https://doi.org/10.5812/numonthly-129587

Correlation of Body Composition and Quality of Life in Maintenance Hemodialysis Patients

authors:

avatar Pringgodigdo Nugroho ORCID 1 , 2 , * , avatar Surya Wijaya 2 , avatar Rudi Putranto 2 , avatar Cleopas Martin Rumende 2 , avatar David William 2 , avatar Aida Lydia ORCID 1 , 2

Nephrology and Hypertension Division, Department of Internal Medicine, Dr. Cipto Mangunkusumo Hospital, Jakarta, Indonesia
Departement of Internal Medicine, Faculty of Medicine, University of Indonesia, Jakarta, Indonesia
Nephro-Urology Monthly: Vol.14, issue 4; e129587
published online: November 7, 2022
article type: Research Article
received: July 30, 2022
accepted: October 3, 2022

how to cite: Nugroho P, Wijaya S, Putranto R, Rumende C M, William D, et al. Correlation of Body Composition and Quality of Life in Maintenance Hemodialysis Patients. Nephro-Urol Mon. 2022;14(4):e129587. https://doi.org/10.5812/numonthly-129587.

Abstract

Background:

Due to the occurrence of hemodialysis, the life expectancy of end-stage renal disease (ESRD) patients is lengthening. However, their quality of life (QoL) is decreasing. The QoL of ESRD patients is composed of physical component score (PCS), mental component score (MCS), and kidney-related component score (KDCS) components. Nutritional status is one of the influencing factors of QoL. It is unclear whether free fat mass (FFM) or fat mass contributes to the increase of QoL in ESRD patients.

Objectives:

This study aimed to identify the correlation between FFM percentage and the QoL of ESRD patients.

Methods:

This cross-sectional study was conducted in Hemodialysis Unit, Division of Kidney and Hypertension, Dr. Cipto Mangunkusumo General Hospital, Jakarta from June to July 2018. In this study, 102 subjects were included. Blood samples were collected for laboratory examination. FFM percentage was measured using bioimpedance analysis (BIA). Meanwhile, QoL was evaluated using KDQOL SF-36 version 1.3. Statistical analysis was done using Pearson correlation test. Analysis of QoL was done based on fat percentage using ANOVA test.

Results:

In this study, the overall KDQOL score was 47.86 ± 6.56 with PCS 40.97 ± 9.66, MCS 46.6 (22.05 - 59.95), and KDCS 55.98 ± 9.02. FFM percentage was 74.21 ± 1% with mean mass of 43.06 ± 8.16 kg. Statistical analysis showed no correlation between FFM percentage and overall KDQOL score (r 0.032, P 0.750). However, there was a significant correlation between PCS and FFM (r 0.223, P 0.024).

Conclusions:

There is a weak positive correlation between FFM in kg and the physical quality of end-stage kidney disease (ESKD) patients.

1. Background

Chronic kidney disease (CKD) is one of the most common chronic diseases in the world (1). Chronic kidney disease is a progressive disease, which leads to end-stage kidney disease (ESKD) (2). Among the treatment modalities for ESKD, hemodialysis is considered the mainstay of dialysis treatment for renal failure (3, 4). Based on data from the Indonesian Renal Registry, the prevalence of hemodialysis patients in 2015 increased four times compared to 2007 (5). Hemodialysis can increase the life expectancy of ESKD patients; however, their quality of life (QoL) will decline in the long term (6).

The QoL is defined as an individual’s perception of their own well-being. Therefore, QoL is subjective to each individual. It has several components, including physical, functional, psychological, and the ability to work or to do activities (7). Quality of life in ESKD patients can be assessed using Kidney Disease Quality of Life Short Form-36 (KDQOL SF-36). This instrument has been validated in Indonesian with alpha Cronbach of 0.78 (8). In the United States, the QoL of ESKD patients is lower than the normal population in which the mean physical score before hemodialysis was 31.1 ± 1 and after hemodialysis was 35.2 ± 0.6 (standard reference score was 50). The mean mental component score before hemodialysis was 44.8 ± 1.2, and after hemodialysis was 47.9 ± 0.7 (standard reference score was 50) (9).

The effect of nutrition on the QoL of end-stage renal disease (ESRD) patients has been investigated for a long time. Based on previous studies, higher body mass index (BMI) is associated with higher QoL. This phenomenon is also known as “the obesity paradox” (10, 11). However, other studies suggested that it is lean body mass (LBM) that has a positive effect on the QoL. Meanwhile, patients with higher BMI and fat content have a higher mortality rate (12).

