Vineetha K Ramdas Nayak  ( Department of Physiology, Sree gokulam medical college and research foundation, Venjaramoodu,Trivandrum, )
Praseeda Satheesh  ( Department of Physiology, Government Medical College, Trivandrum )
Mohan T Shenoy  ( Department of Endocrinology, Sree Gokulam Medical College and research foundation, Venjaramoodu, Trivandrum )
Sanjay Kalra  ( Department of Endocrinology, Bharti Hospital, Karnal, India. )

The predictability of triglyceride glucose index (TyG index) as a biomarker for identification of insulin resistance (IR) is being extensively studied in various ethnic populations. TyG index could be a beneficial tool for identification of IR and populations at high risk for developing diabetes in future. However, more studies are required to standardize optimal cut-off values in different ethnicities and populations. The present review describes existing literature, and identifies merits and demerits of TyG index as a surrogate marker for IR.

 

Keywords: Triglyceride Glucose Index, Insulin resistance, Type 2 Diabetes Mellitus.

DOI:  https://doi.org/10.47391/JPMA.22-63

 

Introduction

 

Type 2 diabetes mellitus (T2DM) is a global epidemic and a major public health burden of this century.¹ It is the fifth leading reason of mortality globally.¹ The global surge in diabetes can be considered as a public health crisis.¹ The International Diabetes Federation (IDF) has estimated that a population of 333 million would have T2DM by the end of this decade.¹ Diabetes adds on to massive economic and health burden for society.¹ Hence it is important to design efficient cost-effective tools for predicting diabetes. Insulin resistance (IR) is a dysregulation in glucometabolism which contributes to development of T2DM.¹ Researchers have collected evidences establishing that complications of diabetes are present in the pre-diabetic phase as well.²

IR is considered as the primary pathological mechanism of development of T2DM. IR is defined as a "metabolic condition comprising of increased insulin levels which stems from insulin insensitivity and co-related with higher triglycerides levels". Hypertriglyceridaemia results in augmented transportation of free fatty acids towards the liver contributing to higher hepatic glucose production. Elevated triglyceride levels also contribute to development of IR through dysregulation of muscle glucose metabolism. However, early screening of at-risk population is crucial if IR has to be detected and managed in a timely manner.

 

Employing TyG index has been utilized as an efficient predictor of IR

 

The Hyperinsulinaemic-Euglycaemic Clamp (HIEC) is regarded as the gold standard diagnostic test to diagnose IR. There are several demerits to this diagnostic test namely it is complicated to perform with laborious method with its limited applications in clinical settings. Hence it is necessary to devise alternate surrogate predictors.³

Various researchers have studied several cost effective and simple biomarkers to screen IR, including HOMA-IR, TGC/HDL, QUICKI and McAuley Index.³ TyG index was first developed by Simental-Mendia et al.⁴ In recent times, this has gained popularity as a surrogate index to measure IR.4 TyG index is obtained as a product of fasting Triglycerides (TG) and plasma glucose levels, which serves as arithmetical expression of IR.⁴ The diagnostic accuracy of TyG index in comparison to HIEC and HOMA-IR has been of keen interest to researchers across the globe.^4-6

 

Utility of TyG index

 

TyG index based on a product of fasting TG levels and FPG has been recognized as a novel marker for identification of IR. TyG index has reported high-level sensitivity and specificity in screening of IR and it is considered as a valuable predictor of IR in different populations.^4,5

Fasting plasma glucose (FPG) and triglycerides (TG) are known traditional risk factors for development of future T2DM. Increased FPG levels are posed as an independent risk factor for developing T2DM even in normoglycaemic subjects.⁷ Elevated TG levels over long period of time also amplifies the risk of development of T2DM in various ethnic populations.8 In the current scenario the diagnostic accuracy of TyG index needs to be explored in different ethnic populations. The predictive performance of TyG index in diagnosing IR, prediabetes and T2DM in comparison to other surrogate markers with respect to gender also needs to be addressed.

 

Advent of Novel Lipid Combined Anthropometric Indices

 

Researchers have validated the efficacy of the TyG index in combination with anthropometric measurements like Triglyceride glucose waist circumference (TyG-WC), triglyceride glucose-body mass index (TyG-BMI) or triglyceride glucose-waist hip ratio (TyG-WHR).^9,10 HOMA-IR is estimated using the arithmetic calculation as shown in Table-1. Quantification of 1 or greater indicates IR.¹¹ Drawbacks are this method can be utilized in subjects who are diabetic.¹¹ Another limitation is broad range of "Normal" values of fasting plasma insulin. Though extensively utilized for the screening of IR, the cut-offs of HOMA-IR are variable between different populations.¹²

 

 

 

