RESEARCH ARTICLE

The Association between Body Mass Index

OPEN ACCESS and Mortality in Incident Dialysis Patients
Citation: Badve SV, Paul SK, Klein K, Clayton PA,
Hawley CM, et al. (2014) The Association between Sunil V. Badve1., Sanjoy K. Paul2*., Kerenaftali Klein2, Philip A. Clayton3, Carmel
Body Mass Index and Mortality in Incident Dialysis
Patients. PLoS ONE 9(12): e114897. doi:10.1371/ M. Hawley1, Fiona G. Brown4, Neil Boudville5, Kevan R. Polkinghorne4, Stephen P.
journal.pone.0114897 McDonald6, David W. Johnson1
Editor: Rachel A. Nugent, University of 1. Department of Nephrology, Princess Alexandra Hospital, Brisbane, Queensland, Australia, 2. Clinical Trials
Washington, United States of America & Biostatistics Unit, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia, 3.
Department of Renal Medicine, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia, 4.
Received: July 29, 2014
Department of Nephrology & Medicine, Monash Medical Centre and Monash University, Melbourne, Victoria,
Accepted: November 15, 2014 Australia, 5. School of Medicine and Pharmacology, University of Western Australia, Perth, Western Australia,
Australia, 6. Australia and New Zealand Dialysis and Transplant Registry, University of Adelaide at Central
Published: December 16, 2014 Northern Adelaide Renal & Transplant Services, Adelaide, South Australia, Australia
Copyright: ß 2014 Badve et al. This is an open-
*Sanjoy.paul@qimrberghofer.edu.au
access article distributed under the terms of the
Creative Commons Attribution License, which . These authors contributed equally to this work.
permits unrestricted use, distribution, and repro-
duction in any medium, provided the original author
and source are credited.

Data Availability: The authors confirm that all data

underlying the findings are fully available without Abstract
restriction. The primary dataset for this manuscript
was generated and made available to the authors Objectives: To study the body mass index (BMI) trajectory in patients with incident
by the Australia and New Zealand Dialysis and
Transplant (ANZDATA) Registry, Adelaide, end-stage kidney disease and its association with all-cause mortality.
Australia. The ANZDATA Data Use Agreement Methods: This longitudinal cohort study included 17022 adult patients commencing
between the ANZDATA Registry and the authors
does not allow the authors to make the data hemodialysis [HD] (n510860) or peritoneal dialysis [PD] (n56162) between 2001
publicly available. The authors confirm that all data
underlying the findings can be obtained without and 2008 and had $6-month follow-up and $2 weight measurements, using the
restriction from the ANZDATA Registry. The Australia and New Zealand Dialysis and Transplant Registry data. The association
interested researchers are advised to contact the
ANZDATA Registry independently (email address of time-varying BMI with all-cause mortality was explored using multivariate Cox
requests@anzdata.org.au). regression models.
Funding: The authors have no funding or support Results: The median follow-up was 2.3 years. There was a non-linear change in
to report.
the mean BMI (kg/m2) over time, with an initial decrease from 27.6 (95% confidence
Competing Interests: Drs. Badve, Klein and
Clayton have reported that they have no potential interval [CI]: 27.5, 27.7) to 26.7 (95% CI: 26.6, 26.9) at 3-month, followed by
conflicts of interest relevant to the contents of this
increments to 27.1 (95% CI: 27, 27.2) at 1-year and 27.2 (95% CI: 26.8, 27.1) at 3-
paper to disclose. Dr. Paul has acted as a
consultant and speaker for Novartis and Amylin year, and a gradual decrease subsequently. The BMI trajectory was significantly
Pharmaceuticals, LLC. He has received grants in
support of investigator and investigator initiated lower in HD patients who died than those who survived, although this pattern was
clinical studies from Merck, Novo Nordisk and not observed in PD patients. Compared to the reference time-varying BMI category
Pfizer, Amylin Pharmaceuticals and Hospira. Dr.
Hawley has received consultancy fees from of 25.1–28 kg/m2, the mortality risks of both HD and PD patients were greater in all
Amgen, Shire, Genzyme and Fresenius; research
categories of time-varying BMI ,25 kg/m2. The mortality risks were significantly
grant from Baxter and payment for lectures from
Amgen and Shire. Dr. Brown has served as a lower in all categories of time-varying BMI .28.1 kg/m2 among HD patients, but
member of the Fresenius ANZ Clinical Advisory
Board and has received payment for lectures from only in the category 28.1–31 kg/m2 among PD patients.
Baxter ANZ PD Academy. Dr. Boudville has
received research grant and payment for lectures
from Roche; and travel grants from Roche, Amgen
and Janssen Cilag. Dr. Polkinghorne has received

