Using Quantitative Medicine to control Type 2 Diabetes

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Title: Using Quantitative Medicine to control Type 2 Diabetes
By: eclaireMD Foundation
Date: 10/20/2016 – 3/8/2017

Table of Contents

Section 1: Introduction (2 figures)
Section 2: Methods (3 figures)
Section 3: Weight and Diabetes (8 figures)
Section 4: Glucose (8 figures)
Section 5: Glucose and Food & Exercise (17 figures)
Section 6: Glucose and Others (8 figures)
Section 7: Hyperlipidemia and Hypertension (10 figures)
Section 8: Metabolism Index (MI) and General Health
Status Unit (GHSU) (12 figures)
Section 9: Conclusions (0 figure)
Section 10: Acknowledgement (0 figure)

Total 86 pages and 68 figures

Abstract of paper:
“Using Quantitative Medicine (a branch of Translational Medicine) to Control Type 2 Diabetes”
By: eclaireMD Foundation
Date: November 28, 2016

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

The author spent 7 years (2010-2016) 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.

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 (2015-2016), 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

His entire research and development efforts have been based on lifestyle management as part of a preventive medicine, by collecting, processing, and analysis of quantitative medical and health data. The results are displayed in more than 70 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 and 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: eclaireMD Foundation
Date: 10/20/2016 – 3/8/2017

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.

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 was measured at 350 mg/dL. Between 1998 and 2000, I suffered several bouts of severe low blood sugar known as insulin shock. Attached below 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 that I needed to begin using insulin immediately and would most likely end up on dialysis within 3 years. In the following 3 months, I moved to a new city with fewer stressors, shut down all of my business enterprises, and started the journey to save my life. After more than 6 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 over one year), triglycerides level 67 mg/dL, HDL 48 mg/dL, LDL 103 mg/dL, total cholesterol 156 mg/dL, and BMI 25.

Figure 1.1







Figure 1-1:

Comparison of health data

My overall diabetes conditions during the past 15 years can be seen in Figure 1-2: A1C over 15 years (2000-2016). 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.

Figure 1.2








Figure 1-2:

A1C over 15 years (2000-2016)


Date:   10/21/2016 17:00

Section 2:   Methods

During the late part of 2010, I came to realize 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.  During 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 “cell” 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 that route. Therefore, I took stock of my strengths, which were mathematics, computer science, and various engineering disciplines.  I have never received any formal training in the biomedical area; therefore, I used a nonlinear dynamic engineering model and Finite Element concept of structural engineering for inorganic materials to simulate the human body’s organic metabolism system.  And then, I applied advanced mathematics to develop this model’s 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 life pattern or regularity for longevity.  These 10 categories contain several hundred detailed elements.  For example, just “Stress” category contains 33 different stressors for both “normal” person and “abnormal” person (e.g. people who suffer personality disorders).  I also included “Time” as my 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 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 (cancer is also one kind of chronic diseases).  It should be noted that my research is focused on preventive medicine, therefore, drugs for treatment medicine is only included as a part of the glucose prediction tool.

Given the average 3-4 month lifespan of blood cells, which carry glucose and lipids throughout the 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 “solve the equation” (in a mathematical sense), and afterward, the system starts to learn by itself through AI.  In 2014 and 2015, I began building this “organic” bio-medical math model and two practical prediction models for both body weight (after one night of sleep) and 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 Figure 2-1 and 2-2, I have reached a 99.9% accuracy on body weight prediction (4/11/2015-10/21/2016) and a 99.0% accuracy on glucose prediction (6/1/2015-10/21/2016).

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 be able 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 case), it was clear that my overall health conditions had improved significantly (as of now, my MI and GHSU are at around 57%) through a better lifestyle management.

Figure 2.1








Figure 2-1:

Predicted and actual body weight (4/11/2015 – 10/21/2016)

Figure 2.2








Figure 2-2:

Predicted and actual daily averaged glucose (6/1/2015 – 10/21/2016)

Figure 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 198 lbs., in 2010 it was 194 lbs., and in 2016 it was 172 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 number 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.

Figure 3.1








Figure 3-1

Weight (2012-2016)

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.

