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Xue Tian et al 2018 - Novel Equations for Estimating Lean Body Mass in Patients With Chronic Kidney Disease (1)

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ORIGINAL RESEARCH
Novel Equations for Estimating Lean Body
Mass in Patients With Chronic Kidney Disease
Xue Tian, MD,*,†,‡,§ Yuan Chen, BN,*,†,‡,§ Zhi-Kai Yang, MD, PhD,*,†,‡,§
Zhen Qu, MD, PhD,*,†,‡,§ and Jie Dong, MD, PhD*,†,‡,§
Objective: Simplified methods to estimate lean body mass (LBM), an important nutritional measure representing muscle mass and
somatic protein, are lacking in nondialyzed patients with chronic kidney disease (CKD). We developed and tested 2 reliable equations
for estimation of LBM in daily clinical practice.
Design and Methods: The development and validation groups both included 150 nondialyzed patients with CKD Stages 3 to 5. Two
equations for estimating LBM based on mid-arm muscle circumference (MAMC) or handgrip strength (HGS) were developed and vali-
dated in CKD patients with dual-energy x-ray absorptiometry as referenced gold method.
Results:We developed and validated 2 equations for estimating LBM based on HGS andMAMC. These equations, which also incor-
porated sex, height, and weight, were developed and validated in CKD patients. The new equations were found to exhibit only small
biases when compared with dual-energy x-ray absorptiometry, with median differences of 0.94 and 0.46 kg observed in the HGS
and MAMC equations, respectively. Good precision and accuracy were achieved for both equations, as reflected by small interquartile
ranges in the differences and in the percentages of estimates that were 20% of measured LBM. The bias, precision, and accuracy of
each equation were found to be similar when it was applied to groups of patients divided by the median measured LBM, the median
ratio of extracellular to total body water, and the stages of CKD.
Conclusions: LBM estimated from MAMC or HGS were found to provide accurate estimates of LBM in nondialyzed patients with
CKD.
� 2017 by the National Kidney Foundation, Inc. All rights reserved.
Introduction
PROTEIN-ENERGYWASTING (PEW) is a commoncomplication in patients with chronic kidney disease
(CKD), adversely affecting patient survival1-3 through its
association with cardiac comorbidity and inflammation.4
It is observed in patients undergoing dialysis but not exclu-
sively so. With the decline of renal function, protein catab-
olism can be gradually aggravated via complex mechanisms
and become apparent in abnormal nutritional indices such
as somatic and visceral protein storage.5 Reduced lean body
*Renal Division, Department of Medicine, Peking University First Hospital,
Beijing, China.
†Institute of Nephrology, Peking University, Beijing, China.
‡Key Laboratory of Renal Disease, Ministry of Health of China, Beijing,
China.
§Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Pe-
king University), Ministry of Education, Beijing, China.
Support: This work is supported in part by Capital Characteristic Clinic
Research Grant from Beijing Science &Technology Committee
(Z111107058811110), New Century Excellent Talents from Education
Department of China, Clinic Research Award from ISN GO R&P Committee,
Ketosteril Research Award from Fresenius Kabi Deutschi and GmbH.
Financial Disclosure: The authors declare that they have no relevant financial
interests.
Address correspondence to Jie Dong, MD, PhD, Renal Division and Institute
of Nephrology, Peking University First Hospital, No. 8 Xishiku Street, Xicheng
District, Beijing 100034, P. R. China. E-mail: jie.dong@bjmu.edu.cn
� 2017 by the National Kidney Foundation, Inc. All rights reserved.
1051-2276/$36.00
https://doi.org/10.1053/j.jrn.2017.09.004
156
mass (LBM) is an important index for somatic protein
deficit and is predictive of high mortality for nondialyzed
CKD Stages 4 and 5 patients.6-8 Hence, accurate
assessment of LBM can be a way to identify PEW during
routine care and initiate intervention and especially for
patients at CKD Stage 3 to 5.
However, LBM measurements are not routinely
performed for CKD patients because of the lack of strai-
ghtforward and accurate measurement techniques. The
gold-standard (tracer dilution) and reference (dual-energy
X-ray absorptiometry [DEXA]) techniques are laborious,
invasive, and unsuitable for routine care.Noninvasive and in-
direct methods, such as creatinine kinetics, anthropometry,
and bioelectrical impedance, are not verified and reliable
methods for determining LBM.9-12 Simple, practical, and
reliable methods for estimating LBM are urgently needed.
Recently, it has been shown that LBM estimated via hand-
grip strength (HGS) and mid-arm muscle circumference
(MAMC) agree well with LBM measured by DEXA
(LBM-DEXA) in hemodialysis (HD) patients.13 We devel-
oped and validated 2 new equations based on HGS and
MAMC for estimating LBM for patients on peritoneal dial-
ysis (PD).14 These new equations show good precision and
accuracy and small bias and perform better than the creatinine
kinetics and anthropometry methods. Since nondialyzed
CKD, HD, and PD are distinct conditions, specific equations
for estimating LBM are also needed for nondialyzed CKD
patients. The present study aimed to develop such equations
for nondialyzed patients at CKD Stage 3 to 5, based on
Journal of Renal Nutrition, Vol 28, No 3 (May), 2018: pp 156-164
Delta:1_given name
Delta:1_surname
Delta:1_given name
Delta:1_surname
Delta:1_given name
https://doi.org/10.1053/j.jrn.2017.09.004
http://crossmark.crossref.org/dialog/?doi=10.1053/j.jrn.2017.09.004&domain=pdf
mailto:jie.dong@bjmu.edu.cn
NOVEL EQUATIONS FOR ESTIMATING LEAN BODY MASS 157
MAMC and HGS, and validate them using DEXA-measured
LBM as the reference method.
