GH-METHODS

Math-Physical Medicine

NO. 375

A new mathematical index or biomarker for preventing, measuring, and controlling various chronic diseases based on GH-Method: math-physical medicine

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

Abstract
The main purpose of this study is to develop a new mathematical biomarker, the General Health Index (GHI) which can indicate the general health state for patients with chronic diseases based on their common biomarker tests, which include glucose, blood pressure, and lipids.

After reading several medical papers regarding the triglyceride and glucose index (TyG) biomarker (References 2, 3, 4, 5, and 6), he decided to apply the TyG equation to examine his conditions for insulin resistance based on the pancreatic beta cells over the past 8 years (References 10 and 11).  After completing papers No. 373 and No. 374, he was inspired to broaden the concept of the TyG equation involving general health conditions by using his own data from 2012 to 2020.  During this self-examination and research process, he created two new general health indices, GHI-1 and GHI-2.

This article will explain the concept and practice of this new biomarker, GHI, step by step.

The author has identified that his general health conditions change into a heathy state when his GHI value reflects a negative value. Particularly, when the GHI value reaches to -10%, his metabolic disorder conditions indicate them to be well controlled.  For example, the GHI reached -19% in 2020, which depicts the “healthiest year” over his 25-year diabetes history.

In addition, he also verified that the GHI-1 value and GHI-2 value are extremely close to each other, with a -0.4% deviation.  This means that either of the equations are suitable as practical applications for other patients to use.  As a result, the author will develop a software for the mobile phone APP, which utilizes the concept and equations for other patients to use.

Introduction
The main purpose of this study is to develop a new mathematical biomarker, the General Health Index (GHI) which can indicate the general health state for patients with chronic diseases based on their common biomarker tests, which include glucose, blood pressure, and lipids.

After reading several medical papers regarding the triglyceride and glucose index (TyG) biomarker (References 2, 3, 4, 5, and 6), he decided to apply the TyG equation to examine his conditions for insulin resistance based on the pancreatic beta cells over the past 8 years (References 10 and 11).  After completing papers No. 373 and No. 374, he was inspired to broaden the concept of the TyG equation involving general health conditions by using his own data from 2012 to 2020.  During this self-examination and research process, he created two new general health indices, GHI-1 and GHI-2.

This article will explain the concept and practice of this new biomarker, GHI, step by step.

Methods
1. Background
To learn more about the author’s GH-Method: math-physical medicine (MPM) methodology, readers can refer to his article to understand his developed MPM analysis method in Reference 1.

2. The authors chronic diseases
Since 1995, he has suffered three chronic diseases, including diabetes, hyperlipidemia, and hypertension.  He has also endured 5 cardiovascular episodes (CVD) from 1994 to 2006, chronic kidney disease (CKD) in 2010, bladder infection, foot ulcer, diabetic retinopathy (DR), and hyperthyroidism for the past decade.  By 2017, most of his metabolic disorders induced chronic diseases and complications have been well controlled.

3. Metabolism model
Since 1/1/2012, he has spent 10 years collecting big data (~2 million data) of his health conditions and lifestyle details in order to conduct his own research on chronic diseases and their various complications.  In 2014, by using topology concept of mathematics and finite-element modeling techniques of engineering, he developed a mathematical model of metabolism, including 4 categories of disease control (health conditions as body outputs) and 6 categories of lifestyle details (body inputs).  In total, these 10 categories contain approximately 500 elements.  He has further assembled these categories into two terms: the metabolism index (MI), which is a combined daily score to validate the body’s health situation, and general health status unit (GHSU), which is the 90-days moving average number of MI in order to show its trend.

Overall, for both the MI and GHSU, he has identified a “break-even line” at 0.735 (73.5%) to separate his metabolism conditions between healthy (below 0.735) and unhealthy (above 0.735).

4. Metabolism categories in this study
Within the health conditions, he has defined 4 categories: weight as m1, glucose as m2, blood pressure as m3, lipid as m4.  In this study, he omits the category m1 of weight to exclude obesity; therefore, he combines m2, m3, and m4 together and using natural logarithm operations to develop another new term of general health index (GHI).

