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Published in: Diabetology & Metabolic Syndrome 1/2018

Open Access 01-12-2018 | Research

Metabolic clustering of risk factors: evaluation of Triglyceride-glucose index (TyG index) for evaluation of insulin resistance

Authors: Sikandar Hayat Khan, Farah Sobia, Najmusaqib Khan Niazi, Syed Mohsin Manzoor, Nadeem Fazal, Fowad Ahmad

Published in: Diabetology & Metabolic Syndrome | Issue 1/2018

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Abstract

Background

Metabolic syndrome over the years have structured definitions to classify an individual with the disease. Literature review suggests insulin résistance is hallmark of these metabolic clustering. While measuring insulin resistance directly or indirectly remains technically difficult in general practice, along with multiple stability issues for insulin, various indirect measures have been suggested by authorities. Fasting triglycerides-glucose (TyG) index is one such marker, which is recently been suggested as a useful diagnostic marker to predict metabolic syndrome. However, limited data is available on the subject with almost no literature from our region on the subject.

Objective

1. To correlate TyG index with insulin resistance, anthropometric indices, small dense LDLc, HbA1c and nephropathy. 2. To evaluate TyG index as a marker to diagnose metabolic syndrome in comparison to other available markers.

Design-cross-sectional analysis

Place and duration of study-From Jun-2016 to July-2017 at PSS HAFEEZ hospital Islamabad.

Subjects and methods

From a finally selected sample size of 227 male and female subjects we evaluated their anthropometric data, HbA1c, lipid profile including calculated sdLDLc, urine albumin creatinine raito(UACR) and insulin resistance (HOMAIR). TyG index was calculated using formula of Simental-Mendía LE et al. Aforementioned parameters were correlated with TyG index, differences between subjects with and without metabolic syndrome were calculated using Independent sample t-test. Finally ROC curve analysis was carried out to measure AUC for candidate parameters including TyG Index for comparison.

Results

TyG index in comparison to other markers like fasting triglycerides, HOMAIR, HDLc and non-HDLc demonstrated higher positive linear correlation with BMI, atherogenic dyslipidemia (sdLDLc), nephropathy (UACR), HbA1c and insulin resistance. TyG index showed significant differences between various markers among subjects with and without metabolic syndrome as per IDF criteria. AUC (Area Under Curve) demonstrated highest AUC for TyG as [(0.764, 95% CI 0.700–0.828, p-value ≤ 0.001)] followed by fasting triglycerides [(0.724, 95% CI 0.656–0.791, p-value ≤ 0.001)], sdLDLc [(0.695, 95% CI 0.626–0.763, p-value ≤ 0.001)], fasting plasma glucose [(0.686, 95% CI 0.616–0.756, p-value ≤ 0.001)], Non-HDLc [(0.640, 95% CI 0.626–0.763, p-value ≤ 0.001)] and HOMAIR [(0.619, 95% CI 0.545–0.694, p-value ≤ 0.001)].