Figure 3.2








Figure 3-2

Waistline (2012-2016)

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 & 3-4. For example, from June 2015 to October 2016, my weight has been around 172 lbs. while my food and meal quantity has been around 91% of my normal portions. But, if I want to further reduce my average weight below 170 lbs., I must cut down my food & meal quantity to around 80% of my normal portions.

Figure 3.3








Figure 3-3

Food & Meal Quantity (6/1/2015-10/20/2016)

Figure 3.4








Figure 3-4

Body Weight in the morning (6/1/2015-10/20/2016)

Now, let us examine the correlation between weight and glucose. Other than genetic factors, being overweight or obese is the main fundamental 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 3-5 and 3-6 below. My tool’s functionality of predicting post-meal glucose levels before consuming food is very crucial for controlling my PPG (Postprandial Plasma Glucose).

Figure 3.5








Figure 3-5

Weight over 180 lbs

Figure 3.6








Figure 3-6

Daily averaged glucose over 160 mg/dL

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 year, my average weight gain between bedtime and the morning of the same day is 2.8 lbs. My weight reduction between bedtime and the next morning is also 2.8 lbs. That is why I can maintain a constant weight at 172 lbs. (+/- 5 lbs.) over the past year. My tool’s functionality of predicting the next morning’s weight is a crucial component of controlling my weight; therefore, my weight control is a crucial part of controlling my diabetes condition. Figures 3-7 & 3-8 show my two kinds of weight changes.

Figure 3.7








Figure 3-7

Weight gain between bedtime and morning of the same day

Figure 3.3








Figure 3-8

Weight reduction between bedtime and next morning


Date:   10/24/2016  13:00

Section 4:   Glucose

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 warning from my 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 cloud database, I’ve stored about one million data points regarding my body and lifestyle, which includes original inputted data, system calculated data, and AI processed data.  In Figure 4-1: daily glucose and 90-days average glucose, my glucose levels varied between 86 mg/dL and 227 mg/dL with an average value of 129 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%. My mathematical simulated A1C values (between 6.3% and 8.1% with an average value of 7.1%) had an accuracy of 92.4%.

The laboratory A1C test result reflects the average glucose values for the past 90 days. The eclaireMD Wellness APP 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 artificial intelligence to automatically customize the calculation according to the changes of the user’s biomedical system’s parameters.

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 2016. This means that my simulated A1C can provide me with an upper-bound 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 changes with time. There are many elements that affect glucose values and I will display more of my research results in following discussions regarding these elements.

Although during 2012 through 2016, the overall A1C curve remains at a somewhat steady state, i.e. around 6.6, the most important factor of controlling my diabetes condition is that I have been decreasing the dosage of my medication over two years’ time frame and finally completely removed all of my diabetes medications about one year ago.  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 and also reduce dosage amounts from the beginning of 2014.  By the end of 2015, I had completely eliminated taking any diabetes medication.

During this period, I witnessed different degrees of “withdrawal symptoms.”  Within one month of removing or reducing medication, my glucose chart fluctuated greatly, with many ups and downs, without any clear and reasonable explanation.

Figure 4.1








Figure 4-1:

Daily Glucose and 90-days Averaged Glucose (2012-2016)


Figure 4.2








Figure 4-2:

Mathematical Simulated A1C and Lab-tested A1C Comparison


Figure 4.3


















Figure 4-3:

List of My Past Lab-tested A1C Values and Corresponding Mathematically Simulated A1C Values

I have been a long-term diabetic patient for almost 20 years.  I realized that having knowledge, a reliable tool, and will power are the three main ways to control this disease.  During the past 2 years, I have applied my acquired medical knowledge and developed my APP tools to help control both my weight and glucose.  The main tools are my weight predictor developed on April 11, 2015 and my glucose predictor developed on June 1, 2015.  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 had to be followed in terms of using these two prediction tools.  First, I had to follow the prediction model’s suggestions regarding input value as precisely as possible.  Second, after measuring my weight or glucose, I was not allowed to go back to readjust my original input values in order to change the prediction’s accuracy (unless there were some new findings or facts that I had just learned or realized from applying this prediction experience).  Some degree of artificial intelligence (AI) has been built into the system as well, but the entire bio-medical system needed to be continuously observed and modified along the way.  That is why I created a non-profit medical research foundation to continuously work in depth on this subject even after my death.

