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Using Quantitative Medicine to Control Type 2 Diabetes

Posted by on Aug 31, 2017 in News | 0 comments

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.

 

 

Bunions (Q & A)

Posted by on May 20, 2015 in News | 0 comments

FootBUNIONS (Q & A)

What is a bunion?
Bunion is an abnormal bony bump at the base of the big toe. Medically, it is referred to as hallux valgus deformity. This enlarged and misaligned joint causes the big toe to point toward the second toe instead of staying straight forward in a normal position. Sometimes, it may even overlap the second toe. The pressure from the big toe may cause the little toes to be misaligned as well. The skin covering the joint may look red and inflamed.

Can you have bunion on the other toes?
Yes, you can have bunion on the base of your little toe (fifth toe). It is called bunionette or tailor’s bunion.

Are bunions painful?
Yes, bunions can be very painful especially with walking. Larger bunion may cause difficulty with walking.

What are the complications of bunion?
Bunion causes your big toe to look distorted and misshapen cosmetically. In addition, the fluid-filled sac around the joint can get inflamed and a condition called bursitis may develop. Over a long period of time, arthritis may develop on the affected joint.

Do bunions affect both genders?
Yes, bunions affect both men and women. But it is estimated that bunions are ten times more common in women than men.

Do bunions run in the families?
Yes, bunions run in the families. If you have a defect in the structure of your foot, you are more likely to develop bunions.

What are the causes of bunions?
Bunions develop from wearing a tight-fitting, narrowed or pointy toe shoes or high heels. It could also be genetic or due to abnormal structure in your foot that you are born with.

What are the signs and symptoms of bunions?
Common symptoms include pain, swelling, redness and tenderness around the joint on the big toe. Common signs are having abnormal bony bump on the side of the big toe and big toe points toward the little toes. The pressure from the big toe may cause the crowding of the little toes and develop a condition called hammertoe. The skin on the bottom of your foot may get thickened and calluses may develop on the skin along the big toe. If bunion is large and severe, you may have pain with walking.

How are bunions diagnosed?
There are no specific tests to diagnose bunions. The doctor will make the diagnosis based on the examination of your foot. He or she will look for redness, swelling, inflammation and the skin changes on your foot and big toe. The doctor will assess the range of motion of the joint on the big toe. Some doctors may order X-ray of the foot to see if you have deformity or abnormal structure of the bone. X-ray can also detect arthritis of the foot.

How are bunions treated?
Treatment depends on severity of pain and effects on your daily life. If you have mild pain that comes on and off, the doctor may try conservative treatments first. He or she will recommend that you wear comfortable shoes with wide toes or sandals. You can find over-the-counter arch support, pads for your soles and spacers between the toes. There are some orthopedic shoes or devices you can get with a prescription.

As for pain, you can take acetaminophen (Tylenol) or ibuprofen (Motrin, Advil), naproxen (Aleve, Naprosyn) or NSAIDs (Aspirin, Ecotrin). You may apply ice pack to the affected area to help reduce inflammation and swelling. If you have a more severe pain, your doctor can give you corticosteroid injection to the area.

If you do not find relief with conservative treatment and pain medication, surgery is an option. There are different surgical procedures available and the doctor will discuss the right one for your case. You can go home on the same day for bunion surgery. Bunionectomy is one of the surgical procedures in which abnormal bony growth is removed and the big toe is realigned back to the normal position. The doctors will instruct you to wear proper shoes after the procedure to ensure a recovery and prevent new bunions from forming. Surgical option is available for bunionette (bunion on fifth toe) as well.

Surgery is not an option for teenagers because the bones are not fully grown yet. Bunions in teenagers are often treated with conservative treatments.

Contributed by Patricia Hsiao M.D.
Sources: aaos.org, mayoclinic.com, medicinenet.com, ncbi.nlm.nih.gov

Cervical Cancer (Q & A)

Posted by on Apr 22, 2015 in News | 0 comments

CERVICAL CANCER (Q & A)

Female anatomyWhat is cervical cancer?
Cervical cancer is cancer of the cervix, which is part of a woman’s reproductive organ. Cancer occurs when cells are dividing rapidly and abnormally. Eventually, it becomes a tumor. Cancer cells can spread to other organs such as liver, lungs and bones by traveling through the blood vessels.
There are two types of cell in the cervix and the name of the cancer indicates which type of cells is involved.
The middle and right images showed how cervix looks from the vagina.

Where is cervix located?
Cervix is located in the pelvic and it is the lower end of the uterus (womb), connected to the vagina. Uterus is where the baby grows during pregnancy. During pregnancy, the cervix is tightly closed and it is opened at childbirth.

How common is cervical cancer?
In the U.S and developed countries, cervical cancer is declining due to preventative screening test. However, in the developing countries, cervical cancer is still the second most common cancer in women.

What are the names of the cervical cancer?

Squamous cells, which are on the outer layer of the cervix, are involved in squamous cell carcinoma of the cervix. Columnar cells are involved in adenocarcinoma of the cervix and they are the lining of the cervical canal. Squamous cell carcinoma of the cervix is a more common one.

What causes cervical cancer?

Cervical cancer is associated with human papilloma virus or HPV infection. While there are many strains of HPV, only infection with HPV 16 and 18 are high risk for developing cervical cancer. HPV is passed from one person to another via sexual intercourse. There are many women who are infected with HPV but not all of them will develop cervical cancer. In some women, the infection goes away on its own. HPV infection can also cause genital warts, which is a non-cancerous condition.

