Accuracy of the triglyceride-glucose index as a surrogate marker for identifying metabolic syndrome in non-diabetic individuals

The discriminative ability of the triglyceride-glucose index to identify metabolic syndrome among adults of the northern Sri Lankan population

BACKGROUND

The triglyceride-glucose index (TyG index) is a simple surrogate marker for Insulin Resistance (IR). However, the relationship between the TyG index and Metabolic Syndrome (MetS) remains unknown in the Northern Sri Lankan population.

METHODS

This was a descriptive, cross-sectional study of adults aged between 18 and 65 years living in Jaffna, Sri Lanka. This study aimed to verify the discriminative ability of the TyG index to identify MetS using the International Diabetes Federation (IDF-2006) criteria and to determine the gender-specific TyG index cut-off values for better prediction of MetS in Northern Sri Lankan adults. TyG index was calculated as Ln[Triglycerides (TG) (mg/dl) × Fasting plasma glucose (FPG) (mg/dl)/2].

RESULTS

A total of 540 individuals were included in this study, with a mean age of 42.18 (± 13.89) years for males and 43.80 (± 12.56) years for females. The mean value of the TyG index in the total study population was 8.54 (± 0.53). Individuals in the higher quartiles of the TyG index had a significantly increased risk of MetS compared with those in the lowest quartile (p < 0.01). TyG index showed a stronger association with MetS than the FPG and all the conventional lipid components and the unadjusted odds ratio was 5.47. The area under the curve (AUC) of ROC revealed values of 0.914 (95% confidence interval (CI): 0.884, 0.944) for females, 0.881 (95% CI: 0.830, 0.932) for males and 0.897 (95% CI: 0.870, 0.924) for the total study population. TyG index had a stronger discriminative ability to identify MetS as per IDF criteria in the study population with a cut-off value of 8.60. The mean level of the TyG index significantly increased with the increasing number of MetS components.

CONCLUSIONS

The mean value of the TyG index increased as the number of MetS components in the study population increased. Individuals with a higher TyG index had a significantly increased risk of having MetS compared with the lowest quartile of the TyG index. TyG index had a good discriminative ability to diagnose MetS as per IDF criteria among the northern Sri Lankan population.

Triglyceride-glucose index demonstrates reasonable performance as a screening tool but exhibits limited diagnostic utility for insulin resistance: Data from the ELSA-Brasil cohort

Insulin resistance (IR) is a key risk factor for chronic metabolic diseases, but its laboratory diagnosis is still costly; thus, the triglyceride-glucose (TyG) index has been proposed as a surrogate method. Our aim was to provide a detailed analysis of cutoffs and test the hypothesis that the TyG index would present reasonable performance parameters for IR screening. This is a cross-sectional study with baseline data from 12,367 eligible participants of both sexes (aged 35-74 years) from the Brazilian Longitudinal Study of Adult Health. TyG correlation and agreement with the Homeostasis Model Assessment for Insulin Resistance were analyzed. Positive and negative predictive values (PV+, PV-) and likelihood ratio (LR+, LR-) were calculated. A moderate positive correlation between TyG and Homeostasis Model Assessment for Insulin Resistance was observed (Pearson r = 0.419). The area under the receiver operating characteristic curve of TyG for IR diagnosis was 0.742 and the optimal cutoff was 4.665, reaching a kappa agreement value of 0.354. For this cutoff, a PV+ of 59.3% and PV- of 76.0%, as well as an LR+ of 2.07 and LR- of 0.45 were obtained. Alternatively, because high sensitivity is desired for screening tests, selecting a lower cutoff, such as 4.505, increases the PV- to 82.1%, despite decreasing the PV+ to 50.8%. We conclude that TyG has important performance limitations for detecting IR, but that it may still be reasonably useful to help screening for IR in adults because it can be calculated from low-cost routine blood tests.

Diagnostic efficacy of the triglyceride-glucose index in the prediction of contrast-induced nephropathy following percutaneous coronary intervention

Introduction

Contrast-induced nephropathy (CIN) is a common complication of percutaneous coronary intervention (PCI). Identifying patients at high CIN risk remains challenging. The triglyceride-glucose (TyG) index may help predict CIN but evidence is limited. We conducted a meta-analysis to evaluate the diagnostic value of TyG index for CIN after PCI.

Methods

A systematic literature search was performed in MEDLINE, Cochrane, and EMBASE until August 2023 (PROSPERO registration: CRD42023452257). Observational studies examining TyG index for predicting CIN risk in PCI patients were included. This diagnostic meta-analysis aimed to evaluate the accuracy of the TyG index in predicting the likelihood of CIN. Secondary outcomes aimed to assess the pooled incidence of CIN and the association between an elevated TyG index and the risk of CIN.

Results

Five studies (Turkey, n=2; China, n=3) with 3518 patients (age range: 57.6 to 68.22 years) were included. The pooled incidence of CIN was 15.3% [95% confidence interval (CI) 11-20.8%]. A high TyG index associated with increased CIN risk (odds ratio: 2.25, 95% CI 1.82-2.77). Pooled sensitivity and specificity were 0.77 (95% CI 0.59-0.88) and 0.55 (95% CI 0.43-0.68) respectively. Analysis of the summary receiver operating characteristic (sROC) curve revealed an area under the curve of 0.69 (95% CI 0.65-0.73). There was a low risk of publication bias (p = 0.81).

Conclusion

The TyG index displayed a noteworthy correlation with the risk of CIN subsequent to PCI. However, its overall diagnostic accuracy was found to be moderate in nature. While promising, the TyG index should not be used in isolation for CIN screening given the heterogeneity between studies. In addition, the findings cannot be considered conclusive given the scarcity of data. Further large-scale studies are warranted to validate TyG cutoffs and determine how to optimally incorporate it into current risk prediction models.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023452257, identifier CRD42023452257.