On July 1, 2016, I entered the following note in my diary:

“Based on the period of 6/1/2015 to 6/30/2016, 13 months of study on actual data analysis, my Chronic Tool has reached 99.2% accuracy rate on my A1C prediction and 98.4% accuracy rate on my 90-days averaged predicted glucose value.  Therefore, starting today 7/1/2016, I will not use the traditional blood test (test strip method) entirely to measure my glucose.  I will mainly depend on my mathematical model to predict my glucose in order to control my diabetes.  I will then go to a hospital or clinic to measure my A1C after 10/1/2016 to make sure that my diabetes condition is still under control.  During the upcoming 3 months, I must be extremely careful in monitoring my diet and meal contents and do my post-meal exercise diligently.  In addition, there are several primary factors to be monitored as well.  If this test is finally successful, my invented mathematical biomedical model on metabolism to control diabetes can reduce both cost and pain for worldwide diabetes patients.”

Since that day, I decided to decrease the amount of testing using traditional glucose meter (finger piercing and test strip method, or glucose meter / glucometer).  However, I still needed to continue testing my fasting glucose by using the glucometer because of my ongoing research of A1C variation due to different weighting contribution factors of FPG vs. PPG.  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.  For the period of July 1, 2016 to October 23, 2016, I have 115 days’ worth of 460 glucose data, 115 FPG data (25% of total glucose data), and another 345 PPG data (33% of total PPG and 25% of total glucose data) as actual measured glucose data.  The remaining 230 data (50% of total data) were relied on my predicted glucose values. The tentative conclusion from this experiment is that I could eliminate the finger-piercing method and still get a 99.6% accuracy on my glucose results.  In order to validate my findings, I plan to continue this experiment for at least another year.  This is an important temporary finding that, if this conclusion is true and my prediction method is proven to be reliable, then most Type 2 diabetes (T2D) patients whose average daily glucose level fall between 100 mg/dL and 400 mg/dL will be able to use my prediction tool to control their diabetes.  It was my original goal of conducting this kind of reliability research to remove the burden and cost associated with finger piercing and test strips.  Hopefully, by using an easy and useful tool as an option, we can further reduce patients’ reluctance on their efforts of controlling diabetes.  Please see Figure 4-4: 50% of glucose data based on predictions (7/1/2016-10/23/2016).

Figure 4.4









Figure 4-4:

Preliminary Data Analysis to determine the reliability and accuracy of glucose prediction when patient dropping off finger-piercing and testing-strip method. These preliminary data have 50% of glucose values based on predictions (7/1/2016-10/23/2016)

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 FPG vs. PPG during the complete period of 6/30/2015 through 3/24/3017.  The results of both r (3.8%) and r2 (0.14%) 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 bio-medical point of view.  Fasting glucose is the combination effect of glucose produced by liver and insulin produced by pancreas during sleeping hours.  On the other hand, during the awakening hours, both the diet control and exercise have direct impact on PPG values.

Figure 4.5




















Figure 4-5

No correlated relationship between FPG and PPG during a period (6/30/2015 – 3/7/2017)

Multiple Factor Analysis (Weighted contribution factors) of FPG and PPG on A1C

I have collected my PPG values for more than 5 years and FPG values for almost 2 years.  Therefore, I can conduct a big data analytics on the composite impact of FPG and PPG on A1C value.   First of all, I have 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 of 6/1/2015 through 1/3/2017 (my latest A1C lab test date).  During this period, I have collected 9 sets of laboratory tested A1C values which are used as the base for this comparison study.  I then choose 5 sets of different weighted contribution factors for my data sensitivity analysis:

a = 0%, 2%, 5%, 8%, 11.4%;

and the corresponding

b = 100%, 98%, 95%, 92%, 88.6%.

