Latin America has about 8.5%, while Africa has about 5% of its people with this disease.  Particularly in developing nations, they do not have sufficient resources to do frequent glucose testing, let alone A1C testing.  An easily accessible and free tool for early prediction will be very helpful for diabetic patients to control their disease, especially in the developing nations.  In addition, Apple devices are too expensive for many to purchase.  Another fact is that most senior citizens are more likely to be diabetic candidates and they may not be as in tune with modern technology and APP’s.  Therefore, my charitable medical research organization, eclaireMD Foundation, has developed both iPhone-based and PC web-based tools for different type of users.  The PC version of my tools are much easier to access by patients in developing countries and elderly populations.  My weight and glucose prediction tool can be downloaded for free from Apple’s APP Store by searching for the keyword of eclairemd, and then click the “Wellness” product on the screen.

On August 3, 2010, my lab test results showed my A1C as 10.0%, ACR as 116 mg/mmol, and triglyceride level as 1,161 mg/dL.  After receiving a serious warning from my primary physician, I decided to change my overall lifestyle.  I began collecting my health and lifestyle data on January 1, 2012.  To date, approximately 5-years’ worth of complete data has been collected.  In my Amazon’s cloud database, I have stored about one million data points regarding my health and lifestyle, which includes original inputted data, system calculated data, and AI generated data.  In Figure 4-1: Daily glucose and 90-days average glucose, within 5.5 years, my glucose levels varied between 86 mg/dL and 227 mg/dL with an average value of 127 mg/dL.  In Figure 4-2: My lab-test A1C values were between 6.3% and 7.1%, with an average value of 6.6%, and my mathematical simulated A1C values (between 6.3% and 8.3% with an average value of 7.3%) had a prediction accuracy of 89%.  This reduced accuracy resulted from my build-in “conservative” design in order to provide patient early warning.

The laboratory A1C test result reflects the average glucose values for the past 90 days. The eclaireMD tool calculates a mathematically simulated A1C value based on the user’s daily glucose input data.  However, this mathematically simulated A1C value has a built-in AI to automatically customize the calculation according to the changes of the user’s biomedical conditions.  Frequent calibration by inputting laboratory test results can improve the accuracy of the mathematically simulated A1C results.  My mathematical A1C curve spectrum has completely “enveloped” the actual lab-tested A1C data from 2012 to 2017. This means that my simulated A1C can provide me with an upper-bound early warning before I get tested for the laboratory A1C.

Figure 4-3 lists all of my lab-test A1C values. Although I have taken into account both the lifespan of red blood cells and developed different linear and nonlinear mathematical models, glucose still has a somewhat unpredictable output value due to a highly nonlinear biomedical body system that it changes with time.  There are many elements that affect glucose values and I will discuss more of my research results in following sections regarding these elements.

From 2012 through 2017, the overall A1C curve remains at a somewhat steady state, i.e. around 6.6%, the most important factor of controlling my diabetic condition is that I have been decreasing the dosage of my medication for over two years’ time frame and completely removed all of my diabetes medications in December of 2015.  Between 2012 and 2013, I was taking three different diabetes medications, such as Januvia 100 mg, Actoplus Met 15 mg/850 mg, and Metformin 2000 mg.  I started to decrease the number of drugs along with reducing the dosage amounts from the beginning of 2014.  By December 8, 2015, I completely stopped taking my diabetes medication.  I must point it out that during this period, I witnessed different degrees of “withdrawal symptoms” which were similar to a drug addict’s detoxification process. Within one month of removing or reducing medication, my glucose chart fluctuated greatly, with many ups and downs, without any clear and reasonable explanation.

I have been a long-term diabetic patient for almost 20 years.  I realized that having knowledge, a reliable tool, and strong willpower are the three main keys to control this disease.  During the past 4 years, I have applied my acquired medical knowledge and developed my own tools to help control both my weight and glucose.  The main tools are my weight predictor and my post-meal glucose predictor, which were developed on April 11, 2015 and June 1, 2015 respectively.  I also developed my fasting glucose predictor on July 31, 2017.  It should be noted that the metabolism model (MI and GHSU) I developed in 2014 is the foundation for improvements in my overall health.  When I could predict my weight and glucose beforehand, it became much easier for me to adjust the amount and quality of my food, along with the frequency and intensity of my exercise.  In other words, if I can wisely adjust my input parameters, my output values will most likely be automatically adjusted for the better.  It should also be noted that the body is organic (nonlinear) and dynamic (changes with time), so we have to constantly monitor for signs of change.

Two fundamental rules must be followed in terms of using these prediction tools.  First, I had to follow the prediction model’s precise suggestions regarding how to input those value.  Second, after measuring my weight or glucose, I was not allowed to go back to randomly readjust my original input data in order to increase the accuracy of prediction (unless there were some new findings or facts that I had just learned or realized from applying this prediction experience).  Some degree of AI has been built into the system as well, but the entire biomedical system must be continuously observed and monitored along the way.  That is why I created a non-profit medical research foundation to persistently work in depth on this subject, even after my passing.

I have conducted an accuracy study of the PPG prediction for two adjacent periods of 115 days each: period A from 3/1/2016 to 6/23/2016 and period B from 7/1/2016 to 10/23/2016.  All of my post meal glucose values during period A were obtained via the glucometer, while during period B, only 33% of my post meal glucose values were measured. The remaining 67% was based on the predicted PPG as the actual PPG values. Whenever I ate my meals at restaurants where no reliable nutrition information was readily available, or I cooked a new dish at home using new food materials, I would measure my post meal glucose by the finger piercing method.  Period A of 100% measured PPG via the glucometer has a slightly lower accuracy rate (~97%) than period B of 30% measured PPG via the glucometer (~98%).  In theory, if 100% of the actual PPG values are based on the predicted PPG data (i.e., 0% of PPG values via the glucometer), then the accuracy rate will be 100%.  In addition, the correlation coefficients between predicted PPG and actual PPG of both periods are quite high (>76%), see Figure 4-4:  Accuracy analysis of predicted PPG values.

The conclusion from this experiment is that I could eliminate the finger-piercing method and still get a very high accuracy rate (>97%) on my post meal glucose results.  This is an important finding that my PPG prediction method is proven to be extremely reliable for most Type 2 diabetes (T2D) patients, whose average daily glucose level fall between 100 mg/dL and 400 mg/dL.  They will be able to use my PPG prediction tool to control their diabetes.  This was my original goal to conduct the reliability study to remove the burden and cost associated with finger piercing and test strips.  Hopefully, this glucose prediction model and tool can reduce both cost and pain for worldwide diabetes patients, especially for patients in underdeveloped nations.

No significant Correlation between FPG and PPG

I have conducted a statistical analysis of the correlation coefficient (r) and coefficient of determination (r2: r square) of 90-days moving average values of FPG vs. PPG during the complete period of 6/1/2015 through 7/26/3017.  The results of both r (-3.3%) and r2 (0.11%) from these two sets of data are very low, which means that the FPG values almost have no correlation with PPG values at all, see Figure 4-5.  The outcome makes a lot of sense from the biomedical point of view.  Fasting glucose is the combination effect of glucose produced by the liver and insulin produced by the pancreas during sleeping hours.  On the other hand, during the awakening hours, both the diet control and exercise have direct impact on PPG values.

Weighted contribution factor analysis of FPG and PPG on A1C

Important dates and events regarding my glucose are as follows:

(1) I started to record and collect my PPG data on 1/1/2012;

(2) I started to record my FPG values on 6/1/2015;

(3) I stopped to take my diabetes medication on 12/8/2015;

(4) My FPG values suddenly jumped from ~110 mg/dL to above 140 mg/dL on 11/23/2016 and remained high for 5 months.  My FPG values dropped again on 6/5/2017 after my weight was reduced.

By now, there are sufficient glucose data in my cloud server, so I can conduct a big data analytics on the composite impact of FPG and PPG on A1C value.  First of all, I defined a new term, Adjusted A1C, as an interim processed variable to conduct this study.  The equation is:

Adjusted A1C = a x FPG + b x PPG    &       b = 100% – a

where a and b are A1C’s weighted contribution factor (%) of FPG and PPG, respectively.

I have used the data from the period from 12/8/2015 through 7/27/2017 to eliminate the data impact from medication.  During this period, I have collected 8 sets of lab-tested A1C data (test performed quarterly) over 2 years, which are used as the base for this comparison study.  I then chose 4 different sets of weighted contribution factors for data analysis:

a = 0%, 15%, 25%, 50%;

and the corresponding

b = 100%, 85%, 75%, 50%.

