Utility of the hypertriglyceridemic waist phenotype in the cardiometabolic risk assessment of youth stratified by body mass index.

BACKGROUND: It is unclear whether the hypertriglyceridemic waist phenotype (HTWP) can be used to identify those at most risk of cardiometabolic disorders. OBJECTIVES: The utility of the HTWP as a useful predictor of cardiometabolic risk in youth stratified by body mass index was assessed. METHODS: Three hundred and eighty-seven children (12-17.5 years) were used within this cross-sectional study. Participants were classified as normal weight or overweight/obese according to the International Obesity Task Force criteria. The HTWP phenotype was defined as having a waist circumference ≥90th percentile for age and gender with concomitant triglyceride concentrations ≥1.24 mmol L(-1) . Cardiometabolic risk profiles were compared using MANCOVA. RESULTS: Normal weight participants with the HTWP had significantly higher levels of C-reactive protein 2.6 ± 0.4 vs. 1.6 ± 0.3 mg L(-1) (P < 0.05) and cardiometabolic risk scores (1.3 ± 0.3 vs. -0.7 ± 0.2 and 2.1 ± 0.4 vs. -0.5 ± 0.2; both P < 0.05) compared with those of a normal weight without the HTWP. Overweight/obese participants with the HTWP had significantly higher C-reactive protein levels (3.5 ± 0.6 vs. 2.6 ± 0.5; P < 0.05) as well as both cardiometabolic risk scores (1.6 ± 0.6 vs. 0.9 ± 0.2 and 2.2 ± 0.6 vs. 0.8 ± 0.2; both P < 0.001) when compared with overweight/obese participants without the HTWP. CONCLUSIONS: The HTWP may serve as a simple and clinically useful approach to identify youth at increased cardiometabolic risk.

Assessment of biochemical liver markers, physical activity, fitness and body mass index for a cardiometabolic risk model in childhood.

AIM: The aim of this study was to investigate clustered cardiometabolic risk scores in healthy 10- to 12-year-olds using anthropometric characteristics, measurements of cardiorespiratory fitness (CRF) and physical activity and blood markers of metabolic disease. We also evaluated how including markers of liver cell injury would affect the clustered cardiometabolic risk assessment model. METHODS: This cross-sectional study focused on 99 children aged 10-12 years. The main outcome included assessing participants with increased and low cardiometabolic risk factors using a clustered risk score model that incorporated markers implicated in metabolic syndrome pathogenesis. Two clustered risk scores were calculated, one incorporating markers of liver cell injury. RESULTS: Children classified as 'increased risk' exhibited significantly lower CRF and higher body mass index Z-scores than their 'low-risk' peers. No significant differences in physical activity were observed. This trend remained unchanged when markers of liver injury were included in the clustered risk assessment model. CONCLUSION: The clustered risk score model is a scientifically robust method of cardiometabolic risk assessment, which reiterates the importance of weight reduction and CRF promotion in childhood. Our study did not show a significant contribution of liver injury markers, and further research is needed to evaluate their effect on cardiometabolic risk stratification in childhood.