GH-METHODS

Math-Physical Medicine

NO. 316

Detailed Contribution Analysis of Metabolism Index Categories on Risk Probability Percentage of Having Cardiovascular Disease or Stroke Using GH-Method: Math-Physical Medicine

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

Abstract
The author uses his developed GH-Method: math-physical medicine approach to investigate a more detailed contribution analysis of Metabolism Index (MI) on the subcategories for three medical conditions and three lifestyle details based on his risk probability percentages of having Cardiovascular Disease (CVD) or stroke for over a period of 10+ years.

Listed below is a table of his annualized risk probability percentage based on MI of having CVD or stroke:

  • Y2010: 100%
  • Y2011: 90%
  • Y2012: 83%
  • Y2013: 85%
  • Y2014: 72%
  • Y2015: 60%
  • Y2016: 55%
  • Y2017: 54%
  • Y2018: 54%
  • Y2019: 55%
  • Y2020: 51%

This article describes the individual contributions from MI which includes three medical conditions sub-categories (glucose, BP, and lipids) and three lifestyle sub-categories (food, exercise, and others) of different modeling associated with artery blockage and artery rupture scenarios based on the risk probability percentage of having CVD or stroke. His research results from the past 10+ years have demonstrated the importance of maintaining an excellent healthy state for the entire body via a stringent lifestyle program in order to reduce the risk of having CVD or stroke.

Emphasis has been placed on the significance and contributions for a patient’s overall metabolism state. Therefore, three sub-categories of specific medical conditions (diabetes, hypertension, and hyperlipidemia) along with three sub-categories of lifestyle (food, exercise, and others) including weight and waistline are quantified. As a result, the findings corroborated with the advice from healthcare professionals to their patients.

Introduction
The author uses his developed GH-Method: math-physical medicine approach to investigate a more detailed contribution analysis of Metabolism Index (MI) on the subcategories for three medical conditions and three lifestyle details based on his risk probability percentages of having Cardiovascular Disease (CVD) or stroke for over a period of 10+ years.

Methods
To learn more about the MPM method, readers can review the article [1], along with the outlined history of his personalized diabetes research and application tools development [2].

In 2014, the author applied topology concept of mathematics and finite-element method of engineering, to develop a ten dimensional complex mathematical model of metabolism which contains four output categories (weight, glucose, BP, and lipids) and other lab-tested data (ACR, TSH, and others.), and six input categories (food, water intake, exercise, sleep, stress, and routine life patterns), and ~500 detailed elements. He further defined two new parameters, Metabolism Index (MI), as the combined score of the above 10 metabolism categories (dimensions) and 500 detailed elements, and general health status unit (GHSU), as the 90-days moving average value of MI. Please noted that Mi (where i=1 through 10) represents individual metabolism score of each category. Since 2012, he has collected ~2 million data of his own biomedical conditions and personal lifestyle details. He only utilized a part of his big database for analysis work in this article.

Next, he developed a few suitable algorithms containing some different weighting factors which include a patient’s baseline data (gender, age, race, family genetic history, medical history, bad habits, BMI, weight, and waistline), medical conditions (diabetes, hypertension, and hyperlipidemia), and lifestyle details (food, exercise, and others). After continuously collecting sufficient input data for a decade, he can then conduct the following three sets of calculations:

  • (A) Medical conditions-individual M2 through M4 for diabetes, hypertension, hyperlipidemia and others. These 3 metabolic disorder values include a patient’s self-collected biomedical data and the lab-tested medical examination results. Through his previous research for the past 5-years, he already detected that glucose is the “principal criminal” and blood pressure with lipids are the “accessory criminals” in terms of induced complications from chronic diseases, specifically CVD, stroke, renal problems, diabetic retinopathy, and even cancers. More precisely, his mathematical model for CVD or stroke includes two scenarios. The first scenario is the artery blockage situation which involves diabetes (glucose), hypertension (blood pressure or BP), and hyperlipidemia (lipids) where he applied his acquired fluid dynamics concept. The second scenario is the artery rupture situation which involves diabetes (glucose), and hypertension (blood pressure or BP) where he applied his acquired solid and fracture mechanics concept.
  • (B) Lifestyle details-individual M5 through M10 which affect medical conditions directly or indirectly. In this category, he includes the following 3 sub-categories with a total of 9 detailed elements. (B-1) 3 foods: quantity, quality, and carbs/sugar amount; (B-2) 2 exercises: daily walking steps and post-meal waking steps; (B-3) 4 others: water intake, sleep, stress, and daily life routines.
  • (C) MI and GHSU scores-MI is a combined score of M1 through M10 using engineering finite element method. GHSU is the 90-days moving average MI curve which can show the MI’s trend clearly. The break-even point for MI is 73.5%, where the levels of risk percentage for the separated groups of medical conditions and lifestyle details are 62%. In other words, being above the break-even line is worse but staying below the break-even line is better.

