Nephrol Dial Transplant (2004) 19: 2289–2295
DOI: 10.1093/ndt/gfh381
Advance Access publication 13 July 2004
Original Article
Assessment of body composition by dual energy X-ray absorptiometry,
skinfold thickness and creatinine kinetics in chronic kidney disease
patients
Carla Maria Avesani, Sergio Antonio Draibe, Maria Ayako Kamimura, Miguel Cendoroglo,
Alessandra Pedrosa, Marise Lazaretti Castro and Lilian Cuppari
Division of Nephrology and Nutrition, Graduate Program of the Federal University of São Paulo, São Paulo, Brazil
Abstract
Background. Finding a method that can be routinely
used to assess body composition with minimum
error is still a challenge for those who work with
chronic kidney disease (CKD) patients. This study
aimed to compare the value of two surrogate
techniques, skinfold thickness (SKF) and creatinine
kinetics (CK) with dual energy X-ray absorptiometry
(DEXA) as the reference method for measuring body
fat and fat-free mass in non-dialysed CKD patients.
Methods. The body fat and fat-free mass of 50 nondialysed CKD patients (38 male, 12 female) were
measured by DEXA and compared with measurements
obtained by SKF and CK.
Results. The mean values of body fat and fat-free
mass obtained by SKF and CK differed significantly
from measurements made by DEXA. The intra-class
correlation coefficient (r) for body fat between SKF
and DEXA (r ¼ 0.74) and between CK and DEXA
(r ¼ 0.47) indicated a moderate degree of reproducibility. A Bland and Altman plot analysis showed a
better agreement between SKF and DEXA [5.8 ±
3.9% (2.0 to 13.6)] than between CK and DEXA
[8.8 ± 8.8% (–8.8 to 26.4)]. Regarding fat-free mass,
the intra-class correlation coefficient (r) between
SKF and DEXA (r ¼ 0.85) indicated a good degree
of reproducibility, while that between SKF and CK
(r ¼ 0.57) indicated a moderate degree of reproducibility. The Bland and Altman plot analysis for fatfree mass showed that DEXA agreed better with SKF
[3.1 ± 3.4 kg (9.9 to 3.7)] than with CK [5.5 ±
6.4 kg (18.2 to 7.3)].
Conclusion. Skinfold thickness seems to be the method
of choice for evaluating body fat. The limitations
Correspondence and offprint requests to: Carla Maria Avesani,
Federal University of Sao Paulo, Nephrology, Sao Paulo SP,
Brazil. Email: [email protected]
inherent to DEXA in evaluating fat-free mass makes
it difficult to designate an alternate method of choice
for assessing this body compartment.
Keywords: body fat; chronic kidney disease; creatinine
kinetics; DEXA; fat-free mass; skinfold thickness
Introduction
Malnutrition is widespread among patients with
chronic kidney disease (CKD), and it is one of the
main factors that increase morbidity and mortality in
these patients [1]. Therefore, monitoring body composition is important for prescribing adequate nutritional
therapy and preventing protein–energy malnutrition.
Hence, in this context it is important to identify
a technique for assessing body composition that is
simple, reliable, non-invasive and cost-effective and
could be routinely used in the clinical setting. Among
the commonly used methods, dual energy X-ray
absorptiometry (DEXA) has the advantage of assessing
the three main body components (fat mass, fat-free
mass and bone mineral mass) with high precision
and with minimal exposure of patients to radiation [2]. DEXA, however, has the disadvantages of
being expensive, only moderately reliable and of
including body water in the fat-free mass compartment [3, 4]. Although DEXA is not a gold standard, it
has been proposed by the Kidney Disease Outcomes
Quality Initiative (K-DOQI) as a reference method to
assess body composition in CKD patients [5]. The
sum of skinfold thicknesses (SKF) provides an estimation of the percent of body fat, and by subtraction
from total body weight, yields fat-free mass [6]. SKF is
very useful clinically, as it is a non-invasive and costeffective method, but it has moderate reliability and
high inter-observer variation [7]. The creatinine kinetics
Nephrol Dial Transplant Vol. 19 No. 9 ß ERA–EDTA 2004; all rights reserved
2290
(CK) method can also be routinely used to estimate
fat-free mass. It is based on creatinine excretion and
has the advantage of being little influenced by the
hydration status of the body [8]. The disadvantage of
this method is that it requires great cooperation
from the patient for the 24 h urine collection [8]. In
addition, SKF and CK have the disadvantage of using
predictive equations, derived from regression analyses
that were based on data from a specific population.
