## GH-METHODS

Math-Physical Medicine

### NO. 376

A study on the relationships between body weight versus triglyceride, fasting plasma glucose, along with triglyceride and glucose index biomarker based on GH-Method: math-physical medicine

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

Abstract
This study analyzes the relationships between body weight versus triglyceride (TG), fasting plasma glucose (FPG), along with triglyceride and glucose index biomarker (TyG) using the correlation analysis.  The equation for the TG and TyG biomarker is:

• TyG = ln(TG) * Fasting glucose (mg / dl)] / 2

or in an abbreviated format:

• TyG = (ln(TG) + ln(FPG)) / 2

The author’s defined alternative New TyG equation is:

• New TyG = ln(TG+FPG) – ln (2)

The author utilizes the correlation analysis of statistics to evaluate the strength of connections among different variables and also confirm their biomedical interpretations.

In summary, the following table lists the six sets of correlation coefficients between weight versus TG, FPG, TyG, and New TyG:

• Weight vs. FPG:  67% (strong)
• Weight vs. TG:  35% (moderate)
• TG vs. FPG: 28% (weak)
• Weight vs. TyG:  47% (moderate strong)
• Weight vs. New TyG: 48% (moderate strong)
• TyG vs. New TYG:  99% (extraordinarily strong)

The biomedical interpretations of the relationships between the biomarkers are:

1. TyG and New TyG would yield almost the same results.
2. Weight has an extremely strong tie with FPG.
3. Weight has moderate strong connections with both TyG and New TyG.
4. TG has a weak connection with FPG, but when using the logarithm to bind them together, it can describe the health situation of insulin resistance.
5. TG is only one of the four components in the lipid category in his model, but it has special characteristics and behavior patterns. It is proven to be an important factor in insulin resistance, non-alcohol fatty liver disease (NAFLD), and cardiovascular disease (References 1, 2, 3, 4, 5).

Introduction
This study analyzes the relationships between body weight versus triglyceride (TG), fasting plasma glucose (FPG), along with triglyceride and glucose index biomarker (TyG) using the correlation analysis.  The equation for the TG and TyG biomarker is:

• TyG = ln(TG) * Fasting glucose (mg / dl)] / 2

or in an abbreviated format:

• TyG = (ln(TG) + ln(FPG)) / 2

The author’s defined alternative New TyG equation is:

• New TyG = ln(TG+FPG) – ln (2)

The author utilizes the correlation analysis of statistics to evaluate the strength of connections among different variables and also confirm their biomedical interpretations.

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 9.

2. Input data
The author has had 36 blood draws at medical laboratories or hospitals in the past 8 years.  Approximately 90% of them were performed at the same location; therefore, the consistency and reliability of the test results are not a concern.  He has removed 14 test results from this study that include HbA1C with no TG data.

For the past 11 years, his major focus centers on his diabetes conditions and their various complications.  Since 1/1/2012, he has collected FPG data once daily and postprandial plasma glucose (PPG) data 4 times daily via finger-piercing and test-strip method.  In summary, he utilized his own 22 lab-tested TG data and finger-pierced FPG data for over 8 years with an average time intervals of ~130 days between two adjacent health examinations of FPG and HbA1C at medical laboratories or hospitals.

He calculated the average weight value during each period within two adjacent lab-tested dates and used them as his weight input data.

3. TyG index
The triglyceride and glucose index (TyG) is a screening method for insulin resistance, which only requires two laboratory determinations: serum triglycerides and serum glucose.  According to a study by Salazar et al., the insulin resistance cut off is placed at the TyG index value of 4.49, with a sensitivity of 82.6% and specificity of 82.1% (AUC=0.889, 95% CI: 0.854-0.924). Subjects with an index of 4.49 or greater are likely to suffer from insulin resistance (References 1, 2, 3, 4 and 5).

The TyG equation is defined as:

• TyG = (ln[Fasting triglyceride (mg / dl) * Fasting glucose (mg / dl)] / 2  or,
• TyG = ( ln[Fasting triglyceride (mg / dl)] + ln[Fasting glucose (mg / dl)] ) / 2

Furthermore, let us re-express it with an abbreviated format as follows:

• TyG = (ln(TG) + ln(FPG)) / 2

The TyG is considered a screening tool for large-scale studies.  According to Fedchuk et al., the TyG values above 8.38 indicates a positive predictive value (PPV) of 99% in predicting steatosis equal to or greater than 5%.  A recent cross-sectional study by Zhang et al. aimed to determine whether TyG has any predictive value for NAFLD by comparing the predictive value of TyG with the determinations of alanine aminotransferase (ALT) in a cohort of 10,761 patients.

