Abstract
♦ Objectives:
To develop and validate equations for estimating lean body mass (LBM) in peritoneal dialysis (PD) patients.
♦ Methods:
Two equations for estimating LBM, one based on mid-arm muscle circumference (MAMC) and hand grip strength (HGS), i.e., LBM-M-H, and the other based on HGS, i.e., LBM-H, were developed and validated with LBM obtained by dual-energy X-ray absorptiometry (DEXA). The developed equations were compared to LBM estimated from creatinine kinetics (LBM-CK) and anthropometry (LBM-A) in terms of bias, precision, and accuracy. The prognostic values of LBM estimated from the equations in all-cause mortality risk were assessed.
♦ Results:
The developed equations incorporated gender, height, weight, and dialysis duration. Compared to LBM-DEXA, the bias of the developed equations was lower than that of LBM-CK and LBM-A. Additionally, LBM-M-H and LBM-H had better accuracy and precision. The prognostic values of LBM in all-cause mortality risk based on LBM-M-H, LBM-H, LBM-CK, and LBM-A were similar.
♦ Conclusions:
Lean body mass estimated by the new equations based on MAMC and HGS was correlated with LBM obtained by DEXA and may serve as practical surrogate markers of LBM in PD patients.
Keywords: Lean body mass, peritoneal dialysis, mortality, mid-arm muscle circumference, hand grip strength, dual-energy X-ray absorptiometry, creatinine kinetics, anthropometry
According to the International Society for Renal Nutrition and Metabolism, lean body mass (LBM) is an important index of protein-energy wasting (1). Several studies have reported that muscle atrophy predicts high mortality rates in chronic kidney disease, post-kidney transplant, and dialysis patients (2–10). The gold standard (i.e., tracer dilution) and reference (i.e., dual-energy X-ray absorptiometry or DEXA) methods for measuring LBM are time-consuming and not readily available; therefore, other methods of estimating LBM have been studied. Lean body mass can be assessed by anthropometry (LBM-A), creatinine kinetics (LBM-CK), and bioelectrical impedance (LBM-BIA). However, the precision of these methods has been put into question. The creatinine clearance method is affected by dietary meat intake, physical activity, hormonal balance, diurnal variations of creatinine clearance, and catabolic states (11,12). Furthermore, LBM-CK is highly variable and underestimates LBM (12,13). On the other hand, LBM-A and LBM-BIA overestimate LBM, especially in obese patients or patients with fluid retention (13,14).
Therefore, simple, practical, and reliable methods for estimating LBM are required. Body composition measurements, which have been recommended in routine practice and intervention studies (1,15), are dependent on muscle and fat mass (16,17). It has been reported that hand grip strength (HGS) and mid-arm muscle circumference (MAMC) measurements can reflect LBM. Hand grip strength and MAMC are associated with good health outcomes for patients with end-stage renal disease (2,8,18–21). Lean body mass measured by HGS or MAMC has had good agreement with LBM measured by DEXA (LBM-DEXA) in hemodialysis (HD) patients (22). As a result of differences in the distribution of body composition among HD and peritoneal dialysis (PD) patients (17,23), specific equations for estimating LBM for PD patients need to be developed.
The objectives of this study were to develop equations for estimating LBM based on MAMC and HGS, compare the LBM values obtained from these equations with LBM measured by DEXA, and compare the developed equations with LBM-CK and LBM-A. Additionally, we assessed the prognostic values of LBM in all-cause mortality risk in a large retrospective PD cohort study because LBM should be a significant predictor of outcome events in PD patients (3,8–10).
Methods
Subjects
This study was approved by the Ethics Committees of Peking University First Hospital with strict adherence to the Declaration of Helsinki. Informed written consent was obtained from each patient.
Cross-sectional study design: There were 2 cross-sectional datasets: 1) data from a development group (106 PD patients) obtained between April 1, 2011, and August 1, 2011; and 2) data from a validation group (107 PD patients) obtained from April 1, 2012, to August 1, 2012. The inclusion criteria consisted of 1) ≥ 18 years of age; 2) PD diagnosis ≥ 3 months; 3) clinically stable; and 4) willingness to be medically examined. Patients were excluded if they had systemic infections, acute cardiovascular events, active hepatitis, tumors, surgeries, or physical trauma 1 month prior to the study.
Retrospective cohort study design: In this study, 889 incident PD patients were enrolled between January 1, 2003, and March 31, 2011. Demographic and clinical data were collected at baseline. Blood pressure, biochemistry data, and nutritional indices were measured during the first 3 months. Dialysis adequacy and residual renal function were measured during the first 6 months. All patients were followed up to death, HD or renal transplant, or the end of the study (November 1, 2011).
