Síndrome metabólica em diferentes fases da vida: predição e importância de seus componentes

Abstract

Objectives: To propose predictive models to Metabolic Syndrome (MetS) in adolescents and to evaluate the most important MetS components in adults. Methods: The data used were from two cohorts of the RPS Consortium: Ribeirão Preto-SP, initiated in 1978-1979, and São Luís-MA, initiated in 1997-1998. Adolescents aged 18-19 years from this cohort (n=2,064) and adults aged 23-25 years and 37-39 years from that cohort (n=1,098) were evaluated. The criteria of Cook et al. (2003), De Ferranti et al. (2004), International Diabetes Federation - IDF (2005) and Joint Interim Statement (2009) were used to diagnose MetS. Artificial neural networks (ANNs) were trained to predict MetS in adolescents, based on noninvasive clinical and anthropometric data, and also to determine the relative importance of MetS components. Results: The prevalence of MetS in adolescents ranged from 5.7% to 12.3%, and was 12.1% at 23-25 years and 41.6% at 37-39 years. The ANN that included age, sex, waist circumference (WC), weight, and systolic and diastolic blood pressure showed better performance and discriminatory power in predicting MetS in adolescents (sensitivity 89.8% and accuracy 86.8%). The most important component for the diagnosis of MetS at 23-25 years was systolic blood pressure, and at 37-39 years of age, it was HDL. The least relevant was WC, at the first time point, and triglycerides, at the second. Therefore, systolic and diastolic blood pressure, and HDL were among the most important components for the diagnosis of MetS. Conclusions: Predicting MetS through non-invasive, easy and quick measurements, as well as knowing the relative importance of each component of MetS in each lifetime, allows the targeting of public health policies, with the concentration of resources for controlling and reducing the prevalence of MetS.