Prognosis of Systolic Pressure 130 to 139 According to Risk. A Prospective Cohort Study Between 1992 and 2019.

BACKGROUND: Guidelines recommend pharmacological treatment for systolic blood pressure (SBP) of 130 to 139 mm Hg in secondary prevention. However, uncertainty persists in primary prevention in low cardiovascular risk patients (CVR). METHODS: Cohort study representative of the general population of Albacete/Southeast Spain. We examined 1029 participants with untreated blood pressure and free of cardiovascular disease, followed-up during 1992 to 2019. Cox regression modeled the association of SBP with cardiovascular morbidity and mortality (outcome-1) and cardiovascular morbidity and all-cause mortality (outcome-2). RESULTS: Participants' mean age was 44.8 years (53.8%, women; 77.1% at low-CVR); 20.3% had SBP 120 to 129; 13.0% 130 to 139 at low-CVR and 3.4% at high-CVR; and 27.4% ≥140 mm Hg. After a 25.7-year median follow-up, 218 outcome-1 and 302 outcome-2 cases occurred. Unadjusted hazard ratios of outcome-1 for these increasing SBP categories (versus <120) were 2.72, 2.27, 11.54, and 7.52, respectively; and 2.69, 2.32, 10.55, and 7.34 for outcome-2 (all P<0.01). After adjustment for other risk factors, hazard ratio (95% CI) of outcome-1 were 1.49 (0.91-2.44), 1.65 (0.94-2.91, P=0.08), 1.36 (0.72-2.57), and 1.82 (1.15-2.88), respectively, and 1.39 (0.91-2.11), 1.69 (1.05-2.73), 1.09 (0.63-1.88), and 1.64 (1.11-2.41) for outcome-2. Compared with 130 to 139 at low-CVR, hazard ratio for 130 to 139 at high-CVR was 4.85 for outcome-1 (P<0.001) and 4.43 for outcome-2 (P<0.001). CONCLUSIONS: In this primary prevention population of relatively young average age, untreated SBP of 130 to 139 mm Hg at low-CVR had long-term prognostic value and might benefit from stricter SBP targets. High-CVR patients had nonsignificant higher risk (limited sample size) but 4-fold greater risk when compared with low-CVR. Overall, results indicate the importance of risk stratification, supporting risk-based decision-making.

Clusters of Cardiovascular Risk Factors and Their Impact on the 20-Year Cardiovascular Risk in a General Population.

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.

Comparison Between Non-High-Density Lipoprotein Cholesterol and Low-Density Lipoprotein Cholesterol to Estimate Cardiovascular Risk Using a Multivariate Model.

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.

Valoración del colesterol no HDL como predictor de episodios cardiovasculares no mortales en una cohorte prospectiva de origen poblacional / Evaluation of non-HDL cholesterol as a predictor of non-fatal cardiovascular events in a prospective population cohort

Introducción: El colesterol no transportado por las lipoproteínas de alta densidad (c-no-HDL) está adquiriendo relevancia en su participación en la valoración del riesgo cardiovascular y como diana terapéutica. El objetivo del presente estudio ha sido valorar la capacidad predictiva independiente, tanto del c-no-HDL como del colesterol de las lipoproteínas de baja densidad (cLDL), principal prioridad en las dislipidemias para reducir el riesgo cardiovascular (RCV), en la morbilidad de causa cardiovascular, en una muestra de origen poblacional. Métodos: El diseño del estudio corresponde a una cohorte prospectiva en la que han participado 1.186 individuos en el grupo c-no-HDL y 1.177 en el grupo cLDL, seguidos durante 10,7años (DE=2,2), los cuales no habían padecido ningún episodio cardiovascular (CV) previo. Las variables predictoras incluidas en el ajuste han sido: género, edad, hipertensión arterial, diabetes mellitus, estado de fumador y c-no-HDL en un grupo. En el otro grupo, formado por pacientes que presentaban niveles de triglicéridos ≤400mg/dl, se sustituyó el c-no-HDL por el cLDL. Se calcularon curvas de supervivencia (Kaplan-Meier) y se aplicaron dos modelos de regresión de Cox, uno por cada grupo.Resultados: El grupo c-no-HDL presentó un 6,2% de episodios CV no mortales durante el seguimiento, y el grupo cLDL, un 6,0%. Después del ajuste, por cada aumento de 30mg/dl de c-no-HDL, la incidencia de nuevos episodios CV no mortales aumentó un 31% (HR=1,31; IC95%: 1,06-1,61; p=0,018) y en el grupo del cLDL un 27% (HR=1,27; IC95%: 0,97-1,61; p=0,068). Conclusiones: Tras un seguimiento de 10,7años, el c-no-HDL se ha mostrado en nuestra población como un factor pronóstico de enfermedad CV no mortal, pero no el cLDL, aunque su HR se encuentra próxima a la significación estadística (AU)