Dexa
Dual-energy X-ray absorptiometry was performed in an Orland Series XR-800 Pen Beam X-ray Bone Densitometer equipped with software Illuminatus DXA 4.4.0 (CooperSurgical, Trumbull, CT, USA). Subjects were requested to wear clothing without any metal artifacts following an overnight fast. Patients were in supine position and not allowed to eat or drink during the scan. The resulting scans were analyzed for the determination of LBM, fat mass, and bone mineral content.
Anthropometric Measurements and HGS
Anthropometric measurements were performed by the same trained personnel using standard skin fold calipers. Measurements were repeated 3 times and averaged. Midarm muscle circumference was calculated from mid-arm circumference and triceps skin fold thickness. Lean body mass estimated from anthropometry was calculated using the following formulas (24):
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Hand grip strength was measured in the dominant and non-dominant arms using a dynamometer; measurements were repeated 3 times and expressed in Newtons (25).
Biochemical Measurements, Dialysis Adequacy, and Dietary Variables
Biochemical measurements were performed with a Hitachi chemistry analyzer. Twenty-four-hour dialysate and urine collections were performed to calculate weekly urea clearance (Kt/V) and total creatinine clearance rate (TCcr). Dietary variables were evaluated from 3-day dietary records using a computer software program. Lean body mass estimated from creatinine kinetics was calculated using the following equation (12):
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where CE is creatinine excretion (mmoles/d) and CD is creatinine degradation (mmoles/d).
CE and CD were calculated from the following formulas:
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where UCO is the urinary creatinine output (mmoles/d), DCO is the dialysate creatinine output (mmoles/d), and PC is the plasma creatinine (μmoles/L).
Statistical Analyses
Data with normal distribution were expressed as mean ± standard deviation; otherwise, data were expressed as median values with their lower and upper quartiles. Categorical variables were expressed as percentage or ratio. Data from the patients were compared by the chi-square test or Mann-Whitney U-test.
To estimate LBM based on MAMC and HGS, stepwise linear regression analyses were performed to select potentially biologic and easily-available predictors (e.g., age, gender, dialysis duration, height, and weight). Therefore, the equation for estimating LBM from MAMC and dominant HGS incorporated gender, height, weight and dialysis duration (LBM-M-H equation). In the clinical practice, MAMC is more difficult to assess than HGS; therefore, a second equation, which incorporated HGS, gender, height, weight, and dialysis duration, was developed (LBM-H equation).
To compare the performance of these equations with those of traditional equations, LBM was calculated by LBM-M-H, LBM-H, LBM-CK, and LBM-A equations. The estimated LBM and measured LBM by DEXA were compared by Bland-Altman analyses. Bias was defined as the median of the difference between the estimated LBM and measured LBM by DEXA; precision was defined as the interquartile range (IQR) of this difference. Accuracy was calculated as the percentage of estimates that differed by > 20% from the measured LBM (1-P20) (26). Confidence intervals (CI) were calculated by means of bootstrap methods (2,000 bootstraps). Differences among the equations were determined by the Wilcoxon sign rank test, IQR by the bootstrap method, and 1-P20 by McNemar's test.
We assessed whether LBM estimated by LBM-M-H, LBM-H, LBM-CK, or LBM-A could predict all-cause mortality risk using Cox regression analysis. Omnibus tests were used to assess the predictive power of the equations. We reported the hazards ratios (HRs) with 95% CIs. All probabilities were 2-tailed and the level of significance was set at 0.05. Statistical analyses were performed by SPSS for Windows software version 13.0 (SPSS Inc., Chicago, IL, USA) and by Medcalc for Windows software version 9.2.1.0 (Medcalc software, Broekstraat, Belgium).
Results
Patient Characteristics
The basic demographic and clinical data of the cross-sectional and retrospective cohort studies are shown in Table 1. According to the results, there were no significant differences in demographic data, laboratory measurements, and nutritional indices between the 2 datasets (p > 0.05). The dominant HGS values were significantly higher than the non-dominant HGS values (p < 0.001); therefore, dominant HGS was used for the remainder of the study.
TABLE 1.