Since the late 90s, dual-energy X-ray absorptiometry (DEXA) has been validated as the gold standard for measuring body composition (BC) by comparing chemical analysis, and anatomy-based imaging methods (CT or MRI) (13, 14). Bioimpedance analysis (BIA) is a non-invasive testing method that allows accurate analysis of body composition using the electrical resistance of various body tissues (15). The BIA is a less expensive, more accessible, and more accurate method of determining LBM than the gold-standard examination, DEXA.

2. Objectives

In this study, we aimed to analyze the relationship between body composition evaluated using BIA and QoL in the maintenance hemodialysis patients.

3. Methods

This is a cross-sectional study that was conducted in Hemodialysis Unit, Division of Nephrology and Hypertension, Dr. Cipto Mangunkusumo General Hospital, Jakarta. The population of this study consisted of 102 ESRD patients who underwent maintenance hemodialysis. The inclusion criteria included patients who were willing to participate and signed the consent, underwent routine hemodialysis twice a week with a duration of 4 - 5 hours/session for at least 3 months, were conscious and cooperative, and were able to read and write, or had relatives who could read for them.

The exclusion criteria were patients with decreased conditions (such as hemodynamic instability, severe heart failure, severe infection, or received intensive care), patients who had other chronic diseases (HIV, decompensated cirrhosis, cancer, stroke, CHF NYHA functional class III-IV), also patients who had difficulty in understanding the questionnaire. All subjects were interviewed for their basic personal information and medical history. A physical examination, including vital signs and general status, was performed. The subjects were then asked to fill in KDQOL SF-36 version 1.3.

Anthropometric profiles of the subjects, such as height, weight, and BMI were measured. Body composition was measured using multifrequency bioimpedance analysis. Free fat mass was measured using the following formula: 100% - fat percentage that was measured by multifrequency BIA. Free fat mass percentage, overall KDQOL score, as well as physical component score (PCS) and kidney-related component score (KDCS) (laboratory parameters) were analyzed using Pearson correlation test. As for mental component score (MCS), statistical analysis was performed using the Spearman correlation test because the data distribution was not normal. Fat percentage (< 20%, 20 - 30%, > 30%) and KDQOL score were analyzed using ANOVA test followed by a post hoc LSD test.

4. Result

This study recruited 102 subjects that met the inclusion and exclusion criteria. Table 1 summarizes the study’s baseline characteristics.

Table 1.

Baseline Characteristics of the Subjects a

CharacteristicsValues
Gender
Male57 (55.9)
Female45 (44.1)
Age (y)49.93 ± 11.98
BMI (kg/m2)23.09 ± 4.37
Underweight (< 18.5)17 (16.7)
Normoweight (18.5 - 22.9)41 (40.2)
Overweight (23 - 24,9)19 (18.6)
Obesity gr I (25 - 29.9)24 (23.6)
Obesity gr II (> 30)6 (5.9)
FFM (kg)43.06 ± 8.16
Percentage of FFM (%)74.21 ± 1.00
Dialysis treatment time, median months (min - max)60 (11 - 240)
Vascular access
Cimino62 (60.8)
CDL30 (29.4)
Femoral access10 (9.8)
Underlying disease
Hypertension34 (33.3)
Glomerulonephritis33 (32.35)
Hypertension and diabetes16 (15.68)
Diabetes12 (11.76)
Others (stones, pyelonephritis, polycystic)7 (0.68)

Abbreviations: BMI, body mass index; FFM, free fat mass; CDL, catheter double lumen.

a Values are expressed as No. (%) or mean ± SD unless otherwise indicated.

Table 2 shows the statistical analysis of free fat mass (FFM) percentage and QoL in ESRD patients. Analysis was also performed for each component of KDQOL (PCS, MCS, and KDCS). No statistically significant correlation was observed between the percentage of FFM and all components of QoL. However, there is a statistically significant correlation between PCS and FFM (r = 0.223, showing a weak correlation, P = 0.024). Other components showed no significant correlation with FFM.

Table 2.

