Ekramzadeh et al. BMC Pediatrics(2024) 24:549
https://doi.org/10.1186/s12887-024-04974-x

BMC Pediatrics

RESEARCH

Open AccessComparing body composition measuresin children with end stage liver disease using
noninvasive bioimpedance analysis
Maryam Ekramzadeh1,2, Seyed Ali Moosavi3,4, Amirali Mashhadiagha3,4, Ali Ghorbanpour4, Nasrin Motazedian3*,
Seyed Mohsen Dehghani3, Homa Ilkhanipoor5 and Alireza Mirahmadizadeh6
Abstract
Background Chronic liver disease (CLD) in children, often leads to cirrhosis and end-stage liver disease (ESLD). CLD
poses significant challenges in management and prognosis. Assessing body composition, including sarcopenia, isincreasingly recognized as important in understanding outcomes in this population.
Methods We conducted a prospective observational study, involving children aged 2 to 18 years with ESLD awaiting
liver transplantation. Socio-demographic, clinical, and laboratory data were collected, and body composition wasassessed using Bioelectrical Impedance Analysis (BIA). Sarcopenia was defined using age-specific cut-off points forappendicular skeletal muscle mass (aSMM) and fat-free mass (FFM).
Results The study included 57 children (42.1% girls, 57.9% boys; median age: 10.9 years) with liver cirrhosis. Of them
11 (19.3%) died during the study. The mean duration of living with end-stage liver disease prior to participation was
5.43 years [IQR: 3.32, 8.39]. The most common etiology was biliary atresia (24.6%), followed by cryptogenic (22.8%).
Deceased children exhibited significantly higher sarcopenia prevalence, lower basal metabolic rate and growth scorescompared to survivors (P < 0.05), (771.0 vs. 934.0, P = 0.166) (65.0 vs. 80.5, P = 0.005). Total body and limb-specified leanmass were lower in deceased children, although not statistically significant. Similarly, total mineral (90% normal) andbone mineral content were lower in deceased children, with a significant difference observed only in water-to-FFM
percentage (72.5 vs. 73.1, P = 0.009).
Conclusion This study highlights the high prevalence of sarcopenia among children with ESLD and its association
with adverse outcomes, including mortality. Bioimpedance analysis emerges as a promising, non-invasive method forassessing body composition in pediatric ESLD, warranting further investigation and integration into clinical practice.
Keywords End-stage liver disease, Sarcopenia, Bioimpedance analysis, Cirrhosis, Pediatric, Body composition*Correspondence:
Nasrin Motazedianmotazediann@yahoo.com
1
Department of Clinical Nutrition, Nutrition Research Center, School of
Nutrition and Food Sciences, Shiraz University of Medical Sciences, Shiraz,
Iran
2
Division of Nephrology and Hypertension, Lundquist Institute, HarborUCLA Medical Center, Torrance, USA

3

Transplant Research Center, Shiraz University of Medical Sciences, Shiraz,
Iran
4
Student Research Committee, Shiraz University of Medical Sciences,
Shiraz, Iran
5
Pediatric Endocrinology and Metabolism Department, Shiraz Universityof Medical Sciences, Shiraz, Iran6
Non-Communicable Diseases Research Center, Shiraz University of
Medical Sciences, Shiraz, Iran

© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
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Ekramzadeh et al. BMC Pediatrics(2024) 24:549