Demographic and Clinical Characteristics of PD Patients in the Cross-Sectional Datasets
Equations for Estimating LBM
In the development data set, correlation coefficients between LBM-DEXA and MAMC or HGS were 0.68 and 0.75, respectively (p < 0.001 for both). The scatterplots, linear regression lines, and 95% CIs between LBM-DEXA and MAMC and between LBM-DEXA and HGS are shown in Figures 1 and 2, respectively. There were no correlations between LBM-DEXA and serum albumin, LBM-DEXA and daily protein, or LBM-DEXA and energy intake (p > 0.05), similar to our previous findings (3). Stepwise linear regression analyses were performed to select variables (e.g., age, gender, dialysis duration, height, weight) that could be incorporated into MAMC and HGS or HGS to develop 2 new equations. Tables 2 and 3 show the regression coefficients of the developed equations using these variables. The R-square of the LBM equations from MAMC and HGS (LBM-M-H) and from HGS (LBM-H) were 0.86 and 0.85, respectively.
Figure 1 —
Scatterplots, regression lines, and 95% CIs of the correlations between LBM measured by DEXA (LBM-DEXA) and MAMC in the development cohort dataset (106 PD patients). Dotted lines represent 95% CIs. CI = confidence interval; LBM = lean body mass; DEXA = dual-energy X-ray absorptiometry; MAMC = mid-arm muscle circumference; PD = peritoneal dialysis.
Figure 2 —
Scatterplots, regression lines, and 95% CIs of the correlations between LBM measured by DEXA (LBM-DEXA) and dominant HGS in the development cohort dataset (106 PD patients). Dotted lines represent 95% CIs. CI = confidence interval; LBM = lean body mass; DEXA = dual-energy X-ray absorptiometry; HGS = hand grip strength; PD = peritoneal dialysis.
TABLE 2.
Regression Coefficients of LBM-DEXA with Selected Variables by Multiple Linear Regression Analysis Based on Development Datasets and LBM-M-H
TABLE 3.
Regression Coefficients of LBM-DEXA with Selected Variables by Multiple Linear Regression Analysis Based on Development Datasets and LBM-H
Comparisons Among LBM-M-H, LBM-H, LBM-CK, LBM-A, and LBM-DEXA
Four LBM values were estimated by LBM-M-H, LBM-H, LBM-CK, and LBM-A per patient and compared to the LBM value measured by DEXA (Figure 3). The estimated LBM values by LBM-M-H and LBM-HGS were numerically close to the measured LBM by DEXA (42.5 kg; 36.6 – 50.0 kg). Lean body mass from LBM-CK (38.8 kg; 32.2 – 45.6 kg) underestimated LBM from DEXA, whereas LBM from LBM-A (46.7 kg; 40.1 – 56.0 kg) overestimated LBM from DEXA. Lean body mass measured by DEXA was significantly correlated with the LBM value estimated from the 4 equations (p < 0.001 for all) with the lowest r obtained from LBM-DEXA and LBM-CK (r = 0.59; Table 4). The LBM-DEXA and LBM values from the 4 equations were significantly correlated to serum creatinine, MAMC, and HGS (p < 0.001 for all), but not to serum albumin, daily protein, or energy intake (p > 0.05).
Figure 3 —
Bar plots of LBM measured by DEXA (LBM-DEXA), LBM estimated by MAMC and HGS (LBM-M-H), HGS (LBM-H), CK (LBM-CK), and anthropometric measurements (LBM-A) in the validation dataset of 107 PD patients. Lower and upper bar lines represent the 25th and 75th percentiles, respectively. The line of the bar is the median. LBM = lean body mass; DEXA = dual-energy X-ray absorptiometry; MAMC = mid-arm muscle circumference; HGS = hand grip strength; CK = creatinine kinetics; PD = peritoneal dialysis.
TABLE 4.
Correlation Coefficients Among LBM-DEXA, LBM-M-H, LBM-H, LBM-CK, and LBM-A
The performance of LBM-M-H, LBM-H, LBM-CK, and LBM-A was assessed using LBM-DEXA as a reference method (Table 5 and Figures 4-1, 4-2, 4-3, and 4-4). The analyses were repeated within 2 mutually exclusive strata that were higher or lower than the median of LBM-DEXA (42.5 kg). The biases between LBM-M-H or LBM-H and LBM-DEXA were significantly lower than those between LBM-CK or LBM-A and LBM-DEXA; this was consistent across the subgroups defined by LBM values that were higher or lower than 42.5 kg (p < 0.001 – 0.05). In general, the IQR differences of LBM-M-H and LBM-H were narrower than those of LBM-CK but similar to those of LBM-A, indicating that the developed equations had higher precision. With respect to accuracy, 1-P20 of LBM was significantly lower from LBM-M-H and LBM-H than from LBM-CK and LBM-A (p < 0.001 – 0.01), indicating that the developed equations had higher accuracy. As previously reported, in patients with relatively high LBM values, the LBM-CK equation has higher bias and variation and worse accuracy than LBM-DEXA (p < 0.001 – 0.01). In contrast, the accuracy of LBM-M-H and LBM-H was consistent or even better in patients with LBM ≥ 42.5 kg (p < 0.05). The bias and precision of LBM-M-H and LBM-H were not significantly different for the whole group (p > 0.05) while the accuracy of LBM-M-H was better than that of LBM-H (p < 0.05).
