## GH-METHODS

Math-Physical Medicine

### NO. 380

A summary report of two biomarkers for triglyceride and glucose index (TyG) and accuracy sensitivity analysis in estimating the insulin resistance status based on GH-Method: math-physical medicine

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

Abstract
The main purpose of this report is to study the role of insulin resistance in diabetes control using the existing triglyceride and glucose index, TyG or TyG-A, and a newly defined index, New TyG or TyG-B.  This article also provides an accuracy sensitivity analysis.  It investigates the deviation of the calculated results for the TyG-A and TyG-B using 25 combination cases within reasonable data ranges between 60 and 200 of the triglycerides (TG) and fasting plasma glucose (FPG).

The author utilized his personal 22 datasets of TG and FPG over the past 8 years, from 1/1/2013 through 10/21/2020, to conduct the first part of his study.  On average, the data has a time interval of ~130 days between the two adjacent medical examinations for TG and HbA1C performed at the medical laboratories or hospitals.  In addition, he used these ~130 moving days average finger-pierced FPG data in this study.  The exact time intervals for both TG and FPG between two adjacent datasets are identical.

The original defined equation shown in Reference 2, 3, 4, and 5 is listed as follows:

• TyG-A = ln [Fasting triglyceride (mg / dl) * Fasting glucose (mg / dl)] / 2

After applying the rule of logarithm, it becomes the following abbreviated format:

• TyG-A = (ln(TG) + ln(FPG)) / 2

For scientists, to develop any mathematical equation for observed physical phenomenon, they should not only demand high accuracy in terms of the physical description via mathematical equation in reflecting the background physical concept or mathematical theory, but the equation should have ease-of-use and be practical for real-life applications. The author is an engineer with mathematics background and 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 has written and published more than 300 medical papers.  Therefore, he understands glucose characteristics and his own health conditions very well.  He wants to identify an easier way to interpret this complex pancreatic beta cells in regard to insulin resistance status, where he can quickly achieve the goal of diabetes control.  Therefore, he made some simple modifications on the existing TyG equation, which he named as TyG-A, by developing an alternative New TyG, which he named as TyG-B as follows (Reference 11):

• TyG-B = ln(TG+FPG) – ln (2)  or  TyG-B = ln((TG+FPG)/2)

The results from the new TyG equation are extremely close to the existing TyG equation within a 99.6% accuracy range using his own collected 22 datasets.  Even using the accuracy sensitivity study results from 25 combined normal cases, the TyG-B values are still within 99.1% accuracy range of TyG-A values.

The author used his 22 personal datasets over the past 8 years to examine the TyG biomarkers with two investigations using both the decoupled components of ln(TG) + ln(FPG) or ln(TG*FPG) for TyG-A, and the combined component of ln(TG+FPG) for TyG-B.  For most patients and physicians, they would have some difficulties to comprehend the biomedical meaning of TG*FPG and the mathematical operation of natural logarithm.  These two terms and amounts do not incite any biomedical comprehensions to them at all.  With the TyG-B, they are dealing with the value of TG+FPG which is much easier for them to comprehend the addition of these two commonly used biomarkers, TG and FPG, instead of the multiplication of them as TG*FPG.  Patients or physicians would only need to remember that in order to keep the insulin resistance level within a healthy range (<4.49), they need to keep each biomarker, TG or FPG, lower than 90 or the combination of TG+FPG lower than 180.  This is the sole reason why the author wants to redefine the existing TyG-A formula into the new TyG-B.

However, in order to keep the integrity of the TyG biomarker with its rich experimental data behind it, the accuracy of the TyG-B results using TyG-A as the base is an especially important issue.  The second part of this study deals with this particular concern.  For the accuracy investigation, he further defined a new equation of “Delta” as the difference between TyG-A and TyG-B as listed below:

• Delta = TyG-B – TyG-A or  Delta =(ln(((TG+FPG)/2)²/(TG*FPG)) / 2

Diabetes conditions contend with glucose production along with the storage of glucose in the liver, and insulin secretion or insulin resistance from the pancreas.  By using the additional TyG biomarker, it involves triglycerides for evaluating insulin resistance in diabetes situation.  It also demonstrates the connectivity of diabetes with both glucose and lipid or both liver and pancreas.