PLOS ONE | DOI:10.1371/journal.pone.0114897 December 16, 2014 1 / 13

 BMI Trajectory and Mortality in Incident Dialysis Patients

payment for lectures from Amgen Australia and

Janssen-Cilag Pty Ltd; and travel grants from Conclusions: BMI changed over time in a non-linear fashion in incident dialysis
Amgen Australia. Dr. McDonald has received patients. Time-varying measures of BMI were significantly associated with mortality
consultancy fees from Amgen and Shire; research
grants from Amgen; and travel grants from Amgen. risk in both HD and PD patients.
Dr. Johnson has received consultancy fees from
Baxter, Shire and Amgen; research grants from
Baxter, Fresenius, Amgen, Janssen Cilag and
Roche; payment for lectures from Baxter,
Fresenius, Amgen, Shire and Boehringer-
Ingelheim; payment for development of educational
presentations from Baxter, Shire, Boehringer- Introduction
Ingelheim and Lilley; and travel grants from Baxter,
Fresenius, Shire, Amgen and Roche. This does not Nearly two-thirds of the adult population in developed countries is either
alter the authors’ adherence to PLOS ONE policies
on sharing data and materials.
overweight or obese, defined as body-mass index (BMI) $25 kg/m2 [1, 2]. High
BMI is a risk factor for end-stage kidney disease (ESKD) [3]. Patients with ESKD
receiving dialysis are no exception to the obesity epidemic [4]. In the USA, the
mean BMI of incident ESKD patients has increased from 25.7 kg/m2 in 1995 to
27.5 kg/m2 in 2002 [4], and to 28.9 kg/m2 in 2007–2009 [5]. In the general
population, obesity is associated with increased risks of all-cause and
cardiovascular mortality [6, 7]. Therefore, weight loss therapy is recommended for
people with BMI $30 kg/m2 or BMI between 25 and 29.9 kg/m2 with $2 absolute
cardiovascular risk factors [8, 9]. However, there are currently no such treatment
guidelines for ESKD patients receiving chronic dialysis for two main reasons.
First, malnutrition is extremely common in dialysis patients and is a major risk
factor of mortality [10]. Second, high BMI is associated with a survival advantage
in dialysis patients [11]. The paradox of better survival with higher BMI has been
well described in ESKD patients receiving hemodialysis (HD) [11–15]. The
association between obesity and survival is less clear in ESKD patients receiving
peritoneal dialysis (PD), with studies variably showing better [16, 17], comparable
[11, 18, 19], or worse [14, 20] survival. Due to this conflicting evidence, except for
severely obese patients who are waiting for kidney transplant surgery, it is largely
unclear whether to recommend weight loss therapy to dialysis patients who are
either overweight or obese. The studies evaluating BMI and mortality are based on
a BMI value at baseline or at the commencement of dialysis therapy. Studies using
follow-up measures of BMI (time-varying) are sparse in both HD and PD patients
[12, 21–27]. Therefore, we examined the BMI trajectory and its association with
all-cause mortality in incident ESKD patients on both HD and PD using data
from the Australia and New Zealand Dialysis and Transplant (ANZDATA)
Registry.