This set of factors is to calculate 5 different adjusted A1C values in order to compare them against the lab tested A1C results which were collected on 9 different lab testing dates.  Figure 4-6 has shown 5 diagrams with 5 sets of adjusted A1C values in comparison with 9 different lab tested A1C values.  These 5 sets of different FPG/PPG weighted contribution factor will result into 5 different Adjusted A1C values.  By comparing Adjusted A1C and lab-tested A1C, I found high correlation coefficients are exited (from 41% to 60%).

Figure 4.6





































Figure 4-6

5 sets of adjusted A1C values in comparison with lab tested A1C data on 9 different lab testing dates

In Figure 4-7, Statistical Comparison study of A1C values based on eclaireMD predicted, lab-tested, and Adjusted, it shows that the case of 0% of FPG and 100% of PPG is the closet mix 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, in reality, my weighting contribution factors of FPG and PPG to A1C value are probably falling within the range of FPG < 10% and PPG > 90%.  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 comparison study.

During this 21 months period since 6/1/2015, my averaged lab-tested A1C is around 6.55% (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.

Figure 4.7







Figure 4-7

Statistical Comparison study of A1C values based on eclaireMD predicted, lab-tested, and Adjusted

Earlier 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 trying to identify most of the important elements which affect 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 function in terms of creating and controlling glucose level, I decided to study the fasting glucose phenomena by using a “macro-viewed” big data analytics approach.  Thus far, I have collected close to 700 morning pre-breakfast fasting plasma glucose (FPG) data.  Since the prediction of FPG is very different from the prediction of PPG (postprandial plasma glucose), and the linear contribution weighting factor for FPG to daily averaged glucose value (4 times glucose collection per day) is somewhat around 25%.  At first, I decided to use my 90-day average daily glucose as the initial condition for predicting FPG.  Please see Figure 4-8:  Predicted FPG value based on my preliminary finding of 360-days’ worth of data.  It indicates that, although 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 averaged glucose value. The deviation between predicted and actual is only 1.6% and my predictions reach 98.4% accuracy.  It should be noted that the above analysis and tentative conclusion were based on data available prior to October 20, 2016.  During this period, most of my fasting glucose data are very close to my averaged daily glucose value.

Figure 4.8







Figure 4-8:

Predicted FPG value based on my preliminary finding of 360-days’ worth of data.

As shown in Figure 4-5, it clearly indicates that there is no influence between FPG and PPG ( correlation coefficient is 3.8%).  The three correlation coefficients between FPG and all of three separated PPG values (between 111 to 116 mg/dL) are also very low.   I am also puzzled about the quantitative characteristics of FPG, including what the exact causes of the surge, how to predict its pattern, and how high it will jump, particularly how to control the high FPG from happening.

Thus far, I have already collected my FPG data for 21 months.  However, my earlier conclusion of using my averaged daily glucose value as my newly predicted fasting glucose was turned over by my recent observed fasting glucose data since my FPG jumped up suddenly starting from November 22, 2016.   After collecting 4 more months of higher FPG values, I have had sufficient amount of data to investigate FPG.   I have chosen a data set of a period of 244 days, from 6/24/2016 to 3/24/2027.  During this period, I further subdivide them into two “equal length” sub-periods of 122 days each.

In the first sub-period from 7/24/2016 through 11/23/2016, the averaged FPG value is 115 mg/dL, while the second sub-period from 11/23/2016 through 3/24/2017 has an averaged FPG of 130 mg/dL.  It showed a 15 mg/dL increased amount of the averaged FPG value.

During this 4-months sub-period, there are no significant changes in my lifestyle, including food, exercise, stress, sleep, water drinking, life regularity, temperature, living environment, etc.  I have also read many articles and papers regarding FPG, tried many different tricks, e.g. taking snacks before sleep, measuring my 3:00 am glucose level, etc., and conducted numerous correlation analyses.  But, I still could not identify any clear pattern to serve as an useful tool on finding how to control my higher FPG in the morning.  I have also performed statistical analyses to compare 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 contributes an increase to the same day’s post-breakfast glucose or receives a “left-over” impact from the previous day’s post-dinner glucose.  Please see both Figure 4-9 & 4-10: correlation studies between FPG and two different PPG values.