These methods have been frequently used to assess
body composition in healthy individuals. However,
abnormalities often present in CKD patients, such
as fluid retention and bone disease, may affect the
validity of these techniques in this population [9].
Previous studies have addressed the validity of simple
methods to assess body composition in patients with
chronic renal disease, but most of them have been
conducted in patients on haemodialysis or peritoneal
dialysis [3,4, 10–17]. Therefore, studies in non-dialysed
CKD patients are lacking.
This study aimed to evaluate the agreement of,
SKF, CK with DEXA for measuring body fat and fatfree mass in non-dialysed CKD patients.
Subjects and methods
We entrolled in this study 50 non-dialysed CKD patients
treated in the outpatient clinic of the Federal University of
São Paulo (UNIFESP). The subjects were older than 18 years
old and clinically stable, and had mild to advanced CKD
with no oedema. Most of the patients (n ¼ 40; 80%) had a diet
consisting of 30–35 kcal/kg/day and 0.6–0.8 g of protein/kg/
day, and were on diuretics and antihypertensive medications (n ¼ 45; 90%). No patient took immunosuppressants
or corticosteroids. Of the cohort, 21 diabetic patients were
being treated with insulin and three were taking oral hypoglycemic agents.
The local Human Investigation Review Committee
approved this study, and informed consent was obtained
from each subject.
Methods
Study protocol. All subjects had their body composition assessed by DEXA, and by the sum of SKF and by
CK. DEXA was used as the reference method, to compare
the results obtained by SKF and CK.
Body mass index (BMI) was calculated for each patient
as weight (kg)/height (m2).
In order to identify other influences, if any, on the
measurements of body composition, the patients were divided
according to gender and the presence of diabetes mellitus.
Dual energy X-ray absorptiometry. DEXA was performed using a Lunar DPX Bone Densitometer scanner
(Lunar Radiation Corporation, Madison, WI) with the
patient in the supine position. The DEXA system performs
rectilinear scans over the length of the body. The scan begins
at the top of the patient’s head and moves downward
toward the feet. The program allows scanning up to 205 lines.
During the scan, the source shutter opens to emit an X-ray
C. M. Avesani et al.
beam. Software calculates the grams of fat tissue, percent
of fat mass, grams of lean tissue and grams of bone mineral
mass. Fat-free mass is calculated as the sum of lean tissue
plus bone mineral mass.
Skinfold thickness. SKF measurements were performed by
a single observer at four sites (triceps, biceps, subscapular
and suprailiac) according to standard techniques. The skinfold measurements were performed in the non-dominant arm
using the LangeÕ skinfold caliper (Cambridge Instrument,
Cambridge, MA, USA). Three sets of measurements were
averaged for each site. Body density was calculated according
to the formula of Durnin and Womersley [6] and percent of
body fat was then derived using Siri’s equation [18]. Fat-free
mass in kilograms was calculated subtracting body fat from
total body weight.
Creatinine kinetics. The CK method calculates fat-free
mass as follows, according to the formula of Keshaviah et al.
[10] based on Forbes’ and Bruining’s work [8]:
Equation 1: FFM (kg) ¼ 0.029 daily creatinine production
(mg/dl) þ 7.38 [8]
Equation 2: Creatinine production (mg/dl) ¼ CE (mg/day) þ
MD (mg/dl) [10]
Equation 3: MD (mg/dl) ¼ 0.38 serum creatinine (mg/dl) BW (kg) [10]
where FFM is fat-free mass, CE is creatinine excretion, MD
is metabolic degradation and BW is body weight. Percent of
body fat was obtained subtracting percent of fat-free mass
from 100.
Since body fat measured by SKF is primarily given
as a percentage figure and fat-free mass measured by DEXA
and CK is primarily given in kilograms, we judged that it
would be more appropriate to report the data regarding
body fat in percent and the data regarding fat-free mass in
kilograms.
Biochemical data
Fasting blood samples were drawn to measure serum
creatinine. Twenty-four hour urine was obtained for the
determination of urinary creatinine concentration. Serum
and urinary creatinine were measured by a Cobas Mira Plus
autoanalyser (Roche Diagnostic System, Basel, Switzerland),
which employs the modified Jaffé reaction. Glomerular filtration rate was estimated using standard creatinine clearance
(24 h urine collection) corrected for body surface area
(1.73 m2).