This 4 sets of factors are to calculate 4 adjusted A1C curves in order to compare them against the eclaireMD simulated mathematical A1C curve and lab tested A1C data points, and the results are shown in Figure 4-6.  By this comparison analysis, I observed the following phenomena:

(1) The contribution ratio of FPG 25% and PPG 75% provides the highest accuracy 100% and correlation 99.8% between adjusted A1C and simulated A1C.  This make a perfect sense since simulated A1C uses 1 FPG and 3 PPG values each day as its base of calculation plus some degrees of AI modification.

(2) The more deviation of the contribution ratios away from the set of 25% & 75%, the lower accuracy and correlation become, however, they are still quite high in all cases.

(3) I deliberately put a case of both FPG and PPG at 50% level, i.e. they have equal contribution on making A1C value.  The turning point date is 11/23/2016, PPG was high prior to that date and FPG was higher after that date.  On adjusted A1C curve, you can see that simulated curve is higher than adjusted prior to 11/23/2016 and vice versa for after 11/23/2017.

In late 2016, I conducted an analysis of lower FPG contribution factors (below 12%) and its results are shown in Figure 4-7, the Statistical Comparison Study of A1C Values based on 3 sets of A1C values: eclaireMD predicted, lab-tested, and Adjusted.  The results show that the case of 0% of FPG and 100% of PPG is the closest combination to eclaireMD predicted A1C, which also possess the highest correlation coefficient (60%).  However, the case of 11.4% of FPG and 88.6% of PPG mix gives a perfect match between adjusted A1C and lab-tested A1C.  This means that, during the period of 8/2/2015 through 1/3/2017, my weighting contribution factors of FPG and PPG to A1C value are probably falling within the range of FPG < 10% and PPG > 90%.  This lower FPG contribution percent is due to my average lower fasting glucose (~110 mg/dL) prior to 11/23/2016.  Please note that I have adopted the common medical community’s convention of using the lab-tested A1C as the basis of all of my glucose statistical comparison study.

During this 20-months period since 12/8/2015, my average lab-tested A1C is 6.6% (real lab-tested values fluctuating between 6.4% and 6.7%) without taking any diabetes medication.  Furthermore, the most probable weighted contribution factors: <11.4% of FPG and >88.6% of PPG, also indicate that my type 2 diabetes is very well under controlled by applying a better lifestyle management without taking any diabetes medications.  However, this observation is based on my highly accurate PPG prediction model and my near-constant low FPG (~110 mg/dL) prior to 11/22/2015.  My FPG values suddenly increased to 158 mg/dL on 11/23/2016 and remained relatively high through 6/3/2017.  I have discovered that, at present day of 7/27/2017, the best mix ratio of FPG & PPG has changed to 25% & 75%.

Figure 4-8: Two periods’ A1C data comparison on actual lab testing dates shows that both of my simulated and adjusted A1C curves are “enveloping” most of my lab-tested A1C data. This is what I called “conservative design” but it still remains a high accuracy of predictions. This proves that a patient can use my tool to predict his or her A1C results before lab tested day.

 

Fig 4.1

 

 

 

 

 

 

 

 

Figure 4-1: Daily Glucose and 90-days Average Glucose (1/3/2012 – 7/24/2017)

Fig 4.2

 

 

 

 

 

 

 

 

Figure 4-2: Mathematical Simulated A1C and Lab-tested A1C Comparison

 

Fig 4.3

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 4-3:  List of My Past Lab-tested A1C Values (2000 – 2017) and Corresponding Mathematically Simulated A1C Values

 

Fig 4.4

 

 

 

 

 

 

 

 

Figure 4-4:  Accuracy analysis of predicted PPG values

 

Fig 4.5

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 4-5: No correlated relationship between FPG and PPG during a period (6/1/2015 – 7/26/2017)

 

Fig 4.6

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 4-6: 4 sets of adjusted A1C values in comparison with eclaireMD simulated A1C and 9 different lab-tested A1C data

 

Fig 4.7

 

 

 

 

 

 

Figure 4-7: Statistical Comparison Study of A1C Values based on (1) eclaireMD predicted A1C, (2) lab-tested A1C, and (3) Adjusted A1C

 

Fig 4.8

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 4-8: Two periods’ A1C data comparison on actual lab testing dates

 

Date: 7/7/2017 – 8/13/2017

Section 5:  Glucose – FPG

My Initial Fasting Glucose Study

During the course of data collection and analysis, I have noticed some deviation that exists between tested data and simulated data.  My first attempt was to identify the most important elements affecting glucose and A1C.  Some of them will be addressed in the following discussions.  Around April 2015, I became intrigued by the difference between higher fasting glucose in the morning (Dawn Phenomenon) and lower fasting glucose in the morning.  Without possessing a complete and clear knowledge of how the liver and pancreas work in terms of creating and controlling glucose level, I decided to study the fasting glucose via a “physics” research approach: i.e. observe phenomena, collect data, create hypothesis, define inputs, derive governing equation, identify outputs, investigate relationship between inputs and outputs, plug all inputs into the equation and calculate outputs, and validate the accuracy of outputs. If the outputs are wrong, then repeat the process again until hypothesis is proven correct.  In modern terminology, this is another way to describe the “macro-view” approach known as ”big data” collection and analysis.  By October of 2016, I have collected nearly 1,000 pre-breakfast fasting plasma glucose (FPG) data.

The prediction of FPG (fasting plasma glucose) is very different from the prediction of PPG (postprandial plasma glucose).  At first, I decided to use my 90-days average daily glucose as the initial condition for predicting FPG.  Please see Figure 5-1: Predicted FPG value based on my preliminary finding of 360-days data.  It indicates that, although the daily FPG goes up and down and it is difficult to predict, but after a long period of time, the averaged FPG settles around the 90-days average glucose value. The deviation between the predicted and actual is 1.6% and my predictions have reached to 98.4% accuracy.  It should be noted that the above analysis and tentative conclusion were based on data available prior to 10/20/2016.  During this period, most of my fasting glucose data are very close to my averaged daily glucose value.

My understanding of FPG was suddenly changed on 11/23/2016

I arrived in Honolulu on 11/22/2016 and stayed there for two months.  The next morning, my FPG jumped to 158 mg/dL.  At first, I thought it was due to traveling and changing living environment; however, it has persistently stayed at a high level, above 140 mg/dL.  I was puzzled by the questions related to quantitative characteristics of FPG, such as: What is the exact causes of the surge? How to predict its pattern? How high will it jump? How to control the high FPG from happening again?  My past understanding and practice of PPG control via diet and exercise have had very negligible impact on my FPG control, since it is produced in early morning hours when I am sleeping.  For the next 3 months, I searched for related information by reading more than 100 papers and articles, asking questions to a few internal medicine doctors, etc.  I also conducted investigations of all possible relationships between FPG and all of my collected input elements on the cloud server, and performed numerous statistical correlation analyses.  For example, I calculated the current day’s FPG against the same day’s post-breakfast glucose and previous day’s post-dinner glucose.  The purpose of these two studies was to see whether FPG contributed to the increase of the same day’s post-breakfast glucose or received a “left-over” impact from the previous day’s post-dinner glucose.  Please see those two low correlation coefficients from Figure 5-2: Two correlation studies between FPG and two different PPG values.

I even tried some “tricks” mentioned in some papers and articles, e.g. eating snacks before sleep, chewing a piece of candy during midnight, measuring my glucose data every hour between 2am and 7am (you can image that the quality of my sleep was badly affected).  None of them worked.  However, I still did not want to retake my diabetes medications.  In addition, I refused to take the suggestion from a few papers to visit a physician to get insulin shots.  These elevated FPG values finally pushed up my lab-tested A1C from 6.4% to 6.7% (5% increase).  By the spring of 2017, I still could not find any clues to develop a useful and accurate FPG prediction model in order to control my higher fasting glucose in the mornings.

