z-logo
open-access-imgOpen Access
Body fat mass and lean mass as predictors of survival in hemodialysis patients
Author(s) -
Ryusuke Kakiya,
Tetsuo Shoji,
Yoshihiro Tsujimoto,
N Tatsumi,
Sawako Hatsuda,
K. Shinohara,
Emi Kimoto,
Hideki Tahara,
Hidenori Koyama,
M. Emoto,
E. Ishimura,
T. Miki,
Tsutomu Tabata,
Yoshiki Nishizawà
Publication year - 2006
Publication title -
kidney international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.499
H-Index - 276
eISSN - 1523-1755
pISSN - 0085-2538
DOI - 10.1038/sj.ki.5000331
Subject(s) - medicine , body mass index , lean body mass , hemodialysis , population , dialysis , proportional hazards model , cohort , confounding , univariate analysis , mass index , multivariate analysis , body weight , environmental health
A higher body mass index (BMI) is a predictor of better survival in hemodialysis patients, although the relative importance of body fat and lean mass has not been examined in the dialysis population. We performed an observational cohort study in 808 patients with end-stage renal disease on maintenance hemodialysis. At baseline, fat mass was measured by dual-energy X-ray absorptiometry and expressed as fat mass index (FMI; kg/m2). Lean mass index (LMI) was defined as BMI minus FMI. During the mean follow-up period of 53 months, 147 deaths, including 62 cardiovascular (CV) and 85 non-CV fatal events, were recorded. In univariate analysis, LMI was not significantly associated with CV or non-CV death, whereas a higher FMI was predictive of lower risk for non-CV death. Analyses with multivariate Cox models, which took other confounding variables as covariates, indicated the independent associations between a higher LMI and a lower risk of CV death, as well as between a higher FMI and a lower risk of non-CV death. These results indicate that increased fat mass and lean mass were both conditions associated with better outcomes in the dialysis population.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom