GH-METHODS

Math-Physical Medicine

NO. 027

From Weight Management via Diabetes Control to Cardiovascular Risk Reduction

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

Introduction
Since 1997, the author has been diagnosed with three chronic diseases such as type 2 diabetes (T2D), hypertension, and hyperlipidemia.  As shown in Figure 1, by 2010, his health reached to a “near collapsed” condition. Therefore, he launched his diabetes research in order to save his own life.  From 2012 to 2018, he has collected and processed  ~1.5M data regarding his own health and body conditions.  During 2010-2013, he self-studied 6 chronic diseases and food nutrition.  In 2014, using mathematics and various  engineering modeling, he developed a mathematical model of the human metabolism system, which contains 11 categories and ~500 elements.  By now, as shown in Figure 1, his chronic disease conditions are near perfectly controlled. This paper described his 8-years effort in terms of annualized segments with different working methods utilized at different stage. It specifically discuss the relationship and results from weight management via diabetes control to lowering his risk probabilities of having heart attack or stroke.

Figure 1: Comparison of Medical conditions (2010 vs 2017)

Method
The author, who is also the patient, measures his body weight twice a day, early morning and bedtime.  Using the finger-piercing method, he measures his fasting plasma glucose (FPG) in the early morning before starting his meals and activities.  He also measures his postprandial plasma glucose (PPG) three times a day, approximately two hours after the first-bite of each meal.  He measures his blood pressure (BP) at least once a day, preferably in the early morning, but sometimes multiple measurements as needed.  Every three months, he goes to hospital to check his lipid conditions.  Therefore, his lipid data amount is only around 1% of less than his other health conditions, e.g. Wright, Glucose, and BP.

Using a customized AI software on his iPhone, he also collected various detailed lifestyle data such as medication, salt intake, stress, sleep, illness, water, food and meal quantity, carbs/sugar intake, exercise, weather, etc.  Finally, he has utilized these big data to conduct many useful correlation analyses among weight, chronic diseases, and lifestyle factors.

From 2015 to 2017, based on his metabolism model, he developed 4 prediction models, including weight, fasting plasma glucose (FPG), postprandial plasma glucose (PPG), and hemoglobin A1C (A1C). All of these prediction models have reached to 95% to 99% data accuracy over a period of approximately four years (~1,500 days).  Utilizing these prediction models, he could successfully reduce both of his weight and blood sugar.  His BMI score has been moved from obesity via overweight and finally to normal range. His diabetes is under control and both of his hypertension and hyperlipidemia conditions are no longer his threats. During Spring of 2018, he further extend his Diabetes research into the study of Risk Probability of having heart attack and stroke due to chronic diseases.

During a period of 2,391 days (1/1/2012 – 7/20/2018) with ~30,000 collected raw data which are directly related to this paper, the author further extracted other relevant data out from this big data pool in order to process them according to his designed process flow.  He finally categorized them according to an annualized format for easier viewing of data trends from year to year.  Table 1 lists this annualized data flow and results.

Table 1: Annualized Data Comparison (2000, 2010, 2012-2018)

Although the author suffered three heart episodes during the decade of 2000 to 2010, he did not link his health risks to his chronic diseases due to his ignorance.  Therefore, he hardly kept any data during that decade except very few spotted significant health data.  In 2010, he got his needed wake-up call from the strong  possibility of having dialysis when his ACR reached to 116.4.  Besides, his A1C reached to 10% and Triglyceride reached to 1161.  He then immediately ceased all of his on-going technology business activities, moved into a stress-free retirement community away from Silicon Valley (one of his stressor), and also launched his own Diabetes research.  However, he had to started this effort by studying the chronic diseases first during 2010 and 2011.  Therefore, after 2012, he could then started to collect his complete set of health data, including both medical conditions (~10%) and lifestyle details (~90%).  Within this total set of ~1.5 million data, there are around 300,000 raw data from collection by computer and other >1 million data from information processing which are also saved in a cloud server for other future studies.  It should be noted that both A1C and lipid data were only available from his quarterly lab tests, not on a daily basis.  However, the author has developed a predicted daily A1C which reaches to 95% accuracy since he added in an uplifted 3% to 8% of “safety margin” in order to provide an early warning to himself. If without this build-in safety margin, his predicted A1C data should have reached a  99% accuracy.