Methods
This longitudinal cohort study was undertaken in accordance with the STROBE
(Strengthening the Reporting of Observational Studies in Epidemiology)
Statement [28]. The study included 20524 patients from the ANZDATA Registry
who commenced chronic dialysis therapy between January 1, 2001 and December
31, 2008, comprising all incident patients with ESKD in Australia and New
Zealand during that time. Patients were excluded if they were younger than 18

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 BMI Trajectory and Mortality in Incident Dialysis Patients

years at the time of renal replacement therapy (RRT) commencement or did not
complete at least 6 months on dialysis since commencement of RRT (due to death
or recovery of kidney function or transplantation or follow-up,6 months) or
underwent pre-emptive kidney transplantation. Patients with ,2 available follow-
up measurements of weight or typographical error (for example, 9 kg instead of
90 kg) were also excluded. Final analysis included 17022 patients (Figure S1 in S1
Information).
The ANZDATA Registry collects information on all patients receiving RRT
from all renal units throughout Australia and New Zealand, and has been
extensively used for clinical epidemiological studies [29, 30]. The data collection is
conducted in accordance with the Australian Commonwealth Privacy Act and
associated state legislation governing health data collection; and individual patient
consent is not required for the registry data. The anonymity of patient
information is maintained by the coding of data during compilation; only
anonymized data is released by the registry to the researchers. The ANZDATA
Registry has approved this study and the submission of this manuscript. The data
were collected every 6 months until March 31, 2004 (survey dates 31st March and
30th September) and annually since December 31, 2004 (survey date 31st
December). The structure of the ANZDATA Registry, the methods of data
collection and validation are described in detail on its website (http://www.
anzdata.org.au). In summary, the collection is complete from the first RRT
procedure in Australia and New Zealand in 1963 and includes all patients from all
renal units in both countries. The data collected included demographic details,
underlying cause of ESKD, a limited range of comorbidities (the presence of
coronary artery disease, peripheral vascular disease, cerebrovascular disease,
chronic lung disease, diabetes mellitus, hypertension, and cigarette smoking), late
nephrologist referral (,3 months before dialysis start), serum creatinine at
dialysis start, the type and dates of each dialysis episode, details about kidney
transplantation, and measurements of height and dry weight.
RRT modality was classified into HD (including hospital-, satellite-, and home-
based HD), PD (including continuous ambulatory PD and automated PD), and
renal transplantation. Dialysis modality was assigned as modality used at 90 days
after commencement of RRT. BMI was calculated from the quotient of the weight
and the square of the height at the commencement of RRT. The values of BMI
were divided into 9 categories (#19, 19.1–22, 22.1–25, 25.1–28, 28.1–31, 31.1–34,
34.1–37, 37.1–40 and .40.1 kg/m2).

Statistical analysis
The basic statistics on study variables were expressed as number (%), mean (SD)
or median (IQR), as appropriate. The distributions of categorical and continuous
study variables by two dialysis modalities were compared using x2 test and non-
parametric Mann-Whitney U test, respectively.
The BMI trajectory was constructed by plotting the mean (95% CI) of baseline
and follow-up measures of BMI against time points. For the BMI trajectory

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 BMI Trajectory and Mortality in Incident Dialysis Patients

analysis, data at the following time points during follow-up were utilized: baseline;
3-, 6-, 9-months; 1-, 1.5-, 2-, 2.5-, 3-, 4-, 5-, 6-, 7- and 8-years. These time periods
were rounded to the nearest survey date of the ANZDATA Registry data
collection. Complete case analysis with follow-up measures of BMI was
conducted, and no missing data imputation was employed. Generalized
Estimating Equation based multivariate regression models were used to compare
the BMI trajectories. Multivariate Cox regression models were used to evaluate
risk factors of all-cause mortality. To evaluate the association between follow-up
BMI measures and all-cause mortality, BMI was used as a time-varying covariate.
Other covariates used in the multivariate regression models were: gender, age,
dialysis modality, ethnicity, cause of ESKD, smoking status, diabetes mellitus,
chronic lung disease, coronary artery disease, cerebrovascular disease, peripheral
vascular disease, and late referral. Due to estimation convergence problems, the
time-varying dialysis modality could not be used, and a fixed dialysis modality
variable (modality used at 90 days after commencement of RRT) was used as a
covariate instead. Robust standard errors of the hazard ratios (HR) were estimated
after adjusting for the effects of study centres as random effects. The
‘‘proportional hazard’’ assumption was tested using standard likelihood ratio test.
Survival time was calculated from the date of commencement of RRT to the date
of event or censoring. Survival analyses were censored for kidney transplantation,
loss of follow-up, recovery of renal function or December 31, 2008.