Methods
Study Design and Subjects
Two cross-sectional data sets were derived from outpa-
tients, with 150 patients between March 1, 2014, and
November 26, 2014, for development group, and another
150 patients between November 27, 2014, and December
1, 2015, for validation group, respectively. The inclusion
criteria for participants were (1) age 18 to 80 years, (2) non-
dialyzed patients with CKD Stage 3 to 5, (3) clinical stable,
and (4) a willingness to be examined with all measurements
being taken simultaneously. Patients were excluded if they
had experienced systemic infections, acute cardiovascular
events, operations, trauma, active hepatitis or tumor, or
severe edema within 1 month before the study. Patients
were also excluded if they had chronic infections, connec-
tive tissue disease, hyperthyroidism or hypothyroidism,
amputations or pregnancy or if they were taking immuno-
suppressive agents and anti-inflammatory medication
chronically. The Ethics Committees of our hospital
approved the study protocol, and we adhered to the Decla-
ration of Helsinki. Informed consent was obtained for each
subject.
Demographic, Biochemical Measurements,
and Dietary Variables
Demographic and clinical data (age, sex, height, weight,
primary renal disease, cardiovascular disease, and diabetes
mellitus) were collected. Cardiovascular disease was re-
corded if 1 of the following conditions was present: angina;
class III and IV congestive heart failure (as defined by the
New York Heart Association); transient ischemic attack;
history of myocardial infarction or cerebrovascular acci-
dent; or peripheral arterial disease.15
Blood samples were drawn after an overnight fast.
Biochemistry data (hemoglobin; serum levels of albumin,
lipids, glucose, uric, urea, creatinine, calcium, phosphate)
were obtained using an automatic chemistry analyzer
(Hitachi Chemicals, Tokyo, Japan). Estimated glomerular
filtration rate was calculated using the Chinese equation
for CKD patients.16 Serum levels of high-sensitivity C-reac-
tive protein were measured by immune rate nephelometry.
Dietary variables are evaluated from3-day dietary records us-
ing a computer software program from PD information
management system in our hospital.
Dual-energy X-ray Absorptiometry
The reference test for assessment of body composition
was DEXA performed with an Orland Series XR-800
Pen Beam X-ray Bone Densitometer. Measurements
were performedas previously described17,18 with
participants wearing a hospital gown, with no metal
snaps and with all artifacts removed. The patient was in
supine position and not allowed to eat or drink during
this study period. The radiation exposure was estimated
to be one-tenth of that for a standard chest X-ray.
Whole-body composition, including total and segmental
lean, total body fat mass, and bone mineral content, were
calculated using the software package ILL Uminatus
DXA 4.4.0.
Anthropometric Measurements and HGS
Anthropometric measurements were taken in millime-
ters by the same trained observers using standard skin-fold
calipers as previously described.19,20 For each site, the
observers obtained 3 readings; the average value of which
was used for further calculations (23, 24). MAMC was
calculated using21
MAMC ðcmÞ5mid2arm circumference ðcmÞ23:142
3triceps skin-fold thickness ðcmÞ
HGS was measured using an adjustable handheld dyna-
mometer. Three consecutive measures of HGS exerted
by both hands were recorded with subjects standing with
their arm at a 180� angle. The dynamometer was held
freely, without support. The participants were told to put
maximal force on the dynamometer. The average of 3 at-
tempts (in Newton) was used for analyses.22 HGS evaluated
in the dominant arm was used to develop the prediction
equation.
Volume Status
Multiple-frequency BIA was performed using the bio-
impedance spectrum analyzer (Xitron Technologies, San
Diego, CA). The procedure is described in detail else-
where. Three consecutive measurements were performed
during a 2-minute period, and values were recorded for
extracellular water (ECW), intracellular water, and total
body water. The ratio of ECW to TBW (E/T) was taken
to reflect the volume status.23,24
Statistical Analysis
Numerical data with normal distributions are presented
as mean 6 standard deviation. Other numerical data are
presented as median values with their lower and upper
quartiles. Categorical variables are expressed as a percentage
or ratio. Patients’ data were compared using the chi-square
test or Mann–Whitney U test as appropriate.
Stepwise linear regression analysis was used to select
easily available biological predictors from age, sex, height,
and weight for incorporation into the new equations,
along with HGS and MAMC. The LBM-H equation
combined HGS with sex, height, and weight. The
LBM-M equation also combined MAMC with sex,
height, and weight.