Correlation of Free Fat Mass Percentage and the Quality of Life in End-stage Renal Disease Patients a

KDQOL ComponentScore% FFMFFM
rP-ValuerP-Value
Overall b (0 - 100)47.86 ± 6.560.0320.7500.080.426
PCS b (0 - 100)40.97 ± 9.660.0250.8060.2230.024 c
MCS d (0 - 100) (max - min)46.62 (22.05 - 59.95)-0.0030.976-0.510.611
KDCS b (0 - 100)55.98 ± 9.020.0460.6460.0650.519

Abbreviations: KDQOL, kidney disease quality of life; FFM, free fat mass; PCS, physical component score; MCS, mental component score; KDCS, kidney disease related component score.

a Values are expressed as mean ± SD unless otherwise indicated.

b Pearson test

c Significant with P < 0.05

d Spearman test

Table 3 shows the correlation between actual muscle mass and QoL in ESRD patients. Analyses were performed for all KDQOL, PCS, MCS, and KDCS. None of the analyses showed a significant statistical correlation. However, there was a tendency for a positive correlation between muscle mass and PCS scores with r = 0.189 and P = 0.052.

Table 3.

Correlation Between Actual Muscle Mass and Quality of Life in End-stage Renal Disease Patients a

KDQOL ComponentScoreMuscle Mass (kg)
rP-Value
Overall score b (0 - 100)47.86 ± 6.560.1210.227
PCS b (0 - 100)40.97 ± 9.660.1890.052
MCS c (0 - 100) (min - max)46.62 (22.05-59.95)-0.0140.886
KDCS b (0 - 100)55.98 ± 9.020.1510.130

Abbreviations: KDQOL, kidney disease quality of life; PCS, physical component score; MCS, mental component score; KDCS, kidney disease related component score.

a Values are expressed as mean ± SD unless otherwise indicated.

b Pearson test

c Spearman test

5. Discussion

End-stage kidney disease requires a long and complicated procedure of monitoring and treatment (16). Physical difficulties such as anemia, comorbidities, malnutrition, elevated creatinine and albumin levels, psychological issues such as depression and sleep disorders, and social issues such as loss of independence, the role of changes, and deterioration in family QoL can all occur in patients with ESKD (17). The Kidney Disease Quality of Life-36 (KDQOL-36) is an instrument used to measure the QoL of CKD patients undergoing routine hemodialysis. Kidney Disease Quality of Life Short Form-36 is extensively used, brief, and easily accessible (18). The score will have three domains, namely PCS, MCS, and KDCS. The Indonesian version of KDQOL SF-36 has been validated by Jos (8). with the alpha Cronbach coefficient of 0.78.

In the present study, the mean ± SD overall KDQOL score was 47.86 ± 6.56. This score is lower than that obtained in Saudi Arabia (60.4 ± 27.3) (19). However, the result needs to be investigated further as there is no currently accepted overall KDQOL-36 score that combines all of its components (20). PCS appeared to have the lowest mean score in the subcomponent analysis of KDQOL (40.97 ± 9.66). The mean ± SD score of PCS is similar to a study in the United States by Porter et al. (21) (41.3 ± 11.5) and Jos (8) in north Borneo, Indonesia (38.51 ± 8.57). Compared to the MCS and KDCS scores, PCS showed the lowest average score. This result corresponds to a study by Hall et al. (22), where the average MCS score exceeded PCS quite significantly (50.9 vs. 34.5). Psychologically, ESKD patients will grow to accept their health conditions and adapt over time. On the other hand, their physical health will decline. Thus, explaining why PCS scored lower than MCS (8, 23).

Malnutrition diagnosis may be difficult and inaccurate in chronic renal patients due to the existence of comorbidities and chronic inflammation (24-26). Studies have looked into the importance of body composition in terms of fat mass (FM) and FFM, as well as the relationship between the two and mortality (27, 28). In patients with ESKD, a decrease in LBM is an essential sign of malnutrition (29), and it was discovered that FM plays a protective function in hemodialysis patient mortality (30). Simple, reliable, and inexpensive protocol is needed to evaluate the nutritional status of ESKD patients in clinical practice (31). The National Kidney Foundation’s Kidney Disease Outcomes Quality Initiative (NKF-KDOQI) considers DEXA the standard reference indicator for estimating body composition and bone mineral density (32). However, due to the high cost and the need for installation, and a large space for the equipment, its usage in clinical practice is limited (33). The BIA is a less expensive, more accessible, and more accurate method of determining LBM than DEXA.