With this developed mathematical risk assessment tool, he can obtain three separate risk probability percentages associated with each of these three calculation models mentioned above. As a result, this tool would offer a range of the risk probability predictions of having CVD or stroke, depending on the patients’ medical conditions, lifestyle details, or the combined metabolism impact on the human body.

Due to the limited space for this paper, he only presents his results based on medical conditions.

The author is a 73-year-old male who has a history of three severe chronic diseases for 25 years. In addition, he also experienced five cardiac episodes from 1994 through 2008 and was diagnosed with an acute renal problem in early 2010. He also suffered from foot ulcer, bladder infection, diabetic retinopathy, and hypothyroidism. He weighed 220 lbs. in 2000 and his HbA1C level was 10.0% in 2010.

In 2014, he developed the mathematical metabolism model and started his stringent and comprehensive lifestyle management program. As a result, his overall health conditions have been noticeably improving since 2013-2015 when he started to reduce the dosage of his three prescribed diabetes medications. By the end of 2015, he completely stopped taking them. During the entire period of 2016-2019, his HbA1C average value was 6.6% without medication. During the recent COVID-19 quarantine period from 1/19/2020 to 8/22/2020, his HbA1C has further decreased to 6.1%.

Results
The author has written a few medical papers regarding the subject of risk probability of having a CVD or stroke based on his annual data when available [3]. The difference between this article and his previous ones is two-fold. First, in this article, he focuses on individual contributions from three sub-categories of biomarkers, including glucose, BP, and lipids, and three major lifestyle sub-categories, comprising of food, exercise, and others, instead of taking the overall performance score based on medical conditions or lifestyle details to conduct his risk assessment calculations. Secondly, he changed a few “weighting factors” in his algorithm in order to reflect some of his newly acquired knowledge on different diseases. However, this weighting factor change only reflects some nominal or insignificant changes on his ending result of risk probability percentages of having CVD or stroke. The overall trend and significant levels remain the same as in his previous papers.

Figure 1 illustrates the background data table of his three analysis including medical conditions, lifestyle details, and MI results.

Figure 1: Background data table of CVD/stroke risk probability % based on MI (genetic and personal, medical conditions, lifestyle details, and 80% of MI)

In Figure 2, the top diagram illustrates three contribution percentage line charts of glucose, BP, and lipids, whereas the bottom diagram reflects three contributions percentage line charts of food, exercise, and other lifestyle details.

In Figure 3, the top line chart diagram shows the contribution percentage of genetic and personal medical conditions, lifestyle details, and 80% of MI. The bottom bar chart diagram depicts the CVD/Stroke risk probability percentage comparison among medical conditions, lifestyle details, and MI.

Figure 2: Contribution % of medical conditions and lifestyle
on CVD/stroke risk probability %
Figure 3: Comparison of various contribution % and CVD/stroke risk
probability % based on MI, medical conditions and lifestyles.

It should be noted that weight and waistline (being overweight or obese) have been included in the baseline category for personal long-term factors because weight conditions similar to personal bad habits, such as smoking cigarettes, drinking alcohol, abusing substances, are difficult to change within a short period of time.