To compare the performance of these 2 equations, with
LBM-DEXA as the reference, LBMs were calculated us-
ing the LBM-M and LBM-H equations for each subject
in the external validation dataset. Differences between
TIAN ET AL158
estimated LBM and measured LBM-DEXA were
compared using a Bland-Altman analysis. Bias was assessed
as the median of the difference between the estimated and
measured LBM values; precision was assessed as the inter-
quartile range (IQR) for the difference; and accuracy was
assessed as the percentage of estimates that differed by
more than 20% from the measured LBM (1-P20).25 Con-
fidence intervals were calculated using bootstrap methods
(2,000 bootstraps). The significance of the differences
among equations was determined using the Wilcoxon
sign rank test for bias, the bootstrap method for the
IQR from the 2,000 bootstrap samples, and McNemar’s
test for 1-P20. For analyses within subgroups, subgroups
were defined by clinical characteristics as follows: LBM
(,44.65 or $44.65 kg); E/T (,0.476 or $0.476); and
CKD stage (estimated glomerular filtration rate 30-59 or
,30 mL/minute/1.73 m2).
Table 1. Demographic and Clinical Characteristics of CKD Patien
Validation
Variables
Cro
Development (n 5 15
Age, y 54.19 6 15.46
Male, n (%) 77 (51.3)
Height, cm 165.24 6 8.08
Weight, kg 65.79 6 12.59
Body mass index, kg/m2 23.99 6 3.77
CKD stage, n (%)
3 66 (44.0)
4 37 (24.7)
5 47 (31.3)
Diabetes, n (%) 38 (25.3)
Laboratory data
Albumin, g/L 40.94 6 4.4
Hemoglobin, g/dL 117.9 6 21.63
C-reactive protein, mg/L 0.73 (0.21-2.86)
Urea nitrogen, mmol/L 6.66 6 1.00
Creatinine, mmol/L 287.52 6 207.55
Triglycerides, mmol/L 1.74 6 0.95
Total cholesterol, mmol/L 4.51 6 1.13
HDL cholesterol, mmol/L 1.15 6 0.37
LDL cholesterol, mmol/L 2.58 6 0.84
Calcium, mmol/L 2.26 6 0.16
Phosphate, mmol/L 1.31 6 0.33
eGFR, mL/min/1.73 m2 27.3 (12.2-45.4)
Nutrition parameters
LBM-DEXA, kg 44.25 6 9.63
HGS (non-dominant), n 290.86 6 104.64
HGS (dominant), n 310.55 6 110.29
MAMC, cm 22.53 6 3.01
Daily protein intake, g/kg/d 0.87 6 0.26
Daily energy intake, kcal/kg/d 26.80 6 5.55
Volume status
ECW, kg 16.75 6 3.82
ICW, kg 18.25 6 4.95
TBW, kg 35.22 6 8.46
E/T 0.45 6 0.11
CKD, chronic kidney disease; DEXA, dual-energy X-ray absorptiometry;
E/T, the ratio of extracellular to total body water; HDL, high-density lipoprot
mass; LDL, low-density lipoprotein; MAMC, mid-arm muscle circumferenc
All probabilities were 2-tailed, and the level of signifi-
cance was set at 0.05. Statistical analysis was performed us-
ing SPSS for Windows software version 21.0 (SPSS Inc.,
Chicago, IL) and Medcalc for Windows software version
9.2.1.0 (Medcalc software, Broekstraat, Belgium).
Results
Characteristics of Participants
A total of 300 patients with Stage 3 to 5 nondialyzed
CKD were recruited. The basic demographic and clinical
data of the development and validation groups are shown
in Table 1. There were no differences in demographic
data, distribution of CKD stage, and comorbidity between
the 2 groups (P..05). Mean hemoglobin was higher in the
validation group but other laboratory measurements were
not different between the 2 groups (P . .05). Patients in
the validation group had significantly higher MAMC
ts in the Cross-sectional Datasets for Development and
ss-sectional Datasets
P Value0) Validation (n 5 150)
52.03 6 15.47 0.22
89 (59.3) 0.16
166.83 6 7.44 0.08
67.8 6 11.27 0.15
24.28 6 3.2 0.47
0.35
75 (50.0)
33 (22.0)
42 (28.0)
38 (25.3) 1.00
41.57 6 4.45 0.23
124.73 6 22.22 0.01
0.99 (0.32-2.70) 0.23
6.59 6 0.84 0.84
298.61 6 249.12 0.68
2.03 6 1.52 0.05
4.53 6 1.51 0.90
1.07 6 0.33 0.56
2.53 6 0.86 0.09
2.27 6 0.16 0.27
1.35 6 0.77 0.43
30.6 (12.65-45.65) 0.71
45.46 6 9.18 0.27
281.4 6 103.08 0.43
296.42 6 101.26 0.25
24.30 6 3.19 ,0.001
0.89 6 0.23 0.54
26.65 6 4.83 0.83
16.48 6 2.99 0.51
18.46 6 4.60 0.70
35.07 6 7.35 0.87
0.45 6 0.10 0.34
ECW, extracellular water; eGFR, estimated glomerular filtration rate;
ein; HGS, handgrip strength; ICW, intracellular water; LBM, lean body
e; TBW, total body water.
Figure 1. Scatterplots, regression lines, and 95% confidence
intervals (CIs) reflecting correlations between lean body mass
measured using DEXA (LBM-DEXA) and mid-arm muscle
circumference (MAMC) in the development group of 150
chronic kidney disease patients. Dotted lines reflect 95% CIs.