Results
Patient characteristics
Baseline patient characteristics are described in Table 1. Compared to those on
PD, HD patients were more likely to be males, Caucasians, Aboriginals or Torres
Strait Islanders, referred late to nephrologist before commencement of dialysis
and to have comorbid conditions, including diabetes mellitus, chronic lung
disease and coronary artery disease (Table 1). PD patients had lower average
weight and BMI values, and were less likely to have BMI .34 kg/m2 compared to
HD patients. Distributions of BMI groups according to the ethnicity and dialysis
modality are described in Table S1 in S1 Information.

BMI trajectory in incident dialysis patients

The median follow-up time was 2.3 years (range: 6 months to 8 years) and the
median number of weight measurements per patient was 4 with a range from 2 to
13 (See Table S2 in S1 Information). BMI changed over time in a non-linear
fashion (Fig. 1). Mean BMI initially decreased significantly from 27.6 (95%
confidence interval [CI]: 27.5, 27.7) kg/m2 at baseline to 26.7 (95% CI: 26.6, 26.9)
kg/m2 at 3 months, then increased to 27.1 (95% CI: 27, 27.2) kg/m2 at 1 year. It
then remained stable over the next 2 years with a mean BMI of 27.2 (95% CI: 26.8,
27.1) kg/m2 at 3 years. Thereafter, mean BMI decreased gradually. The average

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 BMI Trajectory and Mortality in Incident Dialysis Patients

Table 1. Baseline patient characteristics.

Characteristic All Hemodialysis Peritoneal dialysis

n* 17,022 10,860 (63.8) 6,162 (36.2)
Men* 10,249 (60.2) 6,793 (62.6) 3,456 (56.1)
Age (years)# 60.4 (15) 60.6 (15.1) 60.3 (14.7)
Age categories*
18 to 45 years 2,820 (16.6) 1,782 (16.4) 1,038 (16.9)
45 to 65 years 6,791 (39.9) 4,330 (39.9) 2,461 (39.9)
.65 years 7,411 (43.5) 4,748 (43.7) 2,663 (43.2)
Ethnicity*
Caucasians 12,324 (72.4) 8,030 (73.9) 4,294 (69.7)
Aboriginal or Torres Strait Islander 1,397 (8.2) 1,046 (9.6) 351 (5.7)
Maori or Pacific Islander 1,840 (10.8) 1,063 (9.8) 777 (12.6)
Asian 785 (4.6) 370 (3.4) 415 (6.7)
Other 676 (4) 351 (3.2) 325 (5.3)
Primary cause of end-stage kidney disease*
Chronic glomerulopathy 4,155 (24.4) 2,591 (23.9) 1,564 (25.4)
Diabetic nephropathy 5,729 (33.7) 3,627 (33.4) 2,102 (34.1)
Renovascular disease 2,966 (17.4) 1,862 (17.2) 1,104 (17.9)
Polycystic disease 1,033 (6.1) 688 (6.3) 345 (5.6)
Reflux or obstructive nephropathy 827 (4.9) 542 (5) 285 (4.6)
Other 1,256 (7.4) 871 (8) 385 (6.3)
Unknown 1,056 (6.2) 679 (6.3) 377 (6.1)
Smoking status*{
Non-smoker 7,688 (45.2) 4,807 (44.3) 2,881 (46.8)
Former smoker 6,965 (40.9) 4,533 (41.8) 2,432 (39.5)
Current smoker 2,365 (13.9) 1,517 (14) 8,48 (13.8)
#
Weight (kg) 77.4 (19.9) 79.5 (21.4) 73.6 (16.5)
Body mass index (kg/m2)# 27.6 (6.4) 28.2 (6.9) 26.6 (5.3)
Body mass index category*
#19 kg/m2 750 (4.4) 480 (4.4) 270 (4.4)
19–22 kg/m2 2,182 (12.8) 1,295 (11.9) 887 (14.4)
22–25 kg/m2 3,563 (20.9) 2,161 (19.9) 1,402 (22.8)
25–28 kg/m2 3,615 (21.2) 2,180 (20.1) 1,435 (23.3)
28–31 kg/m2 2,711 (15.9) 1,707 (15.7) 1,004 (16.3)
2
31–34 kg/m 1,795 (10.6) 1,179 (10.9) 616 (10)
34–37 kg/m2 1,008 (5.9) 712 (6.6) 296 (4.8)
37–40 kg/m2 615 (3.6) 458 4.2) 157 (2.6)
2
.40 kg/m 783 (4.6) 688 (6.3) 95 (1.5)
Comorbid conditions*
Diabetes mellitus 7,382 (43.4) 4,773 (44) 2,609 (42.3)
Chronic lung disease 2,679 (15.7) 1,775 (16.3) 904 (14.7)
Coronary artery disease 6,807 (40) 4,470 (41.2) 2,337 (37.9)
Cerebrovascular disease 2,492 (14.6) 1,583 (14.6) 909 (14.8)