Who are at risk for cervical cancer?
All women who are sexually active are at risk for HPV infection. But it does not necessarily mean they are at risk for cervical cancer. The following are risk factors for cervical cancer.

• Having multiple sexual partners
• Engaging in sexual activity before the age of 18
• Having a history of sexually transmitted infections (STIs) such as gonorrhea, chlamydia, HIV and syphilis
• Having a dysplasia of the cervix
• Having a weak immune system
• Having a family history of cervical cancer
• Having a mother who took a medication called DES (diethylstilbestrol) during pregnancy
• Smoking

What is dysplasia of the cervix?
Dysplasia refers to the changes in the cells before they become cancerous. It takes several years in order for dysplasia to progress into cervical cancer. There are a number of treatments available for dysplasia. Therefore, early detection via routine screening is an important step in preventing cervical cancer.

What are the signs and symptoms of cervical cancer?
Early cervical cancer does not present with any signs or symptoms. As the cancer advances, a woman may have abnormal period that lasted longer or heavier than usual. Some women may experience bleeding between periods or having period after menopause or bleeding after sexual intercourse. Some may notice abnormal bloody discharge, pelvic pain, back pain and fatigue.

What is the screening test for cervical cancer?

Pap Smear is the screening test for cervical cancer and it detects abnormal cells in the cervix. During Pap Smear exam, your doctor will take a small sample of cells from the cervix and send it to the lab for examination under the microscope. HPV test is also available for women over 30. It may be ordered along with Pap Smear or some doctors may order it after Pap Smear came back abnormal. HPV test is to see if a woman has been infected with certain strains of HPV.

How is cervical cancer diagnosed?
After the abnormal Pap Smear, the doctor will do more tests to see if it is cervical cancer or not. He or she will perform a pelvic exam to see if there is any abnormality in the uterus (womb), ovaries (eggs) and vagina.
Colposcopy is a procedure in which the doctor looks at your cervix with a colposcope (magnifying lens) and takes small sample of tissues from the abnormal area for biopsy. Then, the tissue sample will be sent to the lab for examination under the microscope. Colposcopy is often done in the doctor’s office. Some doctors may do cone biopsy where cone-shaped tissue is taken for biopsy and it contains deeper layer of cells. Once the diagnosis of cervical cancer is made, your doctor will refer you to a cancer specialist.

What is staging?
Once the cervical cancer is diagnosed, your doctor will do staging to determine the extent of the cancer. Staging is based on the size of the cancer, whether it has spread to the surrounding tissues and other organs or not. Staging helps doctors to formulate treatment plan.
Cervical cancer has stage I to stage IV. In stage I, cancer is only in the cervix. Stage II cancer has spread beyond the cervix and it may include uterus. In stage III, cancer has spread to the lower part of the vagina and may include pelvic wall (tissues that line the body). In stage IV, cancer has spread to bladder, rectum or other parts of the body. As part of the staging, your doctor will order other imaging tests such as chest X-ray, CT scan of the pelvis, MRI of the pelvis, cystoscopy to see inside of the bladder, colonoscopy to look at the entire colon and etc.

How is cervical cancer treated?
Treatment of cervical cancer depends on a number of factors such as your age, staging of the cancer and your health condition. There are several surgical options, radiation therapy and chemotherapy available to treat cervical cancer. It is important to have family members and close friends for emotional support during the treatment. Counseling and support groups available for you to meet other women who have been diagnosed with cervical cancer.

What types of surgery are available?

If the cancer is in very early stage, simple hysterectomy can be done. It is a procedure to remove the cervix and uterus. A lot of doctors prefer radical hysterectomy even in early stages of cervical cancer. In this procedure, the doctor removes the cervix, uterus and part of the vaginal, leaving the ovaries (eggs) in place. It can either be done by incision in the belly or laparoscopic procedure in which only small incisions are made to insert the instruments and camera.

What is radiation therapy?
Radiation therapy (RT) uses high energy X-ray to stop the cancer cells from growing and there are two ways to give radiation therapy. In brachytherapy, the device is placed in the cervix and it gives high dose of radiation to the cancerous area. In external beam radiation therapy (EBRT), the radiation is given from outside of the body and it is given five days a week for about 5 to 6 weeks in a clinic setting. In early stages of cancer, your doctor will use one of the radiation methods. For advanced cancer stages, both methods may be used.

What is chemotherapy?
Chemotherapy uses medicines to stop or slow the cancer cells from growing and it is used to enhance the effect of radiation therapy. Medicines are usually injected into a vein once a week. For advanced cancer stages, chemotherapy and radiation are used before the surgery to shrink the tumor.

Is cervical cancer preventable?
Yes, cervical cancer is very much preventable with a routine Pap Smear exam. Also, HPV vaccines are available for girls 9 years of age to 26 years old women who have not been sexually active yet.

How can I prevent myself from getting cervical cancer?
Practice safe sex by using condom, limit the number of sexual partners, do not engage in sexual activity at early ages, get Pap Smear exam routinely, get HPV vaccination and quit smoking if you smoke.

What are the names of the HPV vaccines?
The vaccine called Gardasil prevents infection from HPV strains (6, 11, 16 and 18). Another vaccine called Cervarix prevents infection from HPV strains 16 and 18. Both vaccines are given in a series of three shots over six months.