In this study, we found no significant correlation between the percentage of FFM and overall KDQOL score, as well as the percentage of FFM and PCS score (P = 0.968 and P = 0.873, respectively). Post hoc analysis based on the subject percentage of fat (< 20%, 20 - 30%, and > 30%) also showed no significant correlation. Obesity is associated with a higher risk of cardiovascular disease (CVD) and mortality; however, previous studies reported the protective effects of BMI to lower mortality and increase the QoL in ESKD patients. The phenomenon is called the obesity paradox (10, 11). In contrast to the apparent benefit of FFM in ESKD patients, the effect of fat tissue is still debatable. In our study, the fat proportion does not show a significant correlation with the overall QoL and physical quality of ESKD patients.

We found no significant correlation between FFM and the overall KDQOL score. However, subcomponent analysis revealed a significant correlation between FFM and PCS (r = 0.223, P < 0.05). Wang et al. (34) reported that LBM was inversely correlated with mortality. Martinson et al. (35) revealed that a bigger mid-thigh muscle area (MMA) correlated with better physical function and QoL in ESKD patients undergoing hemodialysis. The BMI, waist circumference, and abdominal fat increase the survival rate but decrease the physical function evaluated by the 6-minute walk test. Patients with higher FFM have more protein reservoir, making them have better health and QoL. Although no statistical significance is found, we can see that a higher muscle mass have a trend toward positive correlation to PCS (r = 0.189, P = 0.052) and KDCS (r = 0.151, P = 0.130). This might be the cause of a significant positive correlation of actual FFM with PCS stated above, as the more actual FFM the patients have, the more muscle mass they will have.

5.1. Conclusions

The percentage of FFM and percentage of fat showed no significant correlation with the overall QoL and physical quality of ESKD patients. However, absolute FFM (in kg) shows a significant correlation with physical component of KDQOL in ESKD patients who undergo routine hemodialysis. Despite not showing a statistically significant correlation with KDQOL, actual muscle mass showed a trend toward a significant correlation with PCS score. According to this study, we suggest ESKD patients may be given muscle training programs to increase their physical quality. We also suggest incorporating muscle function and strength for future studies.

References

  • 1.

    Aggarwal HK, Jain D, Pawar S, Yadav RK. Health-related quality of life in different stages of chronic kidney disease. QJM. 2016;109(11):711-6. [PubMed ID: 27118872]. https://doi.org/10.1093/qjmed/hcw054.

  • 2.

    Levin A, Stevens PE, Bilous RW, Coresh J, De Francisco ALM, De Jong PE, et al. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl. 2013;3(1):1-150.

  • 3.

    Johnson DS, Meyer KB. Delaying and Averting Dialysis Treatment: Patient Protection or Moral Hazard? Am J Kidney Dis. 2018;72(2):251-4. [PubMed ID: 29548781]. https://doi.org/10.1053/j.ajkd.2018.01.042.

  • 4.

    De Rosa S, Samoni S, Villa G, Ronco C. Management of Chronic Kidney Disease Patients in the Intensive Care Unit: Mixing Acute and Chronic Illness. Blood Purif. 2017;43(1-3):151-62. [PubMed ID: 28114127]. https://doi.org/10.1159/000452650.

  • 5.

    Kasih T. [8th Report Of Indonesian Renal Registry]. Bandung, Indonesia: Indonesian Renal Registry; 2015. Indonesian.

  • 6.

    Broers NJ, Usvyat LA, Kooman JP, van der Sande FM, Lacson E, Kotanko P, et al. Quality of Life in Dialysis Patients: A Retrospective Cohort Study. Nephron. 2015;130(2):105-12. [PubMed ID: 26044799]. https://doi.org/10.1159/000430814.

  • 7.

    Fallowfield L. What is quality of life. Health Econ. 2009;1(8).

  • 8.

    Jos W. Kualitas Hidup Pasien yang Menjalani Hemodialisis Rutin di RSUD Tarakan, Kalimantan Utara, 2014. eJournal Kedokteran Indonesia. 2016;4(2). https://doi.org/10.23886/ejki.4.6283.87-91.

  • 9.

    Walters BA, Hays RD, Spritzer KL, Fridman M, Carter WB. Health-related quality of life, depressive symptoms, anemia, and malnutrition at hemodialysis initiation. Am J Kidney Dis. 2002;40(6):1185-94. [PubMed ID: 12460037]. https://doi.org/10.1053/ajkd.2002.36879.

  • 10.

    Kalantar-Zadeh K, Abbott KC, Salahudeen AK, Kilpatrick RD, Horwich TB. Survival advantages of obesity in dialysis patients. Am J Clin Nutr. 2005;81(3):543-54. [PubMed ID: 15755821]. https://doi.org/10.1093/ajcn/81.3.543.