Therefore, the data integrity after 2012 is very complete and quite high.  In comparison, data in 2010 were incomplete and included some guesstimated work.

Results
During the period of 1/1/2012 – 7/20/2018, his medical condition changes and major lifestyle modifications are shown in Table 1 and Figures 2 through 5.  The summarized highlights of results are listed as below:

  1. Weight:
    – 23 lbs (from 289 to 171)
    Period 2013-2015 (3 years) has the most reduction.
  2. BMI:
    – 2.6 (from 27.6 to 25.0)
  3. Waistline:
    – 12 inch (from 44” to 32”)
    Period of 2013-2017 (5 years) has the most reduction.
  4. Food & Meal Quantity:
    – 28% (7 years) from his normal portion of food (from 112% in 2012 to 84% in 2018)
  5. Daily Walking:
    + 6,967 steps in 5 years (from 7,564 in 2013 to 17,976 in 2018)
    Period of 2016-2018 has the the maximum exercise amount.
  6. Post-Meal Walking:
    + 2,599 steps in 4 years (from 1,900 in 2015 to 4,499 in 2018)
    Period of 2016-2018 has the maximum exercise amount.
  7. Daily Glucose:
    – 43 mg/dL (from 169.0 in 2010 to 117.0in 2018)
    Period of 2016-2018 has the most reduction.
  8. A1C:
    – 3.3% (from 10.0% in 2010 to 6.7% in 2018)
    Period of 2016-2018 has the most reduction.
  9. BP (MI 3):
    – 26% (annual averaged SBP/DBP dropped from 127/85 to 96/64).
  10. Risk of heart attack or stroke:
    – 43% (from 74% in 2000 to 31% in 2018, with 3 heart episodes during 2001-2006 and none afterwards)
Figure 2: Comparison of Weight, BMI, Waistline (2000, 2010, 2012-2018)
Figure 3: Comparison of Food Quantity & Walking
Figure 4: Comparison of Lab-tested A1C (2010 - 2018)
Figure 5: Risk Probability of Heart Attack & Stroke (2010, 2012-2018)

Based on his research results published in the past (see references), a few key conclusions can be drawn as follows:

  • Main causes of his weight reduction are food quantity reduction and daily exercise (walking steps) increase.
  • It took 3 years to reduce his weight, however, it takes 5 years to reduce his waistline.
  • Based on his annualized data, he was obese (BMI > 30) prior to 2010, overweight (BMI between 25 and 30) during 2012-2018. Actually, his weight has been less than 170 lbs (BMI is normal, around 24.7) in 2018.
  • His reduced weight has driven his FPG below 120 mg/dL. The combined effect of his carbs/sugar intake around 14.3 gram and post-meal walking around 4,000 steps has driven his PPG below 120 mg/dL as well.
  • His lab-tested A1C value was brought down from 10% in 2010 (averaged daily glucose around 200 mg/dL) to 6.7% in 2018 (averaged daily glucose around 117 mg/dL). It should be noted here that he has completely stopped taking any diabetes medication for almost 3 years (since 12/8/2015).
  • His risk probability of having heart attack or stroke was 74% in 2000. During the period of 2001 through 2006, he suffered 3 heart episodes and none afterwards.  By 2017, the same risk factor has dropped down to 26.4% (comparable with Framingham Study of 26.7% result in 2017).
  • He got hyperlipidemia (in 1992) before diabetes (1997). He noticed his hypertension conditions in 2013-2015.  However, after 2015, both of his hyperlipidemia and  hypertension are no longer his health threats.

He suffered diabetes complication problems, such as kidney, bladder, foot ulcer, eye, etc.  all of these problems seem to be under control.

