## GH-METHODS

Math-Physical Medicine

### NO. 377

A study on the relationships between body weight versus glucose, blood pressure, lipid, and General Health Index biomarker (GHI) based on GH-Method: math-physical medicine

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

Abstract
This article studies the relationships between four medical categories in the metabolism model, specifically body weight (m1) versus glucose (m2), blood pressure (m3), lipid (m4) and the newly developed biomarker, the General Health Index (GHI), using the correlation coefficient analysis of statistics.  The two newly defined equations for the GHI biomarker are:

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

These two formulas can indicate the general health state for patients with chronic diseases based on their routine medical examination results, which usually include glucose, blood pressure, and lipids.  However, this article studies the relationship of weight and other biomarkers since under most circumstances, being overweight or having obesity is one the root causes of  chronic diseases, such as diabetes, hypertension, and hyperlipidemia.

In summary, the following table lists all of the six sets of correlation coefficients between m1 weight versus m2 glucose, m3 blood pressure (BP), m4 lipid, along with GHI-1 and GHI-2:

• Weight vs. Glucose: 69% (strong)
• Weight vs. BP: 93% (strong)
• Weight Lipid: 85% (strong)
• Weight vs. GHI-1: 90% (strong)
• Weight vs. GHI-2: 90% (strong)
• GHI-1 vs. GHI-2: 99.9% (extremely strong, almost identical)

The biomedical interpretations of the relationships between biomarkers are:

1. GHI-1and GHI-2 would yield almost the same results.
2. Weight has a strong tie with Glucose, BP, and Lipid.
3. Weight has a strong connections with both GHI-1 and GHI-2.
4. Weight has a strong connection with all of other three basic biomarkers, but when using the logarithm to bind them together, it can describe the general health situation easily and clearly (positive % means unhealthy and negative % means healthy).

In the turning-around year of 2014, his GHI value became a negative value (-3%) which reflected his general health conditions are starting to improve.  Particularly, when he learned how to control his glucose via diet and exercise in 2016, his GHI value reached -15%, which indicated that his metabolic disorder conditions were under control.  It should be pointed out that his GHI values reached -19% in both 2017 and 2020, where these two years are considered the “healthiest years” over his 30-year diabetes history.

In addition, he has also verified that his GHI-1 and GHI-2 values are extremely close to each other, with a -0.4% small deviation or 99.6% matchability.  This means that either one of these two 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 actual equations.

Introduction
This article studies the relationships between four medical categories in the metabolism model, specifically body weight (m1) versus glucose (m2), blood pressure (m3), lipid (m4) and the newly developed biomarker, the General Health Index (GHI), using the correlation coefficient analysis of statistics.  The two newly defined equations for the GHI biomarker are:

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

These two formulas can indicate the general health state for patients with chronic diseases based on their routine medical examination results, which usually include glucose, blood pressure, and lipids.  However, this article studies the relationship of weight and other biomarkers since under most circumstances, being overweight or having obesity is one the root causes of  chronic diseases, such as diabetes, hypertension, and hyperlipidemia.

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 hypothyroidism 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 (body outputs of medical conditions) 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 into a new biomarker of GHI.

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

• Glucose: 120 mg/dL
• Systolic BP: 120 mmHG
• Diastolic BP: 80 mmHG
• Heart rate: 60 bpm
• Triglyceride: 150
• HDL-C: 40
• LDL-C: 130
• Total cholesterol: 200

By dividing his collected data with the above standard data, he was able to normalize the findings.  When his measured value matches with the standard level, he gets 1.0 (100%).  If his normalized biomarker data is higher than the standard level of 1.0, then it reflects an unhealthy condition, and vice versa (i.e., healthy conditions with less than the standard level of 1.0).  For medical health conditions (m1 through m4), he has established 0.5 as the best condition and 1.5 or higher as the worst condition.  In this investigation report, he will exclude the discussion on lifestyle details (m5 through m10), and focus on glucose for diabetes (m2),  BP 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 daily average glucose, daily average BP, and periodically tested and annual average lipids.  He then normalizes the three-biomarker data using the above “standards” into three sets of annualized metabolism values which are m1 data, m2 data, and m3 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 the above two equations to obtain both GHI-1 and GHI-2 values.  Afterwards, he studied the trend of each GHI variable and waveform and the comparison between these two GHI variables and waveforms.

