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Anthropometric measurements as a potential non-invasive alternative for the diagnosis of metabolic syndrome in adolescents

OBJECTIVE: To identify which anthropometric measurement would be the best predictor of metabolic syndrome (MetS) in Brazilian adolescents. SUBJECTS AND METHODS: Cross-sectional study conducted on 222 adolescents (15-17 years) from a city in southern Brazil. Anthropometric, physical activity, blood p...

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Detalles Bibliográficos
Autores principales: Mastroeni, Silmara Salete de Barros Silva, Mastroeni, Marco Fabio, Ekwaru, John Paul, Setayeshgar, Solmaz, Veugelers, Paul J., Gonçalves, Muryel de Carvalho, Rondó, Patrícia Helen de Carvalho
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Sociedade Brasileira de Endocrinologia e Metabologia 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10118845/
https://www.ncbi.nlm.nih.gov/pubmed/30864629
http://dx.doi.org/10.20945/2359-3997000000100
Descripción
Sumario:OBJECTIVE: To identify which anthropometric measurement would be the best predictor of metabolic syndrome (MetS) in Brazilian adolescents. SUBJECTS AND METHODS: Cross-sectional study conducted on 222 adolescents (15-17 years) from a city in southern Brazil. Anthropometric, physical activity, blood pressure and biochemical parameters were investigated. MetS criteria were transformed into a continuous variable (MetS score). Linear regression analyses were performed to assess the associations of BMI, hip circumference, neck circumference (NC), triceps skinfold, subscapular skinfold and body fat percentage with MetS score. ROC curves were constructed to determine the cutoff for each anthropometric measurement. RESULTS: The prevalence of MetS was 7.2%. Each anthropometric measurement was significantly (p < 0.001) associated with MetS score. After adjusting for potential confounding variables (age, sex, physical activity, and maternal education), the standardized coefficients of NC and body fat percentage appeared to have the strongest association (beta = 0.69 standard deviation) with MetS score. The regression of BMI provided the best model fit (adjusted R(2) = 0.31). BMI predicted MetS with high sensitivity (100.0%) and specificity (86.4%). CONCLUSIONS: Our results suggest that BMI and NC are effective screening tools for MetS in adolescents. The early diagnosis of MetS combined with targeted lifestyle interventions in adolescence may help reduce the burden of cardiovascular diseases and diabetes in adulthood.