Tobacco, hypertension, and vascular disease: risk factors for renal functional decline in an older population.

BACKGROUND: A decline in renal function with age has been noted in some but not all individuals. The purpose of this study was to identify risk factors associated with a clinically significant increase in serum creatinine (of at least 0.3 mg/dL) in an older nondiabetic population. METHODS: A retrospective case-control study was performed analyzing data obtained from 4142 nondiabetic participants of the Cardiovascular Health Study Cohort, all at least 65 years of age, who had two measurements of serum creatinine performed at least three years apart. Cases were identified as participants who developed an increase in serum creatinine of at least 0.3 mg/dL, with controls including participants who did not sustain such an increase. RESULTS: There was an increase in the serum creatinine of at least 0.3 mg/dL in 2.8% of the population. In a multivariate "best-fit" model adjusted for gender, weight, black race, baseline serum creatinine, and age, the following factors were associated with an increase in serum creatinine: number of cigarettes smoked per day, systolic blood pressure, and maximum internal carotid artery intimal thickness. CONCLUSIONS: These data suggest that three very preventable or treatable conditions-hypertension, smoking, and prevalent vascular disease, which are associated with large and small vessel disease-are highly associated with clinically important changes in renal function in an older population.

The effects of measurement error in response variables and tests of association of explanatory variables in change models.

Biomedical studies often measure variables with error. Examples in the literature include investigation of the association between the change in some outcome variable (blood pressure, cholesterol level etc.) and a set of explanatory variables (age, smoking status etc.). Typically, one fits linear regression models to investigate such associations. With the outcome variable measured with error, a problem occurs when we include the baseline value of the outcome variable as a covariate. In such instances, one can find a relationship between the observed change in the outcome and the explanatory variables even when there is no association between these variables and the true change in the outcome variable. We present a simple method of adjusting for a common measurement error bias that tends to be overlooked in the modelling of associations with change. Additional information (for example, replicates, instrumental variables) is needed to estimate the variance of the measurement error to perform this bias correction.