My Recent Fasting Glucose Research after 3/17/2017

After collecting 5 more months of higher FPG values (11/23/2016 – 4/30/2017), followed by another 3 months of efforts on reducing my weight (5/1/2017 – 7/30/2017), I finally have sufficient data to investigate my FPG situation.  I have chosen a data set in a period of 16 months (about 500 days), from 4/1/2016 to 7/28/2017.  During this period, I further subdivided them into two “equal length” sub-periods of 8 months (about 250 days) each.  In the first sub-period from 4/1/2016 through 11/23/2016, my average weight was 172 lbs. and the average FPG was 110 mg/dL.  However, in the second sub-period from 11/23/2016 through 7/28/2017, my average weight was 176 lbs. and the averaged FPG was about 130 mg/dL.  As a result, the averaged FPG has a 20 mg/dL increased amount due to my average weight increase of 4 lbs. Actually, during this entire period (4/1/2016 – 7/28/2017), my minimum weight was 171 lbs. and the maximum weight was 179 lbs.  It should be noted that during the second sub-period after 11/23/2016, there were no significant changes in my lifestyle, including food, exercise, stress, sleep, water intake, life regularity, temperature, living environment, etc.

In the past, my correlation analyses were focused on relationship between input categories (e.g. diet, exercise, living environment, etc.) and output categories (e.g. glucose, weight, blood pressure, etc.).  I am a professional engineer with strong mathematics, physics, and computer science background.  Most of my past 50-years training as an engineer is following this guiding principle: to identify the relationship between inputs and outputs of a system.  I have worked diligently trying to solve this particular FPG problem for almost 4 months.  As a result, around 3am of March 17, 2017, I dreamt about the possibility of one output category could also be served as the influential input factor of another output category.  This influential input category was ”Weight”!  This was my “out-of-box thinking” resulting from my persistent digging into the same problem for 4 months (from 11/23/2015 through 3/17/2017).  As previously indicated, existing input categories, such as food quantity and exercise amount, were inputs of our weight.  By now, I thought about my weight to function as the primary and direct input of my FPG.  Both weight and glucose belong to the category of outputs.  After breaking out from my previously trained engineering thinking pattern and constraints, I was then able to look into the FPG problem with a totally different new angle.

The summary results of the high correlation of 84% between weight and FPG are shown in Figure 5-3: Weight and FPG during the period of 4/1/2016 through 7/28/2017.  In addition, a high correlation can be seen in the charts observed in Figure 5-4, when I plot them out under the selection criteria of weight data of >176 lbs. and FPG data of >130 mg/dL.  I realized that, during the past 5 years, I have already controlled my PPG very tightly via diet, exercise, and others.  However, during this 4-months winter sub-period, I have eaten too much “between-meal” snacks, e.g. nuts.  This eating pattern did not contribute much on my post-meal glucose values; however, it did result in a 4 to 8 lbs. of weight increase which in turn caused the increase of 20 mg/dL on the average FPG.  Lesson to be learned here is to watch out for snacks.

Of course, the increased FPG values will most likely push up my A1C values.  As I mentioned earlier, my FPG contributes approximately 25% and my PPG contributes about 75% to the predicted A1C value.  Therefore, I verified this FPG impact on my A1C from my two following A1C lab tests (4/9/2017 and 6/1/2017), both with 6.7% value, 0.3 increased value from previous A1C of 6.4% (5% increase).

Correlation comparison between Weight & FPG vs. Weight & PPG

My interpretation of the low correlation coefficient (~1%) between post-meal glucose and weight is due to the fact that weight is only a second-level influence on PPG.  The other two lifestyle factors, diet via carbs and sugar control (r = 60%) and regular exercise (r = -27%), are true primary factors for PPG.  Our body weight (one of body’s output) is controlled by our overall lifestyle, food quantity and exercise (two of body’s inputs) are included as well.  During the daytime, we can manipulate both diet and exercise to control our post-meal glucose level to an optimal level.  However, during sleeping hours, it is a completely different story.  For most cases, during sleep time, other lifestyle factors cannot be regulated by us and therefore, our brain takes over the total control of the operation for our internal organs.  When the brain senses that body weight has been increased, it will give orders to the liver to produce glucose around 3am for storing future needed energy.  If liver produces excessive glucose, then the brain gives order to the pancreas to produce insulin to balance the glucose level.  However, for a diabetic patient, the function of the pancreas has been compromised; therefore, the glucose control mechanism will cause our bodies to have a higher fasting glucose value in the morning (FPG).  Since diet and exercise cannot alter FPG directly during sleep, the secondary factor, weight, becomes the input factor and comes into play.  Of course, this is my interpretation without a thorough understanding of biomedicine.

By the end of May 2017, I have figured out the inter-relationship between weight and fasting glucose, and therefore developed a predicted fasting glucose equation.  In Figure 5-5, I have summarized my predicted FPG values under 6 different body weights in the morning based on the least square mean calculation of past 90-days data.

The final comparison results of FPG and PPG of 3 meals between predicted and actual glucose during the period of 6/1/2016 through 7/30/2017 are displayed in Figure 5-6.  It shows that the accuracy percentages of all glucose are extremely high and all of the correlation coefficients are quite high as well.  For the FPG case, the accuracy is around 99% and the correlation is between 48% – 76% (for both daily glucose and 90-days moving average glucose cases).  This Figure illustrates my newly developed fasting glucose prediction model, on 7/30/2017, is highly reliable as with my post-meal glucose prediction model developed two years ago, on 6/1/2015.

The next step is to expand my developed FPG prediction equation to be suitable for the general public, i.e. other diabetic patients.  This part of research, using trial-and-error, took two months (June and July of 2017) to complete.  Figure 5-7: Using my data of weight & FPG to generalize it into BMI & FPG for other patients, illustrates how I developed this predicted fasting glucose step by step, including considerations of its possible future extensions.

Based on my understanding of both biomedicine and mathematics, it is my educated guess that there is a “skewed S-shape” (or a better description of an “escalator” shape) existing for the ranges of both BMI<24.5 and BMI>27.5.  Let me try to explain it in simple terms.  If you just apply the straight line as the prediction tool, you will reach to a glucose value of below 60 mg/dL very quickly (insulin shock) when your BMI is dropping below 24.  So, I bent the straight prediction line upward.  Similarly, if your BMI is rising beyond 28, by applying this straight line as the prediction tool, you will reach to a glucose over 180 mg/dL very quickly.  Unless your physical health has been mistreated for a long period of time, your glucose value should be below 200 mg/dL.  That is why I also bent the straight prediction line downward.  These two bending effects make the prediction equation curve’s shape looks like an escalator.  As of now, I do not have sufficient data to support these two “extreme” scenarios.  I also have no intention to push myself either into insulin shock or become a severe diabetic patient again.  However, as a scientist, it is my responsibility to make the best educated hypothesis and then verify it later.

Since 3/18/2017, I have started to lose weight again and, by 6/1/2017, I finally reduced my weight to 171 lbs. once more.  From 11/23/2016 through 6/1/2017, I have experienced 6 months of higher FPG. Therefore, I needed at least another 6 months of data, until 12/1/2017, to maintain my low-weight and low-FPG level.  Once I reached that day, I will be able to perform another analysis which would cover the following 3 equal length sub-periods:

  1. 6/1/2016-11/30/2016 with low FPG
  2. 12/1/2016-5/31/2017 with high FPG
  3.  6/1/2017-11/30/2017 with low FPG

Through this experiment, I can then confidently verify my conclusion with highly reliable correlation between weight and fasting glucose.  (Note: My latest A1C lab test on 8/18/17 is 6.4%, which is reduced by 5% compared to the previous reading of 6.7% on 6/1/2017. The  decrease is caused by the lower FPG values during this period based on my weight reduction of ~10 lbs.)

As shown in Figure 5-7: Relationship between FPG and BMI for a 16-months period with a higher FPG sub-period and a lower FPG sub-period, I am now highly confident to offer this analysis model to other diabetic patients.  I do understand that the human body is a high degree of nonlinear system and it is difficult to oversimplify it by any simple mathematical equation.  For example, through part of my previous correlation study, I have also discovered the existence of the close relationship between sleep quality and FPG, but my collected data regarding this area is still insufficient.  I will continue my data collection and analysis in this area in order to understand the nonlinearity and other disturbances of fasting glucose prediction.

Based on the collected data from 1/1/2014 through 7/30/2017 (~1,300 days), using BMI and FPG as x- and y-coordinates, a straight-line relationship existed between them, [(24.5, 100) and (27.5, 140)], which is the basic relationship between weight and FPG.  It should be noted that a high correlation coefficient of 82.5% existed between the 30-days moving average weight and 90-days moving average FPG.