He uses the following cutoff points as the “standards” of health:

  • Glucose: 120 mg/dL
  • Systolic blood pressure: 120 mmHG
  • diastolic blood pressure: 80 mmHG
  • Hear rate: 60
  • Triglyceride: 150
  • HDL-C: 40
  • LDL-C: 130
  • Total cholesterol: 200

When his measured value reaches to the standard level, he gets 1.0 (100%).  If his health data is higher than the standard level, then it reflects a number higher than 1.0, and vice versa.  For medical health conditions (m1 through m4), he has established the best condition as 0.5 and the worst condition as 1.5.  He will exclude the discussion on lifestyle details (m5 through m10) in this article, and instead focus on glucose for diabetes (m2),  blood pressure for hypertension (m3), and lipids for hyperlipidemia (m4).

5. Input data
For the period of ~9 years from 1/1/2012 through 12/8/2020, he calculates his average daily glucose, daily average blood pressure, and periodically tested and annual average lipids.  He then normalize these health conditions data according to the above “standard” into three normalized sets of annual metabolism values which are m2 data, m3 data, and m4 data.

6. General Health index (GHI)
He has defined the following two equations for GHI:

  • GHI-1 = (ln(m2)+ln(m3)+ln(m4))/3
  • GHI-2 = ln(m2+m3+m4)-ln(3)

He used his annual average data of m2, m3, and m4 from 2012 through 2020 to plug into these two equations to obtain GHI-1 value and GHI-2 value.  He then studied the trend of each GHI bio marker and the comparison between these two GHI biomarkers.

Mathematically, the natural logarithm (e=2.7183) values of 0.5, 1, 1.5, 2, 3, 4, and 4.5 are calculated and listed below:

  • ln(0.5) = -0.693147
  • ln(1) = 0.0
  • ln(1.5) = 0.405465
  • ln(2) = 0.693147
  • ln(3) = 1.098612
  • ln(4) = 1.386294
  • ln(4.5) = 1.504077

As shown, when the value increases from 0.5 to 4.5, the natural log of the value also increases from a negative value to a positive value.

Results
Figure 1 illustrates the three-line charts of normalized values of m2 glucose, m3 blood pressure, and m4 lipid.  From this diagram, it is clear that the three curves are declining from 1.06 year after year with the exception of bumps during 2018-2019.  His higher values of m2, m3, m4 during 2018-2019 are due to his hectic traveling schedules in attending ~65 international medical conferences to present ~120 papers.  For the 2020 COVID-19 quarantine period, his overall health conditions, including glucose (m2), blood pressure (m3), lipids (m4), and body weight (m1) have been maintained to their best status over the past 30 years.

Figure 1: Annualized m2 glucose, m3 blood pressure, and m4 lipid

Figure 2 reveals the author’s raw data of his collected m2, m3, and m4, and calculated GHI-1 and GHI-2.  The calendar years shown in Column 1 reflects the past 9 years from 2012 to 2020.  

There are two clear observations in the lower table of Figure 2.  First, both the GHI-1 and GHI-2 are positive in 2012-2013 (the unhealthy years); then changed into negative percentages starting in 2014 (the turning-around year); and they end at the lowest percentage of -19% in 2020 (the healthiest year).  Second, the GHI-1 and GHI-2 are almost identical in each year with small deviations.  Their total correlation is 99.6% (100% – 0.4% of delta or difference).  

Figure 2: Raw input data of m2, m3, m4 and calculated GHI-1 and GHI-2

Figure 3 shows the annual bar charts of both GHI-1 and GHI-2.  The above-mentioned two observations for the index turning and trend (see Figure 2 explanation), as well as the traveling impact during 2018-2019 (see Figure 1 explanation) can also be clearly seen in these two bar charts.

Figure 3: Comparison between GHI-1 and GHI-2 and year-to-year change

Conclusions
The author has identified that his general health conditions change into a heathy state when his GHI value reflects a negative value. Particularly, when the GHI value reaches to -10%, his metabolic disorder conditions indicate them to be well controlled.  For example, the GHI reached -19% in 2020, which depicts the “healthiest year” over his 25-year diabetes history.

