by standard methods (ADVIA 2400 Chemistry System, Siemens, Germany).
Quantitative data did not follow a normal distribution and are displayed as median (interquartile range) and compared using the Mann-Whitney test for two independent samples and by the Kruskal-Wallis test for three or more samples. Qualitative variables are displayed as percentages and compared by 2 or Fisher exact test when appropriate. Univariate Cox regression analysis was used to study the predictive power of the biomarkers studied along with these variables: age, sex, body-mass index, diabetes, smoking, hypertension, systolic blood pressure, previous heart failure, peripheral artery disease, cerebrovascular events, atrial fibrillation, ejection fraction40%, LDL (low-density lipoprotein) cholesterol, HDL (high-density lipoprotein) cholesterol, triglycerides, non-HDL cholesterol, glycemia, glomerular filtration rate (Chronic Kidney Disease Epidemiology Collaboration method); therapy with aspirin, clopidogrel, acenocumarol, statins, oral antidiabetic drugs, insulin, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, aldosterone receptor blockers, betablockers, and diuretics; type of last acute coronary event, number of diseased vessels, use of percutaneous revascularization, drug-eluting stents, coronary artery by-pass graft, and existence of complete revascularization at that event. Multivariate Cox regression analysis was performed with forward, stepwise selection, including the biomarkers studied and all the variables mentioned above, in order to 174568-92-4 assess which variables were independently associated with the primary and the secondary outcomes. Variables with p0.05 were entered into the model, and those with p0.10 were removed. Univariate and multivariate linear regression analyses were performed to assess the relationship of NT-proBNP plasma levels with age, glomerular filtration rate, triglycerides and the other biomarkers studied. Analyses were performed with SPSS 19.0 (SPSS Inc., New York).
Five patients were lost to follow-up, leaving 699 patients for analysis. Age was 60.0 (52.0, 72.0) years, and there were 75.3% men in the population studied. After 2.15.98 years of follow-up, 24 patients developed a new cancer. They were located in lungs (4), prostate (4), larynx (3), urinary bladder (3), colon (2), esophagus (2), breast (2), kidneys (1), and pancreas (1). There was also 1 liposarcoma and 1 amigdalar lymphoma. A histological analysis revealed 12 carcinomas, 9 adenocarcinomas, 1 lymphoma, 1 liposarcoma, and 1 urotelial cancer. Only one lung cancer was a small-cell carcinoma. Patients developing malignancies were older (68.5 [61.5, 75.8] vs 60.0 [52.0, 72.0] years; p = 0.011) and had higher NT-proBNP (302.0 [134.8, 919.8] vs 165.5 17764671 [87.4, 407.5] pg/ml; p = 0.040), and high-sensitivity C-reactive protein (3.27 [1.33, 5.94] vs 1.92 [0.83, 4.00] mg/L; p = 0.030), and lower triglyceride (92.5 [70.5, 132.8] vs [112.0 (82.0, 157.0] mg/dl; p = 0.044) plasma levels than those remaining stable, without differences in the other variables studied (Table 1 and Fig 2), including previous or present smoking. The percentage of present smokers at baseline was very low (6.6%). NT-proBNP levels were 301.0 pg/ml in the case of lymphoma, 357.5 (138.8, 803.5) pg/ml in cases of lung cancer, 705.5 (178.0, 1082.0) pg/ml in prostatic cancer, and 291.0 (85.4, 931.0) pg/ ml in all the other patients with malignancies, showing no differences among them (p = 0.720 
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