On top of these findings, I also calculated the following 2 probability ranges of data deviation due to other potential disturbance on the “secondary” factors on FPG:
- Actual FPG value falls into the range of +10 and -10 mg/dL:  51%
- Actual FPG value falls into the range of +20 and -20 mg/dL:  86%

This means that there are approximately 14% of actual FPG data are falling outside of this wider band due to other secondary factors.
These findings are illustrated in Figure 5-8:  Sensitivity study results between weight and FPG for a period of 16 months (4/1/2016 – 7/30/2017).

After consolidating all of my data together, including weight, fasting glucose, post-meal glucose, and daily average glucose, I chose a complete period from 1/1/2014 through 7/30/2017 to conduct the correlation analyses between weight and glucose, including both FPG and PPG.. The results are shown in both Figure 5-9:  A complete period (1/12014 – 7/30/2017) of 1,300 days’ data exhibit a clear and strong correlation between weight and FPG and Figure 5-10: A complete period (1/1/2014 – 7/30/2017) of 1,300 days’ data with Comparison study results between weight and 3 glucose values (FPG, PPG, Daily Glucose).  The conclusion is that glucose is closely tied with weight, which has been known for quite a long time in the medical community.  However, in my research and tool development, I have proved its existence via physics and engineering research approach and big data analytics.  I have also discovered and utilized two very different, yet highly accurate, prediction models for FPG and PPG, respectively. Figure 5-11: Comparison between predicted FPG and actual FPG vs. weight, provides some insights of my FPG model.

Fig 5.1

 

 

 

 

 

 

Figure 5-1: Predicted FPG value based on my preliminary finding of 360-days data

 

Fig 5.2

 

 

 

 

 

 

 

 

 

Figure 5-2: Two Correlation studies between FPG and same day’s post-breakfast PPG and between FPG and previous day’s post-dinner PPG

 

Fig 5.3

 

 

 

 

 

 

 

 

 

 

 

 

Figure 5-3: Weight and  FPG during the period of 4/1/2016 through 7/28/2017

 

Fig 5.4

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 5-4: Strong correlation for weight > 176 lbs. and FPG > 130 mg/dL (4/1/2016 – 7/28/2017)

 

Fig 5.5

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 5-5: My predicted FPG values under 6 different body weights in the morning

 

Fig 5.6

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 5-6: Accuracy and Correlation between predicted glucose and actual glucose for FPG and 3 PPGs

 

Fig 5.7

 

 

 

 

 

 

 

Figure 5-7: Relationship between FPG and BMI for a 26-months period with a higher FPG sub-period and a lower FPG sub-period

 

Fig 5.8

 

 

 

 

 

 

 

 

Figure 5-8: Sensitivity study results between weight and FPG for a period of 16 months (4/1/2016 – 7/30/2017)

 

Fig 5.9

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 5-9:  A complete period (1/1/2014 – 7/30/2017) of 1,300 days’ data exhibit a clear and strong correlation between weight and FPG

 

Fig 5.10

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 5-10: A complete period (1/1/2014 – 7/30/2017) of 1,300 days’ data with Comparison study results between weight and 3 glucose values (FPG, PPG, Daily Glucose)

 

Fig 5.11

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 5-11: Comparison between predicted FPG and actual FPG vs. weight

 

Date:   10/25/2016  13:00

Section 6:   Glucose and Food & Exercise

In order to study the relationship between glucose and food, I have developed an APP known as SmartPhoto for the iPhone.  Within the SmartPhoto, I constructed a relational database structure to attach with each picture stored in the iPhone album.

The data structure has 5 levels:

1. Group: (USA, Japan, France, etc.)

2. Category: (home cooking, chain restaurant, individual restaurant, airline, cruise, etc.)

3. File: (Denny’s, McDonald’s, Greek Restaurant, Asian Food, etc.)

4. Name: (restaurant name, dish name, menu item, etc.)

5. Content: (anything you want to keep a record, e.g. Nutrition ingredients, etc.).

Once the food photos with its data structure are stored in SmartPhoto, they can be sorted and searched any way a person chooses.

Please see Figure 6-1: SmartPhoto Samples of food & meal with glucose level attached with each photo.  From May 1, 2015 through August 13, 2017, I have collected a total of 2,476 pictures of food and meal with an average glucose level (PPG) of 119.7 mg/dL.  During the same period, my daily average glucose level (including both FPG and PPG) is 119.93 mg/dL.

From the years of 2012 through 2014, during my glucose analysis, I came to a tentative conclusion that my high glucose periods (close to 140 mg/dL) were contributed to traveling overseas.  Please see Figure 6-2: Glucose results from 2012 to 2014.  I found that the majority dishes from eastern Asia (excluding Northern China), Hawaii, and Tahiti contain high contents of sugar in their cooking process.  Rice, flour, and/or taro are the main sources of carbohydrates. However, during my extended stay in various eastern Asian countries and Hawaii over 8 months brought another prominent fact.  My average glucose level dropped below 120 mg/dL – a drop of 20 points from previous periods.  Please see Figure 6-3: Glucose results from 2015 through 2016.

After careful analysis of this average glucose decline, I discovered the following four reasons:

(1) I followed my rule of choosing food material and picking menu items more cautiously when I use the glucose prediction capability of my tool;

(2) I spread my daily walking exercise to three post-meal time frames, averaging 4,000 steps after each meal, instead of concentrating on one walk in the evening (this will be discussed in future sections);

(3) I watched my food and meal intake and walking exercise more carefully on traveling days.  For example, after eating a meal at the airport restaurant or airline lounge, I made an effort to walk 3,000 to 4,000 steps in the aisles of the connecting boarding gates;

(4) My SmartPhoto tool’s analysis capability also provided me many insights regarding dining locations, food menus, and cooking material selection.

From examining the big picture data in SmartPhoto, I tabulated the results in Figure 6-4: Summary Table of Average Glucose at Different Eating Locations.  There are a total of 1,591 food and meal pictures with an average of PPG value of 121.8 mg/dL.  During the same period from May 1, 2015 to October 20, 2016, my average daily glucose from my APP tool, including FPG, is 121.41 mg/dL – this is another supporting point of why I decided to use my average daily glucose value as the initial predicted FPG value – while my 90-day average glucose is 123.75 mg/dL as shown in Figure 6-5: Glucose during SmartPhoto Period from May 1, 2015 to October 20, 2016.

My preliminary explanation and interpretation of causes for these summarized results are as follows:

(1) The average glucose values in all the studied nations are similar, measuring between 119.9 and 125.6 mg/dL.  From 2015 to 2016, I followed strict rules for food and meal intake along with the similar ratio between eating at home and eating outside in every country.

(2) Home cooking equates to a 115.3 mg/dL of glucose value, eating in chain restaurants (where nutritional ingredients information is published) equates to 125.2 mg/dL, eating in individual restaurants (where nutrition information is unavailable) equates to 132.3 mg/dL, eating at an airport, in an airline lounge, and in-flight meals equates to 134.0 mg/dL, and eating ready-cooked food from supermarkets equates to 140.6 mg/dL.

(3) Airline-related food produces high glucose points due to the fact that there are limited options on food items and limited space for post-meal exercising.

(4) After studying the nutrition of major food items, I have tried not to eat processed foods.  However, when I have limited options, I can still eat them provided that I read the nutrition facts on the labels carefully (especially the information regarding carbohydrates and sugar).

(5) Further detailed analysis regarding individual restaurants received the following average glucose values:

USA: 129.8 mg/dL

Japan: 139.6 mg/dL

Taiwan: 136.7 mg/dL

Other Nations: 130.8 mg/dL

In general, American and Western food do not include sugar in the cooking process (except in desserts).  Japanese, Korean, southern Chinese, and southeast Asian cultures add both sugar and salt into dishes during the cooking process.  Please see Figure 6-6: Measured Average Glucose for Different Eating Places.

(6) I discovered one interesting observation from analyzing a particular popular brand of chain restaurants.  Usually, I avoid eating lunch or dinner at any chain restaurant.  However, breakfast is an exception since the portions are usually smaller due to economic reasons.  As a result, the portion of carbohydrates and sugar are also greatly reduced in certain chain restaurants’ breakfast foods.  This same particular brand of American chain restaurant has an average glucose value of 122.9 mg/dL, while Japan has 117.4 mg/dL, Taiwan has 125.3 mg/dL, and China has 126.2 mg/dL.  My observation is that this particular chain restaurant in Taiwan and China add some local flavors to the menu items; furthermore, I suspect that its standard operating procedures (SOP) of procurement and cooking may not completely comply with its headquarter’s requirements.

