THU145 Prediction Of Metabolic Syndrome Using DEXA Visceral Adipose Index (DVAI) And Pediatric Metabolic Index (PMI) In Children And Adolescents

Disclosure: K.F. Hearn: None. J.L. Fierstein: None. A. Miller: None. J.A. Canas: None. Introduction: Adverse cardiometabolic risk can be predicted during childhood using risk indices. Dual-energy absorptiometry (DEXA) derived Visceral Adipose Index (DVAI) quantifies Visceral Adipose Tissue (VAT) measured by DEXA in association with dyslipidemia. Pediatric Metabolic Index (PMI) developed for children < 18 years uses anthropometric measures validated against known pediatric markers of insulin resistance. Although Metabolic Syndrome (MetS) is prevalent among children, research on the prediction of MetS using DVAI or PMI is lacking. Aims: 1) To evaluate the predictive accuracy of MetS using DVAI and PMI. We hypothesized models using DVAI would display higher predictive accuracy than models using PMI. Methods: Retrospective review of children aged >5 to <18 years referred for obesity evaluation at Johns Hopkins All Children’s Hospital between 2019- 2022. Anthropometrics, blood pressure, fasting blood samples, and DEXA was collected. MetS were defined using the ATP III, Cook, and International Diabetes Federation (IDF) criteria. DVAI and PMI were calculated using standardized formulas. Multivariable logistic regressions including age, sex, and gynoid-android ratio (GAR), and either DVAI or PMI were fitted. Receiver operating curve (ROC) analyses were used to compare the predictive accuracy of DVAI and PMI models and define VAI and PMI cut-offs for the identification of MetS. Results: Eighty-nine children were included, 53% were female, the median age was 13 years, and 47.9%, 46.8%, and 40.6% had MetS according to, ATP III, Cook, and IDF criteria, respectively. There were significant differences (p<.05) by all criteria of MetS in waist circumference (WC), GAR, DVAI, and PMI, but no differences by any criteria of MetS in the distribution of age, sex, race/ethnicity, or BMI Z-scores. In all regression models, higher both DVAI and PMI were significant predictors for MetS. There were no significant differences in AUC between models based on DVAI and PMI, regardless of MetS criteria. Optimal cut-off values for MetS were 4.3 (C-index [95% Confidence interval {CI}]): (0.69 [0.57 to 0.80]), 2.9 (0.79 [0.69 to 0.89]), and 3.7 (0.76 [0.64 to 0.87]) for DVAI and 5.9 (0.76 [0.66 to 0.87]), 4.7 (0.87 [0.79 to 0.94]), and 5.3 (0.79 [ 0.69 to 0.89]), for PMI based on ATP III, Cook, and IDF criteria respectively. Conclusions: These data suggest that models using PMI to predict MetS defined by Cook criteria had the highest predictive accuracy, while models using DVAI and PMI to predict MetS defined by ATP III and IDF criteria had a similar predictive capability. PMI is a promising score to help identify and manage MetS in children which could be used along with dietary and exercise counseling. Further longitudinal studies are needed to validate the durability of these indices in predicting long-term cardiometabolic risk. Presentation: Thursday, June 15, 2023