Statistical analysis
The results are expressed as mean±standard deviation.
ANOVA for repeated measures was used for comparisons
of the mean values of body fat and fat-free mass assessed by
the three techniques.
The intra-class correlation coefficient (r) was used to test
the reproducibility of body fat and fat-free mass measured
by SKF and CK and compared with DEXA. Values of
the coefficient below 0.4 were considered to indicate poor
reproducibility, values between 0.4 and 0.75, medium reproducibility and values above 0.75, good reproducibility [19].
In addition, Bland and Altman plot analysis was applied to
Assessment of body composition by DEXA
2291
visually assess agreement between the two methods in each
patient [20]. This analysis consists of a graph, in which the
difference between the measurement of each method ( y-axis,
i.e. method A – method B), is plotted against their mean
difference (x-axis, i.e. (method A þ method B)/2). The 95%
limits of individual agreement between the two methods were
calculated as the mean difference between two methods±2.0
standard deviations. The ANOVA and mean±standard
deviation of all variables were tested by True Epistat software (Texas, USA, 1995), while the intra-class correlation
coefficient was calculated by Stata Corp software, version 7.0
(Texas, USA, 2001).
Results
Table 1 shows the main characteristics of the patients.
There was a predominance of males (76 vs 24% female).
Mean BMI indicated the subjects to be overweight.
A more detailed analysis of patients’ BMI showed
that 21 patients (42%) had BMI in the normal range,
20 (40%) were overweight and 9 (18%) were obese.
The mean creatinine clearance indicated, according to
the National Kidney Foundation Classification, stage 3
CKD [21]. The causes of CKD were: diabetes mellitus
in 20 patients (40%), hypertensive nephrosclerosis
in 20 patients (40%), other causes in two patients
(4%), and in eight patients (16%) the causes of CKD
were not determined. In the entire cohort, 25 patients
(50%) had diabetes mellitus.
Body fat
The mean values of percent of body fat measured by the
three techniques are shown in Table 2. As can be
observed, the percent body fat of the entire group
obtained by either SKF or CK was significantly higher
than that derived by DEXA. When the cohort was
divided according to gender (Table 2), results similar to
those found for the entire group were observed for male
and female patients and for diabetic and non-diabetic
patients (data not shown). The results concerning the
intra-class coefficients of correlation are presented
in Table 3. Although the intra-class coefficients of
correlation (r) obtained between both SKF and DEXA
and between CK and DEXA were indicative of a
moderate degree of reproducibility, the coefficient
obtained between SKF and DEXA was higher than
that obtained between CK and DEXA. In addition, the
confidence interval calculated for SKF and DEXA was
shorter than that for CK and DEXA.
The Bland and Altman plot analysis for percent of
body fat is illustrated in Figure 1a and b. The mean
difference and 95% limits of agreement between SKF
and DEXA were smaller than between CK and DEXA.
These results indicate that, although both mean differences are statistically different from zero, SKF
agreed more closely with DEXA than did with CK.
Extreme values of body fat and gender did not
determine larger inter-method differences; there were
homogeneous distributions of the points in the graphs
(Figure 1a and b). Indeed, the inter-method differences
between SKF and DEXA and between CK and DEXA
did not correlate significantly with BMI (SKF –
DEXA vs BMI: r ¼ –0.03, P ¼ 0.6; CK DEXA vs
BMI: r ¼0.26, P ¼ 0.06). Furthermore, these graphs
also demonstrate that, using DEXA as the reference
method, both SKF and CK overestimate body fat in
the majority of the patients (90 and 86%, respectively).
Fat-free mass
The mean values of fat-free mass measured by the
different techniques are shown in Table 2. It can be
noted that the fat-free mass of the entire group measured by SKF and CK was significantly lower than
that obtained by DEXA. When the group was divided
according to gender, only in females were the mean
values of fat-free mass measured by SKF not statistically different from DEXA (Table 2). When the group
Table 2. Body fat and fat-free mass assessed by DEXA, SKF
and CK
DEXA
Body fat
(%)
Table 1. Patients’ characteristics (n ¼ 50)
Age (years)
Weight (kg)
Height (cm)
BMI (kg/m2)
Length of CKD
(months)
Serum creatinine
(mg/dl)
Creatinine clearance
(ml/min/1.73 m2)
Entire group
(n ¼ 50)
Male
(n ¼ 38)
Female
(n ¼ 12)
56.7 ± 11.2
70.2 ± 11.6
163.4 ± 8.5
26.3 ± 4.3
35.1 ± 27.2
56.7 ± 12.5
71.2 ± 11.8
166.6 ± 6.5
25.6 ± 4.1
36.7 ± 8.7
56.4 ± 6.20
67.0 ± 10.6
153.3 ± 5.6a
28.5 ± 4.24a
30.3 ± 17.6
2.7 ± 1.36
2.73 ± 1.5
2.63 ± 1.0
40.1 ± 21.6
41.9 ± 20.1
34.2 ± 25.7
Values given as mean±standard deviation.