In contrast to HOMA-IR model and QUICKI, insulin levels need not be assessed in TyG index. This makes TyG index an easy to measure, simple, cost effective method to assess IR. In contrast with insulin, even triglyceride levels exhibit high within (19.9%) and comparative biological variation (32.7%), but triglyceride levels are measured by enzymatic techniques which are validated and standardised.13 Fasting glucose exhibits narrow variation in healthy subjects and is affected by confounding factors in addition to insulin sensitivity, islet functioning and hepatic glucose output. Hence indices based on blood glucose may have issues related to discriminatory range of insulin sensitivity in healthy subjects.^13,14 Novel lipid combined anthropometric indices which are derived from fusion of TyG- index and anthropometric evaluation like TyG-WC, TyG-BMI and TyG-WHR have also been considered as efficient predictors of IR.¹¹

 

Conclusion

 

The global burden of Diabesity should be addressed by public health screening strategies as they are at increased risk of developing Type 2 DM. Timely intervention and preventive measures have to be initiated. Scientists are on the process of developing predictive models — easy to measure and having a wide range of application and predictability.14 TyG index is simple to calculate with less time or cost constraint — ideal for a larger population. This could prove a practical and pragmatic approach for large-scale screening for diabetes, especially in developing countries.

 

References

 

1.       International Diabetes Federation. IDF Diabetes Atlas, 9th edn. Brussels, Belgium: International Diabetes Federation, 2019.

2.       Jing J, Pan Y, Zhao X, Zheng H, Jia Q, Li H, et al. Prognosis of Ischemic Stroke with Newly Diagnosed Diabetes Mellitus According to Hemoglobin A1c Criteria in Chinese population. Stroke.2016; 47:2038-44.

3.       Bastard JP, Lavoie ME, Messier V, Prud'homme D, Rabasa-Lhoret R. Evaluation of two new surrogate indices including parameters not using insulin to assess insulin sensitivity/resistance in non-diabetic postmenopausal women: a MONET group study. Diabetes Metab. 2012; 38:258-63. doi: 10.1016/j.diabet.2012.01.004. Epub 2012 Mar 8. PMID: 22405724.

4.       Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord.2008; 6:299-304. doi: 10.1089/met.2008.0034. PMID: 19067533.

5.       Guerrero-Romero F, Simental-Mendia LE, Gonzalez-Ortiz M, Martinez-Abundis E, Ramos-Zavala MG, Hernandez-Gonzalez SO, et.al. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic hyperinsulinemic clamp. J Clin Endocrinol Metab 2010; 95:3347-3351.

6.       Abbasi F, Reaven GM. Comparison of two methods using plasma triglyceride concentration as a surrogate estimate of insulin action in nondiabetic subjects: triglycerides × glucose versus triglyceride/high-density lipoprotein cholesterol. Metabolism 2011; 60:1673- 1676.

7.       Song K, Park G, Lee HS, Choi Y, Oh JS, Choi HS, et al. Prediction of Insulin Resistance by Modified Triglyceride Glucose Indices in Youth. Life 2021; 11:286. https://doi.org/10.3390/life11040286.

8.       Mozaffary A, Asgari S, Tohidi M, Kazempour-Ardebili S, Azizi F, Hadaegh F et al Change in fasting plasma glucose and incident type 2 diabetes mellitus: results from a prospective cohort study BMJ Open 2016; 6: e010889. doi: 10.1136/bmjopen-2015-010889

9.       Dotevall A, Johansson S, Wilhelmsen L, Rosengren A. Increased levels of triglycerides, BMI and blood pressure and low physical activity increase the risk of diabetes in Swedish women. A prospective 18-year follow-up of the BEDA study. Diabet Med 2004; 21: 615-622.

10.     Zheng S, Shi S, Ren X, Han T, Li Y, Chen Y, et al. Triglyceride glucose-waist circumference, a novel and effective predictor of diabetes in first-degree relatives of type 2 diabetes patients: cross-sectional and prospective cohort study. J Transl Med 2016; 14:260. pmid:27604550.

11.     Lim J, Kim J, Koo SH, Kwon GC. Comparison of triglyceride glucose index, and related parameters to predict insulin resistance in Korean adults: An analysis of the 2007-2010 Korean National Health and Nutrition Examination Survey. PLoS ONE 2019, 14, e0212963

12.    Lee CH, Shih AZL, Woo YC, Fong CHY, Leung OY, Janus E, et al. Optimal Cut-Offs of Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) to Identify Dysglycemia and Type 2 Diabetes Mellitus: A 15-Year Prospective Study in Chinese. PLoS ONE 2016; 11: e0163424. https://doi.org/10.1371/journal.pone.0163424.

13.    Cakmak O. Evaluation of analytical performance specifications of routine clinical biochemistry tests with biological variation-based total allowable error (TEa) criteria. Int J Med Biochem 2018. https://doi.org/10.14744/ijmb.2018.39974.

14.     Otten J, Ahrén B, Olsson T. Surrogate measures of insulin sensitivity vs the hyperinsulinaemic-euglycaemic clamp: a meta-analysis. Diabetologia 2014; 57:1781-8. https://doi.org/10.1007/s00125- 014-3285-x.