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, Enríquez María Elena Acosta, Aristizabal Juan Carlos, Ramírez-Mena Alberto, Stratakis Nikos, Bustos-Aibar Mireia, Gil Ángel, Gil-Campos Mercedes, Bueno Gloria, Leis Rosaura, Alcalá-Fdez Jesús, Aguilera Concepción María, Anguita-Ruiz Augusto
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.
Pediatr Obes. 2025 Aug;20(8):e70016. doi: 10.1111/ijpo.70016. Epub 2025 May 5.
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.
介绍ObMetrics，这是一款免费且用户友好的Shiny应用程序，可简化儿科人群流行病学研究中根据多种定义对代谢综合征（MetS）结果的计算、数据分析和解读。我们在一项跨队列和定义的患病率比较研究中，使用不同种族人群说明了其有用性。
我们使用来自两个不同种族儿科人群的数据进行了一项案例研究：一个西班牙裔美国队列（N = 1759）和一个西班牙裔欧洲队列（N = 2411）。使用ObMetrics，我们计算了每个参与者的MetS分类（库克、齐默特、阿伦斯）和特定组分的z分数，以比较患病率。
分析显示，不同定义和队列的MetS患病率存在显著异质性。根据库克、齐默特和阿伦斯的定义，西班牙裔欧洲队列中肥胖儿童的MetS患病率分别为25%、12%和48%，西班牙裔美国队列中分别为38%、27%和66%。计算每个队列中特定组分的z分数也突出了脂质代谢和血压方面的种族特异性差异。通过自动执行这些复杂计算，ObMetrics大大减少了分析时间，并将出错可能性降至最低。
ObMetrics被证明是儿科研究的有力工具，可根据各种定义和参考标准生成关于MetS及其组分患病率的详细报告。我们的案例研究进一步为儿科人群代谢健康特征描述的挑战提供了有价值见解。未来的工作应侧重于制定儿科MetS的统一共识指南。同时，ObMetrics能够更早地识别高危儿童和青少年并进行针对性干预。