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 BMI Trajectory and Mortality in Incident Dialysis Patients

Table 1. Cont.

Characteristic All Hemodialysis Peritoneal dialysis

Peripheral vascular disease 4,278 (25.1) 2,762 (25.4) 1,516 (24.6)
Late referral{{ 4,057 (23.8) 2,757 (25.4) 1,300 (21.1)

*n (%).
#
mean (standard deviation).
{
Some missing data.
{
Late referral defined as referral to nephrologist .3 months before dialysis start.

doi:10.1371/journal.pone.0114897.t001

baseline BMI in the HD group was significantly higher by 1.6 kg/m2 (95% CI: 1.4,
1.8, P,0.001) than the PD group, and the BMI trajectory in the HD group
remained at a significantly higher level than the PD group throughout the follow-
up period (Fig. 1). Despite a significant difference in the baseline BMI, the
patterns of non-linear change in BMI were similar in both groups. In PD patients,
the average BMI at 1 year of follow-up was 0.4 kg/m2 (95% CI: 0.2, 0.6) higher
than the baseline level. However, in HD patients, follow-up BMI values were
consistently below the baseline level.

Baseline BMI and all-cause mortality

A total of 5,971 (35%) patients died during follow-up (3,671[34%] in the HD
group vs. 2,300[37%] in the PD group) with a death rate of 12.7 (95% CI: 12.4,
13.1) per 100 person-years. The death rates per 100 person-years in the HD and

Fig. 1. Body Mass Index Trajectories in All Incident Dialysis Patients and According to Dialysis
Modality.

doi:10.1371/journal.pone.0114897.g001

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 BMI Trajectory and Mortality in Incident Dialysis Patients

Fig. 2. Body Mass Index Trajectory and Mortality in Incident Dialysis Patients: A, Hemodialysis; B,
Peritoneal dialysis.