Figure 4.9

















Figure 4-9

Correlation study between FPG and same day’s post-breakfast PPG

Figure 4.10

















Figure 4-10:

Correlation study between FPG and previous day’s post-dinner PPG

Recent Fasting Glucose Study

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 and computer science background.  My past training in engineering disciplines follows this thinking route: develop a governing equation, define prominent input values, and finally derive outputs as the system’s solution.  On March 17, 2017, I suddenly thought about the possibility of one output category could also be served as the influential input factor of another output category.   After I break out from my previously trained thinking constraints, I then be able to look into the FPG problem with a different angle.  In the past, I was trying to identify the correlation and inter-dependency pattern between FPG and other input factors, but totally ignored another output category, weight, even though weight is depending on some inputs, such as food and exercise.   I have already stored my FPG values since 6/1/2015 and all other data in my database.

As shown in Figure 4-11, I have found a high correlation coefficient (47%) exist between FPG and Weight, and a moderate correlation coefficient (24%) exist between Wight & Food Quantity.

Figure 4.11
























Figure 4-11:

Correlation studies between FPG & Weight, and Wight & Food Quantity


Figure 4.12
















Figure 4-12:

Weight increase and FPG increase during the second sub-period (11/23/2016 – 3/24/2017)

A very clear correlation picture can be observed in Figure 4-13, when I plot them out under the selection criteria of weight data of >175 lbs and FPG data of >130 mg/dL.  I also realize 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 PPG value, however, it did result in a 4 to 6 pounds of weight increase which in turn caused the increase of 15 mg/dL on FPG.

Figure 4.13
















Figure 4-13:

Strong correlation for weight > 175 lbs and FPG > 130 mg/dL

Of course, the increased FPG values will most likely also to push up my A1C values.  As I mentioned earlier that my FPG contributes <11.4% and my PPG contributes >88.6% on my predicted A1C value.  Therefore, I am guessing that the forthcoming FPG surge will probably push up my lab-tested results from 6.5 on 1/4/2017 to around 6.7 on 4/1/2017, my scheduled next  medical lab test.

However, if you conduct a statistic analysis (least square mean and correlation coefficient), then you will find that, from Figure 4-14, r is only 14% for weight/PPG comparing against 47% for weight/FPG.  Now, the question is why PPG only has 14% of correlation while FPG has 47%?

Figure 4.14
























Figure 4-14:

Correlation comparison between Weight & FPG and Weight & PPG

My personal interpretation of the correlation coefficient of 14% 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=65%) and regular exercise (r=27%), are true primary factors.  Although our body weight (one of body’s output) is controlled by our lifestyle, diet and exercise (two of body’s inputs) as well.  During 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 different story.  For most cases, during sleep time, other lifestyle factors can not be maneuvered by us and therefore, our brain takes over the total control of operation of our internal organs.  When brain dictates body weight has been increased and then it will give orders to liver to produce glucose around 3AM for storing tomorrow morning’s needed energy.  If liver produces excessive glucose, the brain then gives order to pancreas to produce insulin to balance the glucose level.  But diabetes patient’s pancreas function is malfunctioned, therefore this malfunctioned glucose control mechanism will push up our body’s glucose in the morning (FPG).  Since diet and exercise cannot alter FPG directly during sleep, therefore, the secondary factor, weight, becomes the input factor and comes into play.  This is my understanding and interpretation.

From now on, I will reduce my body weight down to 170-172 lbs range within next month. After achieving this goal, I will continue to monitor both of my weight and FPG for at least another 4-months period of “steady-state” in order to have a complete years of records which composes 3 sub-periods, with averaged FPG of low/high/low.  Through this experiment, I can then confidently draw this conclusion of correlation between weight and fasting glucose and verify it.  In Figure 4-15, I have summarized my predicted FPG values under 6 different body weights in the morning.

Figure 4.15

























Figure 4-15:

My predicted FPG values under 6 different body weights in the morning.

I have selected previous X-months, where X=3,4,5,6,7,8, data of predicted FPG (including the influence from the correlated weight factor) and actual measured FPG to conduct my comparison study.  The results are shown in Figure 4.16 that the accuracy of mean comparison is greater than 98%, while the correlation coefficients fall into the range of 12% to 40%.