What are the recommendations for Pap Smear?
You should get your first Pap Smear at the age of 21 or three years after your first sexual intercourse. For women between 21 and 29 years of age, most organizations recommended to have Pap Smear every two years. If you are between 30 and 65 years of age, the recommendation is every three years or every five years with HPV test. If your Pap Smear shows abnormal cells, you may need more frequent Pap Smear. Once you are 65 or older, you can stop having Pap Smear if you have had three normal results within the last ten years. Your last Pap Smear exam must be in the last five years.

Contributed by Patricia Hsiao M.D.
Sources: cdc.gov, acog.org, mayoclinic.com, uptodate.com, cancer.org, nlm.nih.gov

Uterine fibroids (Q & A)

Posted by on Mar 18, 2015 in News | 0 comments

UterusWhat are uterine fibroids?
Uterine fibroids are benign growth of the uterus (womb) and they are called leiomyomas or myomas in medical terms. A woman can have one or many fibroids and they can vary in size. Fibroids develop from the same muscle tissue as the uterine wall. They can be found inside the uterus, outside the uterus, attached to the outer surface of the uterus or within the uterine wall. They have different names based on their locations and they are usually round shape. Uterine fibroids may either get bigger or smaller with time.

Are uterine fibroids cancerous?
No, uterine fibroids are benign so they are non-cancerous.

How common are uterine fibroids?
Uterine fibroids are very common. Approximately 75 to 80 percent of women have uterine fibroids at some point in their lives and many of them do not have any symptoms.

What age group is more likely to have fibroids?
Uterine fibroids can occur at any age but it is more common among women who are in their 30s and 40s. It seems to be rare among teenagers.

Are there any risk factors for uterine fibroids?
Uterine fibroids tend to run in the families. If your mother or sister has uterine fibroids, you may be at higher risk for developing fibroids. Uterine fibroids are more common among African American women for unknown reason. They tend to have fibroids at younger ages. On the other hand, childbirth and taking oral contraceptive (birth control pills) may decrease your risk of developing uterine fibroids.

What causes uterine fibroids?
Researchers do not know the exact causes of uterine fibroids. Female hormones called estrogen and progesterone appear to be associated with fibroids. Since estrogen and progesterone stimulate the growth of uterine lining to prepare for pregnancy during the monthly cycle, it also promotes the growth of uterine fibroids.
In early pregnancy, fibroids may grow in size due to increased estrogen but it shrinks after childbirth. In addition, uterine fibroids tend to shrink after menopause when estrogen and progesterone are decreased significantly.

What are the symptoms of uterine fibroids?
Many women with uterine fibroids do not have any symptoms. Common symptoms include heavy or prolonged menstrual period and spotting between periods. Too much blood loss during menstruation can cause iron deficiency anemia where you have low blood count and most common symptom is fatigue. Furthermore, you may experience pain or pressure in your lower belly. The pain can be either sharp or dull and you may have pain during sexual intercourse.
If the fibroid is large, it can obstruct nearby structures. For instance, if it is pressing on the bladder, you may experience frequent urination or difficulty urinating. If it presses on the rectum, you will have constipation or difficulty with bowel movement. Some women experience cramps and enlarged uterus. Depending on the location of the fibroids, you may have difficulty getting pregnant. Fibroids can cause miscarriage but it is not common.
Pain the lower belly could be from uterine fibroids.

How are uterine fibroids diagnosed?
Uterine fibroids are often found during a pelvic exam when a woman has no symptoms. Your doctor will notice the irregular shape of the uterus. Pelvic ultrasound is used to confirm the diagnosis. Ultrasound uses sound waves to take pictures of the uterus and the surrounding organs. The probe can be placed on the belly or inside the vagina (transvaginal ultrasound).
Other imaging tests are available but they are often not necessary. Pelvic CT or MRI is hardly used to diagnose uterine fibroids. If a doctor wants to see the fibroids inside the uterus, he or she can use hysteroscopy in an office setting. It is a small device inserted through the vagina. Sonohysterography is a procedure in which fluid is put into the uterus before the ultrasound exam to get better images of the uterine lining. Hysterosalpinography is a procedure that allows doctors to see both uterus and fallopian tubes (uterine tubes).

Does everyone need treatments for uterine fibroids?
No. However, it depends on many factors such as symptoms, age, fertility, size and location. If you are premenopausal, fibroids tend to shrink after menopause. Without any symptoms, treatment may not be necessary.
Treatment is required when it interferes with getting pregnant. Some fibroids grow very slowly or not at all then, treatment is not required. In that case, your doctor will monitor it with pelvic exam and ultrasound periodically.

What are medical treatments for uterine fibroids?
There are several medications available to treat uterine fibroids and those include birth control pills, long-term birth control methods, NSAIDs, GnRH agonists, iron supplement and other medicines.

How do birth control pills affect fibroids?
Oral contraceptive or birth control pills help regulate period so they reduce symptoms such as heavy period and menstrual cramps. However, they do not shrink uterine fibroids. You can get the same benefit from skin patch and vaginal ring. It may take a few months before you see any changes in your period.

What are long-term birth control methods?
Intrauterine device or IUD is a long-term method of birth control and it releases hormone called progestin. The doctor will place it inside the uterus. Even though it has no effect on the fibroids directly, it can reduce heavy period up to five years. Another option for long-term birth control is depo-Provera shot every three months and it also contains progestin hormone. These are good options for women who do not plan to start a family in the near future.