TABLE 5.
Performance of LBM-M-H and LBM-H for Estimating LBM Values with LBM-DEXA as the Reference Method
Figure 4-1 —
Difference between LBM measured by DEXA (LBM-DEXA) and that measured by MAMC and HGS (LBM-M-H) in the validation dataset of 107 PD patients. The solid line in the center of the plot represents the differences; the dashed lines represent the limits of agreement. LBM = lean body mass; DEXA = dual-energy X-ray absorptiometry; MAMC = mid-arm muscle circumference; HGS = hand grip strength; PD = peritoneal dialysis.
Figure 4-2 —
Difference between LBM measured by DEXA (LBM-DEXA) and that measured by HGS (LBM-H) in the validation dataset of 107 PD patients. The solid line in the center of the plot represents the differences; the dashed lines represent the limits of agreement. LBM = lean body mass; DEXA = dual-energy X-ray absorptiometry; HGS = hand grip strength; PD = peritoneal dialysis.
Figure 4-3 —
Difference between LBM measured by DEXA (LBM-DEXA) and that measured by CK (LBM-CK) in the validation dataset of 107 PD patients. The solid line in the center of the plot represents the differences; the dashed lines represent the limits of agreement. LBM = lean body mass; DEXA = dual-energy X-ray absorptiometry; CK = creatinine kinetics; PD = peritoneal dialysis.
Figure 4-4 —
Difference between LBM measured by DEXA (LBM-DEXA) and that measured by anthropometric measurements (LBM-A) in the validation dataset of 107 PD patients. The solid line in the center of the plot represents the differences; the dashed lines represent the limits of agreement. LBM = lean body mass; DEXA = dual-energy X-ray absorptiometry; A = anthropometry; PD = peritoneal dialysis.
Prognostic Values of LBM Estimated from LBM-M-H, LBM-H, LBM-CK, and LBM-A in All-Cause Mortality Risk Among PD Patients
The prognostic values of LBM in the all-cause mortality risk were assessed from the retrospective cohort dataset. At the end of the study, 358 of 889 PD patients died. According to the Cox regression models, the LBM values from the 4 equations could predict mortality risk, with HR of 0.85 (0.79 – 0.92) for LBM-M-H, 0.84 (0.77 – 0.91) for LBM-H, 0.88 (0.83 – 0.94) for LBM-CK, and 0.89 (0.84 – 0.94) for LBM-A. The prognostic values of LBM-M-H, LBM-H, LBM-CK, and LBM-A were similar. Using omnibus tests, the chi-square values of LBM-MAMC, LBM-HGS, LBM-CK, and LBM-A were 16.7, 16.8, 14.9, and 14.1, respectively.
Discussion
We developed 2 equations to estimate LBM in PD patients. Our results revealed that LBM estimated from MAMC and HGS, and LBM estimated from HGS combined with gender, height, weight, and dialysis duration were similar to the LBM value by DEXA. Compared to the LBM value estimated from CK and A, the LBM values estimated from LBM-M-H and LBM-H were more accurate due to their higher precision, lower bias, and higher accuracy. The bias and precision of these 2 new equations were not significantly different from each other; therefore, we suggest choosing 1 of them in clinical practice.
The prognostic value of LBM in the outcome of PD patients has been reported (3,8–10,27,28). Our study confirmed that LBM by MAMC and HGS, HGS, CK, or A predicts all-cause mortality risk in PD patients.
Dual-energy X-ray absorptiometry is not a practical tool for routine patient care due to its complexity, large dimensions, use of radioisotopes, and high costs (29). Therefore, estimating LBM from precise and inexpensive tests such as MAMC and HGS would be convenient. In this aspect, LBM-M-H and LBM-H are promising mathematical equations for assessing the nutritional status and exploring the effect of nutritional interventions on somatic protein stores (30).
