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

• Published in: Diabetology and metabolic syndrome Vol. 10; no. 1; pp. 74 - 8
• Main Authors: Khan, Sikandar Hayat, Sobia, Farah, Niazi, Najmusaqib Khan, Manzoor, Syed Mohsin, Fazal, Nadeem, Ahmad, Fowad
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 05.10.2018; BioMed Central; BMC
• Subjects: Cardiovascular diseases; Complications and side effects; Diagnosis; Metabolic syndrome X; Risk factors; Triglycerides
• DOI: 10.1186/s13098-018-0376-8
• ISSN: 1758-5996

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