Introduction: Non-HDL cholesterol (non-HDL-C) is becoming relevant both in its participation in cardiovascular risk assessment and as a therapeutic target. The objective of the present study was to assess the independent predictive capacity of both non-HDL-C and LDL-C (the main priority in dyslipidemias to reduce cardiovascular risk), in cardiovascular morbidity in a population-based sample. Methods: A prospective cohort study involving 1186 individuals in the non-HDL-C group and 1177 in the LDL-C group, followed for 10.7years (SD=2.2), who had not had any previous cardiovascular event. The predictor variables included in the adjustment were: gender, age, arterial hypertension, diabetes mellitus, smoker status and non-HDL-C in one group. In the other group, consisting of patients presenting TG levels of 400mg/dL, non-HDL-C was replaced by LDL-C. Survival curves (Kaplan-Meier) were calculated and two Cox regression models were applied, one for each group. Results: Non-HDL-C group presented 6.2% of non-fatal cardiovascular episodes during follow-up and the LDL-C group 6.0%. After adjustment, for each 30mg/dL increase in non-HDL-C, the incidence of new non-fatal cardiovascular events increased by 31% (HR=1.31, 95%CI: 1.06-1.61; P=.018) and in the LDL-C group by 27% (HR=1.27, 95%CI: 0.97-1.61, P=.068). Conclusions: After a follow-up of 10.7years, non-HDL-C has been shown in our population as a prognostic factor of non-fatal cardiovascular disease, but not LDL-C, although its HR is close to statistical significance (AU)

Evaluation of non-HDL cholesterol as a predictor of non-fatal cardiovascular events in a prospective population cohort. / Valoración del colesterol no HDL como predictor de episodios cardiovasculares no mortales en una cohorte prospectiva de origen poblacional.

INTRODUCTION: Non-HDL cholesterol (non-HDL-C) is becoming relevant both in its participation in cardiovascular risk assessment and as a therapeutic target. The objective of the present study was to assess the independent predictive capacity of both non-HDL-C and LDL-C (the main priority in dyslipidemias to reduce cardiovascular risk), in cardiovascular morbidity in a population-based sample. METHODS: A prospective cohort study involving 1186 individuals in the non-HDL-C group and 1177 in the LDL-C group, followed for 10.7years (SD=2.2), who had not had any previous cardiovascular event. The predictor variables included in the adjustment were: gender, age, arterial hypertension, diabetes mellitus, smoker status and non-HDL-C in one group. In the other group, consisting of patients presenting TG levels of 400mg/dL, non-HDL-C was replaced by LDL-C. Survival curves (Kaplan-Meier) were calculated and two Cox regression models were applied, one for each group. RESULTS: Non-HDL-C group presented 6.2% of non-fatal cardiovascular episodes during follow-up and the LDL-C group 6.0%. After adjustment, for each 30mg/dL increase in non-HDL-C, the incidence of new non-fatal cardiovascular events increased by 31% (HR=1.31, 95%CI: 1.06-1.61; P=.018) and in the LDL-C group by 27% (HR=1.27, 95%CI: 0.97-1.61, P=.068). CONCLUSIONS: After a follow-up of 10.7years, non-HDL-C has been shown in our population as a prognostic factor of non-fatal cardiovascular disease, but not LDL-C, although its HR is close to statistical significance.