(7) From 2013 to 2014, while I was studying food and nutrition, I drew an incorrect conclusion that I could eat as many vegetables as I wanted.  Later in 2015, after I compiled several million points of data on food nutrition, I discovered the differences among various vegetables.

One way I distinguish between how different vegetables affect my glucose is by color.  Please see Figure 6-7: Summary Contents of Carbs and Sugars in Vegetables.  I came to the conclusion that if I eat large quantities of vegetables, my PPG can increase to a higher value.  I must pay attention to the color of vegetables when I eat them in order to get a more accurate glucose prediction.

(8) When I have a craving for snacks, desserts, and/or fruits, I can definitely consume them, however, I must limit the quantity in order to control both my glucose value and weight.  The best practice for me is to eat limited amount of them between meals, for example at 10 am or 3 pm.  I avoid giving in to my cravings before bedtime to assist with my weight control.  Fruits are important for overall physical health; however, it is important to avoid eating high sugar content fruits (such as pineapples, bananas, etc.) and also limit the quantity consumed.  With this control mechanism, I can maintain a healthy level of glucose.

It would be interesting to analyze the “extreme” cases in my records, e.g. studying glucose over 200 mg/dL.  Figure 6-8 displays all of my 17 meals which contributed to glucose over 200 mg/dL from May 1, 2015 to October 20, 2016.

It should be noted that the 3 major sources contributing to my extremely high PPGs are eating at individual restaurants offering east Asian food, American chain restaurants, and meals on airlines & cruises.  I can still eat at these locations provided that I have knowledge of the food nutrition, use the right tool to predict post-meal glucose value, and have sufficient willpower to resist giving into cravings at the wrong time of day.

Furthermore, as indicated in the following Figure 6-9: Analysis of Causes for Glucose Values Greater Than 140 mg/dL, it is clear that high carbs & sugar food and Asian food have contributed about 58% of higher glucose values (>140mg/dL) causes.  Another interesting fact is that about 10% of unknown reasons occurred, which means I could not explain the actual causes of those high glucose values.

Research has shown that carbohydrates and sugars directly affect glucose levels.  By using the following rules, I can estimate my glucose level before I consume my meal by controlling the food quality and quantity.

(1) I can find the ingredients on the Nutrition Facts label on the food packaging.  I use the amount provided in terms of grams divided by 20 to get the portion estimate.  For example, carbohydrate has 16 grams, then calculate it as 16/20=0.8. The value of 0.8 is entered into the carbs input box of the tool.  I also calculate the sugar amount by using the same method.

(2) When I cook at home, I need to estimate the percentage based on using my open-hand area for estimation or my fist size for volume estimation as 100%.  However, based on my observation for the past few years of portion estimation, I have noticed recently that I need to reduce my 100% estimation to 2/3 of my hand or fist size.  My guess is that my body’s toleration of carbohydrates and sugar has been reduced due to the effects of diabetes.  After collecting more data regarding this phenomenon, I may need to build another layer of AI to address this organic change.

(3) My tool can also search each item of my food components from the food bank, and then add them up to get the total consumption of both carbohydrates and sugar.

(4) Most fruits have both carbohydrates and sugar, but some fruits such as bananas, pineapples, and grapes have higher carbohydrates and sugar content.

(5) It is highly recommended not to eat any desserts, since they contain high carbohydrates, sugar, salt, and fat, which are not healthy for you.  Try to eat plenty of green leafy vegetables; but avoid or reduce non-green vegetables such as beets, carrots, corn, onions, and tomatoes which have higher sugar content.

The most important principle for diabetic patients is to “even out” their glucose wave during the entire day, i.e. push the high tide downward (reduce hyperglycemia) and lift the low tide up (avoid shock from low glucose) like an ocean wave.  Once you are able to maintain your target weight and have a balanced nutrition, your diabetes and other chronic disease conditions should be under control.

Correlation between PPG and Diet (3/8/2017)

PPG (post-meal glucose) values are greatly influenced by our lifestyle, including primarily diet (intake of carbohydrates and sugar) and exercise (within a 2-hour period after each meal).  In Figures 6-10 and 6-11, the correlation results of r = 64.7% and r2 = 41.8% which show that a very strong positive correlation exists between daily average PPG and the average intake of carbs and sugar during daily meals.

More detailed analysis was done for investigating dinner’s glucose and intake of carbs + sugar by using both a Mean (Average) method and Least Square Mean (LSM) method. The results are displayed in both Figure 6.12 and 6.13 which show strong positive correlation values of r = 64.4% and r2 = 41.5%.  These dinner’s correlation results are almost identical as the daily meal average values.  Using the Mean method, I get an average post-dinner glucose of 115.64 mg/dL and an average intake of carbs + sugar of 13.90 gm; however, using the LSM method, I get average post-dinner glucose of 115.66 mg/dL while keeping an average intake of carbs + sugar of 13.90 gm.  This shows that there is no significant difference between using Mean or LSM method for this case.

Glucose and Exercise:

Other than food and meals, exercise was another important factor that contributed to my glucose reduction.  In my APP, I included many different types of exercises.  In my many years of experience, I believe that walking at average speed is the best kind of exercise for many senior citizens.  My average walking speed is 2.5 miles per hour, about 6,000 steps per hour, or 100 steps per minute.

In 2012, I walked an average of 8,000 steps, or 3.3 miles, per day.  During that time, it was difficult for me to walk too long because I was overweight and had weak knees.  By 2016, I gradually increased to 17,200 steps per day, or 7.2 miles per day without any difficulty.  Please see Figures 6-14, 6-15, and 6-16.

In the beginning of 2015, I discovered my PPG would significantly decrease if I spread out my daily walking exercise into 3 segments, i.e. exercising within 2 hours after finishing each meal.  By examining my glucose data for extended periods of time, I also learned that when my average glucose was around 140 mg/dL, with every 1,000 steps taken after a meal, I could reduce my glucose level 7 to 10 points.  However, when my average glucose value dropped to around 120 mg/dL, I could reduce my glucose level 4 to 6 points with every 1,000 steps after a meal.  This difference is due to the assumptions I made in my mathematical models.

Correlation between PPG and Exercise:

In Figure 6-17 the correlation results of r= 27.1% and r2 = 7.4% showed that a weaker but still significant negative correlation between PPG and average walking steps after each meal.  Please note that, as indicated in the same figure, I have walked around 4,000 steps within a 2-hour period after each meal.  In this way, I could take full advantage of walking exercise to improve my daily post-meal metabolism and reduce my PPG values.

Currently, I walk approximately 18,000 steps or 7.5 miles per day.  About 2 months ago, I felt some pain on my heels and I worried about over-exercising my joints.  Therefore, I decided to replace a part of my daily walking exercise with Tai Chi, a slower body motion and stretching movements.  I plan to collect big data over a much longer period to study the effect of Tai Chi on my glucose control situation more precisely.

Summary regarding PPG and Food Quality, Exercise; FPG and Weight:

Figure 6-18: Summary of PPG and Food Quality (Carbs & sugar), Exercise, provides the following two important conclusions:

(1) Every gram of carbs or sugar adds approximate 2 mg/dL of my PPG value.  Since I take about 14 grams of carbs/sugar per meal in average, my average PPG increase amount is 28 mg/dL.

(2) If I walk between 2,000 to 4,000 steps after each meal, I could reduce my PPG by 10 to 20 mg/dL.  This post-meal exercise can bring down my PPG net gain from 28 mg/dL to 8 to 18 mg/dL.  That is why my average PPG falls into the range of 108 mg/dL to 118 mg/dL.

Also shown in Figure 6-19: Correlation coefficients among key variables, we can see the following two conclusions very clearly.

(1) FPG and weight are directly connected.  There are no direct correlation between FPG and carbs & sugar, or exercise.

(2) PPG and carbs & sugar (food quality) are directly connected (60%), while exercise is the secondary factor (-27%).  PPG and weight have no direct connection even though weight is related to food quantity, i.e. meal’s portion.

