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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. ti n g L B M -M a n d L B M -H U s in g L B M -D E X A a s R e fe re n c e M e th o d in th e V a lid a ti o n D a ta s e t s W it h E s ti m a te d L B M P a ti e n ts W it h E s ti m a te d L B M P a ti e n ts W it h E s ti m a te d L B M 5 7 5 ) $ 4 4 .6 k g (n 5 7 5 ) C K D S ta g e 3 C K D S ta g e 4 -5 E /T , 0 .4 7 6 (n 5 7 5 ) E /T $ 0 .4 7 6 (n 5 7 5 ) 1 .9 9 ) 0 .0 2 (2 1 .3 8 to 1 .0 9 ) 1 .0 9 (2 0 .3 3 to 1 .9 8 ) 0 .0 3 (2 0 .3 8 to 0 .8 8 ) 0 .0 2 (2 0 .7 5 to 0 .9 9 ) 1 .1 0 (0 .0 2 to 2 .0 4 ) 2 .1 0 ) 0 .4 5 (2 0 .9 5 to 1 .5 8 ) 1 .3 5 (0 .4 5 to 2 .2 8 ) 0 .5 3 (2 0 .5 4 to 1 .3 7 ) 0 .8 0 (0 .0 4 to 1 .5 8 ) 1 .3 4 (0 .0 7 to 2 .1 6 ) 5 .7 0 ) 5 .0 9 (3 .8 2 to 6 .3 2 ) 4 .5 5 (3 .4 9 to 5 .8 7 ) 4 .5 8 (2 .9 7 to 6 .3 4 ) 4 .1 5 (3 .1 6 to 5 .3 7 ) 4 .9 0 (3 .4 9 to 7 .2 8 ) 5 .2 6 ) 5 .4 6 (3 .9 6 to 6 .4 5 ) 4 .3 9 (3 .0 3 to 5 .7 2 ) 5 .3 2 (3 .1 7 to 6 .3 8 ) 4 .1 0 (3 .1 8 to 5 .5 6 ) 5 .3 1 (3 .2 7 to 6 .7 6 ) 6 .6 7 ) 1 .8 5 (0 .0 0 to 5 .3 3 ) 2 .6 7 (0 .0 0 to 6 .6 7 ) 4 (0 .0 0 to 9 .3 3 ) 1 .3 3 (0 .0 0 to 3 .2 5 ) 4 .0 5 (0 .0 0 to 9 .3 1 ) 5 .3 3 ) 0 .6 7 (0 .0 0 to 2 .6 7 ) 2 .6 7 (0 .0 0 to 6 .6 7 ) 2 .6 7 (0 .0 0 to 6 .6 7 ) 1 .3 3 (0 .0 0 to 3 .2 5 ) 2 .7 0 (0 .0 0 to 6 .8 2 ) im a te s th a t d if fe re d fr o m th e m e a s u re d L B M b y m o re th a n 2 0 % ; C I, c o n fi d e n c e in te rv a l; D E X A , d u a l- e n e rg y X -r a y a b s o rp ti - ,L B M e s ti m a te d fr o m h a n d g ri p s tr e n g th ;L B M -M ,L B M e s ti m a te d fr o m m id a rm m u s c le c ir c u m fe re n c e ;E /T ,t h e ra ti o o fe x tr a - 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 a b le 5 . P e rf o rm a n c e o f th e N e w E q u a ti o n s fo r E s ti m a A ll P a ti e n ts (n 5 1 5 0 ) P a ti e n t , 4 4 .6 k g (n B ia s -m e d ia n d if fe re n c e (9 5 % C I) L B M -M e q u a ti o n 0 .4 6 (2 0 .2 0 to 1 .1 9 ) 0 .9 9 (0 .0 7 to L B M -H e q u a ti o n 0 .9 4 (2 0 .2 1 to 1 .6 0 ) 1 .3 4 (0 .7 2 to P re c is io n -I Q R o f th e d if fe re n c e (9 5 % C I) L B M -M e q u a ti o n 4 .4 6 (3 .7 4 to 5 .8 3 ) 4 .2 1 (3 .1 3 to L B M -H e q u a ti o n 4 .3 0 (3 .5 5 to 5 .8 5 ) 3 .9 2 (2 .8 0 to A c c u ra c y, % (9 5 % C I) 1 2 P 2 0 † (% ) L B M -M e q u a ti o n 3 .3 3 (0 .6 7 to 6 .6 7 ) 2 .6 7 (0 .0 0 to L B M -H e q u a ti o n 2 .6 7 (0 .6 7 to 5 .3 3 ) 2 .0 0 (0 .0 0 to 1 2 P 2 0 † , a c c u ra c y w a s c a lc u la te d a s th e p e rc e n ta g e o f e s t o m e tr y ;I Q R ,i n te rq u a rt ile ra n g e ;L B M ,l e a n b o d y m a s s ;L B M -H c e llu la r w a te r (E C T ) to to ta l b o d y w a te r (T B W ). 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. 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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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