## GH-METHODS

Math-Physical Medicine

### NO. 379

A new mathematical biomarker of general health index for lifestyle management for preventing and controlling chronic diseases based on GH-Method: math-physical medicine

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

Abstract
The author has had chronic diseases for 30 years.  For a long time, he could not comprehend and manage the difficult medical terminologies, biomarker units, acceptance levels, numerical meanings, biomedical interpretations, along with their real applications applied to daily life.  Therefore, he has been searching for a simpler and easier way to help other patients to overcome these hurdles.

The main purpose of this paper is to describe his newly developed mathematical biomarker, the General Health Index for detailed lifestyle management (GHI-LM) using some simple mathematical operations of data normalization and natural logarithm (ln).  This new biomarker can clearly reflect the general health state for patients with chronic diseases based on their daily lifestyle details, which include diet, exercise, sleep, stress, water intake, and daily routine life regularity.  For most patients, they do not need to know the mathematical terms of “logarithm” and “normalization”.  Instead, they should focus on the calculated values of GHI-LM and then check whether they are unhealthy (positive value) or healthy (negative value) based on the lifestyle management viewpoints.

After reading several medical papers regarding the triglyceride and glucose index (TyG) biomarker (References 2, 3, 4, 5, and 6), he was inspired to apply a similar concept of TyG by simply describing the 9-year history of his recorded lifestyle details as mentioned in the previous paragraph. During this self-control examination and extended research process, he has created two new mathematical biomarkers, the GHI-LM1 and GHI-LM2, which are defined as:

• GHI-LM1= sum(ln(Mi, i=5,10)) / 6  and
• GHI-LM2= ln(sum(Mi, i=5,10)) – ln(6)

Where “Mi” values are “metabolism indexes” and M5 is exercise, M6 is water intake, M7 is sleep, M8 is stress, M9 is food and meals, and M10 is daily life routines.  All of these Mi values are normalized with the linear equation of measured value divided by standard value.

This article will explain the GHI-LM concept and its calculated results using his own input data along with its real application and practice, step by step.

The author discovered that his combined score of lifestyle details changes into a heathy state in 2014 when his GHI-LM value reflects a negative value, where the break-even point is zero.  Particularly, when the GHI-LM value reaches approximately -20%, his metabolic disorder conditions indicate as being well controlled.  For example, his GHI-LM reaches -43% to -45% between 2017 and 2020, which depicts the “healthiest period” over his 30-year history of chronic diseases.  As we know, chronic disease conditions, such as obesity, diabetes, hypertension, and hyperlipidemia, are closely interrelated with lifestyle details.  This article offers more in-depth understanding regarding the relationship between chronic diseases and lifestyle details.

In his opinion, not only can this new biomarker, GHI-LM describe the effect of combined lifestyle details accurately, it can easily be understood and applied to daily efforts in controlling chronic diseases by other patients.  Therefore, he has decided to develop a software for the mobile phone APP, which utilizes the GHI-LM concept and equations for other patients to use in their daily managing of their lifestyle details associated with chronic diseases.

Introduction
The author has had chronic diseases for 30 years.  For a long time, he could not comprehend and manage the difficult medical terminologies, biomarker units, acceptance levels, numerical meanings, biomedical interpretations, along with their real applications applied to daily life.  Therefore, he has been searching for a simpler and easier way to help other patients to overcome these hurdles.

The main purpose of this paper is to describe his newly developed mathematical biomarker, the General Health Index for detailed lifestyle management (GHI-LM) using some simple mathematical operations of data normalization and natural logarithm (ln).  This new biomarker can clearly reflect the general health state for patients with chronic diseases based on their daily lifestyle details, which include diet, exercise, sleep, stress, water intake, and daily routine life regularity.  For most patients, they do not need to know the mathematical terms of “logarithm” and “normalization”.  Instead, they should focus on the calculated values of GHI-LM and then check whether they are unhealthy (positive value) or healthy (negative value) based on the lifestyle management viewpoints.

