۱۰-year risk of cardiovascular disease and body mass index in association with the obesity paradox

BACKGROUND: Some recent studies reported an inverse association between obesity and risk of cardiovascular diseases (CVD), heart failure related mortality rate, outcomes of myocardial infarction (MI), and the consequences of cardiovascular events interventions; this inverse association was named the obesity paradox. The present study was conducted with the aim to determine whether the obesity paradox will be detectable when the ۱۰-year risk of CVD is estimated using CVD risk assessment tools.METHODS: The related data of ۲۹۱۰ subjects aged ۴۰-۷۴ years obtained in our cohort study that was carried out among ۶۱۴۰ subjects in Amol, in northern Iran, was included in this study. CVD risk assessment tools were used to estimate the ۱۰-year risk of CVD. Obesity was evaluated using ۴ indices, including waist circumference (WC), waist to height ratio (WHtR), waist to hip ratio (WHR), and body mass index (BMI). The receiver operating characteristic (ROC) curve analysis was utilized to evaluate the discriminatory power of obesity indices for ۱۰-year risk of CVD.RESULTS: Categorizing the participants to with and without obesity according to BMI showed that a significantly higher proportion of men with obesity had a ۱۰-year risk of CVD ≥ ۷.۵% and ≥ ۱۰% according to American College of Cardiology/American Heart Association (ACC/AHA) and the Framingham approaches, respectively. A higher proportion of women without obesity had a ۱۰-year risk of CVD ≥ ۷.۵% than women with obesity based on the ACC/AHA equation (۲۸.۵۴% vs. ۲۴.۱۵%; P = ۰.۰۷۰۷). BMI had a non-significant AUC (< ۰.۵) according to the the ACC/AHA equation.CONCLUSION: BMI showed a weak and non-significant inverse association with ۱۰-year risk of CVD estimated using pooled cohort equations of ACC/AHA in women. However, this result cannot directly provide enough evidence for the obesity paradox.

کلیدواژه ها:

Obesity ، Cardiovascular Diseases ، Risk Assessment ، Body Mass Index