In addition, he also verified that the GHI-1 value and GHI-2 value are extremely close to each other, with a -0.4% deviation.  This means that either of the equations are suitable as practical applications for other patients to use.  As a result, the author will develop a software for the mobile phone APP, which utilizes the concept and equations for other patients to use.

References

  1. Hsu, Gerald C., eclaireMD Foundation, USA, No. 310: “Biomedical research methodology based on GH-Method: math-physical medicine”
  2. Endocrinology and Metabolism Triglyceride Glucose Index Is Superior to the Homeostasis Model Assessment of Insulin Resistance for Predicting Nonalcoholic Fatty Liver Disease in Korean Adults. Endocrinol Metab (Seoul) 2019 Jun;34(2):179-186. doi: 10.3803/EnM.2019.34.2.179.
  3. PubMed, NIH, national center for biotechnology information Lipids Health Dis. 2017 Jan 19;16(1):15. doi: 10.1186/s12944-017-0409-6. “The triglyceride and glucose index (TyG) is an effective biomarker to identify nonalcoholic fatty liver disease”, Shujun Zhang  1 , Tingting Du  1 , Jianhua Zhang  1 , Huiming Lu  2 , Xuan Lin  3 , Junhui Xie  1 , Yan Yang  1 , Xuefeng Yu  4
  4. RESEARCH, The triglyceride-glucose index (TyG) and Nonalcoholic fatty liver in the Japanese population: a retrospective cross-sectional study, Enqian Liu, Yaping Weng, Aiming Zhou, Chunlai Zeng, DOI: 21203/rs.3.rs-21504/v1
  5. Endocrinology Related Meducal Algorithms & Calculators – MDApp, TyG Index Determines insulin resistance and can also identify individuals at risk for NAFLD. Corrected Calcium Calculator.
  6. NCBI/NIH, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6297409/; Journal of Thoracic Disease; Triglyceride glucose index for predicting cardiovascular outcomes in patients with coronary artery disease; Jing-Lu Jin, Ye-Xuan Cao, […], and Jian-Jun Li; J Thorac Dis. 2018 Nov; 10(11): 6137–6146. Doi: 21037/jtd.2018.10.79; PMCID: PMC6297409; PMID: 30622785; Jing-Lu Jin,1 Ye-Xuan Cao,1 Li-Guo Wu,2 Xiang-Dong You,2 Yuan-Lin Guo,1 Na-Qiong Wu,1 Cheng-Gang Zhu,1 Ying Gao,1 Qiu-Ting Dong,1 Hui-Wen Zhang,1 Di Sun,1 Geng Liu,1 Qian Dong,1 and Jian-Jun Li1
  7. Hsu, Gerald C., eclaireMD Foundation, USA, No. 133: “Probable partial recovery of pancreatic beta cells insulin regeneration using annualized fasting plasma glucose  (GH-Method: math-physical medicine)”
  8. Hsu, Gerald C., eclaireMD Foundation, USA, No. 297: “Self-recovery of pancreatic beta cell’s insulin secretion based on annualized fasting plasma glucose, baseline postprandial plasma glucose, and baseline daily glucose data using GH-Method: math-physical medicine”
  9. Hsu, Gerald C., eclaireMD Foundation, USA, No. 339: “Self-recovery of pancreatic beta cell’s insulin secretion based on 10+ years annualized data of food, exercise, weight, and glucose using GH-Method: math-physical medicine”
  10. Hsu, Gerald C., eclaireMD Foundation, USA, No. 373: “Triglyceride and glucose index (TyG) biomarker study along with diabetes control through improvement on insulin resistance using GH-Method: math-physical medicine”
  11. Hsu, Gerald C., eclaireMD Foundation, USA, No. 374: “Using an alternative triglyceride and glucose index biomarker (New TyG) as an easier and more practical tool for diabetes patients to control their insulin resistance conditions based on GH-Method: math-physical medicine”