Classification of metabolic syndrome according to lipid alterations: analysis from the Mexican National Health Nutrition Survey 2006

Abstract

Background: There are 16 possible Metabolic Syndrome (MS) combinations out of 5 conditions (glucose intolerance, low levels of high density lipoprotein Cholesterol (HDL C), high triglycerides, high blood pressure abdominal obesity), when selecting those with at least three. Studies suggest that some combinations have different cardiovascular risk. However evaluation of all 16 combinations is complex difficult to interpret. The purpose of this study is to describe explore a classification of MS groups according to their lipid alterations. Methods: This is a cross sectional study with data from the Mexican National Health Nutrition Survey 2006. Subjects (n = 5,306) were evaluated for the presence of MS; four mutually exclusive MS groups were considered: mixed dyslipidemia (altered triglycerides HDL C), hypoalphalipoproteinemia: (normal triglycerides but low HDL C), hypertriglyceridemia (elevated triglycerides normal HDL C) without dyslipidemia (normal triglycerides HDL C). A multinomial logistic regression model was fitted in order to identify characteristics that were associated with the groups. Results: The most frequent MS group was hypoalphalipoproteinemia in females (51.3%) mixed dyslipidemia in males (43.5%). The most prevalent combination of MS for both genders was low HDL C + hypertension + abdominal obesity (20.4% females, 19.4% males). The hypoalphalipoproteinemia group was characteristic of women less developed areas of the country. The group without dyslipidemia was more frequent in the highest socioeconomic level less prevalent in the south of the country. The mixed dyslipidemia group was characteristic of men, the Mexico City region. Conclusions: A simple system to classify MS based on lipid alterations was useful to evaluate prevalences by diverse biologic sociodemographic characteristics. This system may allow prevention early detection strategies with emphasis on population specific components may serve as a guide for future studies on MS cardiovascular risk.