Comparing the validity of continuous metabolic syndrome risk scores for predicting pediatric metabolic syndrome: the CASPIAN-V study.
Mehri KhoshhaliRamin HeshmatMohammad Esmaeil MotlaghHasan ZiaodiniMahdi HadianTahereh AminaeiMostafa QorbaniRoya KelishadiPublished in: Journal of pediatric endocrinology & metabolism : JPEM (2019)
Background The aim of this study was to compare the validity of various approaches to pediatric continuous metabolic syndrome (cMetS) scores including siMS scores (2 waist/height + fasting blood glucose [FBG]/5.6 + triglycerides [TG]/1.7 + systolic blood pressure [BP]/130 + high-density lipoprotein [HDL]/1.02), Z-scores, principal component analysis (PCA) and confirmatory factor analysis (CFA) for predicting metabolic syndrome (MetS). Methods This nationwide cross-sectional study was conducted on 4200 Iranian children and adolescents aged 7-18 years. The cMetS was computed using data on HDL, cholesterol, TGs, FBG, mean arterial pressure (MAP) and waist circumference (WC). The areas under the receiver operating characteristic curves (AUCs) were used to compare the performances of different cMetS scores. Results Data of 3843 participants (52.4% boys) were available for the current study. The mean (standard deviation [SD]) age was 12.6 (3) and 12.3 (3.1) years for boys and girls, respectively. The differences in AUC values of cMetS scores were significant based on the Delong method. The AUCs (95% confidence interval [CI]) were for Z-scores, 0.94 (0.93, 0.95); first PCA, 0.91 (0.89, 0.93); sum PCA, 0.90 (0.88, 0.92), CFA, 0.79 (0.76, 0.3) and also for siMS scores 1 to 3 as 0.93 (0.91, 0.94), 0.92 (0.90, 0.93), and 0.91 (0.90, 0.93), respectively. Conclusions The results of our study indicated that the validity of all approaches for cMetS scores for predicting MetS was high. Given that the siMS scores are simple and practical, it might be used in clinical and research practice.
Keyphrases
- metabolic syndrome
- blood pressure
- blood glucose
- body mass index
- high density
- insulin resistance
- healthcare
- primary care
- heart failure
- uric acid
- type diabetes
- magnetic resonance
- computed tomography
- adipose tissue
- electronic health record
- young adults
- artificial intelligence
- big data
- glycemic control
- low density lipoprotein