For subjects for whom data on continuous clinical characteristics were incomplete (n = 17), we imputed the population mean. We computed baseline means and proportions of traditional cardiovascular risk factors (age, sex, current smoking, body mass index, hypertension, diabetes mellitus, family history of early myocardial infarction, total cholesterol level, and HDL-C level) for cases and controls. For CRP, we computed the median and interquartile range. Multivariate odds ratios (ORs) for myocardial infarction associated with the higher quartiles of the control distribution of CRP compared with the lowest quartile were computed by logistic regression analysis. Since age squared was a highly significant predictor of myocardial infarction, it was included in the analyses. We used 3 different models. Model 1 was adjusted for age, age squared, and sex. Since likely sources of inflammation include various components of cigarette smoke and adipose tissue,10,11 model 2 additionally included variables that indicated smoking behavior and body mass index. Model 3 was adjusted for all traditional cardiovascular risk factors. Subsequently, we computed a categorical variable ranging from 1 to 4 to indicate 4 situations for each traditional cardiovascular risk factor: (1) CRP level low (not in the highest quartile of the population distribution) and traditional risk factor absent, (2) CRP level high and risk factor absent, (3) CRP level low and risk factor present, and (4) CRP level high and risk factor present. Traditional cardiovascular risk factors that are measured on a continuous scale were considered to be present if they were in the highest quartile of the population distribution. Taking situation 1 (CRP level low and risk factor absent) as the reference, we then computed multivariate ORs for myocardial infarction associated with situations 2, 3, and 4 for each of the traditional cardiovascular risk factors.