BackgroundObesity represents an important health problem and its association with cardiovascular risk factors is well-known. The aim of this work was to assess the correlation between obesity and mortality (both, all-cause mortality and the combined variable of all-cause mortality plus the appearance of a non-fatal first cardiovascular event) in a general population sample from the south-east of Spain.Materials and MethodsThis prospective cohort study used stratified and randomized two-stage sampling. Obesity [body mass index (BMI) ≥30 kg/m2] as a predictive variable of mortality and cardiovascular events was assessed after controlling for age, sex, cardiovascular disease history, high blood pressure, diabetes mellitus, hypercholesterolemia, high-density lipoprotein/triglycerides ratio, total cholesterol and smoking with the Cox regression model.ResultsThe mean follow-up time of the 1,248 participants was 10.6 years. The incidence of all-cause mortality during this period was 97 deaths for every 10,000 person/years (95% CI: 80–113) and the incidence of all-cause mortality+cardiovascular morbidity was 143 cases for every 10,000 person/years (95% CI: 124–163). A BMI ≥35 kg/m2 yielded a hazard ratio for all-cause mortality of 1.94 (95% CI: 1.11–3.42) in comparison to non-obese subjects (BMI <30 kg/m2). For the combination of cardiovascular morbidity plus all-cause mortality, a BMI ≥35 kg/m2 had a hazard ratio of 1.84 (95% CI: 1.15–2.93) compared to non-obese subjects.ConclusionsA BMI ≥35 kg/m2 is an important predictor of both overall mortality and of the combination of cardiovascular morbidity plus all-cause mortality.

Background Clustering of cardiovascular risk factors (CVRFs) is extraordinarily common and is associated with an increased risk of cardiovascular disease (CVD). However, the particular impact of the sum of CVRFs on cardiovascular morbidity and mortality has not been sufficiently explored in Europe. Objective The aim of this study was to analyze the differences in survival-free probability of CVD in relation to the number of CVRFs in a Spanish population. Methods A prospective cohort study was conducted from 1992 to 2016 in a Spanish population that included 1144 subjects with no history of CVD (mean age, 46.7 years) drawn from the general population. We calculated the number of CVRFs for each subject (male sex, smoking, diabetes, hypertension, dyslipidemia, obesity, and left ventricular hypertrophy). Cardiovascular morbidity and mortality records were collected, and survival analysis was applied (competing risk models). Results There were 196 cardiovascular events (17.1%). The differences in total survival-free probability of cardiovascular morbidity and mortality of the different values of the sum of CVRFs were significant, increasing the risk of CVD (hazard ratio, 1.30; 95% confidence interval, 1.13–1.50) per each additional risk factor. Conclusion Differences in survival-free probability of CVD in relation to the number of CVRFs present were statistically significant. Further studies are needed to corroborate our results.

Background: Although studies exist comparing low-density lipoprotein cholesterol (LDL-C) and non-high-density lipoprotein cholesterol (HDL-C) in the development of cardiovascular disease (CVD), most have limitations in the mathematical models used to evaluate their prognostic power adjusted for the other risk factors (cardiovascular risk). Objective: The aim of this study was to compare LDL-C and non-HDL-C in patients with CVD to determine whether both parameters predict CVD similarly. Methods: A cohort of 1322 subjects drawn from the general population of a Spanish region was followed between 1992 and 2006. The outcome was time to CVD. Secondary variables were gender, age, hypertension, diabetes, personal history of CVD, current smoker, body mass index, LDL-C, and non-HDL-C. Two CVD prediction models were constructed with the secondary variables, with only the lipid parameter varying (non-HDL-C or LDL-C). In the construction of the models, the following were considered: multiple imputation, events per variable of 10 or more, and continuous predictors as powers. The validation was conducted by bootstrapping obtaining the distribution of the C statistic (discrimination) and the probabilities observed by smooth curves. These results were compared in both models using graphical and analytical testing. Results: There were a total of 137 CVD events. The models showed no differences in the distributions of the C statistic (discrimination, P = .536) or in the calibration plot. Conclusions: In our population, LDL-C and non-HDL-C were equivalent at predicting CVD. More studies using this methodology are needed to confirm these results.