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 Table of Contents  
ORIGINAL ARTICLE
Year : 2021  |  Volume : 21  |  Issue : 4  |  Page : 184-193

Nutritional-status assessment using body-composition monitor device in a cohort of end-stage renal disease on maintenance hemodialysis


1 Department of Nephrology, Faculty of Medicine, Alexandria University, Alexandria, Egypt
2 Department of Internal Medicine, Minsitry of Health, Egypt

Date of Submission13-Jul-2021
Date of Acceptance07-Sep-2021
Date of Web Publication27-Oct-2021

Correspondence Address:
Dr. Rasha I. Abd Elrazek Gawish
Internal Medicine, Lecturer of Internal Medicine, Nephrology Department, Faculty of Medicine, Alexandria University, 37 Ismail Serry Street, Smouha, Alexandria, 21648
Egypt
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jesnt.jesnt_18_21

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  Abstract 


Background Uremic malnutrition is strongly associated with increased risk of death and hospitalization events in this patient population. Many studies have focused on the association between nutritional status and clinical outcome, supporting for the hypothesis that malnutrition may cause or contribute to mortality. The aim of the present work was to study the nutritional status in patients receiving sessions of maintenance hemodialysis by using the body composition monitor device.
Patients and methods In total, 50 end-stage renal-disease patients on maintenance hemodialysis were enrolled in the study. Assessment of nutritional status using body composition monitor was done for all the patients included in the study. The BCM device takes three steps to display the final output parameters: overhydration, adipose-tissue mass (ATM), and lean-tissue mass (LTM). All output parameters have been validated against the gold-standard reference methods in various studies involving more than 500 patients and healthy controls.
Results The studied group was divided according to subjective global assessment questionnaire score into three groups: 26 (52%) patients were well nourished, 14 (28%) patients were mildly/moderately malnourished, and 10 (20%) patients were severely malnourished. The body composition monitor showed a decrease in the parameters related to the LTM, while an increase in the parameters related to the ATM. There were statistically significant negative correlations between both parameters.
Conclusion Body composition monitor is a noninvasive, bedside, easy, and convenient method of assessment of the body composition by assessing the LTM and ATM that gives a better idea regarding the nutritional status of the patients.

Keywords: body-composition monitor, maintenance hemodialysis, malnutrition


How to cite this article:
Gawish RI, Elrahman NA, Zeid MM. Nutritional-status assessment using body-composition monitor device in a cohort of end-stage renal disease on maintenance hemodialysis. J Egypt Soc Nephrol Transplant 2021;21:184-93

How to cite this URL:
Gawish RI, Elrahman NA, Zeid MM. Nutritional-status assessment using body-composition monitor device in a cohort of end-stage renal disease on maintenance hemodialysis. J Egypt Soc Nephrol Transplant [serial online] 2021 [cited 2022 Aug 16];21:184-93. Available from: http://www.jesnt.eg.net/text.asp?2021/21/4/184/329322




  Introduction Top


Being that common in chronic kidney-disease patients, malnutrition is a source of concern in chronic kidney-disease patients because the parameters of nutritional status are among the most powerful predictors of morbidity and mortality. The nutritional status of patients starting maintenance dialysis is also a powerful predictor of their protein-energy nutritional status 1–2 years later and also of their clinical course on dialysis therapy [1],[2].

Uremic malnutrition is strongly associated with increased risk of death and hospitalization events in this group of patients [3],[4].

Many studies have focused on the association between nutritional status and clinical outcome, supporting for the hypothesis that malnutrition may cause or contribute to increased mortality.

The assessment of nutritional status is one of the main challenges for those working with end-stage renal-disease (ESRD) patients [5].

Nutritional-status evaluation is of great importance in patients with ESRD to ensure appropriate interventions and to reduce the high rate of morbidity and mortality seen in this group of patients [6]. Nutritional assessment is defined as the evaluation of the nutritional status of a patient, based on objective and subjective parameters [7].

Nutritional status in maintenance-hemodialysis patients must be evaluated by a combination of valid, complementary measures instead of a single measure alone as there is no gold-standard method [6],[8].

Ideally, a nutritional marker must predict outcome, be an inexpensive, reproducible, and easily performed test that is not affected by factors like inflammation, sex, age, and systemic diseases. No ideal nutritional marker is available at present. Therefore, the use of a group of anthropometric and biochemical measurements is required to assess protein-energy malnutrition in a given individual [9]. A multidisciplinary approach is required to reliably evaluate the nutritional status in hemodialysis patients, including anthropometric measurements, body- composition measurements, biochemical measurements, functional assessments, dietary assessments, and subjective assessments [10].

