Using Quantitative Medicine to Control Type 2 Diabetes 


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

Date: 11/1/2016 11:00

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.

Figure 9-1: MI & GHSU (2012 – 2016)
Figure 9-2: MI & GHSU (4/11/2015 – 10/20/2016)

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

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

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.

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. (Figure 9-4: Water Score)

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%. (Figure 9-5: Stress Score)

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. (Figure 9-6: Sleep Score)

Figure 9-6: Sleep Score

During this period, I slept 7 hours and 15 minutes per night on average, which is quite sufficient. (Figure 9-7: Sleep Hours)

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. (Figure 9-8:Sleep Disturbance due to Wake Up)

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. (Figure 9-9:Food & Meal Score)

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%. (Figure 9-10: Food & Meal Quantity Score)

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. (Figure 9-11: Food & Meal Quality Score)

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. (Figure 9-12: Daily Routine Score)

Figure 9-12: Daily Routine Score