Are Non-High-Density Lipoprotein Fractions Associated With Pediatric Metabolic Syndrome? the Caspian-V Study Publisher Pubmed



Angoorani P¹ ; Khademian M² ; Ejtahed HS³ ; Heshmat R¹ ; Motlagh ME⁴ ; Vafaeenia M¹ ; Shafiee G¹ ; Mahdivigorabi A¹ ; Qorbani M^{5, 6} ; Kelishadi R² Show Affiliations
Source: Lipids in Health and Disease Published:2018

Abstract

Background: Non-high-density lipoprotein cholesterol (non-HDL-C) is considered as a valuable predictor for dyslipidemia and subclinical atherosclerosis which can be an appropriate index for identifying individuals with metabolic syndrome (MetS). Objective: To evaluate the association between non-HDL-C MetS and determine the optimal cut-points of non-HDL-C fractions for identifying MetS in Iranian children and adolescents. Methods: This nationwide study was conducted in the framework of the fifth survey of a national school-based surveillance program on children and adolescents aged 7-18 years. MetS was defined by the Adult Treatment Panel III (ATP III) criteria modified for the pediatric age group. The analysis of receiver operating characteristic (ROC) curve was applied to determine the optimal cut-points of non-HDL-C, difference between non-HDL-C and LDL-C (Diff-C) and triglycerides (TG) to HDL-C ratio (TG/HDL-C) for the prediction of MetS. Results: Overall, the study participants consisted of 3843 students (52.3% boys) with mean (±SD) age of 12.28 (3.1) years. The odds of high LDL-C, low HDL-C and MetS were increased in subjects with higher non-HDL-C, Diff-C and TG/HDL-C (P < 0.05). Non-HDL-C, Diff-C and TG/HDL-C cut-off points for predicting MetS were 120.5 mg/dl (sensitivity: 44%, specificity: 73%), 19.9 mg/dl (sensitivity: 85%, specificity: 75%) and 2.53 (sensitivity: 82%, specificity: 79%), respectively. Conclusions: This study revealed a strong association between surrogates for serum lipid profile including non-HDL-C, TG/HDL-C and Diff-C and pediatric MetS. Our findings suggest that age- and gender-specific reference values of these markers were appropriate for both risk classification and long-term control of cardiovascular events in clinical assessments. © 2018 The Author(s).