doi:10.1371/journal.pone.0114897.g002

PD groups were 12.2 (95% CI: 11.8, 12.6) and 13.7 (95% CI: 13.1, 14.3),
respectively (See Table S3 in S1 Information for causes of death).
The baseline BMI was higher by 0.6 kg/m2 (95% CI: 0.4, 0.8, P,0.001) in
patients who remained alive than those who died. At baseline, the surviving
cohort had higher BMI by 1 kg/m2 (P,0.01) in HD patients (Fig. 2, panel A).
However, the baseline BMI was not different by mortality status in the PD group
(Fig. 2, panel B). Compared to the reference BMI category of 25–28 kg/m2 at
baseline, all categories of BMI ,25 kg/m2 were associated with increased
mortality risk for all dialysis patients (Table 2). However, the risk estimates were
not consistent between the HD and PD groups. Higher baseline BMI was
associated with significantly lower mortality risk for HD patients with BMI
between 28 and 37 kg/m2. The mortality risk was significantly higher in the PD
group with BMI 34–37 kg/m2.

Time-varying BMI and all-cause mortality

The BMI trajectory was significantly higher on average by 1.22 kg/m2 (95% CI:
1.2, 1.3) in those who remained alive compared to those who died. The BMI
trajectories in HD and PD patients who survived were higher on average by
1.5 kg/m2 (95% CI: 1.4, 1.6) and 0.5 kg/m2 (95% CI: 0, 0.6), respectively, than
those who died. The adjusted risks of mortality associated with different categories
of time-varying BMI for all patients and separately for dialysis modality are
presented in Table 3. Compared to the reference time-varying BMI category of
25–28 kg/m2, there was significantly increased mortality risk in all categories with
time-varying BMI,25 kg/m2. There was also a decreased risk of mortality for all
categories of time-varying BMI .28 kg/m2, except for 37–40 kg/m2. When
analyzed separately for dialysis modalities, increased mortality risks with all
categories of time-varying BMI ,25 kg/m2 were evident in both HD and PD
groups. Decreased mortality risk was evident with all categories of time-varying

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 BMI Trajectory and Mortality in Incident Dialysis Patients

Table 2. The association between baseline BMI and all-cause mortality.

All patients HD patients PD patients

2
BMI (kg/m ) HR 95%CI P HR 95%CI P HR 95%CI P
#19 1.55 1.37, 1.77 ,0.001 1.62 1.37, 1.92 ,0.001 1.43 1.15, 1.78 ,0.01
19 to 22 1.14 1.05, 1.23 ,0.01 1.16 1.03, 1.30 0.02 1.08 0.96, 1.22 0.2
22 to 25 1.12 1.04, 1.20 0.003 1.07 0.98, 1.17 0.1 1.17 1.03, 1.32 0.02
25 to 28 Reference Reference Reference
28 to 31 0.96 0.89, 1.03 0.3 0.90 0.81, 0.99 0.03 1.05 0.94, 1.18 0.4
31 to 34 0.92 0.84, 1.01 0.08 0.82 0.74, 0.90 ,0.001 1.11 0.96, 1.28 0.2
34 to 37 0.95 0.86, 10.4 0.3 0.78 0.68, 0.89 ,0.001 1.33 1.12, 1.57 ,0.01
37 to 40 0.97 0.82, 1.14 0.7 0.90 0.73, 1.11 0.3 1.05 0.76, 1.46 0.8
.40 0.96 0.83, 1.11 0.6 0.89 0.77, 1.03 0.1 1.11 0.77, 1.61 0.6

doi:10.1371/journal.pone.0114897.t002

BMI .28 kg/m2 in the HD group. However, decreased mortality risk in the PD
group was observed only up to the time-varying BMI category of 28–31 kg/m2. In
the PD group, there were no significant differences in the risk estimates for higher
time-varying BMI categories (up to 37 kg/m2); although a trend of increasing
mortality risk was observed for the time-varying BMI category 37–40 kg/m2. The
risk estimates for other risk factors from the multivariate Cox regression models
are presented in Table S4 in S1 Information.

Discussion
This large registry study showed that BMI changed over time in a non-linear
fashion in incident dialysis patients. Although the patterns of non-linear change in
BMI were similar in HD and PD groups, the average BMI in PD patients at 1 year

Table 3. The association between time-varying BMI and all-cause mortality.