The author has self-studied and researched his diabetes conditions for the past 11 years; therefore, he understands the linkage of glucose physical characteristics and mathematical expressions along with his detailed diabetes conditions.  For example, he comprehends the precise mathematical relationships between glucose fluctuations with changes in weight/food/exercise/others and how difficult diet control along with persistent exercise can be for a normal patient.  In addition, he has proven that the glucose reduction rate relates to the self-repair rate of his pancreatic beta cells.  That is why in this study, he conducted a further what-if analysis of the two separate ideal situations using 80 TG and 100 FPG versus 90 TG and 90 FPG.

In 2020, he finally achieved his goal of suppressing his FPG below 100 mg/dL level.  His plan for 2021 and beyond is to accomplish his combined targets of either 80 TG and 100 FPG (TyG-A at 4.49 and TyG-B at 4.50) or 90 TG and 90 FPG (TyG-A at 4.50 and TyG-B at 4.50).  This strategy provides a practical guide on how to reduce his insulin resistance via lowering his combined TG and FPG level to a value lower than 180.

The additional benefits of lowering his risks on nonalcoholic fatty liver disease (NAFLD) and cardiovascular disease (CVD) via lowering his TyG index are extraordinary beneficial.

The accuracy sensitivity analysis results utilized 25 combination cases for a normal patient (25 cases come from the 5 TG levels at 60, 90, 120, 150, 200 multiple by the 5 FPG levels at 60, 90, 120, 150, 200) indicate the following summary of data observations:

• 13 cases (52%) have 100% match.
• 6 cases (24%) have 99% match.
• 4 cases (16%) have 98% match.
• 2 cases (8%) have ~96% match.
• All of the 25 cases (100%) have an average 99.1% of accuracy if using TyG-A values as the base.
• The cases which have a larger than 2% deviation are invoked with either the low-end value of 60 or the high-end value of 200 of both TG and FPG.  Actually, these extremely low or high values of TG and FPG provide a type of amplification effect via their associated TyG-B values.
• 19 cases or 76% have TyG values greater than 4.49.  It indicates the difficulty of maintaining low insulin resistance for diabetes patients.

The conclusions from the sensitivity analysis listed above with accuracy percentages and distribution rates can be calculated directly using the developed equation of Delta, which is “the difference between TyG-A and TyG-B”.

The Delta equation is defined as follows:

• Delta = (TyG-B) – (TyG-A)  or  Delta =(ln(((TG+FPG)/2)²/(TG*FPG)) / 2

In conclusion, the TyG-B is simpler to comprehend and easier to use in real application for patient’s clinical cases.  However, the TyG-A equation has had sufficient statistical support and proof via many clinical data from both lab tests and medical examination records.  Therefore, by knowing the results difference, Delta, the TyG-B results can easily be adjusted and reproduce the same results from using the TyG-A biomarker.

The author is not a medical doctor and lacks access to medical labs and medical records of patients.  Therefore, his newly developed TyG-B biomarker needs additional medical research scientist’s assistance to prove it through big data collected from many clinical patients or existing hospital records.

Introduction
The main purpose of this report is to study the role of insulin resistance in diabetes control using the existing triglyceride and glucose index, TyG or TyG-A, and a newly defined index, New TyG or TyG-B.  This article also provides an accuracy sensitivity analysis.  It investigates the deviation of the calculated results for the TyG-A and TyG-B using 25 combination cases within reasonable data ranges between 60 and 200 of the triglycerides (TG) and fasting plasma glucose (FPG).

The author utilized his personal 22 datasets of TG and FPG over the past 8 years, from 1/1/2013 through 10/21/2020, to conduct the first part of his study.  On average, the data has a time interval of ~130 days between the two adjacent medical examinations for TG and HbA1C performed at the medical laboratories or hospitals.  In addition, he used these ~130 moving days average finger-pierced FPG data in this study.  The exact time intervals for both TG and FPG between two adjacent datasets are identical.

The original defined equation shown in Reference 2, 3, 4, and 5 is listed as follows:

• TyG-A = ln [Fasting triglyceride (mg / dl) * Fasting glucose (mg / dl)] / 2

After applying the rule of logarithm, it becomes the following abbreviated format:

• TyG-A = (ln(TG) + ln(FPG)) / 2

For scientists, to develop any mathematical equation for observed physical phenomenon, they should not only demand high accuracy in terms of the physical description via mathematical equation in reflecting the background physical concept or mathematical theory, but the equation should have ease-of-use and be practical for real-life applications. The author is an engineer with mathematics background and 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 has written and published more than 300 medical papers.  Therefore, he understands glucose characteristics and his own health conditions very well.  He wants to identify an easier way to interpret this complex pancreatic beta cells in regard to insulin resistance status, where he can quickly achieve the goal of diabetes control.  Therefore, he made some simple modifications on the existing TyG equation, which he named as TyG-A, by developing an alternative New TyG, which he named as TyG-B as follows (Reference 11):

• TyG-B = ln(TG+FPG) – ln (2)  or  TyG-B = ln((TG+FPG)/2)

The results from the new TyG equation are extremely close to the existing TyG equation within a 99.6% accuracy range using his own collected 22 datasets.  Even using the accuracy sensitivity study results from 25 combined normal cases, the TyG-B values are still within 99.1% accuracy range of TyG-A values.

The author used his 22 personal datasets over the past 8 years to examine the TyG biomarkers with two investigations using both the decoupled components of ln(TG) + ln(FPG) or ln(TG*FPG) for TyG-A, and the combined component of ln(TG+FPG) for TyG-B.  For most patients and physicians, they would have some difficulties to comprehend the biomedical meaning of TG*FPG and the mathematical operation of natural logarithm.  These two terms and amounts do not incite any biomedical comprehensions to them at all.  With the TyG-B, they are dealing with the value of TG+FPG which is much easier for them to comprehend the addition of these two commonly used biomarkers, TG and FPG, instead of the multiplication of them as TG*FPG.  Patients or physicians would only need to remember that in order to keep the insulin resistance level within a healthy range (<4.49), they need to keep each biomarker, TG or FPG, lower than 90 or the combination of TG+FPG lower than 180.  This is the sole reason why the author wants to redefine the existing TyG-A formula into the new TyG-B.

However, in order to keep the integrity of the TyG biomarker with its rich experimental data behind it, the accuracy of the TyG-B results using TyG-A as the base is an especially important issue.  The second part of this study deals with this particular concern.  For the accuracy investigation, he further defined a new equation of “Delta” as the difference between TyG-A and TyG-B as listed below:

• Delta = TyG-B – TyG-A  or  Delta =(ln(((TG+FPG)/2)²/(TG*FPG)) / 2

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. 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 removed 14 test results from this study that include HbA1C with no triglyceride data.

For the past 11 years, his major concerns center around his diabetes conditions and their induced 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.

3. TyG index (TyG-A)
The “triglyceride and glucose index” is a screening method for insulin resistance, which 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).  Patients with a TyG index of 4.49 or greater are likely to suffer from insulin resistance (References 1, 2, 3, 4 and 5).

The TyG equation is:

• 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. It can be calculated with data obtained from medical records.

A recent cross-sectional study by Zhang et al. aimed to determine whether TyG has any predictive value for non-alcoholic fatty liver disease (NAFLD) by comparing the predictive value of TyG with the determinations of ALT (alanine aminotransferase) in a cohort of 10,761 patients.

The association between a screening method using triglycerides and fasting 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:

4. New TyG index (TyG-B)
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 physical concept or mathematical theory, but the equation must also be practical for real-life applications.  The author is an engineer with mathematics background and 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 or TyG-B equation as follows:

• TyG-B = ln(TG+FPG) – ln (2)  or  TyG-B = ln((TG+FPG) / 2)

5. Sensitivity analysis:
The most common blood test used to check triglyceride levels is called a lipid panel.  A standard lipid panel will test for the following:

• Total cholesterol
• HDL (good) cholesterol
• Triglycerides
• Cholesterol/HDL ratio
• Non-HDL cholesterol

Normal triglyceride levels are < 150 mg/dL.  Triglyceride levels between 150 and 199 mg/dL are borderline high.  High triglyceride levels occur at 200–499 mg/dL.  Anything over 500 mg/dL is considered extremely high.  (Note: The author had a triglyceride value of 1,161 mg/ dL and a FPG value of 280 mg/dL in 2010 with a TyG level at 6.58).  Currently, the author cannot find a defined range for low triglyceride levels.  However, if someone’s triglyceride levels are exceptionally low, this may indicate an underlying condition or disease.