  • 11.

    Friedman AN. Adiposity in dialysis: good or bad? Semin Dial. 2006;19(2):136-40. [PubMed ID: 16551291]. https://doi.org/10.1111/j.1525-139X.2006.00139.x.

  • 12.

    Beddhu S, Pappas LM, Ramkumar N, Samore M. Effects of body size and body composition on survival in hemodialysis patients. J Am Soc Nephrol. 2003;14(9):2366-72. [PubMed ID: 12937315]. https://doi.org/10.1097/01.asn.0000083905.72794.e6.

  • 13.

    Kim J, Wang Z, Heymsfield SB, Baumgartner RN, Gallagher D. Total-body skeletal muscle mass: estimation by a new dual-energy X-ray absorptiometry method. Am J Clin Nutr. 2002;76(2):378-83. [PubMed ID: 12145010]. https://doi.org/10.1093/ajcn/76.2.378.

  • 14.

    Scafoglieri A, Deklerck R, Tresignie J, De Mey J, Clarys JP, Bautmans I. Assessment of regional adipose tissue depots: a DXA and CT comparison in cadavers of elderly persons. Exp Gerontol. 2013;48(9):985-91. [PubMed ID: 23871847]. https://doi.org/10.1016/j.exger.2013.07.005.

  • 15.

    Rozdzynska-Swiatkowska A, Jurkiewicz E, Tylki-Szymanska A. Bioimpedance Analysis as a Method to Evaluate the Proportion of Fatty and Muscle Tissues in Progressive Myopathy in Pompe Disease. JIMD Rep. 2016;26:45-51. [PubMed ID: 26253708]. [PubMed Central ID: PMC4864712]. https://doi.org/10.1007/8904_2015_473.

  • 16.

    Nabolsi MM, Wardam L, Al-Halabi JO. Quality of life, depression, adherence to treatment and illness perception of patients on haemodialysis. Int J Nurs Pract. 2015;21(1):1-10. [PubMed ID: 24124912]. https://doi.org/10.1111/ijn.12205.

  • 17.

    Kim K, Kang GW, Woo J. The Quality of Life of Hemodialysis Patients Is Affected Not Only by Medical but also Psychosocial Factors: a Canonical Correlation Study. J Korean Med Sci. 2018;33(14). e111. [PubMed ID: 29607636]. [PubMed Central ID: PMC5879041]. https://doi.org/10.3346/jkms.2018.33.e111.

  • 18.

    Aiyegbusi OL, Kyte D, Cockwell P, Marshall T, Gheorghe A, Keeley T, et al. Measurement properties of patient-reported outcome measures (PROMs) used in adult patients with chronic kidney disease: A systematic review. PLoS One. 2017;12(6). e0179733. [PubMed ID: 28636678]. [PubMed Central ID: PMC5479575]. https://doi.org/10.1371/journal.pone.0179733.

  • 19.

    A. L-Jumaih A, Al-Onazi K, Binsalih S, Hejaili F, Al-Sayyari A. A study of quality of life and its determinants among hemodialysis patients using the KDQOL-SF instrument in one center in Saudi Arabia. Arab J Nephrol Transplant. 2011;4(3):125-30. [PubMed ID: 22026335]. https://doi.org/10.4314/ajnt.v4i3.71024.

  • 20.

    RAND Corporation. Kidney Disease Quality of Life Instrument (KDQOL). Santa Monica, USA: RAND Corporation; 2018, [cited 10 Nov 2018]. Available from: https://www.rand.org/health-care/surveys_tools/kdqol.html.

  • 21.

    Porter AC, Lash JP, Xie D, Pan Q, DeLuca J, Kanthety R, et al. Predictors and Outcomes of Health-Related Quality of Life in Adults with CKD. Clin J Am Soc Nephrol. 2016;11(7):1154-62. [PubMed ID: 27246012]. [PubMed Central ID: PMC4934840]. https://doi.org/10.2215/CJN.09990915.

  • 22.

    Hall RK, Luciano A, Pieper C, Colon-Emeric CS. Association of Kidney Disease Quality of Life (KDQOL-36) with mortality and hospitalization in older adults receiving hemodialysis. BMC Nephrol. 2018;19(1):11. [PubMed ID: 29334904]. [PubMed Central ID: PMC5769495]. https://doi.org/10.1186/s12882-017-0801-5.

  • 23.