Conclusion

TyG index, having the highest AUC in comparison to fasting glucose, triglycerides, sdLDLc, non-HDLc and HOMAIR can act as better marker for diagnosing metabolic syndrome.
Literature
1.
go back to reference Ceriello A, Motz E. Is oxidative stress the pathogenic mechanism underlying insulin resistance, diabetes, and cardiovascular disease? The common soil hypothesis revisited. Arterioscler Thromb Vasc Biol. 2004;24(5):816–23.CrossRef Ceriello A, Motz E. Is oxidative stress the pathogenic mechanism underlying insulin resistance, diabetes, and cardiovascular disease? The common soil hypothesis revisited. Arterioscler Thromb Vasc Biol. 2004;24(5):816–23.CrossRef
4.
go back to reference Muniyappa R, Madan R, Quon MJ. Assessing insulin sensitivity and resistance in humans. In: De Groot LJ, Chrousos G, Dungan K, Feingold KR, Grossman A, Hershman JM, et al. editors. Endotext [Internet]. South Dartmouth (MA): MDText.com, Inc.; 2000–2015. Muniyappa R, Madan R, Quon MJ. Assessing insulin sensitivity and resistance in humans. In: De Groot LJ, Chrousos G, Dungan K, Feingold KR, Grossman A, Hershman JM, et al. editors. Endotext [Internet]. South Dartmouth (MA): MDText.com, Inc.; 2000–2015.
10.
go back to reference Bakker AJ. Detection of microalbuminuria. Receiver operating characteristic curve analysis favors albumin-to-creatinine ratio over albumin concentration. Diabetes Care. 1999;22(2):307–13.CrossRef Bakker AJ. Detection of microalbuminuria. Receiver operating characteristic curve analysis favors albumin-to-creatinine ratio over albumin concentration. Diabetes Care. 1999;22(2):307–13.CrossRef
11.
go back to reference Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(7):412–9.CrossRef Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(7):412–9.CrossRef
18.
go back to reference Pansuria M, Xi H, Li L, Yang XF, Wang H. Insulin resistance, metabolic stress, and atherosclerosis. Front Biosci (Schol Ed). 2012;1(4):916–31. Pansuria M, Xi H, Li L, Yang XF, Wang H. Insulin resistance, metabolic stress, and atherosclerosis. Front Biosci (Schol Ed). 2012;1(4):916–31.
19.
go back to reference van Houwelingen HC. The future of biostatistics: expecting the unexpected. Stat Med. 1997;16(24):2773–84.CrossRef van Houwelingen HC. The future of biostatistics: expecting the unexpected. Stat Med. 1997;16(24):2773–84.CrossRef
20.
go back to reference Wallace TM, Levy JC, Matthews DR. Use and abuse of HOMA modeling. Diabetes Care. 2004;27(6):1487–95.CrossRef Wallace TM, Levy JC, Matthews DR. Use and abuse of HOMA modeling. Diabetes Care. 2004;27(6):1487–95.CrossRef
21.
go back to reference Kang ES, Yun YS, Park SW, Kim HJ, Ahn CW, Song YD, et al. Limitation of the validity of the homeostasis model assessment as an index of insulin resistance in Korea. Metabolism. 2005;54(2):206–11.CrossRef Kang ES, Yun YS, Park SW, Kim HJ, Ahn CW, Song YD, et al. Limitation of the validity of the homeostasis model assessment as an index of insulin resistance in Korea. Metabolism. 2005;54(2):206–11.CrossRef
22.
go back to reference Levy JC, Matthews DR, Hermans MP. Correct homeostasis model assessment (HOMA) evaluation uses the computer program. Diabetes Care. 1998;21(12):2191–2.CrossRef Levy JC, Matthews DR, Hermans MP. Correct homeostasis model assessment (HOMA) evaluation uses the computer program. Diabetes Care. 1998;21(12):2191–2.CrossRef
23.
go back to reference Geloneze B, Vasques AC, Stabe CF, Pareja JC, Rosado LE, Queiroz EC. Tambascia MA; BRAMS Investigators. HOMA1-IR and HOMA2-IR indexes in identifying insulin resistance and metabolic syndrome: Brazilian Metabolic Syndrome Study (BRAMS). Arq Bras Endocrinol Metabol. 2009;53(2):281–7.CrossRef Geloneze B, Vasques AC, Stabe CF, Pareja JC, Rosado LE, Queiroz EC. Tambascia MA; BRAMS Investigators. HOMA1-IR and HOMA2-IR indexes in identifying insulin resistance and metabolic syndrome: Brazilian Metabolic Syndrome Study (BRAMS). Arq Bras Endocrinol Metabol. 2009;53(2):281–7.CrossRef
25.
go back to reference Sánchez-Villanueva R, Estrada P, del Peso G, Grande C, Díez JJ, Iglesias P, Grupo de Estudios Peritoneales de Madrid de REDINREN (Red Renal de Investigación de la RETICS 06/0016, del Instituto de Salud Carlos III); del IRSIN (Instituto Reina Sofía de Investigación Nefrológica), et al. Repeated analysis of estimated insulin resistance using the HOMAIR index in nondiabetic patients on peritoneal dialysis and its relationship with cardiovascular disease and mortality. Nefrologia. 2013;33(1):85–92. https://doi.org/10.3265/nefrologia.pre2012.nov.11430.CrossRefPubMed Sánchez-Villanueva R, Estrada P, del Peso G, Grande C, Díez JJ, Iglesias P, Grupo de Estudios Peritoneales de Madrid de REDINREN (Red Renal de Investigación de la RETICS 06/0016, del Instituto de Salud Carlos III); del IRSIN (Instituto Reina Sofía de Investigación Nefrológica), et al. Repeated analysis of estimated insulin resistance using the HOMAIR index in nondiabetic patients on peritoneal dialysis and its relationship with cardiovascular disease and mortality. Nefrologia. 2013;33(1):85–92. https://​doi.​org/​10.​3265/​nefrologia.​pre2012.​nov.​11430.CrossRefPubMed
28.
Metadata
Title
Metabolic clustering of risk factors: evaluation of Triglyceride-glucose index (TyG index) for evaluation of insulin resistance
Authors
Sikandar Hayat Khan
Farah Sobia
Najmusaqib Khan Niazi
Syed Mohsin Manzoor
Nadeem Fazal
Fowad Ahmad
Publication date
01-12-2018
Publisher
BioMed Central
Published in
Diabetology & Metabolic Syndrome / Issue 1/2018
Electronic ISSN: 1758-5996
DOI
https://doi.org/10.1186/s13098-018-0376-8

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