Do I need iron supplement?
If your period is heavy, taking iron supplement may help lower the risk of anemia.

What other medicines can I take for menstrual cramps?
NSAIDs or nonsteroidal anti-inflammatory drugs are recommended for cramps and pain but they do not reduce heavy bleeding. You can obtain over-the-counter ibuprofen (Motrin, Advil) and naproxen (Aleve, Naprosyn) for pain relief.

What medication can reduce the size of fibroids?
Gonadotropin-releasing hormone (GnRH) agonists and androgen (Danazol) are medicines that can reduce the size of fibroids. GnRH agonist is often given three to six months before the surgery to shrink the fibroids. Androgen is a steroid hormone and less commonly used due to its side effects such as weight gain, unwanted hair growth, acne, reduced breast size, mood swing and depression.

How do GnRH agonists work?
GnRH agonists work by decreasing estrogen and progesterone which are made by the ovaries. Without these hormones, fibroids shrink. However, it also stops your period so put you in menopause temporarily. You may experience menopausal symptoms such as hot flashes, vaginal dryness and sleep problem. It is not used for long-term because of high risk for osteoporosis (thinning of the bones).

What types of surgery are available to treat fibroids?
Surgery options are often discussed after medical treatment fails or shows very little improvement. Hysterectomy and myomectomy are surgical procedures for treating fibroids. If you do not want to have any more children, hysterectomy will be an option for you. The surgeon will remove your entire uterus and it is an invasive procedure. You may leave the ovaries so you do not go into menopause.
Myomectomy is a procedure where the doctor only removes the fibroids, leaving your uterus in place. There is a chance that fibroids may come back in the future and you may need treatment again. It is an option for women who still want to have children.
There are different ways to do myomectomy and it depends on the size and location of the fibroids. For larger fibroids, abdominal myomectomy is required. The doctor will make an incision in the lower belly to remove the fibroids. Smaller and fewer fibroids can be removed by laparoscopic myomectomy where only very small incisions are made in your belly to insert the camera and tools. If the fibroids are completely inside the uterus, the doctor can perform hysteroscopic myomectomy. The doctor will remove the fibroids through vagina and cervix.

Are there any less invasive procedures beside surgery to treat fibroids?
Yes, there are a few less invasive procedures to treat fibroids. They include endometrial ablation, uterine artery embolization (UAE), magnetic resonance guided focused ultrasound and others.

What is endometrial ablation?
Endometrial ablation destroys the lining of the uterus by heat or electric current. It can only be used for fibroids inside the uterus and it is done to reduce heavy period. In some women, endometrial ablation may stop the period. Pregnancy is not recommended after endometrial ablation, therefore; you will need some forms of birth control after the procedure.

What is uterine artery embolization (UAE)?
Uterine artery embolization (UAE) is a procedure that cuts off the blood supply to the fibroids by injecting tiny particles into the blood vessels leading to the uterus. Without adequate blood supply, fibroids shrink within weeks or months. Pregnancy is not recommended after the procedure so you will need some forms of birth control afterward.

What is magnetic resonance guided focused ultrasound?
This procedure uses multiple ultrasound waves to directly destroy the fibroids through the skin and it is guided by MRI machine. Again, pregnancy is not recommended after the procedure.

Are there any complications with uterine fibroids?
Pain, discomfort and anemia from heavy period are common complications from fibroids. If the fibroid is outside the uterus and attached to it by a stem, it can get twisted. When it happens, you may have sharp pain in the lower belly and nausea.
Fibroids usually do not interfere with pregnancy and may not require treatment. Some fibroids depending on the location may require removal because it may block the uterine tube in order for conception to occur or prevent implantation of the embryo in the uterus.

Contributed by Patricia Hsiao M.D.
Sources: uptodate.com, acog.org, mayoclinic.com, medicinenet.com, ncbi.nlm.nih.gov

Fighting childhood obesity with healthy snacks

Posted by on Feb 25, 2015 in News | 0 comments

ObeseThe term “obesity” refers to having too much fat while overweight means having excess body weight. Childhood obesity has increased significantly in the last three decades in the United States and it is becoming more of a medical condition rather than just a social aspect. Majority of the time, childhood obesity is due to overconsumption of foods and lack of physical activity. Genetic factors and hormonal imbalance may contribute to childhood obesity but those are not common causes. Childhood obesity places children at greater risks for developing diabetes, high blood pressure, high cholesterol, sleep disorder, bone and joint problems. It also affects psychological factors such as having low self-esteem, poor self-image and being bullied at school. These problems can be lead to depression and suicide later in life.

Many of the weight loss diets recommend smaller, more frequent meals than having one large meal a day while watching out the calories you eat. Researchers found that children today eat on average of three snacks per day compared to one snack per day three decades ago. Meanwhile, children consume more sugar-sweetened beverages, high calorie, non-nutritious snacks nowadays. One study showed that kid’s meals today have very high content of salt. In addition, children spend more time in front of TV and play video games in today society than in the past decades.

To fully understand the underlying problems in childhood obesity, researchers conducted a study where they analyzed different types of snack and measured the satiety in children after the snacks. The study group consists of 183 children and average age is around eight. BMI on each child were recorded and 38 of those children are considered overweight while 43 were considered obese. BMI measurement for children (2-20 years old) is different from adults. Please refer to CDC (The Centers for Disease Control) for standard BMI calculator for children.