Table 2. Regression Coefficients Between LBM-DEXA and
Variables Selected by Multiple Linear Regression Analysis
Based on the Development Dataset and the New Equations
Parameters
LBM-DEXA
R-squareCoefficients T P Value
Gender 6.82 9.14 ,.001 0.900
Height, cm 0.18 3.60 ,.001
Weight, kg 0.40 15.52 ,.001
HGS, n 0.01 3.66 ,.001
Constant 218.12 22.50 .014
Equation 1: LBM-H 5 (1 if male; 0 if female) 3 6.82 1 height
(cm) 3 0.18 1 weight (kg) 3 0.40 1 HGS (n) 3 0.01 2 18.12
DEXA, dual-energy X-ray absorptiometry; HGS, handgrip strength;
LBM, lean body mass; LBM-H, LBM calculated from HGS.
NOVEL EQUATIONS FOR ESTIMATING LEAN BODY MASS 159
than those in the development group (P,.001), but com-
parable LBM, as determined using DEXA,HGS for 2 sides,
and dietary protein and energy intake (P . .05). For the
remainder of this research article, HGS is taken to mean
dominant HGS. There were no significant differences in
volume status, including ECW, intracellular water, TBW,
and E/T between groups (P . .05).
Development of New Equations for Estimating
LBM
We constructed 2 LBM-estimating equations using the
development dataset, LBM-H, and LBM-M, based on
HGS and MAMC, respectively.Stepwise procedures
were performed to select potential variables for the regres-
sion equations from age, sex, height, and weight. The scat-
terplots showing linear regression lines and 95% confidence
intervals between LBM-DEXA and HGS and MAMC are
shown in Figures 1 and 2. Spearman correlation analyses
showed that LBM-DEXA was correlated significantly
Figure 2. Scatterplots, regression lines, and 95%confidence
intervals (CIs) reflecting correlations between lean body
mass measured using DEXA (LBM-DEXA) and handgrip
strength (HGS) in the development group of 150 chronic kid-
ney disease patients. Dotted lines reflect 95% CIs.
with sex, height, weight, HGS (r 5 0.72), and MAMC
(r5 0.66; P,.001 for all). As in our previous study, no cor-
relations were observed between LBM-DEXA and serum
albumin or daily protein and energy intake (P . .05).2
Hence, 2 multiple regression equations were established
using HGS or MAMC in combination with the selected
demographic variables of sex, height, and weight.
Tables 2 and 3 list the regression coefficients for LBM-
DEXA using these variables. The R-square values for the
LBM equations were 0.900 and 0.894, for LBM-H and
LBM-M, respectively.
Comparisons Between LBM Values Estimated
Using the LBM-H, LBM-M Equation, and LBM
Values Measured Using DEXA
The 2 LBM formulae were applied to the validation
group and compared with measurements made using
LBM-DEXA (Fig. 3). LBM values estimated using LBM-
HGS and LBM-M were numerically close to those
measured using DEXA (44.6 kg [37.2–52.8 kg]) and signif-
icantly correlated with them (P , .001 for both; Table 4).
Values estimated using the equations and measured using
LBM-DEXA were significantly correlated with HGS and
Table 3. Regression Coefficients Between LBM-DEXA and
Variables Selected by Multiple Linear Regression Analysis
Based on the Development Dataset and the New LBM-M
Equation
Parameters
LBM-DEXA
R-squareCoefficients t P Value
Gender 7.36 9.81 ,.001 0.894
Height, cm 0.22 4.37 ,.001
Weight, kg 0.37 11.03 ,.001
MAMC, cm 0.24 1.89 .061
Constant 226.34 23.29 .001
Equation 2: LBM-M 5 (1 if male; 0 if female) 3 7.36 1 height
(cm) 3 0.22 1 weight (kg) 3 0.37 1 MAMC (cm) 3 0.24 2 26.34
DEXA, dual-energy X-ray absorptiometry; LBM, lean body mass;
LBM-M, LBM calculated from MAMC; MAMC, mid-arm muscle
circumference.
Figure 3. Bar plots of lean body mass (LBM) measured
directly using dual-energy x-ray absorptiometry (LBM-
DEXA), estimated by middle arm circumference (LBM-M)
and handgrip strength (LBM-H) in the validation databases
of 150 chronic kidney disease patients. Lower and upper
bar boundaries are 25th and 75th percentiles, the line within
the bar is the median.
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TIAN ET AL160
MAMC (P , .001 for all) but not with serum creatinine,
daily protein, and energy intake (as observed in the
development dataset). LBM-H and LBM-M, but not
LBM-DEXA, were also significantly correlated with serum
albumin.
The performances of the new equations were further
examined, still using LBM-DEXA as the reference method
(Table 5). Both the LBM-H and LBM-M formulae slightly
overestimated the LBM, but the biases between each of the
formulae and LBM-DEXA were small. To compare the
performance of the equations in different ranges of LBM,
the analyses were repeated in mutually exclusive strata:
higher and lower than the LBM-DEXA median
(44.6 kg); at CKD Stage 3 and CKD Stages 4 and 5; and
higher and lower than the E/T median (0.476). The
observed biases were consistent across all the groups
Table 4. Correlation Coefficients Between LBM-DEXA and
LBM-M-H, LBM-H, and Other Nutritional Indices, Based on
the Validation Dataset
LBM-DEXA LBM-M LBM-H
LBM-DEXA — 0.91** 0.91**
LBM-M 0.91** — 0.99**
LBM-H 0.91** 0.99** —
Serum creatinine 0.10 20.01 20.02
MAMC 0.70** 0.72** 0.70**
HGS 0.72** 0.71** 0.77**
Serum albumin 0.12 0.18* 0.20*
Daily protein intake 0.11 0.07 0.07
Daily energy intake 20.03 20.08 20.08
DEXA, dual-energy X-ray absorptiometry; HGS, handgrip strength;
LBM, lean body mass; LBM-H, LBM estimated from handgrip
strength; LBM-M, LBM estimated from mid-arm muscle circumfer-
ence; MAMC, mid-arm muscle circumference.
