Prediction of Metabolic Syndrome in U.S. Adults Using Homeostasis Model Assessment-Insulin Resistance

Abstract

Background: The prevalence of obesity among U.S. adults has risen steadily over recent decades. Consequently, interest in identification of those at greatest metabolic risk necessitates the periodic assessment of underlying population characteristics. Thus, the aim of this study is to assess the efficacy of using insulin resistance (IR) as a predictor of metabolic syndrome (MetS). Methods: We performed a serial, cross-sectional analysis of nationally representative data from the National Health and Nutrition Examination Survey (NHANES). Data included nonpregnant adults who participated in NHANES between 2011 and 2018. IR was estimated using the homeostasis model assessment (HOMA). Optimal HOMA-IR cut points for MetS were identified using receiver operating characteristic curve analysis. Results: Data from 8897 participants representing 222 million individuals were analyzed. The estimated prevalence of MetS was 31.7% (n = 2958; 95% confidence interval 30.1-33.3). The optimal HOMA-IR to discriminate between individuals with and without MetS in the general population was 2.83 (sensitivity = 73.8%; specificity = 73.8%; area under the curve = 0.82). Conclusion: The HOMA-IR is a sensitive and specific method of screening for individuals with MetS. Prospective evaluation of this approach's efficacy in identifying those at risk for progression to MetS is warranted.

Keywords: HOMA; HOMA-IR; insulin resistance; metabolic syndrome.