GH-METHODS

Math-Physical Medicine

NO. 331

Geriatric study applying both biomedical trend analysis and metabolism pattern recognition to analyze relationships between health and longevity using GH-Method: math-physical medicine

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

Abstract
In this article, the author describes his research method and analysis approach, while demonstrating his research results regarding longevity for his health age using his collected personal data over the past 9 years.

Longevity is a proof that a person’s body has overcome attacks from many different diseases.  Most diseases can be prevented or controlled from the deepest core area and at the most fundamental level via a lifestyle management program.

Once lifestyle details improve and medical conditions are under control, then the overall metabolism situation will be greatly enhanced.  As a result, the immune system will also be strengthened since metabolism and immunity are two sides of the same coin. A strong immunity is the ultimate and most effective defense force to fight against many diseases that can result in death.

Using the results from his previous medical research, he has applied the same geometric presentation model for this particular study.  He uses scores of his medical conditions as the x-axis along with scores of his lifestyle details as the y-axis.  He then selects the ultimate measurement biomarker for longevity via the effective health age as the z-axis values.   Finally, he folds-over or compresses the z-axis data to superimpose with the x-y data on a planar space with a special format of radio-waves to create this “pseudo-3D” graphic representation.

In this analysis, he focuses on using his metabolism index (MI) value from his developed mathematical metabolism model.  This combines four medical conditions and six lifestyle details as two measuring yardsticks of his body’s strength of metabolism and immunity to fight against various death-causing diseases to achieve his objective of longevity.  The measurement of longevity can be attained by developing an “effective health age(Health Age), which has a nonlinear moving path.  It is in contrast from “biological real age” (Real Age) which has a linear moving path that is increased by 1 when a new year arrives.  The “Biological Real Age” or “Chronological Age” is defined as the actual time a person has been alive.

Under this special three-dimensional (3D) data graphic presentation on a two-dimensional (2D) planar space, the health age’s moving path and its recognized curve pattern with their related medical conditions and lifestyle details become ultra-clear.

In summary, as shown in Figure 5, his health ages (gray stars) moves from the upper-right corner in 2012 (health age: 74).  Except in 2013, when he was extremely unhealthy, the moving path has a slight upward trend back to 76; otherwise, the moving path followed a 45-degree straight line toward a data cluster” near the bottom-left corner of health ages between 63 to 65 (2015 -2020). This indicates that health age will be difficult to achieve more noticeable improvements (i.e. around -10 years of age difference).  This is due to the fact that there are small increments available for further improvement on both his medical conditions and lifestyle details.  In 2014, the chart indicates him having the health age of 70, while his real age was 68.  This was the turning-point” year for his health age.  Incidentally, he developed his mathematical model of metabolism in 2014 and then started to pay special attention on his overall health improvement via his metabolism model.  That is why after 2015, his health ages have been lower (younger) than his real ages.

This conclusive figure demonstrates that his persistent efforts of controlling his medical conditions via a stringent lifestyle management program has ultimately improved his health ages.  Hopefully, if he persistently strengthens his metabolism and immunity, he can achieve his goal of longevity via a scientific and mathematical method. 

This article has not only shown the changes of his health ages due to metabolism improvement, but also exhibited his strong determination, willpower, and persistence along with his continuous struggle on controlling his existing medical conditions as well as maintaining his stringent lifestyle management program over the past 9 years.  The only driving force behind him is that he wants to enjoy a long, healthy life and not suffer from the dreadful chronic diseases, cancers, and various infectious diseases which could ultimately lead to death.

Figure 5: Trend and pattern of health age

Introduction
In this article, the author describes his research method and analysis approach, while demonstrating his research results regarding longevity for his health age using his collected personal data over the past 9 years.

Method
1. Background
To learn more about the GH-Method: math-physical medicine (MPM) research methodology, readers can review his article in Reference 1, “Biomedical research methodology based on GH-Method: math-physical medicine (No. 310)”, to understand his MPM analysis method.

2. Longevity
The author learned the following biomedical inter-relationships between cause and consequence which are listed from top to bottom:

  • Poor Lifestyle management
  • Unhealthy Metabolism
  • Obesity and Chronic diseases
  • Disease Complications
  • Weak Immunity
  • Various diseases leading to death

According to US records in 2018, about 50% of Americans died from chronic diseases and their complications, about 29% died from various cancers, and about 11% died from all kinds of infectious diseases. The remaining 10% of them died from non-disease related death.

The author was diagnosed with severe type 2 diabetes (T2D) in 1995 and then developed many serious complications, including CVD, CKD, foot ulcer, diabetic retinopathy, hypothyroidism, bladder infection, and others that became life-threatening by 2010.  Therefore, he decided to self-study six chronic diseases such as obesity, diabetes, hypertension, hyperlipidemia, cardiovascular diseases, stroke, as well as food nutrition, in order to save his own life.

