GH-METHODS

Math-Physical Medicine

NO. 373

Study of triglyceride and glucose index biomarker (TyG) for diabetes control through improvement on insulin resistance using 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 triglyceride and glucose index (TyG) biomarker in diabetes control.  He utilized his collected 22 lab-tested triglyceride (TG) data and finger-pierced fasting plasma glucose (FPG) data over the past 8 years from 1/1/2013 through 10/21/2020 to conduct this study.  On average, the data has a time interval of ~130 days between the two adjacent health examinations for TG and HbA1C from the medical laboratories or hospitals.  The author utilized these 130 moving days average finger-pierced FPG data in this study.

In addition, he compared the correlation coefficients among the four biomarkers, consisting of the Lab-tested TG, Daily glucose, Lab-tested HbA1C, and carbs/sugar intake per meal, to study the degree of closeness between any two biomarkers which ultimately can present an overall picture of the influences among these 4 biomarkers.

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.

The author used his own data over the past 8 years to examine the TyG indices with an investigation on the decoupled components of ln(TG) and ln(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.  His plan for 2021 is to accomplish his combined targets of either 80 TG and 100 FPG (TyG 4.49) or 90 TG and 90 FPG (TyG 4.50).

Not only is this article demonstrating the moderate inter-relationship between diabetes and hyperlipidemia (glucose/HbA1C and TG), but it also indicates a practical route on how to reduce his insulin resistance via lowering his TG index level.

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

Introduction
The main purpose of this report is to study the role of triglyceride and glucose index (TyG) biomarker in diabetes control.  He utilized his collected 22 lab-tested triglyceride (TG) data and finger-pierced fasting plasma glucose (FPG) data over the past 8 years from 1/1/2013 through 10/21/2020 to conduct this study.  On average, the data has a time interval of ~130 days between the two adjacent health examinations for TG and HbA1C from the medical laboratories or hospitals.  The author utilized these 130 moving days average finger-pierced FPG data in this study.

In addition, he compared the correlation coefficients among the four biomarkers, consisting of the Lab-tested TG, Daily glucose, Lab-tested HbA1C, and carbs/sugar intake per meal, to study the degree of closeness between any two biomarkers which ultimately can present an overall picture of the influences among these 4 biomarkers.

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 is simple to use, and 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:

  • 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. Its accuracy and simplicity can be calculated with data obtained from medical records.

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:

Results
Figure 1 shows the author’s raw data of the lab-tested TG, finger-pierced average FPG, and calculated HbA1C.  In addition, it also contains data of lab-tested HbA1C, finger daily average glucose, and AI calculated carbs/sugar intake amount per meal.  The calendar dates shown in Column 1 reflects the 22 selected lab-testing dates which contain both TG and HbA1C result 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 average 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 4.67 which is 4% higher than the TyG cutoff point of 4.49.  

By comparing the average TyG of these two parts, in the second part, the TyG is 5% better than the first part which means 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 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 glucose data only.  However, by using the 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. Nevertheless, his health improvement of pancreatic beta cells through this “improvement of insulin resistance” is quite obvious by comparing the TyG values between TyG of 4.90 in the first part of 2013-2015 and TyG of 4.67 in the second part of 2016-2020.  The author’s interpretation of this low recovery rate is due to the very 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 depicts the two-line charts of TG & FPG in the upper diagram and TyG in the lower diagram, respectively.  It is obviously that his 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.  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 the past 30 years.  Therefore, his TyG index is 4.52 which is quite close to the cutoff point of 4.49.  

Figure 2: TG, FPG, and TyG

Figure 3 reveals three Correlations among TG, HbA1C, and Carbs/Sugar, and Figure 4 illustrates three Correlations among Daily Glucose, HbA1C, and Carbs/Sugar.  These two figures are a type of “related but side-tracked analysis” of the TyG study.  As we know, many identified biomarkers and all of human organs are inter-connected with each other under a single command center of brain.  The only difference is the degree of connectivity or the strength of inter-connections.  

