RESUMO
INTRODUCTION: Metabolic Syndrome (MS) is a construct relating to a series of metabolic dysfunctions attributable to insulin resistance and obesity. Here, we estimate the incidence of MS according to their individual components using a Mexican open-population cohort. METHODS: We evaluated data of 6144 Mexicans amongst whom 3340 did not have MS either by IDF or ATP-III definitions using data from an open-population cohort. We estimated the incidence of MS and each of its traits after a median follow-up of 2.24 (IQR 2.05-2.58) years and evaluated risk factors for MS incidence and each of its traits. We also explored individuals without any MS trait to evaluate trait and MS incidence after follow-up. RESULTS: We observed a high incidence of MS-IDF (115.11 cases per 1000 person-years, 95% CI 107.76-122.47), followed by MS-ATP-III (75.77 cases per 1000 person-years, 95% CI). The MS traits with the highest incidence were low HDL-C and abdominal obesity, which was consistent for subjects without MS and those without any MS trait. When assessing predictors of MS incidence, obesity, insulin resistance, and increased apolipoprotein B levels predicted MS incidence. Weight loss >5% of body weight and physical activity were the main protective factors. Obesity was a main determinant for incident MS traits in our population, with weight loss being also a protective factor for most MS traits. CONCLUSION: We observed a high incidence of MS in apparently healthy Mexican adults. Low HDL-C and abdominal obesity were the most frequent incident MS traits, with obesity being the main determinant of its incidence.
RESUMO
BACKGROUND AND AIMS: A novel method to estimate low density lipoprotein cholesterol (LDL-C) has been proposed by Martin et al. This may permit a more accurate estimation of cardiovascular risk, however, external validation is needed. Here, the performance of LDL-C using this new method (LDL-N) is compared with LDL-C estimated with Friedewald equation (LDL-F) in familial combined hyperlipidemia (FCHL), a common primary dyslipidemia in which apolipoprotein B containing particle composition is abnormal and interferes with LDL-C estimation. METHODS: A total of 410 FCHL subjects were included. LDL-C was estimated with both the Friedewald equation (LDL-F) and the novel formula (LDL-N). Apolipoprotein B levels and non- HDL-C were recorded. The correlation and concordance between LDL-F and LDL-N and both Apolipoprotein B and non-HDL-C levels were calculated. Analysis stratifying for triglyceride tertiles and FCHL lipid phenotypes was also carried out. RESULTS: The correlations between LDL-N and Apo B and non-HDL-C were ρâ¯=â¯0.777 (95%CI 0.718-0.825) and ρâ¯=â¯0.735 (95%CI 0.648-0.816), respectively. The corresponding correlations for LDL-F were ρâ¯=â¯0.551(95%CI 0.454-0.637) and ρâ¯=â¯0.394 (95%CI 0.253-0.537), respectively. In mixed dyslipidemia or isolated hypertriglyceridemia, these correlations were significantly better using LDL-N. With respect to concordance, LDL-N performed significantly better than LDL-F when considering apoB <90â¯mg/dL (κLDL-Nâ¯=â¯0.495 vs. κLDL-Fâ¯=â¯0.165) and non-HDL-C <130 (κLDL-Nâ¯=â¯0.724 vs. κLDL-Fâ¯=â¯0.253). CONCLUSIONS: In FCHL, LDL-C estimation using Martin's formula showed greater correlation and concordance with non-HDL-C and Apo B compared with the Friedewald equation.