## GH-METHODS

Math-Physical Medicine

### NO. 374

Using an improved 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

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

__Abstract__

This article provides an alternative equation for the triglyceride and glucose index (TyG) biomarker. The original defined equation shown in Reference 2, 3, 4, and 5 is listed as follows:

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

*or in **an abbreviated format: *

*TyG = (ln(TG) + ln(FPG)) / 2*

For scientists, to develop any mathematical equation for an 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 must also be practical for real-life applications. The author is a mathematician 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 identify an easier way to interpret his complex pancreatic beta cells status in regard to insulin resistance, where he can quickly achieve the goal of diabetes control. Therefore, he made some simple modifications on the defined TyG equation by developing an alternative “New TyG” equation as follows:

**New TyG = ln(TG+FPG) – ln (2)**

The results from the new equation are extremely close to the original TyG equation with a 99.6% accuracy rate based on his own collected data (see attached figure).

Diabetes conditions deal 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.

The author used his own data over the past 8 years to examine the TyG indices with two investigations using both the decoupled components of ln(TG) and ln(FPG) and the combined component of ln(TG+FPG). He has self-studied and researched his diabetes conditions for the past 11 years; therefore, he knows his diabetes conditions very well. For example, he understands how difficult diet control and persistent exercise can be and how they relate to glucose reduction. Furthermore, he has proven that glucose reduction rate relates to the degree of 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 has finally achieved his goal of suppressing his FPG below 100 mg/dL level.

This New TyG equation would be easier for patients or doctors to use instead of forcing them to take the multiplication result of TG*FPG and then dealing with a mathematical term of logarithm, because most patients and doctors would have difficulties to understand the biophysical meaning of TG*FPG and mathematical implication of logarithm. The wonderful part in using this New TyG formula is that patients can add their TG value and FPG value together easily and then confirm the resulted summation as equal to or less than 180 mg/dL. Under this controlled circumstance, their insulin resistance situation will then be well managed as well.

In addition, the variance bandwidth of FPG is usually much narrower than the variance bandwidth of TG. For example, from 2010 to 2020, the author’s TG variance bandwidth is between 39 and 1160 and his FPG variance bandwidth is between 60 and 200. Therefore, he established his 2021 targeted ideal situation of TyG 4.49 for his own insulin resistance control by maintaining his TG below 80 mg/dL and his FPG below 100 mg/dL.

__Introduction__

This article provides an alternative equation for the triglyceride and glucose index (TyG) biomarker. The original defined equation shown in Reference 2, 3, 4, and 5 is listed as follows:

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

*or in **an abbreviated format: *

*TyG = (ln(TG) + ln(FPG)) / 2*

For scientists, to develop any mathematical equation for an 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 must also be practical for real-life applications. The author is a mathematician 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 identify an easier way to interpret his complex pancreatic beta cells status in regard to insulin resistance, where he can quickly achieve the goal of diabetes control. Therefore, he made some simple modifications on the defined TyG equation by developing an alternative “New TyG” equation as follows:

**New TyG = ln(TG+FPG) – ln (2)**

The results from the new equation are extremely close to the original TyG equation with a 99.6% accuracy rate based on his own collected data.

__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 serious concerns. He has 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**

The “triglyceride and glucose index” 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 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 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 physical concept or mathematical theory, but the equation must also be practical for real-life applications. The author is a mathematician / engineer 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” equation as follows:

**TyG = ln(TG+FPG) – ln (2)**

__Results__

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

###### Figure 1: Data table and TyG calculations

There are two parts of these 22 testing dates and data. The first part, from 2013 to 2015, contains 8 datasets in a 3-year period, or 2.7 datasets per year. The second part, from 2016 to 2020, includes 14 datasets in a 5-year period, or 2.8 datasets per year. The averaged New TyG value for the first part is 4.90 which is 9% higher than the TyG cutoff point of 4.49, while the average TyG value for the second part is 4.69 which is 4.5% higher than the TyG cutoff point of 4.49.