Conclusion
This report is about one patient (himself) over 20 years period.  However, he has collected and processed ~ 1.5 million data, especially over the period of 2012-2018.  He has spent 20,000 hours conduct his research regarding diseases and his own health. This dynamic big data analytics using methodologies, including advanced  mathematics, physics, engineering modeling, machine learning and artificial intelligence, which should be equally effective for other patient’s conditions. Especially, data may be altered somewhat, but conclusions should be similar and applicable to other T2D cases.  In this article, there are no new earth-shaking findings.  However, the author provided quantitative proof to many old recommendations existed in modern medical community.

Biography
The author received an honorable PhD in mathematics and majored in engineering at MIT.  He attended different universities over 17 years and studied 7 academic disciplines.

He has spent 20,000 hours in T2D research.  First, he studied six metabolic diseases and food nutrition during 2010-2013, then conducted his own diabetes research during 2014-2018.  His approach is “quantitative medicine” based on mathematics, physics, optical and electronics physics, engineering modeling, signal processing, computer science, big data analytics, statistics, machine learning, and AI.  His main focus is on preventive medicine using prediction tools.  He believes that the better the prediction, the more control you have.

References

  1. Hsu, Gerald C. (2018). Using Math-Physical Medicine to Control T2D via Metabolism Monitoring and Glucose Predictions. Journal of Endocrinology and Diabetes, 2018(1), 1-6. Retrieved from http://www.kosmospublishers.com/wp-content/uploads/ 2018/06/JEAD-101-1.pdf
  2. Hsu, Gerald C. (2018). Using Math-Physical Medicine to Analyze Metabolism and Improve Health Conditions.

The author created this “math-physical medicine” approach by himself in order to save his own life.  Although he has read many medical books, journals, articles, and papers, he did not specifically utilize any data or methodology from other medical work.  All of his research is his original work based on data he collected from his body and using his own computer software developed during the past 8-years.  Therefore, no major problems were associated with data interference or data contamination.  In addition, his knowledge, information, technique, and methodology of mathematics, physics, engineering, and computer science came from his lifelong learning from schools and industries and should not be listed as medical references.   This is the reason his references only contain his own published papers.

Limitation of Research
This article is based on data of metabolic conditions and lifestyle details collected from one T2D patient (himself).  It does not cover genetic conditions and lifestyle details of other diabetes patients.  However, the author’s research approach is based on his solid inter-disciplinary academic background and successful multiple industrial experiences.  His academic background and working experience have prepared him to conduct his diabetes research with the following thorough process and carefully chosen  steps:

  • observing and identifying a system’s basic characters like a pure physicist;
  • developing related but rigorous mathematical equations like a mathematician;
  • applying suitable engineering models and useful statistical models to address the real world challenges;
  • using modern computer science tools and sophisticated AI techniques to aid in problem solving.

Nevertheless, his conclusions and findings should be re-verified and proceed with caution when applying to other patients who are under different metabolic conditions or lifestyles.

Other Declarations
During the past 8 years of self-study and research, the author has never hired any research assistant or research associate to help with his work except for a part-time computer programmer (~3 evening hours per day) to focus on Apple iOS annual upgrades and system interface problems.  He applied his own invention of a “Software Robot” created during 2001-2009 and AI knowledge he learned to produce the architecture and structure of his customized computer software.  He uses this software to collect and analyze his big data, conduct his medical  research, and then control his diabetes disease.

This project was 100% self-funded by using his own money that was earned from a successful high-tech venture in Silicon Valley.  He did not receive any financial assistance or grants from any public or private institution or organization.  Therefore, there are no concerns regarding any conflict of interest.

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

The author would also like to thank Professor James Andrews at the University of Iowa.  He helped and supported him tremendously when he first came to the United States.  He believed in him and prepared him to build his solid engineering and computer science foundation with a combination of warm heart and strong push during his undergraduate and master’s degree work at Iowa.