 

Fig 6.1

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 6-1: SmartPhoto Sample Pictures of Food & Meal

 

Fig 6.2

 

 

 

 

 

 

 

 

 

Figure 6-2: Glucose during period of 2012-2014

 

Fig 6.3

 

 

 

 

 

 

 

 

Figure 6-3: Glucose during period of 2015-2016

 

Fig 6.4

 

 

 

 

 

 

 

 

 

 

Figure 6-4: Summary Table of Average Glucose and Different Eating Places

 

Fig 6.5

 

 

 

 

 

 

 

 

Figure 6-5: Glucose during SmartPhoto Period (05/01/2015-10/20/2016)

Fig 6.6

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 6-6: Measured Average Glucose for Different Eating Places

 

Fig 6.7

 

 

 

 

 

Figure 6-7: Summary Contents of Carbs and Sugars in Vegetables

 

Fig 6.8

 

 

 

 

 

 

 

 

 

Figure 6-8: 17 Meals contributed to PPG over 200 mg/dL (5/1/2015-10/20/2016)

 

 

Fig 6.9

 

 

 

 

 

 

 

 

 

 

Figure 6-9: Analysis of Causes for Glucose Values Greater Than 140 mg/dL

 

Fig 6.10

 

 

 

 

 

 

 

 

Figure 6-10: Correlation between PPG and diet (carbs and sugar in gm)

 

Fig 6.11

 

 

 

 

 

 

 

 

Figure 6-11: Detailed daily average intake of carbs and sugar per meal (around 15 gm per meal)

 

Fig 6.12

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 6-12: Least Square Mean Calculation for correlation between PPG and diet

 

Fig 6.13

 

 

 

 

 

 

 

 

Figure 6-13: Average Glucose and Carbs/Sugar using Mean Calculation

 

Fig 6.14

 

 

 

 

 

 

 

 

 

 

 

Figure 6-14: Walking Exercise (2012 – 2016)

 

Fig 6.15

 

 

 

 

 

 

 

 

 

Figure 6-15: Waking Exercise Concentrating in the Evening (2012 – 2014)

 

Fig 6.16

 

 

 

 

 

 

 

 

Figure 6-16: Walking Exercise spread out after 3 meals (2015 – 2016)

 

Fig 6.17

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 6-17: Correlation between PPG and exercise (post-meal walking steps)

 

Fig 6.18

 

 

 

 

 

 

 

 

 

 

 

Figure 6-18:  Summary of PPG and Food Quality (Carbs & sugar), Exercise

 

Fig 6.19

 

 

 

 

 

 

 

 

 

 

 

Figure 6-19: Correlation coefficients among key variables

 

Date:   10/28/2016  13:00

Section 7:  Glucose and Others (Stress, Travel, Temperature)

Glucose and Stress

Stress causes many health problems.  When people go through a long and continuous stressful lifestyle pattern, it can severely affect their health.  During my demanding thirty-year career, I endured a constant stressful lifestyle. This led me to have severe chest pains on 5 different occasions, and severe Type 2 diabetes, which resulted in chronic toe injuries, bladder damage, gum infection, and kidney damage.  However, after retiring from my business career, I have enjoyed a peaceful, non-eventful lifestyle (except for the year 2014).  During that year, I went through 3 episodes of higher than normal stressful events from March through June, then from September through October, and again from November through December. Please see Figures 7-1: Comparison of Stressful Periods from March to December 2014 and Peaceful Period from January 2015 to October 2016.

From the following Figures 7-2, 7-3, 7-4, 7-5, and 7-6, we can observe the clear correlation among stress, blood pressure readings, glucose values, and A1C levels.

Since the second and third stressful events occurred back to back, the charts reflect the high stress score, hypertension, and glucose to align with the two-time spans from March through June and again from September through December. However, the A1C value peaked approximately 3 months later than these time spans because A1C takes 3 to 4 months’ worth of average glucose values.

Furthermore, I suffered two separate physical traumas in 2015.  The first incident occurred on June 23rd, where I fell on a sloped walkway resulting in a face injury and an emergency room visit.  My recorded glucose values for the following three days after the accident were 152 mg/dL, 208 mg/dL, and 154 mg/dL, but then on the 4th day, the value dropped to normal level around 120 mg/dL.  The second incident occurred on December 4th, where I sustained a leg injury on a construction site and went to the emergency room again.  My recorded glucose values for the following three days after the accident were 145 mg/dL, 175 mg/dL, 165 mg/dL and then dropped to normal level around 120 mg/dL on the 4th day.

As a result of these two stressful incidents, my glucose level increased temporarily. Both cases took 4 days to allow the traumatic impact on my glucose to diminish.  On April 8, 2016, I fell after missing a step on a stairway, but I did not sustain any physical injury.  However, two hours after eating my normal low-carbohydrate and low-sugar breakfast, my PPG had spiked to 148 mg/dL – even with having the same breakfast as I always did and walking 4,000 steps after the meal.  Although this was a minor incident, it demonstrated that stress can affect the glucose level.

Glucose and Travel

Throughout my life as a businessman, I have traveled extensively worldwide.  To make it simple for my glucose discussion, I have only compiled my traveling record from 2012 to 2016.  I define long trips as air travel time with more than 3 hours (along with +/-2 hours going in and out of the airport) which can affect about 2 meals.  I define short air travel trips as flying time with less than 3 hours (along with +/-2 hours going in and out of the airport) which can affect about 1 meal.  During the past 5 years, on average, I have flown every two weeks, or more precisely every 12.9 days.  From my analysis of my health status on the flying days, I noticed that both my glucose and metabolism were affected noticeably by the traveling.  The two main reasons my glucose and metabolism were affected are due to airline food, not being the best option for diabetics, and limited space for exercise. Once I figured out the main causes, I managed my air travel meals very carefully by watching what I can eat safely and to walk as much as I can after my meal in the crowded airport space. Therefore, during 2015 to 2016, both my glucose and metabolism index during air travel days have greatly improved, almost reaching my normal level of 120 mg/dL.  These analysis results are in Figure 7-7.

Glucose and Weather

I spent 40 years living in different states within the U.S. with less pollution, great weather, and mild climate (with temperatures ranging from 15℃ to 25℃).  During the first half of 2016, I stayed in East Asia continuously for more than 6 months, throughout winter, spring, and early summer.  Although I was traveling to different cities, I disciplined myself to maintain a routine lifestyle, which includes monitoring food, exercise, stress, sleep, water intake, other routines, etc.  However, I noticed my glucose level continued an upward trend during February through June, when the temperature was getting hotter in Asia. I could not explain why but I wondered if the hot weather conditions affected my metabolism.  Figure 7-8 provides a preliminary and short period (about 4.5 months) of data observation.   I wrote this information here to invite other researchers’ attention and input on this topic.

 

Fig 7.1

 

 

 

 

 

 

 

Figure 7-1: Stress Scores Comparison of Stressful period (3/2014 – 12/2014) and Peaceful Period (1/2015 – 10/2016)

 

 

Fig 7.2

 

 

 

 

 

 

 

Figure 7-2: Stress Score During 2014

 

Fig 7.3

 

 

 

 

 

 

 

 

Figure 7-3: Higher Blood Pressures During Stressful Periods

 

Fig 7.4

 

 

 

 

 

 

 

Figure 7-4: Higher Daily Glucose During Higher Blood Pressure and Stressful Periods

 

Fig 7.5

 

 

 

 

 

 

 

 

Figure 7-5: Higher A1C peaks Appear around 3 months Later of High Glucose

 

Fig 7.6

 

 

 

 

 

 

 

 

Figure 7-6: Putting Higher Stress Scores and Higher 90-days Average Glucose Together

 

Fig 7.7

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 7-7: Correlation Among Glucose, Metabolism and Air Travel

 

 

Fig 7.8

 

 

 

 

 

 

 

Figure 7-8: Correlation Between Glucose and Atmosphere Temperature

 

Date:   10/29/2016  15:00

Section 8:  Hyperlipidemia and Hypertension

I have compiled my physical examination data and entered them into my tool since the year 2000.  The plotted lipid graphics are reflected in Figures 8-1, 8-2, 8-3, and 8-4.  Results have shown that I suffered from hyperlipidemia from 2000 to 2012.  Since 2012, although my focus was to control my diabetes, the overall strategy was to utilize preventive medicine via a better and effective lifestyle management.  The mathematical simulated metabolism model developed in 2014 provided an effective tool for lifestyle management.  As a result, while my glucose values were under control, it assisted me in changing my unhealthy lipid data into a healthy state.  Further discussions about metabolism will be found in the next section.

