ObMetrics: A Shiny app to assist in metabolic syndrome assessment in paediatric obesity Torres-Martos, Álvaro Requena, Francisco López-Rodríguez, Guadalupe Hernández-Cabrera, Jhazmin Galván, Marcos Solís-Pérez, Elizabeth Romo-Tello, Susana Jasso-Medrano, José Luis Vilchis-Gil, Jenny Klünder-Klünder, Miguel Martínez-Andrade, Gloria Acosta Enríquez, María Elena Aristizabal, Juan Carlos Ramírez-Mena, Alberto Stratakis, Nikos Bustos-Aibar, Mireia Gil Hernández, Ángel Gil-Campos, Mercedes Bueno, Gloria Leis, Rosaura Alcalá Fernández, Jesús Aguilera García, Concepción María Anguita-Ruiz, Augusto adolescent anthropometry cardiometabolic risk factors child insulin resistance metabolic syndrome metabolic health paediatric obesity This research was supported by the Instituto de Salud Carlos III co-funded by the European Union and ERDF A way of making Europe (grant numbers PI20/00563, PI20/00711, PI20/00924, P20/00988, PI23/00028, PI23/00129, PI23/01032, PI23/00165 and also PI23/00191), and by the European Union through the Horizon Europe Framework Programme (eprObes project, grant number GA 101080219). The authors also acknowledge Instituto de Salud Carlos III for personal funding of Álvaro Torres-Martos and Mireia Bustos-Aibar: i-PFIS and PFIS contracts: IIS doctorates—company in health sciences and technologies of the Strategic Health Action (IFI22/00013 and FI23/00042). We also thank the support from the grant FJC2021-046952-I by MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRPT. Funding for open access charge: Universidad de Granada/CBUA. 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. 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. 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. 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. 2025-05-06T08:45:11Z 2025-05-06T08:45:11Z 2025-05-05 journal article Torres-Martos A, Requena F, López-Rodríguez G, et al. ObMetrics: A Shiny app to assist in metabolic syndrome assessment in paediatric obesity. Pediatric Obesity. 2025;e70016. https://doi.org/10.1111/ijpo.70016 2047-6310 https://hdl.handle.net/10481/103956 10.1111/ijpo.70016 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Wiley