Biostatistics Research - Statistics, Uncertainty, Probability, Modeling

Biostatistics Research Today is a free monthly online journal that collates and summarizes the latest research about Biostatistics, including details on statistics, uncertainty, probability, modeling.


Biostatistics Research Today

Home

View Latest Issue

Information About Biostatistics

Books on Biostatistics

Advertising in Research Today

View Other Research Today Publications



Body mass index and mortality in CKD.

Madero M, Sarnak MJ, Wang X, Sceppa CC, Greene T, Beck GJ, Kusek JW, Collins AJ, Levey AS, Menon V

Department of Medicine, Division of Nephrology, Tufts-New England Medical Center, Boston, MA 02111, USA.

BACKGROUND: Greater body mass index (BMI) is associated with worse survival in the general population, but appears to confer a survival advantage in patients with kidney failure treated by hemodialysis. Data are limited on the relationship of BMI with mortality in patients in the earlier stages of chronic kidney disease (CKD). STUDY DESIGN: Cohort study. SETTING & PARTICIPANTS: The Modification of Diet in Renal Disease (MDRD) Study examined the effects of dietary protein restriction and blood pressure control on progression of kidney disease. This analysis includes 1,759 subjects. PREDICTOR: BMI. OUTCOMES & MEASUREMENTS: Cox models were used to evaluate the relationship of quartiles of BMI with all-cause and cardiovascular disease (CVD) mortality. RESULTS: Mean GFR and BMI were 39 +/- 21 (SD) mL/min/1.73 m(2) and 27.1 +/- 4.7 kg/m(2), respectively. During a mean follow-up of 10 years, there were 453 deaths (26%), including 272 deaths (16%) from CVD. In unadjusted Cox models, quartiles 3 (hazard ratio [HR], 1.45; 95% confidence interval [CI], 1.11 to 1.90) and 4 (HR, 1.58; 95% CI, 1.21 to 2.06) were associated with increased risk of all-cause mortality compared with quartile 1. Adjustment for demographic, CVD, and kidney disease risk factors and randomization status attenuated this relationship for quartiles 3 (HR, 0.81; 95% CI, 0.60 to 1.09) and 4 (HR, 0.83; 95% CI, 0.61 to 1.20). In unadjusted Cox models, quartiles 3 (HR, 1.66; 95% CI, 1.17 to 2.36) and 4 (HR, 1.63; 95% CI, 1.15 to 2.33) were associated with increased risk of CVD mortality. Multivariable adjustment attenuated this relationship for quartiles 3 (HR, 0.92; 95% CI, 0.63 to 1.36) and 4 (HR, 0.85; 95% CI, 0.57 to 1.27). LIMITATIONS: Primary analyses were based on single measurement of BMI. Because the MDRD Study cohort included relatively young white subjects with predominantly nondiabetic CKD, results may not be generalizable to all patients with CKD. CONCLUSIONS: In this cohort of subjects with predominantly nondiabetic CKD, BMI does not appear to be an independent predictor of all-cause or CVD mortality.

Published 27 August 2007 in Am J Kidney Dis, 50(3): 404-11.
Full-text of this article is available online (may require subscription).

Place a permanent text-link or advertisement here for just US$15.

© 2005-2008 Biostatistics Research Today. All Rights Reserved.



Biostatistics Research Today Archive:

Volume 1 (2005)
  Issue 1 (September)
  Issue 2 (October)
  Issue 3 (November)
  Issue 4 (December)

Volume 2 (2006)
  Issue 1 (January)
  Issue 2 (February)
  Issue 3 (March)
  Issue 4 (April)
  Issue 5 (May)
  Issue 6 (June)
  Issue 7 (July)
  Issue 8 (August)
  Issue 9 (September)
  Issue 10 (October)
  Issue 11 (November)
  Issue 12 (December)

Volume 3 (2007)
  Issue 1 (January)
  Issue 2 (February)
  Issue 3 (March)
  Issue 4 (April)
  Issue 5 (May)
  Issue 6 (June)
  Issue 7 (July)
  Issue 8 (August)
  Issue 9 (September)
  Issue 10 (October)
  Issue 11 (November)
  Issue 12 (December)

Volume 4 (2008)
  Issue 1 (January)
  Issue 2 (February)
  Issue 3 (March)
  Issue 4 (April)
  Issue 5 (May)
  Issue 6 (June)
  Issue 7 (July)
  Issue 8 (August)



Biostatistics Books

Applied Survival Analysis: Regression Modeling of Time to Event Data (Wiley Series in Probability and Statistics)

Applied Survival Analysis: Regression Modeling of Time to Event Data (Wiley Series in Probability and Statistics)