Lipids have a close relationship with the “quality” of food.  In order for me to consume a better quality of food, I included a list to serve as both reminder and record for the food quality.  Please see Figure 8-5: Reminder and Record of Quality for Food & Meal.  In this table, if you follow all the rules, then you will get a score of 0.5. If you violate all the rules, then you will get a score of 1.5. Please see Figure 8-6: Score of Quality for Food & Meal from mid-2014 to October 20, 2016.  My “Quality for Food & Meal” satisfaction level is 96% – reflecting a score of 0.54, satisfaction level = (1.5-0.54)/(1.5-0.5).

My blood pressure data is shown in Figures 8-7, 8-8, and 8-9. As I mentioned in an earlier section, from March through December of 2014, I had 3 consecutive stressful periods and their impact on my blood pressure can be seen on these diagrams.  I have already included “limit salt intake” as one of the requirements in “Quality for Food & Meal,” which I firmly follow in my diet.  From Figure 8-10: Analysis of Causes for High Blood Pressure, my data showed that major stressful events are highly correlated to hypertension, which is then followed by overseas traveling, jet-lag, post exercise measurement, extreme weather condition, etc.

Fig 8.1

 

 

 

 

 

 

 

Figure 8-1: Triglycerides (2000 – 2016)

 

Fig 8.2

 

 

 

 

 

 

 

 

Figure 8-2: HDL-C (2000 – 2016)

 

Fig 8.3

 

 

 

 

 

 

 

Figure 8-3: LDL-C (2000 – 2016)

 

 

Fig 8.4

 

 

 

 

 

 

 

Figure 8-4: Total Cholesterol (2000 – 2016)

 

 

Fig 8.5

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 8-5: Reminder and Record of Quality of Food & Meal

 

Fig 8.6

 

 

 

 

 

 

 

Figure 8-6: Score of Quality of Food & Meal

 

Fig 8.7

 

 

 

 

 

 

 

Figure 8-7: Highest Daily SBP & DBP

 

Fig 8.8

 

 

 

 

 

 

 

Figure 8-8: Average Daily SBP & DBP

 

Fig 8.9

 

 

 

 

 

 

 

Figure 8-9: Average Daily Heart Rate

 

Fig 8.10

 

 

 

 

 

 

 

 

 

Figure 8-10: Analysis of Causes for High Blood Pressure

 

Date:   11/1/2016  11:00

Section 9:  Metabolism Index (MI) and General Health Status Unit (GHSU)

For the entire year of 2014, I have conducted research and development on the subject of overall health and chronic diseases.  At the beginning, I tried to find a good definition of metabolism but failed.  For example, the Webster Dictionary defines it as “metabolism = the organic processes (in a cell or organism) that are necessary for life.”  Finally, I tried to define metabolism in a quantitative way.

I created a new term called Metabolism Index (Ml). It is based on four categories of the human body health’s daily output data and six categories of human body health’s daily input data related to chronic diseases.  The four categories of daily output include body weight, blood sugar, blood pressure, and lipid. The six categories of daily input include exercise, water intake, sleep, stress, food and meals, and daily routines.  Since input, output, and the biomedical system are dynamic, i.e. they are changing with time; I included “Time” as the eleventh category.

Within each category, there are many more elements.  For example, there are 9 elements in sleep, 33 in stress (not all elements are suitable for everyone), and approximately 100 for food and meal, etc.  At the end, there are several hundred of elements that need to be addressed, recorded, and analyzed.  Of course, it is a huge burden to figure them out effectively on a daily basis.  The biggest challenge is how to solve the inter-connectivity issues among 11 different categories and hundreds of elements.  As a result, I utilized the finite element concept and dynamic plastic behavior of structural engineering to model this system.  I was able to build a set of mathematical governing equations with various boundary conditions.  With these efforts, the remaining problem to solve was to apply computer science, especially computational automation, and artificial intelligence.  This is where big data analysis and analytic come into play.

The General Health Status Unit (or GHSU) is the moving average of Metabolism Index (MI) over the most current 90 days.  Originally, I defined MI to fall within the range of 0.5 (best condition) to 1.5 (the worst condition).  When both MI and GHSU are under 1.0, it means that your health is generally good.  However, if these values are over 1.0, you may have some health issues or related lifestyle problems.  For myself, I finalized an optimal set of elements within each category and also defined my desired healthy level status: 170 lbs. for body weight; 120 mg/dL for glucose; 120/80 for SBP/DBP; and 150/40/130/200 for triglycerides/HDL-C/LDL-C/total cholesterol.  The “break-even” level for both MI and GHSU is actually 73.5%, i.e. above 73.5% is unhealthy whereas below 73.5% is healthy.  Please note that I have adopted the general medical practice of the lower value to represent as being better or healthy.

As of August 13, 2017, my MI and GHSU are at 56.6% and 55.2% respectively, which indicate that I am healthy.  My physicians have also confirmed that my general conditions are very healthy based on various laboratory test results for the past 3 years. This is an actual application of how to control chronic diseases via applying quantitative medicine on lifestyle management, i.e. a branch of preventative medicine and translational medicine.  Please see Figures 9-1 and 9-2 regarding my MI and GHSU for a period of 2012 through 2016 and another period of April 11, 2015 to October 20, 2016.

With the introduction of basic concepts regarding MI and GHSU, let us examine scores of some major categories. In previous sections, we have already seen many figures of collected data summary, such as weight, waistline, glucose, blood pressure, lipids, food & meal, exercise, and stress.  The remaining missing categories are also important but not so directly linked with the health output data, particularly diabetes related.  I will repeat food & meal data and figures in this section.  Figure 9-3 lists the summary category scores derived from my mathematical computational model and their transformed “satisfaction level” which is a self-explanatory phrase.

Besides the Metabolism Model that was developed in 2014, three other major breakthroughs were produced: The Weight Prediction released on April 11, 2015, Post-meal Glucose Prediction released on June 1, 2015, and Fasting Glucose Prediction released on July 3, 2017.  These four models provided tremendous help and accurate guidance to help control my diabetes and other chronic diseases.  Therefore, in this section’s data and figure display, I selected the period from April 11, 2015 to October 20, 2016 as the standard common period for comparison.

 

Fig 9.1

 

 

 

 

 

 

 

Figure 9-1: MI & GHSU (2012 – 2016)

 

Fig 9.2

 

 

 

 

 

 

 

Figure 9-2: MI & GHSU (4/11/2015 – 10/20/2016)

 

Fig 9.3

 

 

 

 

 

 

 

 

Figure 9-3: Conversion Table of MI Category Scores to Satisfaction Levels

 

My water intake score is 0.74 and its satisfaction level is 95% (100% is defined as drinking 6 bottles or 3,000 ml of water per day). During this period, I have been drinking 5.7 bottles or 2,850 ml of water on average per day.

Fig 9.4

 

 

 

 

 

 

 

Figure 9-4: Water Score

 

Three major stressful events happened to me in 2014; however, during this period (4/11/2015-10/20/2016), I did not encounter stressful situations. Therefore, my stress score is 0.51 and its satisfaction level is 99%.

Fig 9.5

 

 

 

 

 

 

 

Figure 9-5: Stress Score

 

Sleep category has 9 elements.  Among them, sleep hour and sleep interruption due to waking up are the most important two elements for my case.  My total sleep score is 0.74 and its satisfaction level is 86%, not bad at all.

Fig 9.6

 

 

 

 

 

 

 

 

Figure 9-6: Sleep Score

 

During this period, I slept 7 hours and 15 minutes per night on average, which is quite sufficient.

Fig 9.7

 

 

 

 

 

 

 

 

Figure 9-7: Sleep Hours

 

For most male senior citizens, night time urination due to prostate enlargement is the most disturbing factor affecting sleep.  In my case, I was told by my physician that my bladder was damaged due to the long term effects and severity of diabetes.  During the years from 2012 to 2014, I used to wake up 4 times at night to use the bathroom. However, during the period from 2015 to 2016, I only wake up 1.8 times (less than 2) per night on average. This improvement was entirely due to a better lifestyle management, not from taking urological medication.

Fig 9.8

 

 

 

 

 

 

 

 

Figure 9-8: Sleep Disturbance due to Wake Up

 

My food and meal score is simply the average of both quantity score and quality score.  It is 0.73 and its satisfaction level is 77%, which is a decent score.

Fig 9.9

 

 

 

 

 

 

 

Figure 9-9: Food & Meal Score

 

I will repeat both the food quantity and food quality scores in order to emphasize their different roles in controlling chronic diseases.  Food & Meal Quantity control is important for weight control, and in turn to control multiple chronic diseases.  My score is 0.91, or 91% of my normal food consumption (portion size) which allows me to maintain my weight at 172 lbs. I have started another push to drop my weight down to 168-169 lbs. level by reducing my portion size to between 80% to 90%.