The established targets of his MI components are as follows:

  • Medical Conditions:
    Glucose: <120 mg/dL
    SBP: <120 mm Hg
    DBP: <80 mm Hg
    Heart rate: >60 and <100 bpm
    Triglycerides: <150 mg/dL
    HDL-cholesterol: >40 mg/dL
    LDL-cholesterol: <130 mg/dL
    Total cholesterol: <200 mg/dL
  • Lifestyle Details:
    Food quantity: 73.5%
    Food quality: 73.5%
    Carbs/sugar: 20 grams
    Daily walk: 15,000 steps
    Post-meal walk: 4,000 steps
    Water intake: 2,500 cc
    Sleep quality: 73.5%
    Stress: 73.5%
    Daily life routine: 73.5%

If the patients meet all of the above targets, they will get a “break-even” score of ~53%. Since no person would get a “perfect” score on his or her genetic factors and personal long-term factors, the author could provide an average score of ~10% for the worst-case scenario of 20%. As a result, the combined “break-even” risk probability percentage of having CVD or stroke resulting from medical conditions is 62%. However, the break-even MI level is 64%. If the risk percentage is higher than 62-64%, this indicates a higher risk. However, if the risk percentage is lower than 62-64%, this implies a lower risk.

In Figure 2, the top diagram displays all of his three curves of contribution percentages from medical conditions (glucose, BP, and lipids) are decreasing year after year due to his continuous improvements on the control of metabolic disorders via his stringent lifestyle management program, where he completely stopped taking medication since late 2015. The three medical contribution percentage curves stabilized after 2013. His annual average glucose showed the most noticeable reduction from 43% of contribution (280 mg/dL) in 2010 down to 23% of contribution (133 mg/dL). From 2010 to 2013, he took high dosages of three different diabetes medications which was similar to the period prior to 2010; however, the major differential factors to reduce hyperglycemia was due to his awareness on the importance of his daily lifestyle by implementing a stringent lifestyle management program based on scientific evidence [4].

In Figure 2, the bottom diagram presents all of his three percentages of lifestyle details (food, exercise and others) are being reduced year after year due to his stringent lifestyle management program. From judging his three curves over the period of 2017-2020 and his knowledge of this mathematical model, he has possibly reached a “near-optimal” state (i.e. high cost/return ratio) in terms of further potential improvements on managing his food, exercise, and others. Another observation is that it can take many years to change our health state, including both medical condition reversal and lifestyle improvements [5-7].

The following information from 2020 demonstrates the above observations and highlights of his improvement on chronic diseases over the recent COVID-19 quarantine period (1/19/20-8/22/20):

  • Medical Conditions:
    Weight: 172 lbs.
    BMI: 25.0
    Waistline: 33 inch
    SBP: 108 mm Hg
    DBP: 60 mm Hg
    Heart rate: 59 bpm
    Triglycerides: 110*
    LDL-cholesterol: 123*
    HDL-cholesterol: 49*
    Total cholesterol: 168*
    ACR: 19*
    TSH: 2.66*
    PSA: 110*
  • Lifestyle Details:
    Food quantity: 67.7% of normal
    Food quality: 50.1% (50% best)
    Carbs/sugar: 12.2 grams/meal
    Daily walk: 15,904 steps
    Post-meal walk: 4,280 steps/meal
    Water intake: 2,984 cc/day
    Sleep quality: 59.7%
    Stress: 50.0% (50% best)
    Daily life routine: 70.0%

Due to COVID-19, the author was unable to get the necessary blood work done for his lipids, ACR, TSH, PSA, and others at the laboratory; therefore, the asterisk (*) next to the numbers is the average measured values in 2019.

Since 2012, the author kept detailed and completed data from the past 8.5 years. In 2010 and 2011, he could only use some spotted records for guesstimated results, but the data still represent his previous years accurately.

In Figure 3, the top diagram shows the final calculated total contribution percentage of medical condition, lifestyle, and MI are declining year after year. However, the baseline curve (genetic factors and personal long-term factors) remains at a constant level, approximately 9% to 10%. Baseline conditions usually do not change, except for age (getting older each year) and weight (possible fluctuations). In the bottom diagram, the CVD/stroke risk probability percentage bars are also diminishing year after year. Even though 2010 and 2011, involved guesstimated data, they were alarmingly high with 98% based on risk percentage for medical conditions and 95% for lifestyle. This explains why he suffered many diabetes-related complications during this time. The “turning-point” was 2015 when all of his three CVD risk percentages decreased to 62% to 63% level. Finally, during 2020, his lifestyle changed dramatically due to the quarantine impact on his diet, exercise, sleep, stress, daily routines, and so forth. In other words, he worked even harder to maintain his lifestyle management in 2020. Finally, his CVD risk percentage reduced to 56% for medical condition, 52% for lifestyle, and 51% for MI.

