Corresponding Author: Gerald C. Hsu, eclaireMD Foundation, USA.
This paper describes the author’s theory and daily practice of preventive medicine via big data analytics based on a quantitative and precision medicine approach. Undeniably, medical conditions are significant and require continuous monitoring. Four major lifestyle categories such as food, exercise, sleep, and water intake make up the building blocks of medical conditions; therefore, the author monitors, analyzes, and reports them from a longevity viewpoint. In this particular article, he includes his macro-view relationship between life longevity and overall metabolic status.
The author spent ~30,000 hours over the past 10 years, from 2010 to 2020, to conduct his research on chronic diseases and complications, along with endocrinology, specifically focusing on metabolism and glucose.
In the beginning, from 2010 to 2013, he self-studied internal medicine and food nutrition. He specifically focused on six chronic diseases, i.e. obesity, diabetes, hypertension, hyperlipidemia, cardiovascular disease (CVD) & stroke, and chronic kidney disease (CKD). In 2014, he allotted the entire year to develop a complex mathematical metabolism model which includes 4 output categories (weight, glucose, blood pressure, lipids) and 6 input categories (food, water, exercise, sleep, stress, life routine regularity). There are about 500 detailed elements included in these 10 categories. Since using a theoretical approach to deal with a dataset of 10 categories with 500 elements, the problem of identifying and solving all possible interactive relationships among them would be an immense task. This task would include complicated calculations of 500 ! steps. This kind of approach is a huge undertaking without any obvious benefit; therefore, he adopted an approach of applying mathematical concept that is topology. In addition, he applied a practical engineering modeling technique such as finite element method to seek a quicker but rather accurate solution for this complicated biomedical system. At the end, he was able to develop a mathematical metabolism model embedded in a specially designed application software on the iPhone (“eclaireMD”) for his daily use in order to maintain his health status and also serve as a useful research tool for his ongoing various medical research projects. During the development process, he has defined two more new variables, metabolism index (MI) and general health status unit (GHSU), where GHSU is the 90-days moving average MI that is similar to HbA1C’s 90-days moving average glucoses. The results of this dynamic model can be expressed through these two newly defined variables, MI and GHSU, to describe a person’s overall health status and identify shortcomings in any specific area at any moment in time.
In lifestyle management domain, food, water intake, exercise, sleep, stress, and daily life routines directly affect the medical conditions associated with chronic diseases. He wrote two papers to address medical conditions which focuses on stress and daily life routine regularity, which are frequently overlooked. Therefore, in this particular article, he wants to finish his investigation regarding these remaining four important lifestyle categories. Listed below are the highlights of each category.
- Food and Meals:
Control food quantity (especially carbs/sugar amount for diabetes) for weight and glucoses control, the target is ~80% of his normal portion per meal, food quality with nutritional balance for overall health and longevity, energy for body health and metabolism.
Focus on “walking” exercise, daily walking steps for metabolism and general health, post-meal walking steps for glucose control, and sunlight to strengthen immunity.
For metabolism and general health, particularly to strengthen immune systems to fight against various diseases.
- Water intake:
For metabolism and general health, diseases prevention, and body detoxification.
He started to collect input data for his metabolism model on 1/1/2012; however, the data collection of these four specific categories actually started on 5/1/2015. Therefore, he chose his study period from 5/1/2015 through 6/26/2020, or for 62 months.
Listed below are the summary of his performance results for each category (Figures 1 through 4) from his collected data stored in his application software (5/1/2015 – 6/26/2020):
- Food & Meals:
quantity: 84% of normal portion
quality: 96% satisfaction level
overall: 0.6766 (90% satisfaction)
carbs/sugar: 14.4 grams per meal (generate ~26 mg/dL PPG)
- Exercise ( Walking steps):
daily walking: 17,000 steps
post-meal walking: 4,200 steps
(reduce ~21 mg/dL PPG)
sleep hours: >7 hours per night
wake-up times: 1.6 times per night
overall: 0.6260 (89% satisfaction)
- Water Drinking:
2,900 cc per day (97% satisfaction level)
- MI & GHSU:
performance score: 58% (good)
For this particular 5+ years study duration, it belongs in the category of “good performance” period because, by mid-2015, the author studied and learned a lot about chronic diseases, food nutrition, and metabolism.
Now, let us examine each figure with more identified findings.