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

• 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.  Therefore, a negative natural logarithm (ln) value is associated with a healthy condition and a positive natural logarithm (ln) value is associated with an unhealthy condition.

Results
Figure 1 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.

###### Figure 1: Raw data of m1, weight and m2 glucose, m3 blood pressure, m4 lipid, and GHI-1, GHI-2

Figure 2 illustrates the four-line charts of normalized values of m1 weight, m2 glucose, m3 BP, and m4 lipid.  From this diagram, it is clear that the four curves are declining from 1.1 year after year with the exception of bumps during 2018-2019 for both m3 BP and m4 lipid.  His higher values of m3 and m4 during 2018-2019 are due to his hectic and stressful 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), BP (m3), lipids (m4), and body weight (m1) have been maintained to their best status over the past 30 years.

There are two clear observations from Figures 1 and 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 both 2017 and 2020 (the healthiest year).  Second, the GHI-1 and GHI-2 are almost identical each year with small deviations.  Their total matchability is 99.6% (100% of perfect match minus 0.4% of delta or difference).

Figure 3 illustrates the annual bar charts of both GHI-1 and GHI-2.  The 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 be clearly seen in these two bar charts.

###### Figure 3: Correlation coefficients among Weight, GHI-1, GHI-2

Figure 4 and Figure 5 show the 6 calculated correlation coefficients of weight versus, glucose, BP, lipid, GHl-1, and GHI-2.  The following table summarizes their results:

• Weight vs. Glucose: 69% (strong)
• Weight vs. BP: 93% (strong)
• Weight Lipid: 85% (strong)
• Weight vs. GHI-1: 90% (strong)
• Weight vs. GHI-2: 90% (strong)
• GHI-1 vs. GHI-2: 99.9% (extremely strong, almost identical)

The biomedical interpretations of the above relationships between biomarkers are:

1. GHI-1and GHI-2 would yield almost the same results.
2. Weight has a strong tie with Glucose, BP, and Lipid.
3. Weight has a strong connections with both GHI-1 and GHI-2.
4. Weight has a strong connection with all of other three basic biomarkers, but when using logarithm to bind them together, it can describe the general health situation easily and clearly (positive % means unhealthy and negative % means healthy).
###### Figure 5: Correlation coefficients among Weight, Glucose, BP, Lipid

Conclusions
In summary, the following table lists all of the six sets of correlation coefficients between m1 weight versus m2 glucose, m3 blood pressure (BP), m4 lipid, along with GHI-1 and GHI-2:

• Weight vs. Glucose: 69% (strong)
• Weight vs. BP: 93% (strong)
• Weight Lipid: 85% (strong)
• Weight vs. GHI-1: 90% (strong)
• Weight vs. GHI-2: 90% (strong)
• GHI-1 vs. GHI-2: 99.9% (extremely strong, almost identical)

The biomedical interpretations of the relationships between biomarkers are:

1. GHI-1and GHI-2 would yield almost the same results.
2. Weight has a strong tie with Glucose, BP, and Lipid.
3. Weight has a strong connections with both GHI-1 and GHI-2.
4. Weight has a strong connection with all of other three basic biomarkers, but when using the logarithm to bind them together, it can describe the general health situation easily and clearly (positive % means unhealthy and negative % means healthy).

In the turning-around year of 2014, his GHI value became a negative value (-3%) which reflected his general health conditions are starting to improve.  Particularly, when he learned how to control his glucose via diet and exercise in 2016, his GHI value reached -15%, which indicated that his metabolic disorder conditions were under control.  It should be pointed out that his GHI values reached -19% in both 2017 and 2020, where these two years are considered the “healthiest years” over his 30-year diabetes history.

In addition, he has also verified that his GHI-1 and GHI-2 values are extremely close to each other, with a -0.4% small deviation or 99.6% matchability.  This means that either one of these two 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 actual equations.

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”