In the time being there are several methods for the evaluation of nutritional status, including assays of serum albumin concentration, anthropometric parameters, protein catabolic rate, interdialytic weight gain, skinfold thickness, dual-energy radiograph absorptiometry, subjective global assessment (SGA), modified quantitative SGA, malnutrition-inflammation score, and bioelectrical-impedance analysis (BIA). However, no system is a ‘criterion standard’ in methodology because of the advantages and disadvantages of each system [6].


  Nutritional-status assessment using body-composition monitor Top


Body composition monitor device is currently used mainly for assessing volume overload in hemodialysis patients, but it also offers the possibility of evaluating nutritional status of the patients [11].

The main principle of body composition monitor is bioimpedance that has many advantages. It is not expensive, easy, noninvasive, and able to distinguish fat and lean tissue [12].

BIA, widely used for the prediction of body composition, is based on the principle that the measured impedance of the human body to the flow of an alternating electric current (at 50 kHz) is inversely related to the conductive volume, principally that of the total body water (TBW) [12].

In the last two decades since the introduction of BIA for body-composition assessment, many different equations have been published that relate the measured impedance to TBW or its derivative, fat-free mass [12],[13].

The body composition monitor uses the latest bioimpedance-spectroscopy techniques measured at 50 frequencies over a range from 5 to 1000 kHz to determine the electrical resistances of the TBW and the extracellular water (ECW). While high-frequency current can pass through the TBW, low-frequency current is unable to penetrate cell membranes and thus flows exclusively through the ECW [14].

Assessment of fat-tissue index (FTI), lean-tissue index (LTI), body-cell mass (BCM), and intracellular water and ECW percent by BCM device following the midweek session of hemodialysis can be performed to achieve the accurate dry weight of the patient [15].

The aim of the present work was to study the nutritional status in patients receiving sessions of maintenance hemodialysis by using the body composition monitor device in El-Moassah University Hospital.


  Patients and methods Top


Study design

A cross-sectional study was conducted on 50 ESRD patients on maintenance HD.

Ethics approval: the performed procedures were in accordance with the ethical standards of the responsible committee and with the Helsinki Declaration of 1975 as revised in 2000. An informed consent was obtained from the participants, date of approval 16/11/2017, serial number: 0105264, and IRB NO: 00012098.

Patients aging 65 years old or more, chronic liver-disease patients, patients known to have malabsorption syndromes, and patients known to have any type of malignancy or receiving chemotherapeutic agents were excluded.

Detailed history taking, including nutritional questionnaire by SGA [16], as well as thorough clinical examination, including anthropometric measurements [weight, height, and mid-upper-arm circumference (MUAC)] and BMI calculation [17], was performed for all the patients.

Laboratory investigations included complete blood picture [18], serum urea–serum creatinine [19], and serum albumin [20].

Assessment of the patients by the body-composition monitor device

The body composition monitor device takes three steps to display the final output parameters: overhydration, adipose-tissue mass (ATM), and lean-tissue mass (LTM).

All output parameters have been validated against the gold-standard reference methods in various studies involving more than 500 patients and healthy-controls.
  1. The measurements were taken 30 min after the midweek hemodialysis session of the patients.
  2. Electrodes were attached to the dorsum of one hand and the dorsum of one foot with the patient in a supine position on the contralateral side of the arteriovenous fistula.


To obtain the clinically relevant output parameters, two advanced physiological models are used in the body composition monitor [12],[21],[22],[23]:

A volume model measuring electrical conductance in a cell suspension allowing the TBW and ECW as well as the intracellular water to be calculated.

A body-composition model calculating the three principal body compartments such as overhydration, lean tissue, and adipose tissue from ECW and TBW information.



[13]

The device gave the following data:
  • LTI=lean-tissue index.
  • FTI=fat-tissue index.
  • LTM=lean-tissue mass.
  • FTM=fat tissue.
  • ATM=adipose-tissue mass.
  • BCM=body-cell mass.


SGA form [16]:





Statistical analysis

Data were fed to the computer and analyzed using IBM SPSS software package, version 20.0 (IBM Corp., Armonk, New York, USA). Qualitative data were described using number and percent. The Kolmogorov–Smirnov test was used to verify the normality of distribution. Quantitative data were described using range (minimum and maximum), mean, SD, median, and interquartile range. Significance of the obtained results was judged at the 5% level.

The used tests were Pearson coefficient: to correlate between two normally distributed quantitative variables.


  Results Top


The studied group was divided according to SGA-questionnaire score into three groups: 26 patients were well nourished (52%), 14 patients were mildly/moderately malnourished (28%), and 10 patients were severely malnourished (20%) as shown in [Figure 1] ([Table 1],[Table 2],[Table 3]).
Figure 1 Distribution of the studied cases according to SGA (n=50). SGA, subjective global assessment.