Listed below is a table of his annualized risks probability percentage based on MI of having CVD or stroke (Figure 3):

  • Y2010: 100% (weight 198 lbs., BMI 29.2, waistline 44 inches, glucose 280 mg/dL)
  • Y2011: 90% (glucose 200 mg/dL)
  • Y2012: 83% (glucose 128 mg/dL)
  • Y2013: 85% (glucose 133 mg/dL)
  • Y2014: 72% (developed metabolism model, glucose 135 mg/dL)
  • Y2015: 60% (FPG control, glucose 129 mg/dL, stopped medication)
  • Y2016: 55% (PPG control, glucose 119 mg/dL)
  • Y2017: 54% (BMI 25, glucose 117 mg/dL)
  • Y2018: 54% (heavy traveling, glucose 116 mg/dL)
  • Y2019: 55% (heavy traveling, glucose 114 mg/dL)
  • Y2020: 51% (weight 172 lbs., BMI 25, waistline 33 in, glucose 109 mg/dL).

Due to his heavy travel schedules of attending more than 60 medical conferences from 2018-2019, his MI risk probability percentage increased from 54% in 2018 to 55% in 2019; however, during the recent stabilized quarantined life in 2020, it actually assisted him in lowering his risk to a record low of 51%.

It should be noted that the risk probability percentages are expressed on a “relative” scale and not on an “absolute” scale. However, by keeping the “break-even” line of 63% (actually between 62% to 64%) in mind, one can quickly judge the different severe levels from the calculated CVD/stroke risk probability percentages.

Conclusion
This article describes the individual contributions from MI which includes three medical conditions sub-categories (glucose, BP, and lipids) and three lifestyle sub-categories (food, exercise, and others) of different modeling associated with artery blockage and artery rupture scenarios based on the risk probability percentage of having CVD or stroke. His research results from the past 10+ years have demonstrated the importance of maintaining an excellent healthy state for the entire body via a stringent lifestyle program in order to reduce the risk of having CVD or stroke.

Emphasis has been placed on the significance and contributions for a patient’s overall metabolism state. Therefore, three sub-categories of specific medical conditions (diabetes, hypertension, and hyperlipidemia) along with three sub-categories of lifestyle (food, exercise, and others) including weight and waistline are quantified. As a result, the findings corroborated with the advice from healthcare professionals to their patients.

References

  1. Hsu Gerald C (2020) Biomedical research methodology based on GH-Method: math-physical medicine (No. 310). J App Mat Sci & Engg Res 4: 116-124.
  2. Hsu Gerald C (2020) Glucose trend pattern analysis and progressive behaviour modification of a T2D patient using GH-Method: math-physical medicine (No. 305). J Nutr Diet Pract 4: 1-6.
  3. Hsu Gerald C (2020) Using mathematical model of metabolism to estimate the risk probability of having a cardiovascular diseases or stroke during 2010-2019 via GH-Method: math-physical medicine (No. 255). Archives of Infect Diseases & Therapy 4: 28-30.
  4. Hsu Gerald C (2020) Detailed lifestyle contribution Analysis is of various lifestyle management on risk probability percentage of having a CVD or stroke using GH-Method: math-physical medicine (No. 314). J Diabet Res Rev Rep 2: 1-4.
  5. Hsu Gerald C (2020) Detailed contribution analysis of various medical conditions on risk probability percentage of having a CVD or stroke using GH-Method: math-physical medicine (No. 315). J Diabet Res Rev Rep 2: 1-4.
  6. Hsu Gerald C (2020) Using GH-Method: math-physical medicine to investigate the triangular dual-correlations among weight, glucose, blood pressure with a Comparison of 2 Clinic Cases (No. 43). Acta Scientific Pharmacology 1: 8-10.
  7. Hsu Gerald C (2020) Using GH-Method: math-physical medicine and signal processing techniques to predict PPG (No. 13). J Nutr Diet Pract 4: 1-2.

Copyright: © 2020 Hsu GC. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.