Figure 4.16











Figure 4.16

Data Sensitivity Study for predicted FPG

Since I developed my post-meal glucose prediction model on 6/1/2015 and my fasting glucose prediction model on 3/18/2017, I have collected 668 days of glucose data.  In the background of predicted glucose, I collected and analyzed data for the following categories: exercise, food, carbs, sugar, stress, weight, etc. which included other calculated and processed information amounting to a total of several hundred thousand data. The final results of FPG along with the PPG comparison between predicted and actual glucose during the period of 7/11/2015 through 3/30/2017 are displayed in Figures 4.17 and 4.18.  As a result, the accuracy of mean values of glucose is greater than 95% while correlation coefficients are within the range of 66% to 89%. This conclusion proves the accuracy and reliability of my glucose prediction models. Furthermore, the lab-tested A1C values also confirmed its accuracy.


Figure 4.17























Figure 4.17


Figure 4.18






























Figure 4.18

Please see the following chart, Figure 4.19.  I have used the previous 90-days body weight correlated model to predict my FPG values (7/11/2015-4/8/2017), which is very accurate. The correlation coefficient is almost 73%.

Figure 4.19









Figure 4.19

Date:   10/25/2016  13:00

Section 5:   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 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 food photos with its data structure are stored in SmartPhoto, they can be sorted and searched any way a person chooses.

Please see Figure 5-1: SmartPhoto Samples of food & meal with glucose level attached with each photo.  From May 1, 2015 through October 20, 2016, I collected a total of 1,591 pictures of food and meal with an average glucose level (PPG) of 121.8 mg/dL.  During the same period, my daily averaged glucose level (including FPG) is 121.41 mg/dL.

Figure 5.1



















Figure 5-1:

SmartPhoto Sample Pictures of Food & Meal


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 5-2: Glucose results from 2012 to 2014.  I found that the majority dishes of 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 recent extended stay in various eastern Asian countries and Hawaii over 8 months has introduced another prominent fact.  My average glucose level dropped below 120 mg/dL – a drop of 20 points from previous periods.  Please see Figure 5-3: 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 make the effort to walk 3,000 to 4,000 steps in the aisles connecting the boarding gates;

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

Figure 5.2








Figure 5-2:

Glucose during period of 2012-2014


Figure 5.3








Figure 5-3:

Glucose during period of 2015-2016

From examining the big picture data in SmartPhoto, I tabulated the results in Figure 5-4: Summary Table of Averaged 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 5-5: Glucose during SmartPhoto Period from May 1, 2015 to October 20, 2016.

Figure 5.4










Figure 5-4: Summary Table of Averaged Glucose and Different Eating Places

Figure 5.5








Figure 5-5:

Glucose during SmartPhoto Period (05/01/2015-10/20/2016)


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

Figure 5.6
















Figure 5-6: Measured Average Glucose for Different Eating Places


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.

(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 5-7: 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 body 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.

Fig 5.7






Figure 5-7:  Summary Contents of Carbs and Sugars in Vegetables

It would be interesting to analyze the “extreme” cases in my records, e.g. studying glucose over 200 mg/dL.  Figure 5-8 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 and cruises.  I can still eat at these locations provided 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.

Figure 5.8










Figure 5-8:

17 meals contributed to PPG over 200 (5/1/2015-10/20/2016)

Furthermore, as indicated in the following Figure 5-9: Analysis of Causes for Glucose Values Greater Than 140, 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.

Figure 5.9









Figure 5-9:

Analysis of Causes for Glucose Values Greater Than 140

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 artificial intelligence 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 contains 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 Figure 5.10 and 5.11, the correlation results of r = 64.7% and r2 = 41.8% which show that a very strong positive correlation exists between daily averaged PPG and the averaged intake of carbs and sugar during daily meals.

Fig 5.10









Figure 5.10

Correlation between PPG and diet (carbs and sugar in gram)

Fig 5.11









Figure 5.11

Detailed daily averaged intake of carbs and sugar per meal (around 15 gram per meal)

More detailed analysis were 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 5.12 and 5.13 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 averaged values.  Using the Mean method, I get an averaged post-dinner glucose of 115.64 mg/dL and an averaged intake of carbs + sugar of 13.90 gram; however, using the LSM method, I get averaged post-dinner glucose of 115.66 mg/dL while keeping an averaged intake of carbs + sugar of 13.90 gram also.  This shows that there is no significant difference between using Mean or LSM method.