The following list shows the 6 correlation coefficients (R).

  • TG vs. HbA1C: R = 18%
  • TG vs. Carbs: R = 32%
  • HbA1C vs. Carbs: R = 52%
  • Glucose vs. HbA1C: R = 47%
  • Glucose vs. Carbs: R = 54%
  • Carbs vs. HbA1C: R = 52%

Based on these six R values, we can see that the relationships among Carbs, Glucose, and A1C are extraordinarily strong (R ~ or > 50%) which indicate glucose control.  On the other hand, both Carbs and HbA1C have a weaker inter-relationships with TG (R between 18% to 32%), while triglycerides belong to the lipid category, they have no strong and direct links with Carbs, glucose, and HbA1C. 

Figure 3: 3 Correlations among TG, HbA1C, and Carbs/Sugar
Figure 4: 3 Correlations among Daily Glucose, HbA1C, and Carbs/Sugar

Figure 5 displays the calculation process of TyG in a more detailed fashion.  

Again, here is the TyG equation:

  • TyG = (ln(TG) + ln(FPG)) / 2

Special attention should be focused on the individual values of both ln(TG) and ln(FPG).  First, there is an exceedingly small difference between the average ln(TG) of 4.73 and the average ln (FPG) of 4.77.  Second, the widths of the data range are quite different.  There is a wider range for ln(TG) from 3.66 (TG 39) to 5.17 (TG 176) similar to TG values, and a narrower range for ln(FPG) from 4.56 (FPG 95) to 4.91 (FPG 135) similar to FPG values. This obviously larger difference among TG values, not among FPG values, is resulted from his attention and efforts on glucose control for diabetes, not on lipid control for hyperlipidemia. 

In the bottom two rows of Figure 5, two special “what-if” analysis results are shown there.  The first ideal situation is when TG is 90 mg/dL and FPG is 90 mg/dL then TyG will be 4.5.  The second ideal situation is when TG is 80 mg/dL and FPG is 100 mg/dL then TyG will be 4.49.  This useful information can provide a guideline for the future control on both diabetes conditions and hyperlipidemia conditions from the viewpoint of insulin resistance.

Figure 5: Step-by-step calculation of TyG with “what-if” ideal cases

Figure 6 has two comparison line charts in it.  The upper diagram shows TG curve vs. FPG curve and the lower diagram shows ln(TG) curve vs. ln(FPG) curve.  These two diagram in Figure 6 have shown the sharp comparison between a rather smooth FPG curves and a fluctuated TG curves.  The “ideal situation” of TG 80 mg/dL and FPG 100 mg/dL with TyG 4.99 are shown using a cross sign inside these two diagrams.

Figure 7 demonstrates a comparison of TG vs. FPG with a low R of 28% and ln(TG) vs. ln(FPG) with a low R of 33%.  Figure 7 also shows another comparison of TG vs. ln(TG) with an extremely high R of 97% and FPG vs. ln(FPG) with an extremely high R of 95%.  These two extremely high R values are not unexpected due to the mathematical natures of the correlation between one specific variable vs. the natural logarithm of this same variable.  

Figure 6: TG vs. FPG, ln(TG) vs. ln(FPG)
Figure 7: biomedical comparison of TG vs. FPG and mathematical proof of variable vs. ln(variable)

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.

The author used his own data over the past 8 years to examine the TyG indices with an investigation on the decoupled components of ln(TG) and ln(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.  His plan for 2021 is to accomplish his combined targets of either 80 TG and 100 FPG (TyG 4.49) or 90 TG and 90 FPG (TyG 4.50).

Not only is this article demonstrating the moderate inter-relationship between diabetes and hyperlipidemia (glucose/HbA1C and TG), but it also indicates a practical route on how to reduce his insulin resistance via lowering his TG index level.

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

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”