The relationship between body mass index (BMI) and patient survival in end-stage kidney disease is not well understood and has been the subject of much debate over recent years.
This study used a latent class joint modeling approach to identify latent groups that underpinned associations between patterns of change in BMI during hemodialysis and two competing events: transplant and death without transplant. We included all adult patients who initiated chronic hemodialysis treatment in Australia or New Zealand between 2005 and 2014.
There were 16,414 patients included in the analyses; 2,365 (14%) received a transplant, 5,639 (34%) died before transplant, and 8,410 (51%) were administratively censored. Our final model characterized patients based on five broad patterns of weight change (BMI trajectories): “late BMI decline” (about 2 years after commencing hemodialysis); “rapid BMI decline” (immediately after commencing hemodialysis); “stable and normal/overweight BMI”; “stable and morbidly obese BMI”; or “increasing BMI.” Mortality rates were highest among classes with declining BMI, and the timing of weight loss coincided with the timing of increases in mortality. Within the two stable BMI classes, death rates were slightly lower among the morbidly obese.
The findings from this descriptive analysis suggest a paradoxical association between obesity and better survival. However, they also suggest that the shape of the BMI trajectory is important, with stable BMI trajectories being beneficial. Future research should be aimed at understanding the causes of weight changes during dialysis, to determine whether there could be strategies to improve patient survival.
From the aDepartment of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
bVictorian Centre for Biostatistics (ViCBiostat), Melbourne, Australia
cClinical Epidemiology and Biostatistics Unit, Murdoch Children’s Research Institute, Melbourne, Australia
dMelbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
eDepartment of Nephrology, Monash Medical Centre, Melbourne, Australia
fDepartment of Medicine, Monash University, Melbourne, Australia
gANZDATA Registry, SA Health and Medical Research Institute, Adelaide, Australia
hDepartment of Medicine, University of Adelaide, Adelaide, Australia
iBiostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, United Kingdom
jDepartment of Environment, Land, Water and Planning, Arthur Rylah Institute for Environmental Research, Victoria, Australia.
Submitted April 9, 2018; accepted September 25, 2018.
S.L.B. is funded by an Australian National Health and Medical Research Council (NHMRC) Postgraduate Scholarship (ref: APP1093145), with additional support from an NHMRC Centre of Research Excellence grant (ref: 1035261) awarded to the Victorian Centre for Biostatistics (ViCBiostat). M.J.C. is partly funded by a UK Medical Research Council (MRC) New Investigator Research Grant (MR/P015433/1). The ANZDATA Registry is funded by the Australian Organ and Tissue Donation and Transplantation Authority, the New Zealand Ministry of Health and Kidney Health Australia.
The dataset used in this study is not publicly available. Data from the ANZDATA registry are available for research purposes, but an application must be submitted to, and approved by, the executive committee. Example computing code is included in the eAppendix (http://links.lww.com/EDE/B423).
Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com).
Correspondence: Samuel L. Brilleman, School of Public Health and Preventive Medicine, Monash University, 553 St. Kilda Road, Melbourne, VIC 3004, Australia. E-mail: email@example.com.