By comparing the average New TyG values of these two parts, in the second part, the New TyG is 4.6% better than the first part. This means that his New TyG index of insulin resistance situation has been improving around 1% per year (actually 0.97% per year) from 3/2016 to 10/2020. In his previous research papers regarding pancreatic beta cells self-recovery (Reference 6, 7 and 8), he has proven that his self-recovery rate of pancreatic beta cells insulin secretion has an annual rate between 2.3% to 3.2%. This higher percentage and wider self-repair rate range were calculated based on his collected glucose data only, including observations of his FPG, PPG, and HbA1C. However, by using this New TyG index which includes dual influences from both TG and FPG, his self-recovery range of pancreatic beta cells of insulin secretion has an annual rate of ~1% only. In addition, the health improvement of his pancreatic beta cells through this “improvement of insulin resistance” is quite obvious by comparing the New TyG values between TyG of 4.90 for the first part of 2013-2015 and TyG of 4.69 for the second part of 2016-2020. His interpretation of this lower recovery rate is due to the exceptionally long lifespan of the pancreatic beta cells in comparison against the relatively shorter lifespans of 120 days for red blood cells and 300-500 days for liver cells.

Figure 2 shows the calculated results using the New TyG equation. It clearly depicts his New TyG has reached to the cutoff point during the second half of 2016 after his 2+ years of glucose control efforts from 2014 to mid-2016. His higher than 4.49 of TyG values during 2017 to 2019 are due to his hectic traveling schedules in attending ~65 international medical conferences to present ~120 papers. During the quarter prior to 2/12/2019, he did not travel which resulted into a low New TyG of 4.20. For the 2020 COVID-19 quarantine period, his overall health conditions, including lipids (TG), glucose (FPG) and weight have been maintained in the best situation compared to his records in past 30 years. Therefore, his New TyG index is 4.52 which is quite close to the cutoff point of 4.49. Both of his two ideal cases of TG 90, FPG 90 and TG 80, FPG 100 have resulted into New TyG value of 4.50.

###### Figure 2: The calculation table for New TyG

Figure 3 reveals numerical results comparison using TyG equation versus New TyG equation. Except two TyG values on both 9/1/2016 and 2/12/2019 have 4% and 14% numerical differences, respectively, the overall difference is only -1.3%. This means that a 98.7% accuracy if the TyG value is considered as the “standard value” for comparison. In other words, the TyG curve and New TyG curve are “almost” identical to one another with a 98.7% match up.

In the bottom two rows of the data tables in Figures 2 and 3, the two special “what-if” analysis results are shown by using both TyG and New TyG. The first ideal situation is when TG is 90 mg/dL and FPG is 90 mg/dL, then TyG will be 4.50 and New TyG will be 4.50 also. The second best-case scenario is when TG is 80 mg/dL and FPG is 100 mg/dL, then TyG will be 4.49 and New TyG will be slightly different at 4.50. For the author’s case, he has chosen to improve his TG to go below 80 mg/dL while keeping his FPG at or below 100 mg/dL starting from 2021.

This useful information on insulin resistance can provide a guideline for other patients to use in controlling their conditions in regard to diabetes and hyperlipidemia.

###### Figure 3: Comparison of results between TyG and New TyG

__Conclusions__

Diabetes conditions deal 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.

The author used his own data over the past 8 years to examine the TyG indices with two investigations using both the decoupled components of ln(TG) and ln(FPG) and the combined component of ln(TG+FPG). He has self-studied and researched his diabetes conditions for the past 11 years; therefore, he knows his diabetes conditions very well. For example, he understands how difficult diet control and persistent exercise can be and how they relate to glucose reduction. Furthermore, he has proven that glucose reduction rate relates to the degree of 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 has finally achieved his goal of suppressing his FPG below 100 mg/dL level.

This New TyG equation would be easier for patients or doctors to use instead of forcing them to take the multiplication result of TG*FPG and then dealing with a mathematical term of logarithm, because most patients and doctors would have difficulties to understand the biophysical meaning of TG*FPG and mathematical implication of logarithm. The wonderful part in using this New TyG formula is that patients can add their TG value and FPG value together easily and then confirm the resulted summation as equal to or less than 180 mg/dL. Under this controlled circumstance, their insulin resistance situation will then be well managed as well.

In addition, the variance bandwidth of FPG is usually much narrower than the variance bandwidth of TG. For example, from 2010 to 2020, the author’s TG variance bandwidth is between 39 and 1160 and his FPG variance bandwidth is between 60 and 200. Therefore, he established his 2021 targeted ideal situation of TyG 4.49 for his own insulin resistance control by maintaining his TG below 80 mg/dL and his FPG below 100 mg/dL.

__References__

- 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.
- 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
- 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
- Endocrinology Related Meducal Algorithms & Calculators – MDApp, TyG Index Determines insulin resistance and can also identify individuals at risk for NAFLD. Corrected Calcium Calculator.
- 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
- 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)”
- 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”
- 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”
- Hsu, Gerald C., eclaireMD Foundation, USA, No. 310: “Biomedical research methodology based on GH-Method: math-physical medicine”
- 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”