In this study, children were randomly placed into four different groups where different snacks were given while watching TV for 45 minutes. One group was given potato chips and the second group was given only vegetables. The third group was given cheese and the fourth was given combination of cheese and vegetables. Researchers used three questions on nine points scale to measure the satiety. Furthermore, the parents of the study group children filled out 20 questionnaires regarding mealtime habits and family involvement in mealtime activities.

For the surveys, the children were questioned before the snack, immediately after the snack, and 20 minutes after the snacks. The cheese group and vegetable group were considered control groups and researchers compared the satiety measurements from the other groups to these. The average of 620 calories was consumed in the potato chips group and 200 calories in the cheese group. The vegetables group consumed about 60 calories on average and 170 calories in the combination group. The study showed that combination of cheese and vegetables fulfilled satiety faster than the potato chips with fewer calories. Often times, obesity results from calorie imbalance (more intake than you use for energy). In conclusion, healthier snacks can meet satiety with lesser amount while getting fewer calories. Furthermore, replacing junk foods with healthier snacks may be more reasonable approach than eliminating junk foods all together in children’s diet.

Contributed by Patricia Hsiao M.D.
Sources: pediatrics.aappublications.org, cdc.gov, mayoclinic.com

Physical activity on bone health

Posted by on Jan 21, 2015 in News | 0 comments

Running womanPhysical activity plays a major role in fighting obesity and management of chronic conditions such as heart disease, diabetes, high blood pressure and high cholesterol. Exercise or physical activity helps children build bones and strengthen muscles and joints. In older adults, physical activity improves strength and mobility, which in turn reduces falls and fall-related injuries.

In a recent study on premenopausal women, researchers found an increase in bone formation markers and high serum IGF-1 (positive effector on bones) in women who have more than 120 minutes of physical activity per week than sedentary women. One study was conducted on women over 65 years of age and participants were randomly placed into either the exercise group or control group. At the end of the 18 months study, researchers measured bone mineral density on both groups. It showed that women in the exercise group have higher bone mineral density than control group. Bone mineral density tells us the amount of calcium and minerals in your bones. Lower bone mineral density indicates higher risk for osteoporosis.

Furthermore, physical activity is important part of the treatment and prevention of osteoporosis. In osteoporosis, weight bearing and strengthening activities are crucial and effective. Physical therapist can help you design an exercise program that is right for you. In conclusion, any amount of physical activities would help you stay healthy and have positive impact on your bone health.

Contributed by Patricia Hsiao M.D.
Sources: medscape.com, jamanetwork.com, ncbil.nlm.nih.gov, uptodate.com

Lipid lowering dietary supplements

Posted by on Dec 5, 2014 in News | 0 comments

SupplementsIf you have high cholesterol, you might want to pay close attention to what you eat. Diet plays a major role in treating chronic conditions such as diabetes, high cholesterol and high blood pressure. There are many commercial products and herbal supplements that claim to lower blood cholesterol. Make sure you talk to your doctor before you start taking any herbal supplements because some may interact with the medications you are taking. On the other hand, there are some dietary supplements you can take to lower your cholesterol. Research studies have shown that fish oil or omega-3-fatty acid, fibers and some nuts have effects on cholesterol.

Fish oil or omega-3-fatty acid
Fish oil has been studied for many potential health benefits and some populations who eat a lot of fish products have lower rate for heart disease. You may hear that fish oil is good for many medical conditions but not all of them have been proven by scientific studies. However, we know for the fact fish oil or omega-3-fatty acids have effect on high cholesterol and high blood pressure. Many studies were done on laboratory animals and some were done on humans for high cholesterol. Studies concluded that fish oil reduces triglycerides (a type of cholesterol) level by large amount in patients with high triglycerides. It may also raise HDL (good cholesterol) level by small amount. It is recommended to consume at least one or two servings of oily fish a week. Fish with lots of omega-3 include tuna, salmon, mackerel, mullet, anchovy, trout, sardines, halibut and herring. You can also get omega-3-fatty acids from flaxseed oil, canola oil and soybean oil. If you do not like to eat fish, you can take fish oil supplement (1 gram daily). Talk to your doctor if you are concerned about your daily dose of fish oil supplement along with your other medicines. Fish oil supplements may contain small amount of vitamin E. You should not take more than the recommended dosage.

Fibers
We know that fibers are good for your intestines and it can help prevent colon cancer. Studies showed that soluble fibers in psyllium, wheat, celery, oatmeal and oat bran can reduce LDL cholesterol (bad cholesterol) level. You can also get fibers from eating fruits. Therefore, it is recommended to have 5-10 grams of fibers a day.

Nuts
Studies showed that walnut has effect on lowering total cholesterol and LDL cholesterol (bad cholesterol). Similar effects are found from almonds and pistachio as well. You should try to consume a handful of nuts a few times a week. Make sure to get the ones which are unsalted or not sugar-coated. Some studies indicated that people who consume nuts more than two times a week may benefit from lowering the risk of heart disease.

Others with small effect on cholesterol level
There are a few studies done on green tea on cholesterol level and researchers found that drinking unsweetened green tea can reduce LDL cholesterol in a small amount. In addition, soy products are rich in unsaturated fats and fibers compared to protein from meats. Studies showed that soy protein improves cholesterol level and blood pressure.