*P , .05 and **P , .001 for correlation coefficients between
variables. T
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Figure 5. Difference plot between lean body mass (LBM),
measured directly using dual-energy x-ray absorptiometry
(LBM-DEXA), and estimated using the equation based on
handgrip strength (LBM-H) in the validation group of 150
chronic kidney disease patients. The medium dashed line is
the difference and the others are the limits of agreement. Sub-
groups were defined by LBM above and below 44.65 kg.
NOVEL EQUATIONS FOR ESTIMATING LEAN BODY MASS 161
(P , .001-.05). In general, the IQR differences with both
equations were small (3.92-5.32 kg) and independent of
the values of LBM-DEXA, E/T, and CKD stage. This
indicates that the 2 new equations are precise. In terms of
percentage accuracy, the LBM 1-P20 was markedly low
for both equations, that is, 1.33% to 4.05%, indicating
higher accuracy. The accuracies of the equations were
even better in patients with LBM $44.6 kg (P , .05) and
significantly lower in patients with E/T . 0.476
(P , .05). These results are shown in Figures 4-9.
Discussion
We developed 2 new equations to estimate LBM in non-
dialyzed CKD that havehigh precision and accuracy. Our
data indicated that LBM estimated using MAMC or
HGS, combined with sex, height, and weight was not
only close to the LBM measured by DEXA but also signif-
icantly correlated. The performance of the new equations
was consistent across subgroups in CKD stage and either
side of the medians of measured LBM and E/T.
To our knowledge, no prior study has used regression
methods to estimate LBM in nondialyzed CKD patients
although 2 studies have done so in patients on HD and
PD.13,14 However, several equations for estimating LBM
based on MAMC and HGS have developed by our group
and Kalantar-Zadeh’s.13,14 These studies indicated that
there was a high correlation between LBM and both
HGS and MAMC across all CKD stages. MAMC reflects
the mass of biceps and triceps, and HGS depends on the
strength of forearm and hands in a particular action.
Because limbs contribute significantly to physical activity
and exercise training, muscle mass and strength in upper
limbs parallels that of the whole body. It is, therefore,
Figure 4. Difference plot between lean body mass (LBM),
measured directly using dual-energy x-ray absorptiometry
(LBM-DEXA), and estimated using the equation based on
mid-arm circumference (LBM-M) in the validation group of
150 chronic kidney disease patients. The medium dashed
line is the difference and the others are the limits of agree-
ment. Subgroups were defined by LBM above and below
44.65 kg.
reasonable to expect a close relationship between HGS
and MAMC and LBM. Previous studies support our data,
showing that HGS and MAMC are in good agreement
with LBM measured by DEXA in HD patients.13,26
These observations can explain why the new equations
for estimating LBM perform well in PD and HD
patients.13,14 In addition, estimating LBM using MAMC
has been found to be more effective that measuring it
using skin fold at 4 sites in PD patients.14 Because
measuring HGS and MAMC are easily performed and
inexpensive bedside tests, we believe that the newequations
are a promising method for monitoring nutritional status
Figure 6. Difference plot between lean body mass (LBM),
measured directly using dual-energy x-ray absorptiometry
(LBM-DEXA), and estimated using the equation based on
mid-arm circumference (LBM-M) in the validation group of
150 chronic kidney disease (CKD) patients. The medium
dashed line is the difference and the others are the limits of
agreement. Subgroups were defined by CKD stage (esti-
mated glomerular filtration rate 30-59 or ,30 mL/minute/
1.73 m2).
Figure 9. Difference plot between lean body mass (LBM),
measured directly using dual-energy x-ray absorptiometry
(LBM-DEXA), and estimated using the equation based on
handgrip strength (LBM-H) in the validation group of 150
chronic kidney disease patients. The medium dashed line is
the difference and the others are the limits of agreement.
Subgroups were defined by the ratio of extracellular water
to total body water (,0.476 or $0.476).
Figure 7. Difference plot between lean body mass (LBM),
measured directly using dual-energy x-ray absorptiometry
(LBM-DEXA), and estimated using the equation based on
handgrip strength (LBM-H) in the validation group of 150
chronic kidney disease (CKD) patients. The medium dashed
line is the difference and the others are the limits of agree-
ment. Subgroups were defined by CKD stage (estimated
glomerular filtration rate 30-59 or ,30 mL/minute/1.73 m2).
TIAN ET AL162
and exploring the effect of nutritional interventions on
somatic protein stores.27 Our previous data have also indi-
cated that LBM estimated by HGS and a combination of
HGS and MAMC predict all-cause mortality in PD pa-
tients.14 The prognostic value of new equations still needs
to be explored in the future.