3. Metabolism
After the first 4 years of self-studying endocrinology, he then spent the entire year of 2014 to develop a complex mathematical model of metabolism.  This model contains four easily measured biomarkers of medical conditions such as body weight, glucose, blood pressure, and lipids, along with six lifestyle details including food portion quantity & nutritional quality balance, water intake, appropriate exercise, sleep amount & quality, stress reduction, and daily life routine regularity.  He applied the concept of topology from mathematics and the modeling technique of finite element method from engineering to develop this mathematical model of metabolism which became the cornerstone of his future medical research work.  As a result, his overall health conditions started to improve after 2015.

In 2014, he also defined two specific output parameters of his metabolism model as metabolism index (MI) and general health status unit (GHSU).  MI is the combined score of the four medical conditions and six lifestyle details which can be calculated on one specific day, a time instant, or over a period of time.  GHSU is defined as the 90-days moving average MI values.  He has also identified a “break-even line” at 0.735 (73.5%) to separate his metabolic conditions between the healthy state (below 0.735) and unhealthy state (above 0.735).

He started to collect his data of weight and glucose beginning on 1/1/2012 and other lifestyle detailed data from 2013-2014.  Thus far, he has collected nearly 2 million data regarding his body health and lifestyle details.  In this particular study, based on some scattered data, he guesstimated his overall medical conditions score, lifestyle details score, and annualized MI values for 2012 and 2013.

4. Health Age Equation
He further developed a simple equation to calculate his effective health age as follows:

  • Effective Health Ag = Real Biological Age * (1+((MI-0.735)/0.735)/AF)

Here AF stands for ”Amplification Factor”, between 1 to 4, and he used 2 for his case.

He then utilized his annualized MI data to calculate his effective health age in order to compare against his real biological age.

5. Heath Age Diagram
In order to demonstrate the results of his health age, he created a modified 2D planar space which can describe 3D data and information.  Initially, he set his x-coordinate as his scales of medical conditions from low scale to high scale with the following 5 segments:

  • Segment A:  85% – 90 %
  • Segment B:  90% – 95 %
  • Segment C:  95% – 100 %
  • Segment D:  100% – 105 %
  • Segment E:  105% – 110 %

Secondly, he set his y-coordinate as his scales of lifestyle details from high scale to low scale with the following 5 segments:

  • Segment 5:  80% – 85%
  • Segment 4:  75% – 80%
  • Segment 3:  70% – 75%
  • Segment 2:  65% – 70%
  • Segment 1:  60% – 65%

Therefore, these x-axis and y-axis constitute a 2D planar space with a total of 25 sub-regions inside, such as A1 through E5 in Figure 5.

Thirdly, he sets his “pseudo-3D” z-coordinate as his health ages from low scale (lower left corner) to high scale (upper right corner) in a “radio-wave” format with the following 4 radio-wave segments:

  • Segment 1: 60 – 65 years old
  • Segment 2: 65 – 70 years old
  • Segment 3: 70 – 75 years old
  • Segment 4: 75 – 80 years old

However, for a better view, he superimposes (folds-over or compresses) this z-axis on 2D planar x-y space with a radio-wave format to show their different levels of health ages (Figure 5).  In this special diagram, the reader can easily observe the health age moving patterns from 2012 throughout 2020 and their respective relationship with both medical conditions and lifestyle details within each year.

From observing this health age moving trend and curve pattern diagrams, patients can modify their behavior one step at a time, by taking little steps on a smaller scale.  This is what the author defined progressive behavioral modification.

Results
In Figure 1, it shows the background data table for scores of 10 detailed metabolic components (m1 through m10), medical conditions (m1-m4) scores, lifestyle details (m5-m10) scores, MI values, health ages, real ages, and age difference (Health age minus Real age) from 2012 to 2020.  However, during 2012-2013, there are no detailed metabolic component scores available, only three overall metabolic values.

Figure 1: Background data tables of MI components and Ages (2012 - 2020)

Figure 2 depicts the MI values from 2012 to 2020.  As illustrated over the 9 years, 2013 had the highest MI score of 93% (the unhealthiest year) and 2020 has the lowest MI score of 53% (the healthiest year).  

Figure 3 shows the comparison between health age and real age based on the effective health age equation described previously.

Figure 2: Bart chart of annualized metabolism index (MI)
Figure 3: Comparison between Real age and Health age (2012 - 2020)

The health age is directly proportional to MI score, which is a linear relationship between these two variables of health age and MI.  Therefore, the curve pattern of health age bars completely match the curve pattern of MI core bars in Figure 2.  However, the real age is linearly increasing year after year.  From 2012 to 2014, his health ages were higher than his real ages, where he was actually older than his real age.  From 2015 to 2020, his health ages become lower than his real ages, where he actually becomes younger than his real age.  