Fig 5.12
























Figure 5.12

Least Square Mean Calculation for Correlation between PPG and Diet


Fig 5.13









Figure 5.13

Averaged Glucose and Carbs/Sugar using Mean Calculation

Glucose and Exercise:

Other than food and meals, exercise was another important factor that contributed to my glucose reduction.  In my APP, I include 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 5-14, 5-15, and 5-16.

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.

Fig 5.14












Figure 5-14:

Walking Exercise (2012-2016)

Fig 5.15










Figure 5-15:

Waking Exercise Concentrating in the Evening (2012-2014)


Fig 5.16








Figure 5-16:

Walking Exercise Spreading over After 3 Meals (2015-2016)


Correlation between PPG and Exercise

In Figure 5.17 the correlation results of r= 27.9% and r2 = 7.8% show that a weaker but still significant negative correlation between PPG and averaged 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 also reduce my PPG values.

At present, 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 of time to study the effect of Tai-chi on my glucose control situation more precisely.


Fig 5.17









Figure 5.17

Correlation between PPG and exercise (post-meal walking steps)

Date:   10/28/2016  13:00

Section 6:  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, and kidney damage.  However, after retirement 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 6-1: Comparison of Stressful Periods from March to December 2014 and Peaceful Period from January 2015 to October 2016.

Figure 6.4








Figure 6-1

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


From the following Figures 6-2, 6-3, 6-4, 6-5, and 6-6, 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 two time spans from March through June and again from September through December. However, the A1C value peaks approximately 3 months later than these time spans because A1C takes 3 to 4 months’ worth of average glucose values.

Figure 6.5








Figure 6-2:

Stress Score During 2014

Figure 6.6









Figure 6-3:

Higher Blood Pressures During Stressful Periods

Figure 6.7








Figure 6-4:

Higher Daily Glucose During Higher Blood Pressure and Stressful Periods

Fig 6.5








Figure 6-5 :

Higher A1C peaks Appear around 3 months Later of High Glucose


Figure 6.9








Figure 6-6:

Putting Higher Stress Scores and Higher 90-days Averaged Glucose Together

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 & low-sugar breakfast, my PPG had spiked at 148 mg/dL. Not only had I ate the same breakfast as I always did, but I had also walked 4,000 steps afterward.  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) 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 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 6-7.

Figure 6.10















Figure 6-7:

Correlation Among Glucose, Metabolism and Air Travel


Glucose and Weather:

I spent 40 years living in different states in the U.S. with less pollution, great weather, and mild climate (with temperatures ranging from 15 degree to 25 degree Celsius).  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 6-8 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.

Figure 6.11








Figure 6-8

Correlation Between Glucose and Atmosphere Temperature


Date:   10/29/2016  15:00

Section 7:  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 Figure 7-1, 7-2, 7-3, and 7-4.  Results have shown that I suffered from hyperlipidemia from 2000 to 2012.  Since 2012, although my focus was to control my diabetes, my 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.

Fig 7.1








Figure 7-1:  Triglycerides (2000-2016)


Fig 7.2








Figure 7-2:  HDL-C (2000-2016)


fig 7.3








Figure 7-3:  LDL-C (2000-2016


Fig 7.4








Figure 7-4:  Total Cholesterol (2000-2016)


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 7-5: 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 7-6: 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).

Fig 7.5


















Figure 7-5:

Reminder and Record of Quality of Food & Meal


Fig 7.6








Figure 7-6:

Score of Quality of Food & Meal


My blood pressure data is shown in Figures 7-7, 7-8, and 7-9. 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 7-10: 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 timing, extreme weather condition, etc.

Figure 7.7








Figure 7-7:  Highest Daily SBP & DBP

Figure 7.8








Figure 7-8:  Averaged Daily SBP & DBP

Figure 7.9








Figure 7-9:  Averaged Daily Heart Rate

Figure 7.10









Figure 7-10:  Analysis of Causes for High Blood Pressure


Date:   11/1/2016  11:00

Section 8:  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 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 drinking, 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 8 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 better or healthy.

