Produto de acumulação lipídica: acurácia para identificação de portadores da síndrome metabólica em adultos

Abstract

Metabolic syndrome (MS) has become a major public health problem in recent years both for its high magnitude and for its strong relationship with cardiovascular disease (CVD) and diabetes mellitus type II (DM2), two of the main causes of mortality worldwide. Therefore, it is necessary an early and accurate identification of individuals at high risk of developing MS in a simple and agile way. Accordingly, the Lipid Accumulation Product (LAP) is a novel biomarker of central lipid accumulation related to risk of metabolic syndrome (MS), diabetes and cardiovascular disease. The objective of this study was to analyze the LAP's ability to identify individuals with MS. This was a cross-sectional epidemiological study conducted with 211 employees of health campus at a public university of Belo Horizonte city Minas Gerais State Brazil. Questionnaire was applied to assess demographic, socioeconomic, anthropometric and lifestyle characteristics. MS was defined according to criteria proposed by NCEP-ATP III updated, IDF, AHA/NHBLI and harmonized NCEP-ATP III/IDF. The Receiver Operator Characteristic curve (ROC) was used to find the LAP cutoff that best identified individuals with MS diagnosed according to different definitions. The prevalence of MS was: 27.0% (95% CI: 21.1 to 33.5) NCEP-ATP III updated; 33.2% (95% CI: 26.9 to 40.0) IDF; 28.4% (95% CI: 22.5 to 35.0) AHA/NHBLI and 38.4% (95% CI: 32.0 to 45.2) harmonized NCEP-ATP III/IDF. The analysis of ROC curves showed that the LAP was significant discriminator for MS in all diagnostic criteria. The best point cutoffs to identify the MS were 41.10 NCEP-ATP III updated (86% sensitivity, 82.5% specificity); 37.85 IDF (81.4% sensitivity, 80.9% sensitivity); 39.39 AHA/NHBLI (88.3% sensitivity, 81.5% specificity) and 33.18 harmonized NCEP-ATP III/IDF (sensitivity 84% and specificity 80.8%). It is concluded that the LAP was associated with MS and had strong and reliable diagnostic accuracy for identifying individuals with MS.