It is noteworthy that the bias between LBM-M-H or LBM-H and LBM by DEXA was lower than that reported by Noori et al. in HD patients (22). In that study, DEXA measurements were obtained during a non-dialysis day for the development dataset, whereas near-infrared interactance was performed during a HD treatment for the validation dataset. The intradialytic variation in fluid status and the different reference method used for measuring LBM might explain the relatively high bias of their equations. In contrast, in our study, DEXA was the only reference method for both development and validation datasets. Furthermore, the anthropometric and DEXA measurements were performed by the same trained personnel.
Lean body mass estimated by CK underestimated LBM by 3 kg according to DEXA, which is in accordance with previous findings (12,13). Johansson et al. (12) reported that when compared to total body potassium, LBM-CK significantly underestimated LBM by 2 – 14 kg. Furthermore, when compared to the antipyrine distribution volume, which is the gold standard for measuring LBM, LBM-CK underestimated LBM by 6.9 kg (13). In addition, Negri et al. reported that repeated measurements of LBM by CK in PD patients resulted in a coefficient of variation of 15.39% in a period of 3 to 4 months (11). This phenomenon could be partly explained by changes in the dietary intake of meat, physical activity, hormonal balance, diurnal variations of creatinine clearance, and catabolic states (11,12). Additionally, the absence of gender in LBM-CK can also be a contributor for bias because there are relevant differences in LBM values between females and males. On the other hand, the developed equations included gender as a key variable.
Our data revealed that LBM-A overestimated LBM-DEXA by 5 kg, similar to previous studies (14); the precision of LBM-A was good but its accuracy was worse than that of LBM-M-H and LBM-H. It is interesting that the performance of LBM-M-H and LBM-H, also derived from anthropometric measurements, was better than that of LBM-A. The potential causes for this phenomenon include the following. First, LBM-A was calculated by subtracting LBM from body weight without taking into account bone cell mass. LBM-M-H and LBM-H were directly derived from LBM values measured by DEXA, which consists of fat mass, LBM, and bone mass. Therefore, LBM-M-H and LBM-H should agree with the DEXA method. Second, although LBM-M-H, LBM-H, and LBM-A include anthropometric measurements, LBM-A requires skinfold measurements from 4 sites, which is harder to measure than variables of LBM-M-H and LBM-H, i.e., height, weight, and HGS. The hydration status, obesity, or high intra- or inter-observer measurement variability affects skinfold measurements to a great extent (31,32). Third, LBM-A was originally developed for UK patients and may not be suitable for Chinese patients.
To our knowledge, this is the first study focused on PD patients that developed and validated LBM equations from MAMC and HGS with DEXA as the reference method. Comprehensive nutritional data were collected from both cross-sectional and cohort datasets. All nutritional measurements were performed by a skillful dietitian, thereby reducing the intra-bias of repeated measurements. Second, gender was introduced into the 2 new equations. Dialysis duration was also taken into account in the new equations because muscle atrophy can be aggravated following prolonged dialysis treatments. However, we should recognize that dialysis duration is highly variable because the dialysis practice and quality of care may differ among clinics and patients.
We are aware of some limitations of our study. Our study was conducted in a single center and involved Chinese PD patients, which limits, to some extent, the possibility of making generalizations. However, in our country, a substantial proportion of patients with poor socioeconomic status live far away from hospitals (33); therefore, the assessment of nutritional status using simple and practical methods such as LBM-M-H and LBM-H is of utmost importance. In addition, only clinically stable patients were included in this study because acute comorbidities can rapidly affect fluid status and body composition. Our results may not be directly applicable to all PD patients. Although DEXA is a simple, quick, and highly precise technique, it is also affected by hydration status (14,34,35), which is especially common in PD patients (23,36). Additionally, the lack of data on volume status is a drawback of this study.
In conclusion, the LBM-M-H and LBM-H equations had smaller bias and higher precision and accuracy than the traditional LBM-CK and LBM-A equations. Mid-arm muscle circumference and HGS are easier to measure in clinical practice; therefore, these new equations would be more practical under different circumstances. These 2 new equations provided good surrogate methods for LBM but need to be evaluated in a large-scale PD population.
Disclosures
The authors have no financial conflicts of interest to declare.
Acknowledgments
The authors express their appreciation to the patients and staff of the Peritoneal Dialysis Center of Peking University First Hospital, for their continuing contribution to this study. This work was supported in part by the Capital Characteristic Clinic Research Grant from Beijing Science &Technology Committee (Z111107058811110), the New Century Excellent Talents from Education Department of China, and the Clinic Research Award from ISN GO R&P Committee.
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