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Table 1 Distribution of the studied cases according to demographic data (N=50)

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Table 2 Distribution of the studied cases according to BMI (kg/m2) (N=50)

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Table 3 Descriptive analysis of the studied cases according to blood urea level, serum creatinine, hemoglobin level, and serum albumin level (N=50)

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The body composition monitor results were as the following and as in [Table 4]
Table 4 Descriptive analysis of the studied cases according to different nutritional parameters of the body composition monitor device (N=50)

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LTI ranged between 7.60 and 22.30 kg/m2 with a mean of 11.76±2.75 kg/m2.

FTI ranged between 4.30 and 33.20 kg/m2 with a mean of 16.79±5.91 kg/m2.

LTM ranged between 17.20 and 72.30 kg with a mean of 33.09±10.56 kg.

Fat mass (FAT) ranged between 9.80 and 64.80 kg with a mean of 33.95±11.89 kg.

ATM ranged between 13.30 and 88.10 kg with a mean of 46.20±16.17 kg.

BCM ranged between 7.60 and 44.30 kg with a mean of 17.50±6.88 kg.

MUAC ranged between 21.0 and 47.0 cm with a mean of 30.14±5.22 cm ([Table 5]).
Table 5 Correlation between different parameters

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  Discussion Top


Malnutrition is known to be one of the most common complications of chronic renal failure and it is associated with increased morbidity, mortality, and impaired quality of life [24].

There is no single gold-standard method of assessment for malnutrition in ESRD patients [25].

The BMI of the patients ranged between 19.11 and 41.59 kg/m2 with a mean of 28.82±4.28 kg/m2. The BMI values were similar to those in the study conducted by Delgado et al. [26], where the BMI of the participants was with a mean of 28.9±7.01.

BMI values could be affected by race, ethnic group, and its interpretation is compromised by the inability to assess the distribution of body composition and so BMI values do not really reflect the nutritional status of the hemodialysis patients as they rely on the patient’s body weight as a whole figure, including fat tissue, bone mass, and LTM according to the equation of measurement of the BMI and also the BMI does not assess the percentage of LTM versus fat-tissue mass [26].

Serum albumin level ranged between 3.60 and 5.0 g/dl with a mean of 4.33±0.39 g/dl and those results are similar to the study conducted by Delgado et al. [26], where the serum albumin level was of a mean of 4.0±0.35 g/dl.

Serum albumin level has a good inverse relationship with mortality in hemodialysis patients, but the albumin level is not only influenced by decreased protein intake, but with other factors as well such as inflammation, catabolic and anabolic processes, age, comorbidity, fluid overload (i.e. plasma volume), and urinary albumin losses. Albumin synthesis is reduced during acute-phase response, which means that it can be used also as a marker of acute inflammation [27].

In the study conducted by Espahbodi et al. [28], over 105 hemodialysis patients were included and the SGA-score results were as the following:

Well-nourished: four (3.81%).

Mild-to-moderate malnourished: 98 (93.33%).

Severe malnourished: three (2.86%).

In the study conducted by Gurreebun et al. [29], where over 141 patients were enrolled, the SGA-score results were as the following:

Well-nourished: 128.

Mild-to-moderate malnourished: 13.

While in the current study, the studied group was divided according to SGA-questionnaire score into three groups: 26 (52%) patients were well-nourished, 14 (28%) patients were mildly/moderately malnourished, and 10 (20%) patients were severely malnourished.

As noticed, there is discrepancy between the results among different studies as it is a subjective method of assessment of the nutritional status and it requires special training, also, it depends upon the patients’ own words regarding diet history and gastrointestinal complaints and also it assesses the fluid status by looking for the presence of edema and assesses the muscle status by inspection of the patient and also because of the difference of diets and environments among the studied populations [30].

The MUAC was measured and the mean was 30.14±5.22 (mean±SD), while in the study done by Patiwi et al. [31], who conducted a similar cross-sectional study on 55 ESRD on HD in Indonesia, the mean MUAC was 25.15, which is lower than the mean reported by our study and this may be explained mainly by the difference in ethnic groups, dietary, and lifestyle differences, which lead to higher prevalence of overweight and obesity in the Egyptian population.

The patients in the present study were examined by using a bioimpedance device (body composition monitor, Fresenius Medical Care, Germany). The device measures whole-body bioimpedance through a range of frequencies from 5 kHz to 1 MHz and determines extracellular and intracellular resistance by means of the Cole model [32].

The study conducted by Mathew et al. [33] included 85 hemodialysis patients within its population and they were examined by the body composition monitor given the following results:
  • LTI (kg/m2) 10.66±2.79.
  • FTI (kg/m2) 9.94±4.60.
  • LTM (kg) 28.92±9.19.
  • Fat (kg) 19.32±8.62.
  • ATM (kg) 26.28±11.72.
  • BCM (kg) 14.76±6.08.


