ObMetrics: A Shiny app to assist in metabolic syndrome assessment in paediatric obesity.

Rosaura Leis, María Elena Acosta Enríquez, José Luis Jasso-Medrano, Susana Romo-Tello, Elizabeth Solís-Pérez, Marcos Galván, Jhazmin Hernández-Cabrera, Guadalupe López-Rodríguez, Francisco Requena, Ángel Gil, Mercedes Gil-Campos, Gloria Bueno, Nikos Stratakis, Augusto Anguita-Ruiz, Álvaro Torres-Martos, Miguel Klünder-Klünder, Jesús Alcalá-Fdez, Alberto Ramírez-Mena, Mireia Bustos-Aibar, Jenny Vilchis-Gil, Juan Carlos Aristizabal, Concepción María Aguilera, Gloria Martínez-Andrade

Journal: Pediatric obesity 2025;20(8):e70016

PMID: 40324927

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Abstract

OBJECTIVE

To introduce ObMetrics, a free and user-friendly Shiny app that simplifies the calculation, data analysis, and interpretation of Metabolic Syndrome (MetS) outcomes according to multiple definitions in epidemiological studies of paediatric populations. We illustrate its usefulness using ethnically different populations in a comparative study of prevalence across cohorts and definitions.

METHODS

We conducted a case study using data from two ethnically diverse paediatric populations: a Hispanic-American cohort (N = 1759) and a Hispanic-European cohort (N = 2411). Using ObMetrics, we computed MetS classifications (Cook, Zimmet, Ahrens) and component-specific z-scores for each participant to compare prevalences.

RESULTS

The analysis revealed significant heterogeneity in MetS prevalence across different definitions and cohorts. According to Cook, Zimmet, and Ahrens's definitions, MetS prevalence in children with obesity was 25%, 12%, and 48%, respectively, in the Hispanic-European cohort, and 38%, 27%, and 66% in the Hispanic-American cohort. Calculating component-specific z-scores in each cohort also highlighted ethnic-specific differences in lipid metabolism and blood pressure. By automating these complex calculations, ObMetrics considerably reduced analysis time and minimised the potential for errors.

CONCLUSION

ObMetrics proved to be a powerful tool for paediatric research, generating detailed reports on the prevalence of MetS and its components based on various definitions and reference standards. Our case study further provides valuable insights into the challenges of characterising metabolic health in paediatric populations. Future efforts should focus on developing unified consensus guidelines for paediatric MetS. Meanwhile, ObMetrics enables earlier identification and targeted intervention for high-risk children and adolescents.

Address: Department of Biochemistry and Molecular Biology II, Institute of Nutrition and Food Technology 'José Mataix,' Center of Biomedical Research, University of Granada, Granada, Spain.; Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain.; CIBER Physiopathology of Obesity and Nutrition (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.; Weill Cornell Medicine, New York, New York, USA.; Academic Group of Nutritional Epidemiology, School of Health Sciences, Universidad Autónoma del Estado de Hidalgo, Pachuca, Mexico.; Facultad de Salud Pública y Nutrición, Universidad Autónoma de Nuevo León, San Nicolás de los Garza, Mexico.; Epidemiological Research Unit in Endocrinology and Nutrition, Hospital Infantil de México Federico Gomez, Ministry of Health (SSA), Mexico City, Mexico.; Institute of Health Sciences, Autonomous University of the State of Hidalgo, Hidalgo, Mexico.; Sciences of Health Faculty, Nutrition School, School of Public Health, Montemorelos University, Nuevo Leon, Mexico.; Physiology and Biochemistry Research Group-PHYSIS, Faculty of Medicine, University of Antioquia, Medellín, Colombia.; IT Section, IFMIF-DONES, Granada, Spain.; Barcelona Institute of Global Health, Barcelona, Spain.; Department of Biochemistry and Molecular Biology II, Institute of Nutrition and Food Technology 'José Mataix,' Center of Biomedical Research, University of Granada, Granada, Spain.; CIBER Physiopathology of Obesity and Nutrition (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.; Growth, Exercise, Nutrition and Development (GENUD) Research Group, Institute for Health Research Aragón (IIS Aragón), Pediatric Endocrinology Unit, Facultad de Medicina, Clinic University Hospital Lozano Blesa, University of Zaragoza, Zaragoza, Spain.; CIBER Physiopathology of Obesity and Nutrition (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.; Metabolism and Investigation Unit, Reina Sofia University Hospital, Maimónides Institute of Biomedicine Research of Córdoba (IMIBIC), University of Córdoba, Córdoba, Spain.; CIBER Physiopathology of Obesity and Nutrition (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.; Barcelona Institute of Global Health, Barcelona, Spain.; CIBER Physiopathology of Obesity and Nutrition (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.; Unit of Pediatric Gastroenterology, Hepatology and Nutrition, Pediatric Service, Hospital Clínico Universitario de Santiago. Unit 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.; Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain.; Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain.

DOI: 10.1111/ijpo.70016

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MeSH Terms: Adolescent, Child, Female, Humans, Male, Hispanic or Latino, Metabolic Syndrome, Mobile Applications, Pediatric Obesity, Prevalence, European People