a
P<0.05: male vs female.
SKF
CK
Total (n ¼ 50) 24.7 ± 9.6a,b 30.3 ± 8.7 34.4 ± 12.3
Male (n ¼ 38)
20.3 ± 5.6a,b 26.6 ± 5.9 29.6 ± 11.6
Female (n ¼ 12) 37.7 ± 6.3a,b 42.0 ± 5.2 44.4 ± 8.5
Fat-free
Total (n ¼ 50) 51.7 ± 8.8a,b 48.5 ± 7.9 45.2 ± 7.9
mass (kg) Male (n ¼ 38)
55.0 ± 6.6a,b 51.7 ± 5.9 49.2 ± 6.9
Female (n ¼ 12) 40.9 ± 5.5b 38.5 ± 4.2 36.7 ± 5.5
Values given as mean±standard deviation.
P 0.05: aSKF vs DEXA; bCK vs DEXA.
Table 3. Intraclass coefficients of correlation
Methods
SKF vs DEXA
CK vs DEXA
Body fat (%)
Fat-free mass (kg)
r
95% CI
r
95% CI
0.74
0.47
0.61–0.86
0.25–0.69
0.85
0.57
0.78–0.93
0.39–0.76
2292
C. M. Avesani et al.
35
∆ body fat (%) (SKF−DEXA)
(a)
30
25
20
15
+ 2 SD = 13.6
10
mean= 5.8 ± 3.9%
p ≤ 0.05
5
0
−2 SD = −2.0
−5
−10
0
5
10
15
20
25
30
35
40
45
50
55
Mean body fat (%) [(SKF+DEXA) /2)]
35
∆ body fat (%) (CK−DEXA)
(b)
30
+ 2 SD = 26.4
25
20
15
mean = 8.8 ± 8.8%
p ≤ 0.05
10
5
0
−5
−10
−2 SD = −8.8
0
5
10
15
20
25
30
35
40
45
50
55
Mean body fat (%) [(CK+DEXA) /2)]
Fig. 1. Comparison of the agreements between skinfold thickness (SKF) and DEXA (a), and between creatinine kinetics (CK) and DEXA
(b) in the measurement of body fat (filled diamonds, male; open diamonds, female).
was divided according to the presence of diabetes,
the same pattern observed for the entire group was
seen in diabetic and non-diabetic patients (data not
shown). Table 3 shows that the intra-class coefficient
of correlation (r) obtained between SKF and DEXA
was indicative of good reproducibility, but the coefficient obtained between CK and DEXA represented
moderate reproducibility. It can also be seen that the
confidence interval for SKF and DEXA was shorter
than the one observed for CK and DEXA.
The Bland and Altman comparisons of techniques
for measuring fat-free mass are illustrated in Figure 2a
and b. The mean difference and the 95% limits of
agreement between SKF and DEXA indicate that fatfree mass measured by SKF has a better agreement
with DEXA than with CK. Furthermore, the differences between the fat-free masses measured by SKF
and DEXA and by CK and DEXA were influenced
neither by extreme values for the fat-free mass nor
by gender. In fact, BMI was not correlated with
differences in fat-free mass between SKF and DEXA
(SKF DEXA vs BMI: r ¼ 0.08, P ¼ 0.81) or
between CK and DEXA (CK DEXA vs BMI:
r ¼ 0.31, P ¼ 0.10). These graphs also demonstrate
that, using DEXA as a reference method, SKF and
CK underestimated fat-free mass in 84 and 80% of
the patients, respectively.
Discussion
This study aimed to evaluate the agreement of SKF
and CK with DEXA for measuring body fat and fatfree mass in non-dialysed CKD patients.
