Surrogate indexes of insulin resistance and risk of metabolic syndrome in non-Hispanic White, non-Hispanic Black and Mexican American. Issue 1 (January 2020)

• Record Type: Journal Article
• Authors: Okosun, Ike S.; Okosun, Bryan; Lyn, Rodney; Airhihenbuwa, Collins
• Is Part Of: Diabetes & metabolic syndrome. Volume 14:Issue 1(2020)
• Page Start: 3
• Page End: 9
• Publication Date: 2020-01
• Subjects: Blood pressure -- Triglyceride glucose index -- Visceral adiposity index -- Lipid accumulation product; Diabetes -- Periodicals; Metabolism -- Disorders -- Periodicals; Diabetes Mellitus -- Periodicals; Metabolic Diseases -- Periodicals; Diabète -- Périodiques; Métabolisme, Troubles du -- Périodiques; Endocrinologie -- Périodiques; Diabète -- Physiopathologie -- Périodiques; Diabetes; Metabolism -- Disorders; Electronic journals; Periodicals; 616.462
• DOI: 10.1016/j.dsx.2019.11.012
• Languages: English
• ISSNs: 1871-4021

Abstract

Abstract: Aim: To compare the strength of associations between surrogate indexes of insulin resistance (sIR) and risk of metabolic syndrome (MetS) in non-Hispanic White (NHW), non-Hispanic Black (NHB) and Mexican American (MA) adults. Methods: The 2013–2016 US National Health and Nutrition Examination Survey data (n = 3435) were used for this study. The associations between sIR that includes Triglyceride/HDL cholesterol ratio (TG/HDL-C), triglyceride glucose (TG) index, visceral adiposity index (VAI), lipid accumulation product (LAP), TG-body mass index (TG-BMI), and TG-waist circumference (TG-WC) and risk for MetS were determined using the prevalence odds ratio (OR) from the logistic regression analyses. Pseudo-R-squared tests were used to estimate the proportion of variance in MetS accounted for by each sIR. Akaike Information Criterion and Bayesian Information Criterion from the multinomial logistic regression analysis were used to compare models that included each sIR and its components separately as predictors of MetS. Areas under curves (AUC) from the receiver-operating characteristic (ROC) were used to detect their diagnostic capabilities. Results: Compared with other sIR, TG-WC (AUC = 0.899; 95% CI: 0.884–0.913 in NHW) and (AUC = 0.893; 95% CI:0.871–0.915 in NHB), and LAP (AUC = 877; 95% CI: 0.861–0.894 in MA) exhibited the highest diagnostic and predictive accuracy for MetS. Compared with other sIR, TG-WC (OR = 22.8; 95% CI:16.6–31.0 in NHW) and (OR = 22.7; 95% CI:13.1–39.3 in NHB), and LAP (OR = 10.6; 95%:6.6–17.0 in MA) were most significantly associated with increased odds of MetS, adjusting for eGFR, age, marital status, CHD, CHF, income, education, physical activity, alcohol use, smoking and use of cholesterol-lowering medication. Conclusions: TG-WC in NHW and NHB, and LAP in MA are more powerful than other proxies of IR in predicting MetS. TG-WC and LAP can serve as adjunctive tools for screening for MetS in NHW, NHB, and MA.