After reading several medical papers regarding the triglyceride and glucose index (TyG) biomarker (References 2, 3, 4, 5, and 6), he was inspired to apply a similar concept of TyG by simply describing the 9-year history of his recorded lifestyle details as mentioned in the previous paragraph.  During this self-control examination and extended research process, he has created two new mathematical biomarkers, the GHI-LM1 and GHI-LM2, which are defined as:

• GHI-LM1= sum(ln(Mi, i=5,10)) / 6  and
• GHI-LM2= ln(sum(Mi, i=5,10)) – ln(6)

Where “Mi” values are “metabolism indexes” and M5 is exercise, M6 is water intake, M7 is sleep, M8 is stress, M9 is food and meals, and M10 is daily life routines.  All of these Mi values are normalized with the linear equation of measured value divided by standard value.

This article will explain the GHI-LM concept and its calculated results using his own input data along with its real application and practice, 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 four chronic diseases, including obesity, diabetes, hypertension, and hyperlipidemia.  He has also endured five 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 their 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 final terms: the metabolism index (MI or 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 the trend of combined disease developments and lifestyle management efforts and effectiveness.

His metabolism model was designed for the majority of patients with chronic diseases, not trying to cover some “extreme cases”.  For both the MI and GHSU, he has identified an overall “break-even line” at 0.735 (73.5%) to separate his metabolism conditions between healthy (below 0.735) and unhealthy (above 0.735).  This 0.735 or 73.5% break-even line are for the combined 10 categories of 4 medical conditions and 6 lifestyle details.  However, for the category of the medical conditions, his break-even line is defined at 1.0 (100%) with the best condition at 0.5 (50%) and the worst condition at 1.5 or higher (150% or higher) in order to cover the most-likely data ranges for the majority of type 2 diabetes patients.

4. Metabolism categories in this study
Within the lifestyle management, he has defined 6 categories: M5 is exercise (both daily and post-meal walking steps), M6 is water intake (cc or bottles), M7 is sleep (total 9 elements, including sleeping hours, wake up times, sleep quality, etc.), M8 is stress (total ~40 elements), M9 is food and meals (both food portion quantity and ~20 food quality elements), and M10 is daily life routines (~25 elements).  Figure 1 shows some sample data-entry screens of sleep and daily routines on the iPhone APP.

At the start, he collects his daily data of lifestyles management using the iPhone APP.  Due to the concern of large volume of data entry, various artificial intelligence (AI) techniques were used in this software.   He then “normalizes” the input data into 6 separate metabolism indices, m5 through m10, with established cutoff points as the “standards” of each category.  All of these Mi values are normalized with the linear formula of measured value divided by standard value.

• Food quantity: percentage of patients normal portion % of meals (50% is the best condition and 150% is the worst condition)
• Food quality: percentage of patients % of perfect high-quality meals (50% is the best condition and 150% is the worst condition)
• Daily walking: 10,000 steps
• Post-meal walking: 4,000 steps
• Water intake: 2,000 cc or 4 bottles
• Sleep hours: more than 7 hours of sleep is 50%, the best condition
• Sleep quality: 50% is the best quality condition
• Stress quality: 50% is the best quality condition to reflect a non-stressful life
• Daily life routine: 50% is the best condition

When his measured value reaches to the standard level, he gets a normalized score of 1.0 (100%).  If his health data is higher than the standard level, then it reflects a normalized number higher than 1.0, and vice versa.  When these 6 lifestyle details scores combine with the other 4 medical conditions scores, an overall “cutoff line” at 0.735 (73.5%) is the dividing line between health lifestyle (<73.5%) and unhealthy lifestyle (>73.5%).  In this study, the discussion on medical conditions from m1 through m4 are excluded, instead he will concentrate on lifestyle details from m5 through m10.

After obtaining all 6 normalized metabolism indexes, he then utilizes the natural logarithm (ln) operations to develop two new biomarkers of general health index for lifestyle management, GHI-LM1 and GHI-LM2.

5. Input data
For the period of ~9 years from 1/1/2012 through 12/12/2020, he has collected a large input data of his daily lifestyle details.  He then normalizes these 6 lifestyle details data according to the established standards into 6 sets of normalized annual metabolism indices which are data of m5 through m10.

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

• GHI-LM1
=sum(ln(Mi, i=5,10)) / 6
=(ln(m5)+ln(m6)+ln(m7)+ln(m8)+ln(m9)+ln(m10)) / 6

and

• GHI-LM2
= ln(sum(Mi, i=5,10)) – ln(6)
= ln(m5+m6+m7+m8+m9+m10) – ln(6)

Where “Mi” values are “metabolism indexes” of m5 through m10.