Assessment of body composition by DEXA
(a)
2293
∆ lean body mass (kg) (SKF−DEXA)
8
4
+ 2 SD = 3.7
0
mean = −3.1 ± 3.4 kg
p ≤ 0.05
−4
−8
−2 SD = −9.9
−12
−16
−20
−24
30
35
40
45
50
55
60
65
70
Mean lean body mass (kg) [(SKF+DEXA)/2]
(b)
∆ lean body mass (kg) (CK−DEXA)
8
+ 2 SD = 7.3
4
0
−4
mean = −5.5 ± 6.4 kg
p ≤ 0.05
−8
−12
−16
−2 SD = −18.2
−20
−24
30
35
40
45
50
55
60
65
70
Mean lean body mass (kg) [(CK+DEXA)/2]
Fig. 2. Comparison of the agreements between skinfold thickness (SKF) and DEXA (a) and between creatinine kinetics (CK) and DEXA
(b) in the measurement of fat-free mass (filled diamonds, male; open diamonds, female).
Body fat
Our findings show that the mean values of body fat
assessed by SKF and CK were significantly higher than
those obtained by DEXA; however, there was a better
agreement between SKF and DEXA than between CK
and DEXA. Considering DEXA as a reference method,
our findings suggest that SKF presented an advantage
over CK for assessing body fat.
The worse results obtained by CK for assessing
body fat might be related to methodological features
and to the hydration status of patients. SKF and
DEXA primarily yield the amount of body fat [2,6],
while CK primarily derives fat-free mass [8]. The
estimation of body fat by CK is obtained by the
subtraction of fat-free mass from total body weight.
Because the measurement of fat-free mass by CK
suffers little from the hydration status [3,10], an excess
of body fluid could erroneously result in a higher figure
for body fat. Therefore, if the patient is overhydrated,
CK might yield falsely high values of body fat.
Although our patients had no oedema, it is possible
that some degree of fluid overload may have occurred.
Unfortunately, previous studies cannot confirm our
assumptions, as to the best of our knowledge, our study
is the first to use CK in the estimation of body fat in
non-dialyzed CKD patients.
SKF measurement is a simple, non-invasive, costeffective and very useful method in clinical practice.
The disadvantages of this technique are the large
inter-observer variations and its poor precision in
obese individuals [7]. However, these factors did not
contribute to the significant inter-method difference
found between SKF and DEXA, since SKF was
measured by a single observer and no significant
correlation was found between inter-method differences and BMI.
2294
The comparison of body fat calculated with SKF and
DEXA in patients with chronic renal failure has been
previously studied. Woodrow et al. [12] and Kamimura
et al. [15] found that body fat measures obtained by
SKF were more similar to DEXA than those obtained
using bioelectrical impedance. Similarly, in renal
transplant patients the agreement between SKF and
DEXA in the estimation of body fat was slightly better
than between DEXA and bioelectrical impedance [14].
In view of our findings, SKF seems to be a method of
choice, compared with CK, to estimate body fat. In
order to confirm the reliability of SKF to estimate body
fat in CKD patients, comparisons with gold standard
techniques, such as hydrodensitometry, computerized
tomography and magnetic ressonance, are still necessary.
Fat-free mass
In our study, fat-free mass measured by SKF had a
better agreement with DEXA and a better degree
of reproducibility than it did with CK. The reason for
these findings may be attributed to some features of
CK. The CK method estimates fat-free mass based
upon the assumption that, in the steady state, creatinine
production is proportional to the amount of fat-free
mass [8]. Creatinine production is equal to the sum of
metabolic degradation and creatinine excretion [10].
Thus, the accuracy of this method depends on the
correct estimation of metabolic degradation and creatinine excretion. In our study, metabolic degradation was
calculated using the equation proposed by Keshaviah
et al. [10] (equation 3 shown in the methodology),
which was based on data from haemodialysis and
peritoneal dialysis patients, and was not validated
for non-dialysed CKD patients. Moreover, creatinine
excretion was measured in 24 h urine collection, which
require great cooperation from patients. It has been
shown that a 15 min error in voiding time for a 24 h
collection period leads to a 1% error in the value of
urinary creatinine excretion [8]. Therefore, although
our patients were instructed to collect urine very
carefully, imprecise urine collections may have
occurred, which to a lesser extent, could have led to
errors in the calculation of fat-free mass by CK. Diet
may be another factor to influence the measures of fatfree mass obtained by CK [8]. Although the idea is
controversial, meat intake may raise the amount
of creatinine excreted, leading to the overestimation
of fat-free mass [8]. This possibility did not seem to
come into play in our study, as fat-free mass estimated by CK was significantly lower than the one
estimated by the other techniques. Similarly, previous studies on peritoneal dialysis patients also have
reported that fat-free mass assessed by CK was significantly lower than fat-free mass assessed by other
methods [3, 10, 11, 13].