The author’s TG record during the past 9-years (2012-2020) shows a data range of covering from the lowest at 39, through 67, 85, 88, 90, all the way up to the highest 176, with an average value of 120 mg/dL.  His FPG record during the same 9-years period shows a data range from the lowest 54 to the highest 273 with an average value of 121 mg/dL.  However, the extremely high or low values for both TG and FPG occurred only a few times.  Therefore, in order to be practical for the majority of other patients, the author has selected the following key data ranges for both TG and FPG:

• Low level: 60 mg/dL
• Ideal level: 90 mg/dL
• Average level: 120 mg/dL
• Low-end of borderline: 150 mg/dL
• High-end of borderline: 200 mg/dL

Next, he combined these 5 levels for TG and the same 5 levels for FPG together into 25 possible combined cases, 5*5=25.

Finally, he calculated the two sets of TyG values using the equations of TyG-A and TyG-B and the Delta values between them.

• Delta = (TyG-B) – (TyG-A)  or  Delta =(ln(((TG+FPG)/2)²/(TG*FPG)) / 2

Results
Figure 1 shows the author’s raw data of the lab-tested TG, finger-pierced average FPG, and other glucoses during 1/1/2012 to 12/17/2-20.  It provides information for his lowest and highest values of both TG and FPG over this 9-year period.

By comparing the average TyG of these two sub-periods over the past 8 years (2013-2020), the TyG values of 4.90 in the first time period of 2013-2015 and TyG value of 4.67 in the second time period of 2016-2020.  The TyG improved by 5% which means that his TyG index of insulin resistance situation has been improving at 1% per year from 2016 to 2020.  In his previous research papers (Reference 6, 7 and 8), he has independently proven that his self-recovery rate of pancreatic beta cells insulin secretion has an annual rate between 2.3% to 3.2% using glucose data only.  However, by using the TyG index which includes “dual influences” from both lipid (TG) and glucose (FPG), his self-recovery rate of pancreatic beta cells of insulin secretion is 1% only.  Nevertheless, his health improvement of pancreatic beta cells through this “self-recovery of insulin resistance” is quite obvious.  The author’s interpretation of this low recovery rate is due to the exceedingly long lifespan of the pancreatic beta cells in comparison to the relatively shorter lifespans of 120 days for red blood cells and 300-500 days for liver cells.

###### Figure 1: TG and FPG values during 2012 to 2020

Figure 2 depicts the comparison of TyG-A versus TyG-B using his personal 22 datasets in the upper diagram and the comparison of TyG-A versus TyG-B using 25 combined cases in the lower diagram.  From the upper diagram of his personal 22 cases, it is evident that his two largest delta values between TyG-A and TyG-B occur at his lowest TG value of 39 on 2/12/2019 (14% delta) and his second lowest TG value of 67 on 9/1/2016 (4% delta).  The rest of these 22 datasets have a less than 1% to 2% of Delta, which means they have 98% to 99% of data correlation.

The results from the TyG-B equation are extremely close to the existing TyG-A equation which have a 99.6% accuracy using his own collected 22 datasets.  By using the accuracy sensitivity study results from the 25 combined cases, the TyG-B values are still within 99.1% accuracy range of TyG-A values.

Again, here are the two equations for TyG-A and TyG-B, as well as the equation of Delta between these two TyG values:

• TyG-A = (ln(TG) + ln(FPG)) / 2
• TyG-B = ln(TG+FPG) – ln (2)
• Delta = (TyG-B) – (TyG-A)  or  Delta =(ln(((TG+FPG)/2)²/(TG*FPG)) / 2
###### Figure 2: Comparison of TyG-A vs. TyG-B for both the author’s case and 25 combined cases

Figure 3 shows a long form of data table including the step-by-step calculations of TyG-A, TyG-B, and Delta.