Contributed by Patricia Hsiao M.D.
Sources: ncbi.nlm.nih.gov/pubmed, nlm.nih.gov/medlineplus, mayoclinic.com, uptodate.com

Nutrition in Pregnancy (Q & A)

Posted by on Nov 19, 2014 in News | 0 comments

Healthy pregnancy
What is the best diet in pregnancy?
There is no single diet for women during pregnancy yet, eating well-balanced, healthy foods is important for your health as well as your baby. The baby inside you needs a lot of nutrients to grow and to attain normal development. Regardless of what diet you choose, it should contain a lot of vegetables, fresh fruits, whole grains, lean meat and low-fat dairy products.

What are the recommended servings for carbohydrates, protein, fruits, vegetables and dairy in pregnancy?
The recommendations may vary slightly depending on what sources you are looking at. Generally, about 9 servings of carbohydrate, 3 or more servings of protein, 2 or more servings of fruits, 3 or more servings of green vegetables, and 4 servings of dairy products are recommended during pregnancy. Limit fat consumption to about 2 servings per day and try to drink 8 glasses of water a day.

Do I need extra calories during pregnancy?
Yes, women need to consume more calories to support the growing baby during pregnancy. Daily calorie intake needs to be increased by 300 kcal per day in second trimester (4-6 months) and over 400 kcal per day in third trimester (7-9 months).

How much weight do I need to gain during pregnancy?
The amount of weight you need to gain during pregnancy varies and it depends on your weight before pregnancy. Check with your obstetrician about how much weight you should put on in your pregnancy. The following is a recommended weight gain for general population:
BMI before pregnancy Recommended weight gain during pregnancy
Underweight (BMI of less than 18.5) 28-40 pounds
Normal weight (18.5 and 24.9) 25-35 pounds
Overweight (BMI of 25 to 29.9) 15-25 pounds
Obese (BMI of 30 or greater) 11-20 pounds

Why should I monitor my weight gain?
We encourage women to have better control over their weight gain during pregnancy to prevent large infant, preterm birth and post-term birth. Overweight and obese women are at higher risk for developing gestational diabetes (high blood sugar during pregnancy) and preeclampsia (high blood pressure in pregnancy). Babies are also at higher risk for birth defects and birth-related injuries.

What foods should I eat to get the extra calories?
The extra calories should come from various food sources such as carbohydrates, protein and low-fat dairy products. You can get carbohydrates from whole grain products, cereal and starchy vegetables. Protein is also essential nutrient for fetal growth and development especially in the second and third trimester. Your diet should include more lean meat found in poultry and fish (certain types), tofu, dried beans and peas. Protein can be obtained from dairy products such as egg, milk, yogurt and cottage cheese as well.

What are good sources of protein if I am a vegan?
Soy products, beans, peas and peanut butter are rich in protein.

What is folic acid?
Folic acid or folate is vitamin B that prevents neural tube defects (defects of brain and spinal cord). Brain formation occurs as early as four weeks after conception. You can find folic acid in fortified cereals, green vegetables (spinach, broccoli, asparagus, okra and brussel sprouts), dried beans, peas and citrus fruits (orange, grapefruit, papaya and strawberry).

Can I take folic acid supplement?
Yes, you can take 400 to 800 mcg (micrograms) of folic acid daily. Some prenatal vitamins contain folic acid but the amount varies among different brands. Let your doctor know if you are taking medications for seizure, diabetes, asthma, lupus or inflammatory bowel disease. You may need a higher dose of folic acid. You will be given a higher dose of folic acid if you had a baby with brain or spinal cord defect or you have a family member with spinal bifida. In that case, the doctor will prescribe you 4000 mcg of folic acid daily, which is ten times higher than the normal dose.

When should I start taking folic acid supplement?
Even when you are just planning to get pregnant, you should start eating healthy foods and take multivitamin with folic acid (400 to 800 mcg daily). Brain and spinal cord form very early on in pregnancy before most women find out they are pregnant. On the other hand, neural tube defects are very much preventable with folic acid during pregnancy.

What other vitamins and minerals do I need during pregnancy?
During pregnancy, women need increased amount of iron, calcium and vitamin D. If you are a vegan, you may also need vitamin B12 supplement as well as iron, calcium and vitamin D.

What is iron for?
Iron is part of the red blood cells and red blood cells carry oxygen to the brain, organs and tissues. Your body demand for iron is higher in pregnancy since your baby also produces his or her own blood supply.

What is anemia?
Anemia is a condition where you do not have enough red blood cells to carry oxygen to the organs and tissues. During pregnancy, iron-deficiency anemia is more common and it is due to inadequate iron to make red blood cells. Common symptoms include fatigue, weakness, pale skin and headache.

What types of food are rich in iron?
Iron is found in meats, iron-fortified cereal, spinach, beans and nuts.

How much iron do I need daily?
You need 27 milligrams of iron daily and most prenatal vitamin supplements contain iron.

What are calcium and vitamin D for?
Calcium and vitamin D are needed for bone formation and to strengthen bones.

What foods are rich in calcium and vitamin D?
You can get calcium and vitamin D from milk, yogurt, cheese, eggs, spinach, asparagus, broccoli, cereal and fortified orange juice. Vitamin D is also from sunlight through your skin.

How much calcium do I need daily?
You need 1000 milligrams of calcium daily. If you are a teen mom, you need 1300 milligrams of calcium daily.

How much vitamin D do I need daily?
You need 600 IU (international unit) of vitamin D daily.