In the study conducted by Arias-Guillén et al. [27] on over 91 hemodialysis patients, it gave the following results by the same device:
  • FTI (kg/m2): 12.11±4.6 (mean±SD).
  • LTI (kg/m2): 11.81±2.63 (mean±SD).


In the study conducted by Zhang et al. [34] on over 123 hemodialysis patients, the body composition monitor results were as the following:
  • FTI (kg/m2): 9.9 (mean±SD).
  • LTI (kg/m2): 12.5 (mean±SD).


In the study conducted by Rymarz et al. [35], over 48 hemodialysis patients were examined by body composition monitor, giving the following results:
  • LTI (kg/m2): 12.00±2.42 (mean±SD).
  • FTI (kg/m2): 12.60±6.06 (mean±SD).
  • BCM (kg): 17.89±5.71 (mean±SD).


In another study conducted by Rymarz et al. [36], the studied populations included 48 hemodialysis patients who were examined by the body composition monitor with the following results:
  • LTM (kg) (mean±SD) 33.6±8.6.
  • ATM (kg) (mean±SD) 34.5±16.8.
  • Fat mass (mean±SD) 25.3±12.4.
  • BCM (kg) (mean±SD) 17.9±5.7.


The mean LTI in our study was 11.76±2.75 kg/m2, which is similar to the LTI reported by the previously mentioned studies. The mean FTI in the present study was 16.79±5.91, which is higher than the values reported by the aforementioned studies. The low values of the LTI indicate decreased muscle mass of the patients whether due to decreased physical exercise, sedentary lifestyle due to the multiple complications of ESRD, or the decreased intake of protein or the presence of GIT complications, while the higher value of FTI may be explained by dietary and lifestyle differences. In the current study, there was a statistically significant positive correlation between ATM and FTI (P<0.001), there was a statistically significant positive correlation between ATM and FAT (P<0.001), and there was a statistically significant positive correlation between fat mass (FAT) and FTI (P<0.001).

A statistically significant negative correlation was found between LTI and FTI (P<0.001) and there was a statistically significant negative correlation between LTM and FTI (P<0.001) and those correlations show that decreased muscle mass may happen, although there is fat-mass accumulation, which is a phenomenon called ‘sarcopenic obesity’ [35].

There was a statistically significant positive correlation between BCM and LTI (P<0.001), there was a statistically significant positive correlation between BCM and LTM (P<0.001).

In the study conducted by Rymarz et al. [36], a strong positive correlation was found between BCM and LTM with a correlation coefficient: 0.836 (P<0.001), which is consistent with the results of our study.

In the present study, there was a statistically significant negative correlation between BCM and fat mass (P=0.012), there was a statistically significant negative correlation between BCM and FTI (P<0.001), and there was a statistically significant negative correlation between BCM and ATM (P=0.013).

In the study conducted by Rymarz et al. [36], a strong negative correlation was found between BCM and the amount of fat mass (FM) with a correlation coefficient − 0.691 (P<0.001), which is consistent with the results of our study.

In the present study, there was a statistically significant positive correlation between MUAC and FTI (P=0.001), there was a statistically significant positive correlation between MUAC and FAT (P<0.001), and there was a statistically significant positive correlation between MUAC and ATM (P<0.001).

Zhang et al. [34] reported a statistically significant positive correlation between MAMC (mid-arm muscle circumference) [which is measured using the following formula: MAMC (cm)=MUAC (cm)–3.14×TSF (cm)] and LTI (P<0.001) but a nonstatistically significant positive correlation between MACM and FTI. While our study demonstrated a statistically significant positive correlation between MUAC and LTI and also a nonstatistically significant positive correlation between MUAC and FTI. The MAMC is a better surrogate of muscle mass compared with MUAC because the MAMC is calculated using the triceps skinfold, while the MUAC (which can be used as a good anthropometric measurement as well) might be affected by the fat mass, although the correlation between both parameters did not reach statistical significance.

There was a statistically significant positive correlation between BMI and FTI, which is similar to the results of Zhang et al. [34] with a correlation coefficient 0.644 (P<0.001).


  Conclusions Top


  1. Questionnaires may be misleading in the assessment of the nutritional status if used as the only method of assessment as it is a subjective method that depends mostly on the patients.
  2. BMI values cannot give an accurate estimation the muscle mass of the body.
  3. Serum albumin levels may be within normal even in the presence of decreased LTM.
  4. Body composition monitor is a noninvasive, bedside, easy, and convenient method of assessment of the body composition by assessing the LTM and ATM, which gives a better idea regarding the nutritional status of the patients.


Acknowledgements

Criteria for inclusion of the authors: Dr Montasser M. Zeid formulated the hypothesis and supervised the whole work, Dr Nourhan Abd Elrahman collected the data from the patients, and Dr Rasha I. Abd Elrazek Gawish wrote the paper and supervised the whole work.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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