It has been questioned whether we need MAMC or
HGS-derived LBM to monitor nutritional status because
MAMC and HGS can themselves serve as general indices
of nutritional and prognostic status.13,26,28 In addition,
their reduction, known as sarcopenia,29 can be a sign of
malnutrition or wasting.30 We think that LBM, as a direct
index of lean mass for the whole body, can be directly
Figure 8. Difference plot between lean body mass (LBM),
measured directly using dual-energy x-ray absorptiometry
(LBM-DEXA), and estimated using the equation based on
mid-arm circumference (LBM-M) in the validation group of
150 chronic kidney disease patients. The medium dashed
line is the difference and the others are the limits of agree-
ment. Subgroups were defined by the ratio of extracellular
water to total body water (,0.476 or $0.476).
applied in the diagnosis of PEW, as recommended by the
International Society of Renal Nutrition andMetabolism.5
For patients at CKD Stages 3 to 5, there are often fluctua-
tions in body weight as the result of changes in fat mass, lean
mass, volume status26,31,32 (due to volume overload),
comorbidities, and any conditions that lead to
disturbances in protein, fat, or lipid metabolism. We
should determine whether lean mass, fat mass, or volume
status, or a combination, is contributing to changes in
body weight, and then providing required nutritional care.
Our data also indicated statistically significant but weak
relationships between LBM-H and LBM-Mwith serum al-
bumin. Despite serum albumin level is still by far the most
commonly used nutritional marker in patients with CKD,
it is notable that serum albumin level in general correlated
poorly with other nutritional estimates. Because low serum
albumin level not only reflects poor nutritional status33-35
but also albumin losses in urine (and/or dialysate), the
presence of inflammatory reaction, systemic diseases, old
age, and degree of hydration.33,36-38 Kaysen et al.35,39 also
supported that serum albumin level is related more to
inflammation than nutritional status in dialysis patients,
which may also explain why the present study aimed to
develop such equations for nondialyzed patients at CKD,
instead of using serum albumin as a nutritional index.
Our data showed that the 4-variable LBM formulae
overestimated the LBM measured by DEXA by 0.46
and 0.94 kg. The 95% confidence limits on the median
difference between the methods were very narrow, and
the narrow scatter of differences between the methods
was independent of LBM, volume status, and stage of
CKD. It is noteworthy that the biases between estimated
and measured LBMs were lower than that shown in Noo-
ri’s study in HD patients,13 and the accuracy was better
NOVEL EQUATIONS FOR ESTIMATING LEAN BODY MASS 163
than that we showed in PD patients.14 There are a num-
ber of potential reasons for this. First, in Noori’s study,
DEXA measurements were obtained on a nondialyzed
day for the development dataset, whereas in the valida-
tion dataset, a difference method, that is, near-infrared
interactance, was performed during HD treatment. Sec-
ond, patients in the validation group in Noori’s study
were slightly older and included fewer men. As with
the general population, HGS values for CKD patients
undergoing dialysis are associated with age and
sex28,34,40 and so differences in the characteristics of
people between development and validation group
could affect the results. Third, as shown in the present
study, the performance of the new equations in bias and
precision did not differ between patients with higher or
lower values of E/T. However, there was lower
accuracy in patients with higher E/T, which indicates
that a bigger variation in fluid status may contribute to
a lower accuracy. This could also explain why our
previous study performed in PD patients observed
lower accuracy of the new equations compared with
that shown in nondialyzed CKD patients.
To our knowledge, this is the first study that has
developed and validated LBM equations using HGS
and MAMC for nondialyzed CKD patients using
DEXA as the reference method. Comprehensive nutri-
tional data were collected for the whole dataset and
nutritional measurements were performed by a skillful
dietitian, thereby reducingintrabias between repeated
measurements. Volume status was also evaluated, giving
us a unique chance to explore the bias, precision, and
accuracy of the new equations in patients with different
volume statuses. Because DEXA is influenced by hydra-
tion status, simultaneous evaluation of volume status is
important.12,41,42
We are also aware of limitations of this study. The predic-
tionmodel we described is developed in single-center non-
dialyzed CKD patients and is therefore not necessarily valid
for other populations. Validation of the new equations still
needs to be performed in a larger nondialyzed CKD pop-
ulation from multiple origins. In addition, only clinically
stable patients were included in the study because acute co-
morbidities could rapidly influence fluid status and body
composition. Our new equations also cannot be applied
to any patients who met this study’s exclusion criteria.
The cross-sectional nature of the study limited us to
exploring the prognostic value of the new equations in
nondialyzed CKD patients.
In summary, 2 new models for predicting LBM using
HGS and MAMC were developed and then validated in a
relatively large sample of nondialyzed CKD patients.
Results of the validation indicated that the equations can
provide reliable and accurate estimates of LBM in nondia-
lyzed CKD patients in clinical practice. Further studies are
needed to validate the models in a larger study population,
and longitudinal studies are required to evaluate the suit-
ability of the formulae for detecting changes in LBM in
response to interventions.
Practical Implications
This study developed simple, accurate, reliable and
routine methods for estimating LBM. That can become
an integral part of monitoring nutritional status of CKDpa-
tients in the clinical practice.
Acknowledgments
The authors express their appreciation to the staff of the peritoneal
dialysis center of Peking University First Hospital, for their continuing
contribution to this study.