Figure 4 reflects the age difference (health age minus his real age) from 2012 to 2020.  In 2013, his age difference was +9 years, while in 2020, the age difference becomes -10 years, with the turning-point between 2014 and 2015.  

Figure 5 is the most important and conclusive diagram of this article.

It has demonstrated that his persistent efforts on controlling his medical conditions via a stringent lifestyle management program has ultimately improved his health ages.  Hopefully, if he persistently strengthens his metabolism and immunity, he can achieve his goal of longevity via a scientific and mathematical method. 

In summary, as shown in Figure 5, his health ages (gray stars) moves from the upper-right corner in 2012 (health age: 74).  Except in 2013, when he was extremely unhealthy, the moving path has a slight upward trend back to 76; otherwise, the moving path followed a 45-degree straight line toward a data cluster” near the bottom-left corner of health ages between 63 to 65 (2015 -2020). This indicates that health age will be difficult to achieve more noticeable improvements (i.e. around -10 years of age difference).  This is due to the fact that there are small increments available for further improvement on both his medical conditions and lifestyle details.  In 2014, the chart indicates him having the health age of 70, while his real age was 68.  This was the turning-point” year for his health age.  Incidentally, he developed his mathematical model of metabolism in 2014 and then started to pay special attention on his overall health improvement via his metabolism model.  That is why after 2015, his health ages have been lower (younger) than his real ages.

An interesting fact from the past decade is that three of his physicians indicated his age was about 10 years older after reviewing his various medical examination reports when he was 63 years old in 2010.  However, those same physicians told him in 2017 that he was about 10 years younger when he reached the age of 70.  This range of +10 years to -10 years was their empirical judgements based on their many years of clinical experiences from seeing hundreds of patients.  Now, the author uses a scientific approach which is based on physical phenomena observations, big data analytics, and mathematical derivations to draw a conclusion for the range of +8 years to -10 years of his age difference. Nevertheless, the above two guesstimated age ranges made by his physicians and the author are quite comparable to each other.    

The life expectancy of an American male is 78.69 years (2016 data).  If the author continues his metabolic condition improvements, chronic disease control, as well as his stringent lifestyle management program, he stands a good chance to extend his life for an additional eight years to reach a real biological age of 87 (79 plus 8).  

Figure 4 : Age difference between biological real age and effective health age
Figure 5: Health Age Trend & Pattern diagrams via metabolism

Conclusion
This article has not only shown the changes of his health ages due to metabolism improvement, but also exhibited his strong determination, willpower, and persistence along with his continuous struggle on controlling his existing medical conditions as well as maintaining his stringent lifestyle management program over the past 9 years.  The only driving force behind him is that he wants to enjoy a long, healthy life and not suffer from the dreadful chronic diseases, cancers, and various infectious diseases which could ultimately lead to death.

References

  1. Hsu, Gerald C. eclaireMD Foundation, USA. “Biomedical research methodology based on GH-Method: math-physical medicine (No. 310)”
  2. Hsu, Gerald C. eclaireMD Foundation, USA. “Effective health age resulting from metabolic condition changes and lifestyle maintenance program, No. 223”
  3. Hsu, Gerald C. eclaireMD Foundation, USA. “Longevity analysis by comparing the overall metabolism and life routine regularity for two periods via GH-Method: math-physical medicine (No. 282)”
  4. Hsu, Gerald C. eclaireMD Foundation, USA. “ A geriatric study of longevity via big data analytics of metabolism, stress, and daily life routine  (GH-Method: math-physical medicine) 283”
  5. Hsu, Gerald C., eclaireMD Foundation, USA; “A geriatric study of longevity via big data analytics of metabolism and medical conditions  (GH-Method: math-physical medicine) 284”
  6. Hsu, Gerald C., eclaireMD Foundation, USA; “A geriatric study of longevity via big data analytics of metabolism along with food, exercise, sleep, and water (GH-Method: math-physical medicine) No. 285”
  7. Hsu, Gerald C., eclaireMD Foundation, USA; “How an engineer investigates his expected lifespan based on his metabolic conditions and lifestyle management program, 265”
  8. Hsu, Gerald C., eclaireMD Foundation, USA; “Calibrating the estimated health age via metabolism index using GH-Method: math-physical medicine (No. 313)”
  9. Hsu, Gerald C., eclaireMD Foundation, USA; “Comparison of effective health ages between the sophisticated model for researchers and simplified model for patients using GH-Method: math-physical medicine (No. 323)”
  10. Hsu, Gerald C., eclaireMD Foundation, USA; “Effective age resulting from metabolic changes (No.157)”
  11. Hsu, Gerald C., eclaireMD Foundation, USA: “Applying progressive lifestyle modifications and biomedical trend analysis plus pattern recognition to strengthen metabolism and immunity in order to fight against infectious diseases using GH-Method: math-physical medicine (No. 330)”