In this study, DEXA was used as a reference
method to estimate fat-free mass. However, it is
well known that fat-free mass measured by DEXA
is influenced by the hydration status of the patient.
C. M. Avesani et al.
This has been shown in studies conducted in haemodialysis patients, in whom the amount of fat-free
mass decreased after the dialysis session and the difference was equivalent to the amount of the ultrafiltrate
[4]. Similar results were noted in peritoneal dialysis
patients in whom fat-free mass estimated by DEXA
also decreased after the drainage of the dialysis fluid
from the abdomen [3]. Another limitation of DEXA
for assessing the fat-free mass of CKD patients is that
it assumes that 73% of the lean mass is water. This
may not be true for haemodialysis patients, since
Arkouche et al. [22] measured body water by the
oxygen 18 labelled-water method in 18 patients on
haemodialysis and found that 69.4±3% of the lean
mass of those patients was water. In non-dialysed CKD
patients, it is not known if the hydration status
of the lean mass is equivalent to 73%, as it is assumed
to be for healthy individuals. Estimation of fat-free
mass by SKF might be also influenced by hydration
status, since this method estimates fat-free mass as
the difference of body fat from total body weight. On
the other hand, the influence of body water on the
fat-free mass estimated by the CK method is minimal [10]. Although our patients had no oedema,
we cannot exclude the possibility of some degree of
fluid overload. Thus, the higher mean values of fatfree mass measured by SKF and DEXA may be
explained by the fact that these two techniques
included the excess of body water in the fat-free mass
compartment. Indeed, previous studies in haemodialysis and in peritoneal dialysis patients also have
reported that fat-free mass measured by those techniques, such as DEXA, SKF and bioelectrical impedance, that are readily influenced by body water
were higher than the fat-free mass estimated by CK
[3,10,11,13,17]. Of particular importance is the study of
Lo et al. [11] on peritoneal dialysis patients. They
reported that the fat-free mass assessed by counting
total body potassium, a method that is not influenced
by hydration status, was better correlated with CK than
with SKF, bioelectrical impedance or near-infrared
interactance. The present study does not provide
evidence to conclude which of these two techniques,
SKF or CK, is more reliable for estimating fat-free
mass.
In conclusion, SKF presented a better agreement
with DEXA than it did with CK for both body compartments. As DEXA is a reliable method to assess
body fat, SKF seems to be the method of choice to
evaluate body fat. However, since DEXA might have
some drawbacks for measuring fat-free mass, further
cross-sectional and longitudinal studies concerning
the applicability of SKF or CK for measuring fat-free
mass in CKD patients are necessary.
Acknowledgements. The authors thank Professor Clovis and
Alexandre Shinzato for their helpful statistical assistance, and
Professor Dirce Maria Sigulem for support during the research.
This study was supported by the Oswaldo Ramos Foundation and
by Fundação de Amparo à Pesquisa do Estado de São Paulo
(Fapesp).
Assessment of body composition by DEXA
Conflict of interest statement. The author and co-authors declare
that there is no conflict of interest in this study.
References
1. Combe C, Chauveau P, Laville M et al. Influence of nutritional factors and hemodialysis adequacy on the survival of
1,610 french patients. Am J Kidney Dis 2001; 37 [Suppl 2]:
S81–S88
2. Gotfredsen A, Jensen J, Borg J, Christiansen C. Measurement
of fat-free mass and total body fat using dual photon
absorptiometry. Metabolism 1986; 35: 88–93
3. Bhatla B, Moore H, Emerson P et al. Fat-free mass estimation by creatinine kinetics, bioimpedance and dual energy
X-ray absorptiometry in patients on continuous ambulatory
peritoneal dialysis. ASAIO J 1995; 41: M442–M446
4. Abrahamsen B, Hansen TB, Hogsberg IM, Pedersen FB, BeckNielsen H. Impact of hemodialysis on dual X-ray absorptiometry, biolectral impedance measurements and anthropometry.