References
1. Carrero JJ, ChmielewskiM, Axelsson J, et al. Muscle atrophy, inflamma-
tion and clinical outcome in incident and prevalent dialysis patients.Clin Nutr.
2008;27:557-564.
2. Dong J, Li YJ, Lu XH, Gan HP, Zuo L, Wang HY. Correlations of lean
body mass with nutritional indicators and mortality in patients on peritoneal
dialysis. Kidney Int. 2008;73:334-340.
3. Noori N, Kovesdy CP, Dukkipati R, et al. Survival predictability of lean
and fat mass in men and women undergoing maintenance hemodialysis. Am J
Clin Nutr. 2010;92:1060-1070.
4. Stenvinkel P, Heimburger O, Paultre F, et al. Strong association between
malnutrition, inflammation, and atherosclerosis in chronic renal failure. Kid-
ney Int. 1999;55:1899-1911.
5. Ikizler TA, Cano NJ, Franch H, et al. Prevention and treatment of pro-
tein energy wasting in chronic kidney disease patients: a consensus statement
by the International Society of Renal Nutrition and Metabolism. Kidney Int.
2013;84:1096-1107.
6. Foster BJ, Kalkwarf HJ, Shults J, et al. Association of chronic kidney dis-
ease with muscle deficits in children. J Am Soc Nephrol. 2011;22:377-386.
7. Fried LF, BoudreauR, Lee JS, et al. Kidney function as a predictor of loss
of lean mass in older adults: health, aging and body composition study. J Am
Geriatr Soc. 2007;55:1578-1584.
8. O’Sullivan AJ, Lawson JA, ChanM, Kelly JJ. Body composition and en-
ergy metabolism in chronic renal insufficiency.Am J Kidney Dis. 2002;39:369-
375.
9. Negri AL, Barone R, Veron D, et al. Lean mass estimation by creatinine
kinetics and dual-energy x-ray absorptiometry in peritoneal dialysis. Nephron
Clin Pract. 2003;95:c9-c14.
10. Johansson AC, Attman PO, Haraldsson B. Creatinine generation rate
and lean body mass: a critical analysis in peritoneal dialysis patients. Kidney
Int. 1997;51:855-859.
11. de Fijter WM, de Fijter CW, Oe PL, ter Wee PM, Donker AJ. Assess-
ment of total body water and lean body mass from anthropometry, Watson
formula, creatinine kinetics, and body electrical impedance compared with
antipyrine kinetics in peritoneal dialysis patients. Nephrol Dial Transplant.
1997;12:151-156.
12. Konings CJ, Kooman JP, Schonck M, et al. Influence of fluid status on
techniques used to assess body composition in peritoneal dialysis patients. Perit
Dial Int. 2003;23:184-190.
13. Noori N, Kovesdy CP, Bross R, et al. Novel equations to estimate lean
body mass in maintenance hemodialysis patients. Am J Kidney Dis.
2011;57:130-139.
14. Dong J, Li YJ, Xu R, Yang ZK, Zheng YD. Novel equations for esti-
mating lean body mass in peritoneal dialysis patients. Perit Dial Int.
2015;35:743-752.
15. Smith SC Jr, Jackson R, Pearson TA, et al. Principles for national and
regional guidelines on cardiovascular disease prevention: a scientific statement
from the World Heart and Stroke Forum. Circulation. 2004;109:3112-3121.
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref1
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref1
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref1
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref2
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref2
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref2
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref3
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref3
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref3
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref4
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref4
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref4
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref5
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref5
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref5
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref5
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref6
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref6
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref7
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref7
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref7
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref8
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref8
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref8
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref9
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref9
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref9
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref10
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref10
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref10
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref11
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref11
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref11
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref11
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref11
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref12
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref12
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref12
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref13
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref13
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref13
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref14
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref14
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref14
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref15
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref15
http://refhub.elsevier.com/S1051-2276(17)30228-5/sref15
TIAN ET AL164
16. Ma YC, Zuo L, Chen JH, et al. Modified glomerular filtration rate
estimating equation for Chinese patients with chronic kidney disease. J Am
Soc Nephrol. 2006;17:2937-2944.
17. Kamimura MA, Avesani CM, Cendoroglo M, Canziani ME,
Draibe SA, Cuppari L. Comparison of skinfold thicknesses and bioelectrical
impedance analysis with dual-energy X-ray absorptiometry for the assessment
of body fat in patients on long-term haemodialysis therapy.Nephrol Dial Trans-
plant. 2003;18:101-105.
18. Donadio C, Halim AB, CaprioF, Grassi G, Khedr B, Mazzantini M.
Single- and multi-frequency bioelectrical impedance analyses to analyse
body composition in maintenance haemodialysis patients: comparison with
dual-energy x-ray absorptiometry. Physiol Meas. 2008;29:S517-S524.
19. Garagarza C, Joao-Matias P, Sousa-Guerreiro C, et al. Nutritional sta-
tus and overhydration: can bioimpedance spectroscopy be useful in haemo-
dialysis patients? Nefrologia. 2013;33:667-674.
20. Nelson EE, Hong CD, Pesce AL, Peterson DW, Singh S, Pollak VE.
Anthropometric norms for the dialysis population. Am J Kidney Dis.
1990;16:32-37.
