BackgroundThe question about what risk function should be used in primary prevention remains unanswered. The Framingham Study proposed a new algorithm based on three key ideas: use of the four risk factors with the most weight (cholesterol, blood pressure, diabetes and smoking), prediction of overall cardiovascular diseases and incorporating the concept of vascular age. The objective of this study was to apply this new function in a cohort of the general non Anglo-Saxon population, with a 10-year follow-up to determine its validity.MethodsThe cohort was studied in 1992-94 and again in 2004-06. The sample comprised 959 randomly-selected persons, aged 30-74 years, who were representative of the population of Albacete, Spain. At the first examination cycle, needed data for the new function were collected and at the second examination, data on all events were recorded during the follow-up period. Discrimination was studied with ROC curves. Comparisons of prediction models and reality in tertiles (Hosmer-Lemeshow) were performed, and the individual survival functions were calculated.ResultsThe mean risks for women and men, respectively, were 11.3% and 19.7% and the areas under the ROC curve were 0.789 (95%CI, 0.716-0.863) and 0.780 (95%CI, 0.713-0.847) (P<0.001, both). Cardiovascular disease events occurred in the top risk tertiles. Of note were the negative predictive values in both sexes, and a good specificity in women (85.6%) and sensitivity in men (79.1%) when their risk for cardiovascular disease was high. This model overestimates the risk in older women and in middle-aged men. The cumulative probability of individual survival by tertiles was significant in both sexes (P<0.001).ConclusionsThe results support the proposal for “reclassification” of Framingham. This study, with a few exceptions, passed the test of discrimination and calibration in a random sample of the general population from southern Europe.

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.