All patients HD patients PD patients
BMI (kg/m2) HR 95%CI P HR 95%CI P HR 95%CI P
#19 3.30 2.91, 3.74 ,0.001 3.72 3.27, 4.24 ,0.001 2.56 2.10, 3.12 ,0.001
19 to 22 1.70 1.53, 1.89 ,0.001 1.74 1.54, 1.98 ,0.001 1.63 1.41, 1.89 ,0.001
22 to 25 1.23 1.13, 1.34 ,0.001 1.25 1.11, 1.40 ,0.001 1.22 1.06, 1.40 ,0.01
25 to 28 Reference Reference Reference
28 to 31 0.80 0.73, 0.88 ,0.001 0.80 0.71, 0.91 0.001 0.80 0.69, 0.94 ,0.01
31 to 34 0.82 0.74, 0.90 ,0.001 0.72 0.65, 0.81 ,0.001 0.96 0.80, 1.14 0.6
34 to 37 0.82 0.71, 0.94 ,0.01 0.69 0.59, 0.81 ,0.001 1.06 0.83, 1.34 0.7
37 to 40 0.94 0.81, 1.09 0.4 0.76 0.61, 0.95 0.02 1.30 1.05, 1.62 0.02
.40 0.76 0.64, 0.90 ,0.01 0.74 0.63, 0.87 ,0.001 0.72 0.51, 1.01 0.06

doi:10.1371/journal.pone.0114897.t003

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 BMI Trajectory and Mortality in Incident Dialysis Patients

of follow-up was slightly higher than the baseline level. Time-varying measures of
BMI were significantly associated with mortality risk in both HD and PD patients.
High BMI is a risk factor for ESKD and the prevalence of obesity is increasing
among ESKD patients needing dialysis [3]. Most of the studies evaluating the
association between BMI and mortality have used a single BMI value at baseline.
Few studies have assessed follow-up measures of BMI or weight [12, 21–27]. Using
data from a large dialysis provider, Kalantar-Zadeh and colleagues reported a
significantly increased mortality risk in HD patients with time-varying BMI values
below the reference category of 23 to 25 kg/m2 and a decreased mortality risk with
high BMI categories, including very high BMI values of .45 kg/m2
[12, 21, 22, 25]. Kotanko and colleagues observed a marked decrease in body
weight in 3 months preceding death in HD patients [26]. Using serum creatinine
as a surrogate for muscle mass, decline in muscle mass was a stronger predictor of
mortality than weight loss [25]. The patient characteristics from these studies were
different to those from our study. First, the previous studies included prevalent
dialysis patients that may have led to the introduction of selective survival bias
[31]. Second, the ethnic compositions of the cohort were different as our study
predominantly included Caucasian patients. Third, except one [27], all studies
included patients receiving HD only. Previously reported analyses of the effect of
intra-individual change in (gain or loss of) BMI or weight over time on mortality
could have been affected by regression to the mean [32] and an incorrect
assumption of linear and unidirectional change of weight.
A key strength of our study was the inclusion of all incident adult ESKD
patients receiving both HD and PD. The initial decrease in BMI in the first year of
starting dialysis could be due to the excess burden of illness that these patients
experience at the time of reaching ESKD necessitating dialysis. Indeed, mortality is
highest in the first year of starting dialysis with reported rates ranging from 17.5%
to 25% [5, 33]. Due to the absence of data on the assessment of volume status, we
could not differentiate between weight loss due to fluid removal and true weight
loss. Subsequent weight gain could be due to improved appetite and nutrition
once patients are stabilised on dialysis therapy. As with HD patients, there was an
increased risk of mortality in PD patients with lower time-varying BMI. In
contrast, the survival benefit associated with high BMI in PD patients was limited
to the time-varying BMI category of 28 to 31 kg/m2. There were no significant
differences in the risk estimates for time-varying BMI up to 37 kg/m2, although a
trend of increasing mortality risk was observed that achieved statistical
significance for the time-varying BMI category of 37 to 40 kg/m2. This
discrepancy in mortality risks between the two dialysis modalities at higher BMI
values may be due to differential development of abdominal obesity, which is in
turn associated with increased mortality in ESKD patients [34]. Visceral fat mass
increases by 11–23% within 1 year of initiating PD, probably due to the metabolic
consequences of intraperitoneal administration of glucose-containing PD
solutions [35–37]. In contrast, increases in visceral fat mass are generally not
observed in HD patients [38, 39]. Importantly, our study demonstrated that the
average BMI at 1 year of follow-up was higher than the baseline value in PD