The association between a screening method using triglycerides and glucose should not come as a surprise as NAFLD is considered the liver manifestation of metabolic syndrome, while triglycerides and serum glucose are key components of this process.

The following table summarizes the two cut-off points identified for insulin resistance and NAFLD positive diagnosis likelihood (Reference 4, MDApp):

4. New TyG equation
In order to develop any mathematical equation for describing an observed physical phenomenon, scientists should not only demand high accuracy of physical description via mathematical equation in reflecting the background of the physical concept or mathematical theory, but the equation must also be practical for real-life applications.  The author is a mathematician and engineer along with being a long-term severe type 2 diabetes (T2D) patient.  To date, he has collected ~2 million data of his health conditions and lifestyle details and he understands them very well.  He wants to develop an easier way to interpret his complex pancreatic beta cells status in regard to insulin resistance and to find a quicker path in achieving the goal of his diabetes control.  Therefore, he made some simple modifications of the above defined TyG equation and developed an alternative New TyG equation as follows:

• New TyG = ln(TG+FPG) – ln (2)

Results
Figure 1 shows the author’s raw data of the lab-tested TG, finger-pierced average FPG, weight, calculated TyG and New TyG values.  The calendar dates shown in Column 1 reflects the 22 selected lab-testing dates which contain both TG and FPG values.

From these data, it is clear that both his FPG and weight are trending downward with a narrow band of data variance while TG are fluctuating with a wide band of data variance.  Due to these data characteristics and waveforms, he could predict that his waveforms of both TyG and New TyG are going to be more similar to the waveform of TG.

###### Figure 1: Raw input data of Weight versus TG, FPG, and calculated TyG and New TyG values

Figure 2 shows the following calculated correlation coefficients among Weight, TG, and FPG:

• Weight vs. FPG: 67% (strong)
• Weight vs. TG: 35% (moderate)
• TG vs. FPG: 28% (weak)

In his previous research work based on a much bigger amount of data, his weight and FPG have a greater than 90% of correlation coefficient.  Even utilizing two other clinical cases, one underweight diabetes patient with BMI 18 and another extreme obese diabetes patient with BMI 42, their correlation coefficients between their weight and their FPG are also around or greater than 90%.  For this special study of using a rather smaller amount of data due to a limited number of available lab tests for both TG and HbA1C, the correlation coefficients of his 22 data of weight versus FPG is 67%.  This correlation percentage is still high enough to be considered a strong and tight relationship between these two biomarkers.

###### Figure 2: Correlation coefficients among Weight, TG, FPG

Figure 3 depicts the following calculated correlation coefficients among Weight, TyG, and New TyG:

• Weight vs. TyG:  47% (moderate strong)
• Weight vs. New TyG:  48% (moderate strong)
• TyG vs. New TYG: 99% (extremely strong)

Although the two eauations of TyG and New TyG are somewhat different, their final calculated results are almost identical.  This is important since the validity and applicability of TyG equation has been proven by a large amount of biomedical testing data.  Therefore, the 99.6% correlation of the final results using TyG and New TyG can prove that the New TyG equation is almost equally accurate as the TyG equation in terms of measuring insulin resistance.  This high connection could explain why the 47% and 48% of weight versus these two TyG results as well as the 99% of correlation between TyG and New TyG.

###### Figure 3: Correlation coefficients among Weight, TyG, New TyG

Conclusions
In summary, the following table lists the six sets of correlation coefficients between weight versus TG, FPG, TyG, and New TyG:

• Weight vs. FPG: 67% (strong)
• Weight vs. TG: 35% (moderate)
• TG vs. FPG: 28% (weak)
• Weight vs. TyG: 47% (moderate strong)
• Weight vs. New TyG: 48% (moderate strong)
• TyG vs. New TYG:  99% (extraordinarily strong)

The biomedical interpretations of the relationships between the biomarkers are:

1. TyG and New TyG would yield almost the same results.
2. Weight has an extremely strong tie with FPG.
3. Weight has moderate strong connections with both TyG and New TyG.
4. TG has a weak connection with FPG, but when using the logarithm to bind them together, it can describe the health situation of insulin resistance.
5. TG is only one of the four components in the lipid category in his model, but it has special characteristics and behavior patterns. It is proven to be an important factor in insulin resistance, non-alcohol fatty liver disease (NAFLD), and cardiovascular disease (References 1, 2, 3, 4, 5).

References

1. 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.
2. 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
3. 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
4. Endocrinology Related Meducal Algorithms & Calculators – MDApp, TyG Index Determines insulin resistance and can also identify individuals at risk for NAFLD. Corrected Calcium Calculator.
5. 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
6. 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)”
7. 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”
8. 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”
9. Hsu, Gerald C., eclaireMD Foundation, USA, No. 310: “Biomedical research methodology based on 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”