P Xiao1, H Cheng2, D Q Hou2, A Y Gao3, L G Wang4, Z C Yu5, H J Wang6, X Y Zhao2, H B Li1, G M Huang2, J Mi7. 1. Department of Epidemiology, Capital Institute of Pediatrics, Beijing 100020, China; Graduate School of Peking Union Medical College, Beijing 100730, China. 2. Department of Epidemiology, Capital Institute of Pediatrics, Beijing 100020, China. 3. Dongcheng District Primary and Secondary School Health Center, Beijing 100009, China. 4. Miyun District Primary and Secondary School Health Center, Beijing 101500, China. 5. Tongzhou District Primary and Secondary School Health Center, Beijing 101100, China. 6. Fangshan District Primary and Secondary School Health Center, Beijing 102400, China. 7. Department of Non-communicable Disease Management, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China.
Abstract
Objective: To compare the power of dyslipidemia diagnosis by different sets of cut points in the prediction of cardiovascular metabolic risk factors and identify the appropriate cut points for the diagnosis of dyslipidemia in children and adolescents in China. Methods: Data were obtained from the baseline survey of 'School-based Cardiovascular and Bone Health Promotion Program' in Beijing in 2017. Dyslipidemia was diagnosed by using two set of cut points. Receiver operating characteristic curve analysis was conducted to assess the power of dyslipidemia diagnosis by the two set of cut points to predict the prevalence of hypertension, obesity, high fat mass percentage and impaired fasting glucose. Results: A total of 14 390 children and adolescents were in included in the study. The prevalence rates of high TC, high LDL-C, low HDL-C, and high TG in the participants were 2.7%, 2.7%, 14.4%, and 3.7% according to 'Chinese Reference Standard', and 5.0%, 3.7%, 13.3%, and 3.5% according to 'China Expert Consensus'. Low HDL-C and high TG defined by the 'Chinese Reference Standard' had better performance for the prediction of high fat mass percentage and obesity in boys, but worse performance in girls (P<0.001). Conclusions: Using 'China Reference Standard' can increase the true positive rate in the prediction of obesity or high fat mass percentage in boys, and reduce the false positive rate in girls. The cut points for the diagnosis of dyslipidemia in Chinese children and adolescents need to be further validated by using national representative sample and in longitudinal study.
Objective: To compare the power of dyslipidemia diagnosis by different sets of cut points in the prediction of cardiovascular metabolic risk factors and identify the appropriate cut points for the diagnosis of dyslipidemia in children and adolescents in China. Methods: Data were obtained from the baseline survey of 'School-based Cardiovascular and Bone Health Promotion Program' in Beijing in 2017. Dyslipidemia was diagnosed by using two set of cut points. Receiver operating characteristic curve analysis was conducted to assess the power of dyslipidemia diagnosis by the two set of cut points to predict the prevalence of hypertension, obesity, high fat mass percentage and impaired fasting glucose. Results: A total of 14 390 children and adolescents were in included in the study. The prevalence rates of high TC, high LDL-C, low HDL-C, and high TG in the participants were 2.7%, 2.7%, 14.4%, and 3.7% according to 'Chinese Reference Standard', and 5.0%, 3.7%, 13.3%, and 3.5% according to 'China Expert Consensus'. Low HDL-C and high TG defined by the 'Chinese Reference Standard' had better performance for the prediction of high fat mass percentage and obesity in boys, but worse performance in girls (P<0.001). Conclusions: Using 'China Reference Standard' can increase the true positive rate in the prediction of obesity or high fat mass percentage in boys, and reduce the false positive rate in girls. The cut points for the diagnosis of dyslipidemia in Chinese children and adolescents need to be further validated by using national representative sample and in longitudinal study.
Entities:
Keywords:
Children and adolescents; Diagnostic cut points; Dyslipidemia
Authors: Yan Fang; Xing-Hui Li; Yan Qiao; Nan Wang; Ping Xie; Gang Zhou; Peng Su; Hui-Yuan Ma; Ji-Yang Song Journal: Open Life Sci Date: 2020-05-07 Impact factor: 0.938