Prognostic value of obesity on both overall mortality and cardiovascular disease in the general population.

BACKGROUND: Obesity 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 METHODS: This prospective cohort study used stratified and randomized two-stage sampling. Obesity [body mass index (BMI) ≥ 30 kg/m(2)] 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. RESULTS: The 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/m(2) 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/m(2)). For the combination of cardiovascular morbidity plus all-cause mortality, a BMI ≥ 35 kg/m(2) had a hazard ratio of 1.84 (95% CI: 1.15-2.93) compared to non-obese subjects. CONCLUSIONS: A BMI ≥ 35 kg/m(2) is an important predictor of both overall mortality and of the combination of cardiovascular morbidity plus all-cause mortality.

Framingham risk score for prediction of cardiovascular diseases: a population-based study from southern Europe.

BACKGROUND: The 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. METHODS: The 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. RESULTS: The 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). CONCLUSIONS: The 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.

[Relationship between inflammation marker and all-cause and cardiovascular mortality in a prospective cohort study]. / Relación entre el proceso inflamatorio y la mortalidad de origen cardiovascular y por todas las causas en un estudio de cohortes prospectivo de base poblacional.

INTRODUCTION: Inflammation is present in every stage of the atherosclerosis process, therefore, inflammation hallmarks such as the fibrinogen can be related to the complications in which it intervenes, mortality is one of them. The objective of this study is to assess the association of the fibrinogen with all-cause mortality in men from general population sample obtained by random sampling in the Spanish region of Albacete. METHODS: A total of 506men without cardiovascular events with 10.6years (SD=2.3) of follow-up, volunteered to participate in a prospective cohort study. The assessment of the fibrinogen as a predictor variable has been calculated after adjusting it by age, hypertension, diabetes mellitus, obesity, total cholesterol, HDL-cholesterol/triglycerides ratio, and smoking habit applying a Cox regression model. The adjustment has been made by adding the fibrinogen to the model, as a qualitative variable (<400 and ≥400mg/dl). RESULTS: The average age of the participants was 46.6years old (DE=16.8). After the adjustment, the hyperfibrinogenemia (≥400mg/dl) showed a hazard ratio (HR) for all-cause mortality of 1.85 (95%CI: 1.05-3.26) and for cardiovascular mortality HR=2.69 (95%CI: 1.09-6.63). CONCLUSIONS: In men without cardiovascular events of our study, fibrinogen was showed as an independent predictor of all-cause mortality and cardiovascular mortality.

[Association of insulin resistance to different anthropometric measures and cardiovascular risk factors in a non-diabetic population]. / Relación de la resistencia a la insulina con diferentes medidas antropométricas y factores de riesgo cardiovascular en una población no diabética.

BACKGROUND AND OBJECTIVE: Insulin resistance (IR) has been directly related to obesity, particularly central obesity, and to other cardiovascular risk factors (CVRFs). Direct IR quantification is difficult in clinical practice, and indirect methods such as HOMA (homeostasis model assessment) have therefore been developed. The aim of this study was to assess the association of IR, as measured by HOMA, with different anthropometric measures and some CVRFs. MATERIALS AND METHODS: A cross-sectional, observational study was carried out in a general population sample older than 18 years in the province of Albacete, Spain. Sample size was 678 subjects. Participants completed a survey and underwent physical examinations and laboratory tests. Obesity measures included body mass index, waist perimeter, and sagittal abdominal diameter. Data analysis was performed using SPSS 15.0 software. RESULTS: Mean values of obesity measures were higher in males as compared to females and increased with age. IR prevalence was 39.8%. All assessed anthropometric measures, decreased HDL (high density lipoprotein) cholesterol and increased non-HDL cholesterol were independently associated to the risk of IR. CONCLUSIONS: A clear association exists between different anthropometrical measures and IR in the general population. There is also an association between lipid profile cahnges and the risk of experiencing IR.