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 BMI Trajectory and Mortality in Incident Dialysis Patients

patients, but lower in HD patients. We did not observe an increased mortality risk
in PD patients with time-varying BMI .40 kg/m2, possibly due to small patient
numbers (95 patients).
A major limitation of our study was that BMI does not distinguish fat mass
from lean mass, especially in patients with muscle wasting [40]. Furthermore, BMI
does not reflect body-fat distribution. Given that fat distribution varies
substantially across various ethnic backgrounds at the same level of BMI, BMI is
not an ideal surrogate of fat mass [41]. The ANZDATA Registry does not collect
data on other anthropometric measures, body composition, laboratory indices of
nutrition and inflammation, hospitalizations or cardiovascular events. The
accuracy of reported data on dry-weight could not be validated as data on the
assessment of volume status are not collected. One common limitation with
survival regression models incorporating time-varying covariates is the difficulty
to identify the non-linearity of the time-varying covariates. However, given the
observed BMI trajectory, the hazard estimates obtained through the weighted Cox
regression models should be sufficiently robust. Other limitations of our study
include adjustment for a limited number of baseline variables and variable
frequency of weight reporting. Patients with less than 6 months follow-up on
dialysis were excluded, potentially leading to bias. Since our study predominantly
included Caucasian patients, the results of this study may not be generalizable to
patient populations with different ethnic compositions.
Considering the obesity epidemic in ESKD, the results of our study have
important clinical and research implications, despite its limitations. The finding of
a non-linear change in BMI, especially in the 1st year of starting dialysis, suggests
that particular attention should be paid by clinicians to optimising nutritional
intake in this early period in the hope of preventing early weight loss and
heightened early mortality. Prospective studies are required to understand the
dynamic association between BMI and abdominal obesity in dialysis patients and
its variation according to dialysis modality. Randomised clinical trials are required
to study the effect of nutritional interventions on BMI and important clinical
outcomes, such as quality of life and mortality. Finally, research evaluating the
effects of glucose-sparing PD regimens on visceral fat mass and patient outcomes
is needed.
In conclusion, BMI changed in a non-linear fashion in incident dialysis
patients. Time-varying measures of BMI were significantly associated with all-
cause mortality risk. Lower time-varying BMI categories were associated with
increased mortality risk in both HD and PD patients. Higher time-varying BMI
categories were associated with decreased mortality in HD patients, but not in PD
patients.

Supporting Information
S1 Information. Supporting tables and figure. Table S1. Distributions of BMI
groups according to ethnicity and dialysis modality. Table S2. Number of weight

PLOS ONE | DOI:10.1371/journal.pone.0114897 December 16, 2014 10 / 13

 BMI Trajectory and Mortality in Incident Dialysis Patients

measurements per patient. Table S3. Causes of death. Table S4: Hazard ratio (95%
CI) for mortality associated with individual covariates from the multivariate Cox-
regression model with time-varying BMI as covariate. Figure S1. Flow diagram
describing patient selection in the study.
doi:10.1371/journal.pone.0114897.s001 (DOCX)

Author Contributions
Conceived and designed the experiments: SVB SKP CMH DWJ. Performed the
experiments: SVB SKP KK. Analyzed the data: SKP KK. Contributed reagents/
materials/analysis tools: PAC SPM SKP. Contributed to the writing of the
manuscript: SVB SKP KK PAC CMH FGB NB KRP SPM DWJ.

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