ESPE2024 Poster Category 2 Fat, Metabolism and Obesity (39 abstracts)
Comparative Analysis of the Discriminative Ability of BMI, TMI, and BMI zscore in Establishing Metabolic Syndrome Risk
Julia Domingo-Ajenjo 1,2 , Álvaro Torres-Martos 3,4,5 , Mireia Bustos-Aibar 3 , Maria Arteaga 1 , Rocio Vázquez-Cobela 6,7,8 , Carlos Nagore 9,10 , Pilar De Miguel 11,10 , Jesús Alcalá-Fdez 1 , Augusto Anguita-Ruiz 12,8 , Rosaura Leis 6,7,8 , Concepción M. Aguilera 3,8 & Gloria Bueno-Lozano 9,11,10,8
1Department of Computer Science and Artificial Intelligence, Granada, Spain. 2Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain. 3Department of Biochemistry and Molecular Biology II, School of Pharmacy, ”José Mataix Verdú”, Granada, Spain. 4Institute of Nutrition and Food Technology (INYTA) and Center of Biomedical Research, University of Granada, Granada, Spain. 5CIBER de Fisiopatologı́Madrida de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain. 6Unit of Pediatric Gastroenterology, Hepatology and Nutrition, Pediatric Service, Hospital Clı́nico Universitario de Santiago., Santiago de Compostela, Spain. 7Unit of Investigation in Nutrition, Growth and Human Development of Galicia-USC, Pediatric Nutrition Research Group-Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain. 8CIBER de Fisiopatologı́a de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain. 9Pediatric Endocrinology Unit, Clinic University Hospital Lozano Blesa, Zaragoza, Spain. 10Research Institute of Aragón (IISA), Zaragoza, Spain. 11Growth, Exercise, Nutrition and Development (GENUD) Research Group. University of Zaragoza, Zaragoza, Spain. 12Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
Pediatric obesity is intimately related to the development of Metabolic Syndrome (MetS), which is a cluster of metabolic alterations associated with an increased risk of premature death. Some indexes of obesity, such as the Triponderal Mass Index (TMI), Body Mass Index (BMI), and Body Mass Index z-score (BMI-zscore), are used to identify children at high risk. This study aims to compare the discriminative ability of BMI, BMI and BMI-zscore to determine the risk of developing MetS in a Spanish population of children (MetS z-score calculated by ObMetrics (https://coblabugr.shinyapps.io/obmetrics/). A receiver operating characteristic (ROC) analysis based on the Youden index was performed to define gender-specific cut-off points for each index on a Spanish population of 702 children (normal-weight, overweight, and with obesity) from 8 to 18 years of age from the GENOBOX study (53% boys, 71% pubertal, 24% MetS). These cut-off points were validated on an external spanish population of 639 children (normal-weight and overweight) from 8 to 18 years of age from IBEROMICS study (52% boys, 78% pubertal, 48% MetS). Table 1 shows the results obtained for both populations.
| GENOBOX | IBEROMICS | |||||||
| Cut-offs | Sens | Spe | AUC | Sens | Spe | AUC | ||
| BMI | Boys | 25.44 kg/m2 | 0.812 | 0.681 | 0.809 | 0.847 | 0.376 | 0.612 |
| Girls | 25.26 kg/m2 | 0.875 | 0.699 | 0.848 | 0.939 | 0.377 | 0.658 | |
| TMI | Boys | 16.49 kg/m3 | 0.896 | 0.649 | 0.834 | 0.951 | 0.258 | 0.605 |
| Girls | 16.87 kg/m3 | 0.903 | 0.682 | 0.853 | 0.945 | 0.308 | 0.650 | |
| BMI-zscore | Boys | 2.147 | 0.812 | 0.785 | 0.854 | 0.847 | 0.484 | 0.665 |
| Girls | 2.372 | 0.875 | 0.757 | 0.871 | 0.847 | 0.493 | 0.670 | |
To assess the accuracy of each index, a non-parametric statistical study was conducted using Delong et al. [1] statistical test in IBEROMICS. The hypothesis of equality was rejected only for the BMI-zscore index vs. the BMI and TMI indexes for boys with p-values of 0.001 and 0.005, respectively. In conclusion, although insufficient evidence was found to determine a superior index for discriminating MetS risk, the results showed a higher predictive ability of BMI-zscore for boys. These results highlight the need for a more comprehensive approach that considers other indexes and risk factors to improve the identification of MetS in pediatric populations. [1] DeLong, E.R., DeLong, D.M., and Clarke-Pearson, D.L. (1988). Doi: 10.2307/2531595
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