Can the daily requirements of iron, calcium and vitamin D be fulfilled by taking prenatal vitamins?
No, prenatal vitamins are supplement to your diet. You still need to eat nutritious foods to meet your daily requirements of iron, calcium and vitamin D. Most of them do not contain 1000 milligrams of calcium.

Are there any foods I must avoid during pregnancy?
Yes, there are certain categories of food you must avoid during pregnancy. Those include fish with high mercury content, raw meat, some seafood, unpasteurized milk and cheese products.

What types of fish are high in mercury?
Fish with high mercury content are found to be associated with central nervous system damage and those include swordfish, tuna steaks, mackerel and shark.

What fish are safe to eat during pregnancy?
It is safe to eat canned light tuna, salmon, tilapia, catfish, crab and shrimp up to two times a week.

What other seafood do I need to avoid?
You should avoid oysters and clams as well as sushi that are made with raw fish.

Can I eat organ meats?
No, because they contain too much vitamin A, which are toxic for the fetus.

What foods should I avoid to prevent myself from bacterial food poisoning?
You can get bacterial food poisoning from raw, undercooked meat and poultry and it can be harmful to your baby. If you eat processed deli meats and eggs, make sure they are cooked thoroughly. It may be wise to avoid eating raw sprouts such as clover, radish and alfalfa because it is impossible to wash out the bacteria inside the seeds.

What other hygiene should I adapt to prevent myself from food-borne illnesses?
Always rinse the vegetables and fruits thoroughly before eating. Make a good habit of washing your hands before and after handling foods.

What is listeriosis?
Listeriosis is a condition caused by bacteria called listeria. While it causes mild-flu-like symptoms such as headache, fever, nausea and vomiting in a mother, it is more fatal to the fetus. It can cause miscarriage and stillbirth.

What foods should I avoid to prevent myself from listeriosis?
You must avoid unpasteurized milk or juice, soft cheese, foods that are made from unpasteurized milk, raw or undercooked meat, poultry and smoked seafood. Soft cheese includes Camembert, feta, Brie, blue cheese and Mexican-style cheeses (fresco, queso, blanco and panela).

Can I have caffeine during pregnancy?
Some studies suggested that too much caffeine is associated with miscarriage and preterm birth but not enough evidence to support it. However, it is recommended that pregnant women should consume less than 300 mg of caffeine, which is about two cups of coffee a day.

Are herbal products safe to consume during pregnancy?
We have limited data to support whether herbal teas and products are safe to consume or not. Thus, you should stay away from herbal products during pregnancy.

What other lifestyle modifications do I need to adapt in pregnancy?
As we all know that alcohol causes fetal alcohol syndrome (mental retardation and developmental problems), pregnant women must avoid all alcoholic beverages including wine. In addition, quit smoking if you smoke. Try to exercise for 30 minutes a day as many times as you can in a week.

Contributed by Patricia Hsiao M.D.
Sources: acog.org, ncbi.nlm.nih.gov , mayoclinic.com, ghc.org, uptodate.com

Polycystic Ovary Syndrome (PCOS)

Posted by on Oct 22, 2014 in News | 0 comments

Polycystic
Polycystic ovary syndrome or PCOS is a common hormonal disorder in women and it involves multiple organs in the body. Even though the name implied multiple cysts in the ovaries, some women may not have cysts in the ovaries. It is estimated that about 5 to 10 percent of women in the U.S have polycystic ovary syndrome.PCOS is often found in women who are in their childbearing age but it can also occur in young girls who just reach puberty.Currently, research studies showed that PCOS runs in the families. If your mother or sister is diagnosed with PCOS, you are at higher chance for having it.

Scientists do not know the exact causes of PCOS yet. Nevertheless, it is found to be associated with imbalance of several hormones including estrogen and progesterone (female hormones), androgen (male hormone), LH and FSH (from pituitary gland in the brain) and insulin. Insulin is the hormone that regulates blood glucose. In a normal menstrual cycle, LH, FSH, estrogen and progesterone play a role in oocyte (egg) development and ovulation, which is the released of the matured egg for fertilization.In PCOS, too much insulin is produced but the body cells are resistant to insulin uptake. Therefore, blood glucose remains high. In addition, it causes increased production of androgen (male hormone) in the ovaries. Too much androgen interferes with ovulation. Thus, fertility problem, excess body hair and irregular menstrual cycle are common among women with PCOS. Some women may also develop multiple cysts in the ovaries.

There is a variety of symptoms associated with PCOS and it varies from one woman to another. Most women experience irregular periods where they have less than 8 periods per year. Period may be heavy and prolonged when they have it. Many women with untreated PCOS have difficulty getting pregnant. Because of too much androgen (male hormone), women develop male-like features such as hirsutism in which excess hair growth on the face, chest and belly. Furthermore, they may experience hair loss or male-pattern baldness and deepening of the voice.

Some women develop acne from oily skin due to androgen and they may notice dark patches of skin around the neck, groin area and under arms. Insulin impairment causes weight gain and obesity, which is common among women with PCOS. Some women develop type II diabetes. Both obesity and diabetes increase the risk of heart disease and many women also suffer from sleep apnea in which your breathing stops briefly during sleep. People with sleep apnea experience fatigue and increased daytime sleepiness.