Fig 9.10

 

 

 

 

 

 

 

Figure 9-10: Food & Meal Quantity Score

 

My food quality score is 0.54 and its satisfaction level is 96%.   You can get the 100% score if you follow the ready-defined 20 rules precisely every day.  From my experience, this score helps me to lower my blood lipid to reflect the data in the healthy level status.  Genetically, I was born with low blood pressure, but previously as a businessperson, I encountered many stressful events that caused me to have “temporarily but not so severely” hypertension.   I refuse to take medication for my “high” blood pressure, so instead I changed my lifestyle in order to correct this health problem.

Fig 9.11

 

 

 

 

 

 

 

Figure 9-11: Food & Meal Quality Score

 

Finally, let us examine my daily routine life pattern score of 0.74 and its satisfaction level 95%.  This means that I follow a regular routine in my daily life pattern.  This category has a total of 14 elements to be checked on a daily basis.  Evidence has shown that a simple and regular life pattern contributes a lot to life longevity.  I finally was able to live this kind of life after I retired from a highly competitive business career and to find new interests to pursue, while maintaining a simple but routine lifestyle.

Fig 9.12

 

 

 

 

 

 

 

Figure 9-12: Daily Routine Score

 

Date:  11/1/2016  14:00 & 8/13/2017 23:00

Section 10:  Conclusion

This project “Using Quantitative Medicine (a branch of Translational Medicine) to control Type 2 diabetes” started in August of 2010 and completed in August of 2017.  During the first 4 years, I studied 6 chronic diseases and food nutrition in depth.  In addition, I invested next 3 years on research and development, and then created 4 major mathematical and biomedical prediction models to simulate the human body’s health system.  These 4 prediction models are: Metabolism on 12/31/2014, Weight on 4/11/2015, PPG on 6/1/2015, FPG on 7/30/2017.  Along the way, I created an application software (APP) for patients to use on their iPhone/iPad or PC.  I was able to produce and store more than one million of “clean” data regarding my health and lifestyle in the cloud server from 2012 to 2017.

Although my findings were no different from other research findings and discussions in the medical community, I hope the same conclusions are drawn from my personal quantitative data; therefore, they can provide an extra level of confirmation and credibility for worldwide patients to follow.

As I mentioned previously, I am presenting my personal health data from the past 5.5 years.  However, I am confident that my findings are highly applicable to many other Type 2 Diabetes (T2D) patients whose glucose values are within the range of 90 to 400 mg/dL.  Although there are other patients’ data already available in our cloud server through the use of my tool, I did not have enough time to analyze their data yet.  This will be a part of phase 2 of this project.

In August of 2010, when my physician informed me that both my A1C and ACR values were dangerously high, I was extremely scared and did not know what to do next.  I thought about my health condition long and hard and then I realized that there is only one person who can really help me. And that person is MYSELF.  Yes, I can save my own life!  For cases that require medication, operation, or urgent care, a trained medical doctor is definitely needed for treatment and guidance.  However, with chronic diseases, I did not get this disease overnight thus it cannot be cured overnight.  The obvious way to overcome these conditions is through “preventative medicine,” which requires a lifestyle change.  Since I was diagnosed with diabetes, I already knew that there is no way I could cure my disease completely, but I can do my best to control it from getting worse.

Toward the end of this project, I finally realized what is Lifestyle change: you must be willing to examine your life to see what are your priorities or goals.  Once you decide on a new set of life priorities, you then examine or develop a complete list of associated or related tasks, activities, and behaviors. After that, you can then seek for knowledge, expertise, and tools to help you make changes. This is the true lifestyle change.

From my journey of the past 7 years, I found 3 fundamental problems associated with most diabetic patients:

  1. Lack of disease knowledge;
  2. Lack of a useful tool;
  3. Lack of willpower and persistence.

These are the reasons making it difficult to adapt our lifestyle to control our chronic diseases.  By now, I have acquired sufficient knowledge regarding diabetes and can easily share the most important and fundamental part of it with other patients.  Through my research, I have also developed practical and free mobile tools to control the disease on a daily basis.  However, I am still puzzled about how to influence others to change their lifestyles and health behaviors.  I am currently studying this problem via a “Social Dynamics” approach, i.e. using natural science, including mathematics, physics, computer science, and various engineering concepts and methods to address, analyze, and hopefully, to be able to alter patient’s social-psychological behaviors, especially health habits.  I know this is another long and tough journey, probably more difficult than my research on diabetes.  I founded a non-profit organization, eclaireMD Foundation, with a goal to address medical problems associated with certain chronic diseases, and to conduct tactical activities to help other patients worldwide.  My plan is to incorporate what I learned from “Social Dynamics” into future phases of this project.

 

Date:  11/1/2016  15:00 & 8/13/2017 23:15

Section 11:  Acknowledgement

I would like to thank the following people:

First and foremost, I wish to express my appreciation to Professor Norman Jones, who is a very important person in my life.  Not only did he give me the opportunity to study at MIT, but he also trained me extensively on how to solve problems and conduct scientific research.

I would also like to thank Professor James Andrews.  He helped and supported me tremendously when I failed academically at the University of Iowa.  He believed in me and prepared me the undergraduate requirements on how to build my engineering foundation.

These two great professors helped me, so that I could one day help others.

Jamie M. Nuwer, MD in Palo Alto, California, is a bright, young physician who has compassion in helping her patients.  She also took me under her care while she worked at the Stanford Network in Menlo Park.  Her encouragement and support are deeply appreciated.

Neal Okamura, MD in San Ramon, California, was my primary physician for 20 years, from 1992 to 2012.  He was the one who warned me in 2010 regarding the severity of my diabetic condition, which triggered me to launch this project.

Jeffrey Guardino, MD and Kristine Sherman, MD of the Stanford Network in Menlo Park, California, have been my primary physicians since 2012.  During my many visits with them, we spoke in detail about how I used my tool to improve my health.  It was Dr. Guardino who encouraged me in 2015 to write this paper for others to read.

During my journey, I met two medical doctors in Taiwan, Jia-Lei Loo, MD and Chi-Jen Tseng, MD.  Not only are they my medical advisors, but they are also my social network friends.  We constantly chat about many health-related issues online.  I am grateful to them for their support and encouragement from overseas.

My deep appreciation goes to both James Ratcliff, MD and JoEllenVanZander, MD of the Stanford Network in Menlo Park, California, for their care and encouragement.

I also would like to express my appreciation to both Steven Bhimji, MD and Patricia Hsiao, MD for their early participation of the “knowledge” development for this project.

I want to thank Gay Winterringer, who has a PhD in nutrition and food science, for her impressive input and knowledge.

Dr. Chia-chi Ou, a Ph.D. in Chemistry, has been my best friend since the age of 4.  Throughout my lifetime and the past 7 years of diabetes research, he has listened to my thoughts and feelings patiently and also provided strong moral support and useful intelligent input.

I have known both Clayton Parker and Dennis Heller for many years while working together in the semiconductor industry. After retiring from my business career, they became good friends of mine.  I would like thank them for their time and feedback throughout the project.

I would like to extend my appreciation to a long-time trusted friend since my MIT days, Dr. Toyohiko Muraki, for his input and support on my development of the metabolism model, using multi-dimensional nonlinear engineering modeling technique, and many follow up discussions regarding my research work.

My appreciation also extends to my friend, Professor Toru Nakura at Department of Electrical and Electronic Engineering at Tokyo University in Japan, for his valuable inputs and discussions regarding my mathematical and statistical analysis results interpretation.

My deep appreciation also goes to Janet Kwan, RN for her devoted contribution and whole-hearted support to this project since she joined my team in early 2013.  I have discussed and exchanged numerous ideas regarding diabetes disease and healthcare with her.

I would like to thank to my lovely daughter, Cindy Janus, for her tireless editing of my paper and, incorporating my findings on her own battle to improve her lifestyle.  The same appreciation goes to my dear son, John Hsu, who is using my App in fighting his type 2 diabetes.

Last, but not least, I would like to thank my wonderful wife, Lily, who is a diabetic patient herself and has participated in my program for almost 4 years.  Her participation and daily support of my efforts provided additional data points different from mine, resulting in many insightful revelations.

 

 

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