Triglyceride-Glucose Index and Related Parameters Predicted Metabolic Syndrome in Nigerians

Background: Triglyceride-glucose (TyG) index, a product of triglyceride and fasting plasma glucose, is a novel tool that can identify people with metabolic syndrome (MS). It is unknown if TyG index can identify MS among Nigerians. Methods: Cross-sectional health screening conducted between August and December 2018, among staff and students of Ekiti State University/Ekiti State University Teaching Hospital, Nigeria, Ado-Ekiti. The analysis included 473 participants, aged ≥18 years. Anthropometric indices and blood pressure were measured by standard protocol. Fasting lipid profile and blood glucose were determined. TyG index and product of TyG and anthropometric indices were calculated, and MS defined according to the harmonized criteria. The diagnostic ability of TyG index and related parameters to identify people with MS was determined with the area under curve (AUC) of receiver operating characteristic curves. Stepwise logistic regression analyses were used to generate odd ratios (ORs) for prediction of MS. Results: The mean age of the participants was 39.2 (11.4) years and there were 173 (36.6%) men. In all participants, TyG-waist to height ratio (TyG-WHtR) shows the largest AUC for MS detection (0.863, 95% confidence interval, CI: 0.828–0.892) followed by TyG-waist circumference (TyG-WC) (0.858, 95% CI: 0.823–0.888), TyG-body mass index (TyG-BMI) (0.838, 95% CI: 0.802–0.870), TyG index (0.796, 95% CI: 0.757–0.831), WHtR (0.791, 95% CI: 0.752–0.827), and TyG-waist-to-hip ratio (TyG-WHpR) (0.771, 95% CI: 0.730–0.808) in that order. Gender analysis revealed that TyG-WC and TyG-WHtR have largest AUC in both genders. Before and after adjustment, TyG-WHtR (OR: 6.86, 95% CI: 3.94–11.93) and TyG index (OR: 5.91, 95% CI: 3.01–11.59) presented the highest OR in all participants, respectively. Conclusions: TyG index is effective in identifying MS in this cross-sectional study, and the product of TyG index and anthropometric indices improved identification and prediction of MS.