As of October 20, 2016, my MI and GHSU are at 58.45% and 57.1% 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 of the past 2 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 8-1 and 8-2 regarding my MI and GHSU for a period of 2012 through 2016 and another period of April 11, 2015 to October 20, 2016.

Figure 8.1








Figure 8-1:  MI & GHSU (2012-2016)


Figure 8.2








Figure 8-2:  MI & GHSU (4/11/2015-10/20/2016)


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 8-3 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, two other major breakthroughs were produced: the Weight Prediction released on April 11, 2015 and Glucose Prediction released on June 1, 2015. These three 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 select the period from April 11, 2015 to October 20, 2016 as the standard common period for comparison.

Figure 8.3









Figure 8-3:

Conversion Table of MI Category Scores to Satisfaction Levels


My drinking water 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.

Figure 8.4








Figure 8-4:  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%.

Figure 8.5








Figure 8-5:  Stress Score


Sleep category has 8 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.

Figure 8.6









Figure 8-6:  Sleep Score


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

Figure 8.7








Figure 8-7:  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 intaking of urological medication.

Figure 8.8









Figure 8-8:  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.

Figure 8.9








Figure 8-9:  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 around 80%.

Figure 8.10








Figure 8-10:  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.

Figure 8.11








Figure 8-11:  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 life.

Figure 8.12









Figure 8-12:  Daily Routine Score


Date:   11/1/2016  14:00

Section 9:   Conclusions

This project “Using Quantitative Medicine (a branch of Translational Medicine) to control Type 2 diabetes” started in August of 2010 through the end of 2016.  For 4 years, I self-studied several chronic diseases and food nutrition in depth.  In addition, I invested 3 years of research and develop these 3 major mathematical and biomedical prediction models to simulate the human body’s health system.  Along the way, I created an application software 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 2016.

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

As I mentioned previously, I am presenting my personal health data from the past 5 years.  However, I am confident that my findings are highly applicable to many other Type 2 Diabetes (T2D) patients with cases of having glucose values in the range of 90 to 400 mg/dL.  Although there are other patients’ data available in the cloud server through the use of my tool, but I have not spent 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.  That person is MYSELF.  For those cases that need medication, operation, or urgent care, a trained medical doctor is definitely needed for treatment and guidance.  However, in the case of chronic diseases, I did not get this disease overnight thus it cannot be cured overnight. The clear way to overcome these conditions is through “preventative medicine,” which requires a lifestyle change. Since I was diagnosed with diabetes, I knew already that there is no way I could cure my disease completely, but I can do my best to control it from getting worse.  From my personal journey, 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.

That is why it is difficult to change our lifestyle in order to control our chronic diseases.  By now, I have acquired sufficient knowledge regarding diabetes.  Through my research, I have also developed practical tools to control it on a daily basis. However, I am still puzzled about how to influence others to change their lifestyles and behaviors.  I am currently studying this problem via “Social Physics”, i.e. using natural science, including mathematics, physics, computer science, and various engineering methods to address and alter human beings’ social-psychological behaviors.  I know this is another long and tough journey.  I founded eclaireMD Foundation with a goal to address certain diseases along with medical problems and to conduct nonprofit activities to help other patients worldwide.  My plan is to incorporate what I learn through “Social Physics” and incorporate it in future phases of this project.


Date:   11/1/2016  15:00

Section 10:   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.

My deep appreciation goes to both James Ratcliff, MD and JoEllen VanZander, 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.

I have known Dennis Heller for many years while working together and we became good friends after my retirement from my business career.  I would like to thank him for his time and feedback throughout the project.

I would like to extend my appreciation to my dear old friend from the MIT days, Dr. Toyohiko Muraki, for his input 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 in early 2013.  I have discussed and exchanged numerous ideas regarding diabetes disease and its care with her.

Last, but not least, I would like to thank my wonderful wife, Li Li, who is a diabetic patient herself and has participated in my program for over 3 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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