He used his annual normalized data of m5 through m10 from 2012 through 2020 to plug into the two equations from above to obtain his GHI-LM1 and GHI-LM2 values for each year.  He then studied the trend of his GHI-LM biomarkers and its biomedical interpretations for each year’s value.

Mathematically, the natural logarithm (e=2.7183) values of 0.5, 1, 1.5, 2, 3, 4, and 4.5 are calculated and 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
• ln(5)    = 1.609438
• ln(6)    = 1.791759

As shown, when the variable’s value increases from 0.5 to 6.0, the value of natural logarithm (ln) of the variable also increases from a negative value to a positive value.

Results
Figure 1 shows two sample screens on iPhone APP for both sleep and daily routines.

###### Figure 1: Sample screen of data entry for sleep and daily routines

Figure 2 depicts the raw input data and step by step calculations for GHI-LM1 and GHI-LM2 values.  The calendar years shown in Column 1 reflects the past 9 years from 2012 to 2020.

###### Figure 2: Input data of m5-m10 with calculations of GHI-LM1 & GHI-LM2

Figure 3 reveals the comparison of his two GHI-LM curves and values that is GHI-LM1 versus GHI-LM2, using two bar-chart diagrams.

From Figure 2 and Figure 3, it is clear that the combined lifestyle mi values are declining from the upper range of 7% (Y2012) year after year, trending downward to the lower range of -43% (Y2020), with the exception of little bumps in 2018-2019.  His turning-around period occurred in 2014, where his two GHI-LM values are turning from positive value into negative value.  It should be pointed out that his rising GHI-LM values during 2018-2019 are caused by his stressful and hectic traveling schedules in attending ~65 international medical conferences to present ~120 papers.  However, during the 2020 COVID-19 quarantine period and the entire period of 2017-2020, his overall lifestyle management, including m5 through m10, have declined and maintained to their lowest level (best status of -43% to -45%) over the past 30-year history of chronic diseases.

###### Figure 3: Bar chart diagrams of GHI-LM1 and GHI-LM2

Figure 4 illustrates the direct comparison between GHI-LM1 curve and GHI-LM2 curve.  These two calculated GHI-LM values are almost identical with an extremely high matchability of 99%.

Here is the summary based on the observations from all of the data tables and figures:

1. The overall trend of these two GHI-LM values are declining year after year.
2. The GHI-LM values are positive in 2012-2013, the “unhealthy” years.
3. The GHI-LM values are changed into negative percentages starting from 2014, the “turning-around” year.
4. The traveling impacts on GHI-LM during 2018-2019 are evident.
5. The ending year, 2020 and the period from 2017 to 2020 have the lowest GHI-LM values between -43% and -45%, which are his best conditions or “healthiest” years.

The GHI-LM1 values and GHI-LM2 values are almost identical to each other, with a 99.5% matchability.

###### Figure 4: Line chart of comparison between GHI-LM1 and GHI-LM2 with 99.0% matchability

Conclusions
The author discovered that his combined score of lifestyle details changes into a heathy state in 2014 when his GHI-LM value reflects a negative value, where the break-even point is zero.  Particularly, when the GHI-LM value reaches approximately -20%, his metabolic disorder conditions indicate as being well controlled.  For example, his GHI-LM reaches -43% to -45% between 2017 and 2020, which depicts the “healthiest period” over his 30-year history of chronic diseases.  As we know, chronic disease conditions, such as obesity, diabetes, hypertension, and hyperlipidemia, are closely interrelated with lifestyle details.  This article offers more in-depth understanding regarding the relationship between chronic diseases and lifestyle details.

In his opinion, not only can this new biomarker, GHI-LM describe the effect of combined lifestyle details accurately, it can easily be understood and applied to daily efforts in controlling chronic diseases by other patients.  Therefore, he has decided to develop a software for the mobile phone APP, which utilizes the GHI-LM concept and equations for other patients to use in their daily managing of their lifestyle details associated with chronic diseases.

References

1. Hsu, Gerald C., eclaireMD Foundation, USA, No. 310: “Biomedical research methodology based on GH-Method: math-physical medicine”
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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
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