21. Chumlea WC. Anthropometric and body composition assessment in
dialysis patients. Semin Dial. 2004;17:466-470.
22. Wang AY, Sanderson JE, Sea MM, et al. Handgrip strength, but not
other nutrition parameters, predicts circulatory congestion in peritoneal dial-
ysis patients. Nephrol Dial Transplant. 2010;25:3372-3379.
23. Asghar RB, Green S, Engel B, Davies SJ. Relationship of demographic,
dietary, and clinical factors to the hydration status of patients on peritoneal
dialysis. Perit Dial Int. 2004;24:231-239.
24. Woodrow G, Oldroyd B, Wright A, Coward WA, Truscott JG. The
effect of normalization of ECWvolume as a marker of hydration in peritoneal
dialysis patients and controls. Perit Dial Int. 2005;25(Suppl 3):S49-S51.
25. Inker LA, Schmid CH, Tighiouart H, et al. Estimating glomerular
filtration rate from serum creatinine and cystatin C. N Engl J Med.
2012;367:20-29.
26. Noori N, Kopple JD, Kovesdy CP, et al. Mid-arm muscle circumfer-
ence and quality of life and survival in maintenance hemodialysis patients.
Clin J Am Soc Nephrol. 2010;5:2258-2268.
27. Dong J, Ikizler TA.New insights into the role of anabolic interventions
in dialysis patients with protein energy wasting. Curr Opin Nephrol Hypertens.
2009;18:469-475.
28. Wang AY, Sea MM, Ho ZS, Lui SF, Li PK, Woo J. Evaluation of hand-
grip strength as a nutritional marker and prognostic indicator in peritoneal
dialysis patients. Am J Clin Nutr. 2005;81:79-86.
29. Cano NJ, Miolane-Debouit M, Leger J, Heng AE. Assessment of
body protein: energy status in chronic kidney disease. Semin Nephrol.
2009;29:59-66.
30. Gibney MJ, Elia M, Ljungqvist J. Clinical Nutrition: Anthropometric
Assessment of Body Composition. 3rd ed. Oxford, UK: Blackwell; 2005:20.
31. Pellicano R, Strauss BJ, Polkinghorne KR, Kerr PG. Longitudinal
body composition changes due to dialysis. Clin J Am Soc Nephrol.
2011;6:1668-1675.
32. Choi SJ, KimNR,Hong SA, et al. Changes in body fat mass in patients
after starting peritoneal dialysis. Perit Dial Int. 2011;31:67-73.
33. Heimburger O, Bergstrom J, Lindholm B. Is serum albumin an index
of nutritional status in continuous ambulatory peritoneal dialysis patients?
Perit Dial Int. 1994;14:108-114.
34. Jones CH,Newstead CG,Will EJ, Smye SW, Davison AM. Assessment
of nutritional status in CAPD patients: serum albumin is not a useful measure.
Nephrol Dial Transplant. 1997;12:1406-1413.
35. Kaysen GA. Biological basis of hypoalbuminemia in ESRD. J Am Soc
Nephrol. 1998;9:2368-2376.
36. Kaysen GA, Schoenfeld PY. Albumin homeostasis in patients un-
dergoing continuous ambulatory peritoneal dialysis. Kidney Int.
1984;25:107-114.
37. Jones CH, Smye SW, Newstead CG, Will EJ, Davison AM. Extracel-
lular fluid volume determined by bioelectric impedance and serum albumin in
CAPD patients. Nephrol Dial Transplant. 1998;13:393-397.
38. VIIIth Congress of the International Society for Peritoneal Dialysis.
ISPD 98. Seoul, Korea, August 23-26, 1998. Abstracts. Perit Dial Int.
1998;18(Suppl 2):S1-S94.
39. Kaysen GA, Rathore V, Shearer GC, Depner TA. Mechanisms of hy-
poalbuminemia in hemodialysis patients. Kidney Int. 1995;48:510-516.
40. Stenvinkel P, Barany P, Chung SH, Lindholm B, Heimburger O. A
comparative analysis of nutritional parameters as predictors of outcome in
male and female ESRD patients. Nephrol Dial Transplant. 2002;17:1266-
1274.
41. Horber FF, Thomi F, Casez JP, Fonteille J, Jaeger P. Impact of hydration
status on body composition as measured by dual energy X-ray absorptiometry
in normal volunteers and patients on haemodialysis. Br J Radiol. 1992;65:895-
900.
42. Abrahamsen B, Hansen TB, Hogsberg IM, Pedersen FB, Beck-
Nielsen H. Impact of hemodialysis on dual X-ray absorptiometry, bioelec-
trical impedance measurements, and anthropometry. Am J Clin Nutr.
1996;63:80-86.
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	Novel Equations for Estimating Lean Body Mass in Patients With Chronic Kidney Disease
	Introduction
	Methods
	Study Design and Subjects
	Demographic, Biochemical Measurements, and Dietary Variables
	Dual-energy X-ray Absorptiometry
	Anthropometric Measurements and HGS
	Volume Status
	Statistical Analysis
	Results
	Characteristics of Participants
	Development of New Equations for Estimating LBM
	Comparisons Between LBM Values Estimated Using the LBM-H, LBM-M Equation, and LBM Values Measured Using DEXA
	Discussion
	Practical Implications
	Acknowledgments
	References

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