Interaction between cardiovascular risk factors and body mass index and 10-year incidence of cardiovascular disease, cancer death, and overall mortality.

The effect of above-normal body mass index (BMI) on health outcomes is controversial because it is difficult to distinguish from the effect due to BMI-associated cardiovascular risk factors. The objective was to analyze the impact on 10-year incidence of cardiovascular disease, cancer deaths and overall mortality of the interaction between cardiovascular risk factors and BMI. We conducted a pooled analysis of individual data from 12 Spanish population cohorts with 10-year follow-up. Participants had no previous history of cardiovascular diseases and were 35-79years old at basal examination. Body mass index was measured at baseline being the outcome measures ten-year cardiovascular disease, cancer and overall mortality. Multivariable analyses were adjusted for potential confounders, considering the significant interactions with cardiovascular risk factors. We included 54,446 individuals (46.5% with overweight and 27.8% with obesity). After considering the significant interactions, the 10-year risk of cardiovascular disease was significantly increased in women with overweight and obesity [Hazard Ratio=2.34 (95% confidence interval: 1.19-4.61) and 5.65 (1.54-20.73), respectively]. Overweight and obesity significantly increased the risk of cancer death in women [3.98 (1.53-10.37) and 11.61 (1.93-69.72)]. Finally, obese men had an increased risk of cancer death and overall mortality [1.62 (1.03-2.54) and 1.34 (1.01-1.76), respectively]. In conclusion, overweight and obesity significantly increased the risk of cancer death and of fatal and non-fatal cardiovascular disease in women; whereas obese men had a significantly higher risk of death for all causes and for cancer. Cardiovascular risk factors may act as effect modifiers in these associations.

Risk of Cause-Specific Death in Individuals With Diabetes: A Competing Risks Analysis.

OBJECTIVE: Diabetes is a common cause of shortened life expectancy. We aimed to assess the association between diabetes and cause-specific death. RESEARCH DESIGN AND METHODS: We used the pooled analysis of individual data from 12 Spanish population cohorts with 10-year follow-up. Participants had no previous history of cardiovascular diseases and were 35-79 years old. Diabetes status was self-reported or defined as glycemia >125 mg/dL at baseline. Vital status and causes of death were ascertained by medical records review and linkage with the official death registry. The hazard ratios and cumulative mortality function were assessed with two approaches, with and without competing risks: proportional subdistribution hazard (PSH) and cause-specific hazard (CSH), respectively. Multivariate analyses were fitted for cardiovascular, cancer, and noncardiovascular noncancer deaths. RESULTS: We included 55,292 individuals (15.6% with diabetes and overall mortality of 9.1%). The adjusted hazard ratios showed that diabetes increased mortality risk: 1) cardiovascular death, CSH = 2.03 (95% CI 1.63-2.52) and PSH = 1.99 (1.60-2.49) in men; and CSH = 2.28 (1.75-2.97) and PSH = 2.23 (1.70-2.91) in women; 2) cancer death, CSH = 1.37 (1.13-1.67) and PSH = 1.35 (1.10-1.65) in men; and CSH = 1.68 (1.29-2.20) and PSH = 1.66 (1.25-2.19) in women; and 3) noncardiovascular noncancer death, CSH = 1.53 (1.23-1.91) and PSH = 1.50 (1.20-1.89) in men; and CSH = 1.89 (1.43-2.48) and PSH = 1.84 (1.39-2.45) in women. In all instances, the cumulative mortality function was significantly higher in individuals with diabetes. CONCLUSIONS: Diabetes is associated with premature death from cardiovascular disease, cancer, and noncardiovascular noncancer causes. The use of CSH and PSH provides a comprehensive view of mortality dynamics in a population with diabetes.

Derivation and validation of a set of 10-year cardiovascular risk predictive functions in Spain: the FRESCO Study.

OBJECTIVE: To derive and validate a set of functions to predict coronary heart disease (CHD) and stroke, and validate the Framingham-REGICOR function. METHOD: Pooled analysis of 11 population-based Spanish cohorts (1992-2005) with 50,408 eligible participants. Baseline smoking, diabetes, systolic blood pressure (SBP), lipid profile, and body mass index were recorded. A ten-year follow-up included re-examinations/telephone contact and cross-linkage with mortality registries. For each sex, two models were fitted for CHD, stroke, and both end-points combined: model A was adjusted for age, smoking, and body mass index and model B for age, smoking, diabetes, SBP, total and HDL cholesterol, and for hypertension treatment by SBP, and age by smoking and by SBP interactions. RESULTS: The 9.3-year median follow-up accumulated 2973 cardiovascular events. The C-statistic improved from model A to model B for CHD (0.66 to 0.71 for men; 0.70 to 0.74 for women) and the combined CHD-stroke end-points (0.68 to 0.71; 0.72 to 0.75, respectively), but not for stroke alone. Framingham-REGICOR had similar C-statistics but overestimated CHD risk. CONCLUSIONS: The new functions accurately estimate 10-year stroke and CHD risk in the adult population of a typical southern European country. The Framingham-REGICOR function provided similar CHD prediction but overestimated risk.

Metabolic syndrome as a cardiovascular disease risk factor: patients evaluated in primary care.

To estimate the prevalence of metabolic syndrome (MS) in a population receiving attention in primary care centers (PCC) we selected a random cohort of ostensibly normal subjects from the registers of 5 basic-health area (BHA) PCC. Diagnosis of MS was with the WHO, NCEP and IDF criteria. Variables recorded were: socio-demographic data, CVD risk factors including lipids, obesity, diabetes, blood pressure and smoking habit and a glucose tolerance test outcome. Of the 720 individuals selected (age 60.3 +/- 11.5 years), 431 were female, 352 hypertensive, 142 diabetic, 233 pre-diabetic, 285 obese, 209 dyslipemic and 106 smokers. CVD risk according to the Framingham and REGICOR calculation was 13.8 +/- 10% and 8.8 +/- 9.8%, respectively. Using the WHO, NCEP and IDF criteria, MS was diagnosed in 166, 210 and 252 subjects, respectively and the relative risk of CVD complications in MS subjects was 2.56. Logistic regression analysis indicated that the MS components (WHO set), the MS components (IDF set) and the female gender had an increased odds ratio for CVD of 3.48 (95CI%: 2.26-5.37), 2.28 (95%CI: 1.84-4.90) and 2.26 (95%CI: 1.48-3.47), respectively. We conclude that MS and concomitant CVD risk is high in ostensibly normal population attending primary care clinics, and this would necessarily impinge on resource allocation in primary care.