The accuracy sensitivity analysis results utilized 25 combination cases for a rather normal patient (25 cases come from the 5 TG levels at 60, 90, 120, 150, 200 multiple by the 5 FPG levels at 60, 90, 120, 150, 200) indicate the following summary of data observations:

1. 13 cases (52%) have 100% match.
2. 6 cases (24%) have 99% match.
3. 4 cases (16%) have 98% match.
4. 2 cases (8%) have ~96% match.
5. All of the 25 cases (100%) have an average 99.1% of accuracy if using TyG-A values as the base.
6. The cases which have a larger than 2% deviation are invoked with either the low-end value of 60 or the high-end value of 200 of both TG and FPG.  Actually, these extremely low or high values of TG and FPG provide a type of amplification effect via their associated TyG-B values.
7. 19 cases or 76% have TyG values greater than 4.49.  It indicates the difficulty of maintaining low insulin resistance for diabetes patients.
###### Figure 3: Calculation table for TyG-A, TyG-B, ad Delta values for 25 combined cases

Conclusions
Diabetes conditions contend with glucose production along with the storage of glucose in the liver, and insulin secretion or insulin resistance from the pancreas.  By using the additional TyG biomarker, it involves triglycerides for evaluating insulin resistance in diabetes situation.  It also demonstrates the connectivity of diabetes with both glucose and lipid or both liver and pancreas.

The author has self-studied and researched his diabetes conditions for the past 11 years; therefore, he understands the linkage of glucose physical characteristics and mathematical expressions along with his detailed diabetes conditions.  For example, he comprehends the precise mathematical relationships between glucose fluctuations with changes in weight/food/exercise/others and how difficult diet control along with persistent exercise can be for a normal patient.  In addition, he has proven that the glucose reduction rate relates to the self-repair rate of his pancreatic beta cells.  That is why in this study, he conducted a further what-if analysis of the two separate ideal situations using 80 TG and 100 FPG versus 90 TG and 90 FPG.

In 2020, he finally achieved his goal of suppressing his FPG below 100 mg/dL level.  His plan for 2021 and beyond is to accomplish his combined targets of either 80 TG and 100 FPG (TyG-A at 4.49 and TyG-B at 4.50) or 90 TG and 90 FPG (TyG-A at 4.50 and TyG-B at 4.50).  This strategy provides a practical guide on how to reduce his insulin resistance via lowering his combined TG and FPG level to a value lower than 180.

The additional benefits of lowering his risks on nonalcoholic fatty liver disease (NAFLD) and cardiovascular disease (CVD) via lowering his TyG index are extraordinary beneficial.

The accuracy sensitivity analysis results utilized 25 combination cases for a normal patient (25 cases come from the 5 TG levels at 60, 90, 120, 150, 200 multiple by the 5 FPG levels at 60, 90, 120, 150, 200) indicate the following summary of data observations:

• 13 cases (52%) have 100% match.
• 6 cases (24%) have 99% match.
• 4 cases (16%) have 98% match.
• 2 cases (8%) have ~96% match.
• All of the 25 cases (100%) have an average 99.1% of accuracy if using TyG-A values as the base.
• The cases which have a larger than 2% deviation are invoked with either the low-end value of 60 or the high-end value of 200 of both TG and FPG.  Actually, these extremely low or high values of TG and FPG provide a type of amplification effect via their associated TyG-B values.
• 19 cases or 76% have TyG values greater than 4.49.  It indicates the difficulty of maintaining low insulin resistance for diabetes patients.

The conclusions from the sensitivity analysis listed above with accuracy percentages and distribution rates can be calculated directly using the developed equation of Delta, which is “the difference between TyG-A and TyG-B”.

The Delta equation is defined as follows:

• Delta = (TyG-B) – (TyG-A)  or  Delta =(ln(((TG+FPG)/2)²/(TG*FPG)) / 2

In conclusion, the TyG-B is simpler to comprehend and easier to use in real application for patient’s clinical cases.  However, the TyG-A equation has had sufficient statistical support and proof via many clinical data from both lab tests and medical examination records.  Therefore, by knowing the results difference, Delta, the TyG-B results can easily be adjusted and reproduce the same results from using the TyG-A biomarker.

The author is not a medical doctor and lacks access to medical labs and medical records of patients.  Therefore, his newly developed TyG-B biomarker needs additional medical research scientist’s assistance to prove it through big data collected from many clinical patients or existing hospital records.

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. 310: “Biomedical research methodology based on GH-Method: math-physical medicine”
11. 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”
12. Hsu, Gerald C., eclaireMD Foundation, USA, No. 374: “Using a newly redefined biomarker, triglyceride and glucose index biomarker (New TyG), as an alternative tool for diabetes patients to control their insulin resistance conditions based on GH-Method: math-physical medicine”