Robinson Ramírez-Vélez1, Alejandro Anzola1, Javier Martinez-Torres1, Andres Vivas2, Alejandra Tordecilla-Sanders1, Daniel Prieto-Benavides1, Mikel Izquierdo3, Jorge Enrique Correa-Bautista1, Antonio Garcia-Hermoso4,5. 1. 1 Centro de Estudios para la Medición de la Actividad Física "CEMA", Escuela de Medicina y Ciencias de la Salud, Universidad del Rosario , Bogotá DC, Colombia . 2. 2 Grupo de Investigación en Ejercicio Físico y Deportes. Vicerrectoría de Investigaciones. Universidad Manuela Beltrán . Bogotá DC, Colombia . 3. 3 Department of Health Sciences, Public University of Navarra , Pamplona, Spain . 4. 4 Laboratorio de Ciencias de la Actividad Física, el Deporte y la Salud, Universidad de Santiago de Chile , USACH, Santiago, Chile . 5. 5 Universidad San Sebastián , Santiago, Chile .
Abstract
BACKGROUND: In contrast to the definition of metabolic syndrome (MetS) in adults, there is no standard definition of MetS in pediatric populations. We aimed at assessing the differences in the prevalence of MetS in children and adolescents aged 9-17 years using four different operational definitions for these age groups and at examining the associated variables. METHODS: A total of 675 children and 1247 adolescents attending public schools in Bogota (54.4% girls; age range 9-17.9 years) were included. The prevalence of MetS was determined by the definitions provided by the International Diabetes Federation (IDF) and three published studies by Cook et al., de Ferranti et al., and Ford et al. In addition, we further examined the associations between each definition of MetS in the total sample and individual risk factors using binary logistic regression models adjusted for gender, age, pubertal stage, weight status, and inflammation in all participants. RESULTS: The prevalence of MetS was 0.3%, 6.3%, 7.8%, and 11.0% according to the definitions by IDF, Cook et al., Ford et al., and de Ferranti et al., respectively. The most prevalent components were low high-density lipoprotein cholesterol and high triglyceride levels, whereas the least prevalent components were higher waist circumference and hyperglycemia. Overall, the prevalence of MetS was higher in obese than in non-obese schoolchildren. CONCLUSIONS: MetS diagnoses in schoolchildren strongly depend on the definition chosen. These findings may be relevant to health promotion efforts for Colombian youth to develop prospective studies and to define which cut-offs are the best indicators of future morbidity.
BACKGROUND: In contrast to the definition of metabolic syndrome (MetS) in adults, there is no standard definition of MetS in pediatric populations. We aimed at assessing the differences in the prevalence of MetS in children and adolescents aged 9-17 years using four different operational definitions for these age groups and at examining the associated variables. METHODS: A total of 675 children and 1247 adolescents attending public schools in Bogota (54.4% girls; age range 9-17.9 years) were included. The prevalence of MetS was determined by the definitions provided by the International Diabetes Federation (IDF) and three published studies by Cook et al., de Ferranti et al., and Ford et al. In addition, we further examined the associations between each definition of MetS in the total sample and individual risk factors using binary logistic regression models adjusted for gender, age, pubertal stage, weight status, and inflammation in all participants. RESULTS: The prevalence of MetS was 0.3%, 6.3%, 7.8%, and 11.0% according to the definitions by IDF, Cook et al., Ford et al., and de Ferranti et al., respectively. The most prevalent components were low high-density lipoprotein cholesterol and high triglyceride levels, whereas the least prevalent components were higher waist circumference and hyperglycemia. Overall, the prevalence of MetS was higher in obese than in non-obese schoolchildren. CONCLUSIONS: MetS diagnoses in schoolchildren strongly depend on the definition chosen. These findings may be relevant to health promotion efforts for Colombian youth to develop prospective studies and to define which cut-offs are the best indicators of future morbidity.
Authors: Javier Martínez-Torres; Jorge Enrique Correa-Bautista; Katherine González-Ruíz; Andrés Vivas; Héctor Reynaldo Triana-Reina; Daniel Humberto Prieto-Benavidez; Hugo Alejandro Carrillo; Jeison Alexander Ramos-Sepúlveda; Emilio Villa-González; Antonio García-Hermoso; Robinson Ramírez-Vélez Journal: Int J Environ Res Public Health Date: 2017-02-27 Impact factor: 3.390
Authors: Jeison Alexander Ramos-Sepúlveda; Robinson Ramírez-Vélez; Jorge Enrique Correa-Bautista; Mikel Izquierdo; Antonio García-Hermoso Journal: BMC Public Health Date: 2016-09-13 Impact factor: 3.295
Authors: Osama E Amer; Shaun Sabico; Malak N K Khattak; Abdullah M Alnaami; Naji J Aljohani; Hanan Alfawaz; Abdulaziz AlHameidi; Nasser M Al-Daghri Journal: Children (Basel) Date: 2021-12-03