There is no specific test for PCOS. It is often diagnosed by taking a complete medical history, blood tests and pelvic ultrasound. The doctor will ask you about your period and other symptoms you may have. You do not need to have all the symptoms mentioned above to make a diagnosis of PCOS. He or she will do a physical exam and pelvic exam to check for abnormalities. The doctor will order blood tests for pregnancy test, fasting blood glucose and cholesterol, androgen hormone level and thyroid-stimulating hormone. Some doctors will do glucose tolerance test where your fasting blood glucose and blood glucose after drinking a sugary drink are measured. You may be asked to have a pelvic ultrasound to check for cysts in your ovaries.

PCOS is treated by managing or reducing the symptoms with oral medications. For women who are not planning to get pregnant, your doctor will prescribe birth control pills to regulate your menstrual period. Birth control pills decrease the production of male hormone so it is effective for reducing facial hair growth, deepening of the voice and male-pattern baldness. It can also help clear acne. You may experience nausea, bloating and breast tenderness from birth control pills but those often resolve in a few months. Laser hair removal is an option if you have a lot of excess hair on the face and other parts of the body.

If you are not a candidate for birth control pills, another medication called spironolactone (Aldactone) is an option. It is a medication to reduce androgen hormone production as well as lowering the blood pressure. It is effective for treating excess hair growth, male-pattern baldness and acne problem. However, it can cause birth defects if you become pregnant.
Metformin (Glucophage) is a diabetes medication to improve insulin function and it is given to regulate blood glucose. Metformin also helps regulate menstrual period. Unlike other diabetes medications, metformin does not cause weight gain. The doctors will also recommend lifestyle changes such as diet and exercise if you are overweight or obese. Losing a few extra pounds can lower your blood glucose and keep your periods regular.

There are fertility medications available if you are planning to get pregnant. PCOS causes infertility by blocking ovulation. Clomiphene (Clomid and Serophene) is an oral medication that stimulates ovulation. Studies showed that 50 percent of women with PCOS become pregnant after getting treated with clomiphene. You will need to lose a few extra pounds if you are overweight. If you still do not ovulate with clomiphene, gonadotropin therapy (FSH and LH injections) is recommended. Some doctors may refer you for in vitro fertilization (IVF) treatment. It is a more expensive fertility treatment and it has increased chance of multiple births.

Contributed by Patricia Hsiao M.D.
Sources: ncbi.nlm.nih.gov, pcos.northwestern.edu, mayoclinic.com, womenshealth.gov, uptodate.com

Thyroid Disorders and Diabetes

Posted by on Aug 21, 2014 in News | 0 comments

Profile 2Thyroid disorders and diabetes mellitus are very common in our society. Studies showed that thyroid disorders are much more common among diabetics and up to one third of type I diabetics eventually develop some form of thyroid disorder. Both thyroid disorders and diabetes are part of endocrine disorders where problem is originated in endocrine glands. Thyroid disorders are due to overproduction or underproduction of thyroid hormones by thyroid gland.

Thyroid gland is located in front of the neck below Adam’s apple. Thyroid hormones production is controlled by another endocrine gland in the brain called pituitary gland. Thyroid hormones regulate body’s metabolism which affects many organs including heart, digestive tract, muscles and etc. Diabetes is due to lack of insulin production in the pancreas or body cells are resistant to insulin. Insulin is a hormone that regulates blood glucose. Pancreas is one of the endocrine glands (hormone producing gland) located behind the stomach.

Type I diabetes occurs when the body’s immune system destroys insulin-producing cells in the pancreas. Similarly, thyroid disorders can be due to immune system attacking thyroid gland. That is one of the reasons for thyroid disorders being more common in type I diabetes. For thyroid disorders, you can either have hyperthyroidism (too much thyroid hormones) or hypothyroidism (too little thyroid hormones). The symptoms differ in these conditions. A person with hyperthyroidism may complain of rapid or irregular heartbeats, anxiety, irritability, weight loss despite normal or increased appetite, diarrhea, tremors in the hands, muscle weakness and heat intolerance (sweating more than usual). Women may experience irregular menstrual period.

On the other hand, a person with hypothyroidism may experience fatigue, shortness of breath with exercise, weight gain despite normal appetite, cold intolerance, thin or coarse hair and constipation. Women may have menstrual irregularity. Both hyperthyroidism and hypothyroidism can interfere with your diabetes management. If you are diabetic and develop hyperthyroidism, your blood glucose will be high even when you are taking insulin or oral medications regularly. Medications or insulin could not stay in the bloodstream long enough since your metabolism is faster. Also, hyperthyroidism may be confused with signs of hypoglycemia (low blood glucose) such as sweating, racing heartbeats and tremors because both conditions present with similar symptoms. Having hypothyroidism can affect your blood glucose as well. Individuals with diabetes may experience repeated episodes of low blood glucose because medications or insulin will stay longer in the bloodstream. In addition, hypothyroidism will raise your LDL (the bad cholesterol) and triglycerides levels.

In diabetics, thyroid symptoms can be a bit difficult to diagnose because of the similarity. Therefore, serum TSH is recommended as a screening tool. When you are first diagnosed with type II diabetes, TSH should be done at that time and it should be repeated every few years. If you are diagnosed with type I diabetes, your doctor will check for antibodies (specific proteins) against thyroid hormones as well as serum TSH. If antibodies are found, TSH screening will be done every year. In conclusion, thyroid disorder should be treated promptly especially in diabetics because it will improve blood glucose management greatly.

Contributed by Patricia Hsiao M.D.
Sources: journal.diabetes.org/clinicaldiabetes, spectrum.diabetesjournal.org, thyroidscience.com, ncbi.nlm.nih.gov, uptodate.com