Figure 1 shows his meals quantity is 84% of his normal portion. This is a good level for him to control his weight around 172 lbs. His food quality satisfaction level is an excellent 96% which means that his intake of food and meals are nutritionally well-balanced (Figure 5). His overall food and meals score is at a good level of 90%. All three curves, consisting of food quantity, food quality, and overall food, are declining gradually due to his consistent improvement through a slower speed (lasting longer). However, his carbs/sugar intake amount is 14.4 grams per meal with a “flat and calm” curve based on his knowledge of both diet and diabetes. The 14.4 grams of carbs/sugar will add ~26 mg/dL to his PPG level.
Figure 1: Food and Meals
Figure 5: Sleep & Food quality
Figure 2 depicts his walking exercise level. He walks ~17,000 steps per day which would burn about ~680 calories at 40 calories per thousand walking steps. His post-meal walking of 4,200 steps would reduce his PPG level by ~21 mg/dL. As a result, his net gain of PPG is only 5 mg/dL (26-21=5).
Figure 2: Exercise
Figure 3 illustrates his sleep conditions. He has defined sleep with 9 detailed elements (Figure 5). However, for him, the sleeping hours and wake-up times per night are the most important indicators among 9 elements. During 2013-2014, he suffered bladder infections due to his severe diabetes complications and had to urinate frequently, day and night, sometimes up to 5 times a night. As a result, this situation seriously impacted his quality of sleep and needed rest. In Figure 3, we can see his average sleeping time was 7.08 hours and woke up on an average of 1.6 times per night. His sleep category earned a good score of 0.6260 at an 89% of satisfaction level.
Figure 3: Sleep
Figure 5: Sleep & Food quality
The bottom diagram of Figure 4 reflects his average water intake of 5.8 bottles or 2,900 cc of water per day (96% satisfaction level based on 3,000 cc per day as his target). This is an extremely healthy habit. The combination of food, exercise, sleep, and water intake along with stress, regular daily life routines together with his improvements on his medical conditions (diabetes, hypertension, hyperlipidemia, and obesity), are depicted in the top diagram of Figure 4. The MI and GHSU scores (the lower the better) are 58% which is an excellent status.
Figure 4: MI, GHSU, & Water
Figure 6: From lifestyle through metabolism, immunity, to diseases and death
The author provided a detailed explanation regarding his lifestyle management program, specifically food, exercise, sleep, and water intake. In summary, he improved his health conditions from a severe type 2 diabetes and its many related complications to a healthy individual within 8.5 years due to his stringent lifestyle control.
Lifestyle management is an important topic and scientific project. It requires a strong understanding of its biological and physical contents and mathematical interrelationships with the medical conditions to build a solid foundation in maintaining one’s health and achieving longevity.
This big data analytics is based on ~1.3 million data over 5+ years. His developed metabolism model have shed some light about the impact on his longevity due to his overall metabolism changes, including the improvements on all of lifestyle details and medical conditions.
These significant improvements achieved in managing his lifestyle and his chronic diseases and complications have definitely contributed to the perspective of his longevity.
His metabolism model is a highly effective tool to investigate the subject of geriatrics and longevity.
Moving from the inner circle towards the outside rings, Figure 6 depicts that stringent lifestyle management leading into a good metabolism state, and then converting into a strong immunity to fight against three major disease categories such as chronic diseases and complications (50% of death), cancers (29% of death), and infectious diseases (11% of death), except for the remaining 10% of non-diseases related death cases. This is a logical way to achieve longevity which is also the core of geriatrics research branch. Readers can find more detailed information from References 1 through 6 regarding the author’s previous work results in this arena.
- Gerald C. Hsu, eclaireMD Foundation, USA. April 2020. No. 223: “Effective health age resulting from metabolic condition changes and lifestyle maintenance program.”
- Gerald C. Hsu, eclaireMD Foundation, USA. April 2020. No.280: ”A geriatric study of self-recovering diabetes conditions (GH-Method: Math-physical medicine).”
- Gerald C. Hsu, eclaireMD Foundation, USA. December 2019. No.235: ”Linkage among metabolism, immune system, and various diseases using GH-Method: math-physical medicine (MPM).”
- Gerald C. Hsu, eclaireMD Foundation, USA. December 2019. No.263: ”Risk probability of having a metabolic disorder induced cancer (GH-Method: MPM).”
- Gerald C. Hsu, eclaireMD Foundation, USA. No.283: ”A geriatric study of longevity via big data analytics of metabolism, stress, and daily life routine (GH-Method: math-physical medicine).”
- Gerald C. Hsu, eclaireMD Foundation, USA. No.284: “A geriatric study of longevity via big data analytics of metabolism and medical conditions(GH-Method: math-physical medicine).”