    Mandoorah QM, Shaheen FA, Mandoorah SM, Bawazir SA, Alshohaib SS. Impact of demographic and comorbid conditions on quality of life of hemodialysis patients: a cross-sectional study. Saudi J Kidney Dis Transpl. 2014;25(2):432-7. [PubMed ID: 24626022]. https://doi.org/10.4103/1319-2442.128613.

  • 24.

    Kovesdy CP, Kalantar-Zadeh K. Why is protein-energy wasting associated with mortality in chronic kidney disease? Semin Nephrol. 2009;29(1):3-14. [PubMed ID: 19121469]. [PubMed Central ID: PMC5500837]. https://doi.org/10.1016/j.semnephrol.2008.10.002.

  • 25.

    Mancini A, Grandaliano G, Magarelli P, Allegretti A. Nutritional status in hemodialysis patients and bioimpedance vector analysis. J Ren Nutr. 2003;13(3):199-204. [PubMed ID: 12874744]. https://doi.org/10.1016/s1051-2276(03)00079-7.

  • 26.

    Piccoli A. Estimation of fluid volumes in hemodialysis patients: comparing bioimpedance with isotopic and dilution methods. Kidney Int. 2014;85(4):738-41. [PubMed ID: 24682120]. https://doi.org/10.1038/ki.2013.434.

  • 27.

    Carrero JJ, Chmielewski M, Axelsson J, Snaedal S, Heimburger O, Barany P, et al. Muscle atrophy, inflammation and clinical outcome in incident and prevalent dialysis patients. Clin Nutr. 2008;27(4):557-64. [PubMed ID: 18538898]. https://doi.org/10.1016/j.clnu.2008.04.007.

  • 28.

    Ren H, Gong D, Jia F, Xu B, Liu Z. Sarcopenia in patients undergoing maintenance hemodialysis: incidence rate, risk factors and its effect on survival risk. Ren Fail. 2016;38(3):364-71. [PubMed ID: 26738817]. https://doi.org/10.3109/0886022X.2015.1132173.

  • 29.

    Ikizler TA, Cano NJ, Franch H, Fouque D, Himmelfarb J, Kalantar-Zadeh K, et al. Prevention and treatment of protein energy wasting in chronic kidney disease patients: a consensus statement by the International Society of Renal Nutrition and Metabolism. Kidney Int. 2013;84(6):1096-107. [PubMed ID: 23698226]. https://doi.org/10.1038/ki.2013.147.

  • 30.

    Kakiya R, Shoji T, Tsujimoto Y, Tatsumi N, Hatsuda S, Shinohara K, et al. Body fat mass and lean mass as predictors of survival in hemodialysis patients. Kidney Int. 2006;70(3):549-56. [PubMed ID: 16788699]. https://doi.org/10.1038/sj.ki.5000331.

  • 31.

    de Abreu AM, Wilvert LC, Wazlawik E. Comparison of Body Mass Index, Skinfold Thickness, and Bioelectrical Impedance Analysis With Dual-Energy X-Ray Absorptiometry in Hemodialysis Patients. Nutr Clin Pract. 2020;35(6):1021-8. [PubMed ID: 32141138]. https://doi.org/10.1002/ncp.10481.

  • 32.

    Kopple JD. National kidney foundation K/DOQI clinical practice guidelines for nutrition in chronic renal failure. Am J Kidney Dis. 2001;37(1 Suppl 2):S66-70. [PubMed ID: 11158865]. https://doi.org/10.1053/ajkd.2001.20748.

  • 33.

    Donadio C, Halim AB, Caprio F, 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(6):S517-24. [PubMed ID: 18544819]. https://doi.org/10.1088/0967-3334/29/6/S43.

  • 34.

    Wang J, Streja E, Rhee CM, Soohoo M, Feng M, Brunelli SM, et al. Lean Body Mass and Survival in Hemodialysis Patients and the Roles of Race and Ethnicity. J Ren Nutr. 2016;26(1):26-37. [PubMed ID: 26482246]. [PubMed Central ID: PMC4706466]. https://doi.org/10.1053/j.jrn.2015.07.003.

  • 35.

    Martinson M, Ikizler TA, Morrell G, Wei G, Almeida N, Marcus RL, et al. Associations of body size and body composition with functional ability and quality of life in hemodialysis patients. Clin J Am Soc Nephrol. 2014;9(6):1082-90. [PubMed ID: 24763868]. [PubMed Central ID: PMC4046730]. https://doi.org/10.2215/CJN.09200913.