Am J Clin Nut 1996; 63: 80–86
5. National Kidney Foundation/DOQI. National Kidney Foundation/DOQI clinical practice guidelines for nutrition in chronic
renal failure. Am J Kidney Dis 2000; 35 [Suppl 2]: S34–S35
6. Durnin JVGA, Womersley J. Body fat assessed from total body
density and its estimation from skinfold thickness: measurements on 481 men and women aged 16 to 72 years. Br J Nutr
1974; 32: 77–97
7. Heymsfield SB, Baumgartner RN, Pan SF. Nutritional
assessment of malnutrition by anthropometric methods. In:
Shils ME, Olson JA, Shike M, Ross AC, eds. Modern Nutrition in Health and Disease. Lippincott Williams & Wilkins,
Baltimore, MD: 1999; 895–902
8. Forbes GB, Bruining J. Urinary creatinine excretion and fatfree mass. Am J Clin Nutr 1976; 29: 1359–1366
9. Woodrow G, Oldroyd B, Turney FH, Davies PS, Day JME,
Smith MA. Four-component model of body composition in
chronic renal failure comprising dual-energy X-ray absorptiometry and measurement of total body water by deuterium
oxide dilution. Clin Sci 1996; 91: 763–169
10. Keshaviah PR, Nolph KD, Moore HL et al. A. fat-free
mass estimation by creatinine kinetics. J Am Soc Nephrol 1994;
4: 1475–1485
11. Lo WK, Prowant BF, Moore HL et al. Comparison of different
measurements of fat-free mass in normal individuals in chronic
peritoneal dialysis patients. Am J Kidney Dis 1994; 23: 74–85
2295
12. Woodrow G, Oldroyd B, Smith MA, Turney JH. Measurements of body composition in chronic renal failure: comparison
of skinfold anthropometry and biolectral impedance with dual
energy X-ray absorptiometry. Eur J Clin Nutr 1996; 50:
295–301
13. Arkouche W, Fouque D, Pachiaudi C et al. Total body water
and body composition in chronic peritoneal dialysis patients.
J Am Soc Nephrol 1997; 8: 1906–1914
14. Van Den Ham ECH, Kooman JP, Christiaans MHL et al.
Body composition in renal transplant patients: bioimpedance
analysis compared to isotope dilution, dual energy X-ray
absorptiometry and anthropometry. J Am Soc Nephrol 1999;
10: 1067–1079
15. Kamimura MA, Avesani CM, Cendoroglo M, Canziani MEF,
Draibe SA, Cuppari L. Comparison of skinfold thicknesses
and biolectral impedance analysis with dual-energy X-ray
absorptiometry for the assessment of body fat in patients on
long-term heamodialysis therapy. Nephrol Dial Transplant 2003;
18: 101–105
16. Kamimura MA, Santos NSJ, Avesani CM, Canziani MEF,
Draibe SA, Cuppari L. Comparison of three methods for the
determination of body fat in patients on long-term hemodialysis therapy. J Am Diet Assoc 2003; 103: 195–199
17. Pellé G, Level C, El Khoury, Chauveau P, Combe C. Lean
body mass estimation by creatinine index in heamodialysis
patients. Comparison with the results of dual energy X-ray
absorptiometry (abstract). Nephrol Dial Transplant 2003; 18
[Suppl 4]: 445
18. Siri WE. Body composition from fluids spaces and density:
analysis of two methods. In: Brozec J, Henschel A, eds.
Techniques for Measuring Body Composition. National Research
Council, Washington, DC: 1961; 223–224
19. Fleiss JL. Reliability of measurement. In: Fleiss JL. The Design
and Analysis of Clinical Experiments. John Wiley & Sons,
New York, NY: 1986; 1–33
20. Bland M, Altman D. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet
1986; 1: 307–310
21. National Kidney Foundation/DOQI. National Kidney Foundation/DOQI clinical practice guidelines for chronic kidney
disease: evaluation, classification and stratification. Am J
Kidney Dis 2002; 39 [Suppl 1]: S46–S64
22. Arkouche W, Normand S, Chambrier C, Laville M, Fouque D.
Impaired skeletal cell hydration in maintenance hemodialysis
patients: an oxygen 18–DEXA study (abstract). J Am Soc
Nephrol 2003; 14: 55A
Received for publication: 15.7.03
Accepted in revised form: 18.3.04