Introduction
Chronic liver disease is a forerunner to cirrhosis andend-stage liver disease (ESLD), during which scar tissue replaces healthy hepatocytes, making it incapableof functioning adequately. Therefore, individuals present with jaundice, itching, fatigue, ascites and peripheral
edema, variceal bleeding, and changes in level of consciousness [1]. According to the Global Burden of Disease project, 5400 people lost their lives due to CLD in2017 in Iran (8.12 age-standardized deaths per 100,000),
making it one of the most costly digestive diseases [2].
Orthotopic liver transplantation (LT) has been the only
curative treatment in both adults and children with terminal liver failure for over thirty years [3].
Assessment of body composition is believed to be helpful in children with ESLD. Many factors including nutrition influence body composition. Undernutrition andresultant impaired growth manifests as decreased muscle
tissue and expanded fat mass in the pediatric population[4, 5]. Previous studies have revealed that sarcopenia is apoor prognostic factor since it affects the length of stayon the waiting list and increases the risk of postoperative
mortality in adults [6]. However, the underlying mechanism is not yet elucidated in children [7]. In contrast toassessing sarcopenia using a Computed Tomography(CT) scan which puts children at risk of X-ray exposure,body composition assessment could be more practicaland applicable and also less expensive [8]. There are different tools to assess body composition, including leanmass, fat mass and body water analysis. It is not yet clear
which tool(s) would be predictive in the prognosis of
ESLD [9].
Consequently, more studies should be conductedassessing prognostic roles of body composition measures, specifically among vulnerable population of children. Our goal is to evaluate the distribution of bodycomposition measures including anthropometric parameters and sarcopenia using a Body Composition Analyzer,
among population with ESLD on the waiting list of LT.
Methods
Study population and design

This prospective observational study was conductedfrom April 2021 to April 2022 in Motahari and Imam
Reza Clinics affiliated with Shiraz University of Medical Sciences, Shiraz, Iran. Patients were selected fromthe Shiraz Pediatric Liver Cirrhosis Cohort study (IR.
SUMS.REC.1398.142). We recorded the patients’ statusfrom follow-up information. The participants aged 2 to18 years, of both genders, with ESLD, who were on the
LT waiting list, and were able to stand on their feet were
eligible to enter the study. Patients were excluded if theirparents or legal guardians did not consent to participation or in case of any medical contraindication to test,

Page 2 of 9such as the presence of a cardiac pacemaker or prosthetic
devices.
Data collection and instruments

The socio-demographic characteristics along with medical and laboratory data of participants were extracted
from the pediatric liver cirrhosis registry (IR.SUMS.
REC.1399.530) and missing were collected through interviews. Laboratory findings include complete blood count(CBC), blood urea nitrogen (BUN) and creatinine; andfull liver function test (LFT) including alanine transaminase (ALT), aspartate aminotransferase (AST), alkaline
phosphatase, total and direct bilirubin, total protein,
albumin and gamma-glutamyl transferase (GGT).
Body composition factors were measured with the
Bioelectrical impedance analysis (BIA) technique using
InBody 770 Body Composition Analyzer (InBody USA,
Cerritos, CA) [10]. This six-frequency BIA testing device
provides both comprehensive and segmental extremity
quantitative assessment of body composition and body
water balance parameters, including skeletal muscle
mass (SMM), body fat mass (BFM), intracellular water, or
extracellular water.
To conduct the BIA investigation, we employed aconventional method (based on device protocol). Thismethod entailed utilizing a fixed frequency of 50 kHz andplacing four electrodes at a distant position (two on thehand and two on the foot on the same side of the body).
The patient was after at least 8 h of overnight fasting positioned in a lying position, with the lower limbs spreadout at a 45° angle and the upper limbs spread out at a 30°
angle. Once the measurements reached a stable state, thevalues of resistance and reactance were recorded.
The following quantitative variables were assessed:
Body Composition Analysis (protein, minerals, SMM,
BFM, fat-free mass (FFM) and bone mineral content allin kilograms), Obesity Analysis (body mass index [BMI],
percent body fat and obesity degree), Segmental Lean
Analysis, Body Water Composition (including intracellular, extracellular, and total body water [TBW] in Liters)
and Segmental Body Water Analysis. Segments compriseright and left arms, trunk, and right and left legs. Other
parameters are basal metabolic rate (BMR), waist-hip
ratio, visceral fat area, body cell mass (BCM) as well as
anthropometric parameters in centimeters such as thecircumference of waist, arm, and arm muscle. Nutrition,
obesity, and body balance evaluation were the quantitative variables.
The Growth Score utilized in this study is an index provided by the InBody device, integrating weight, muscle
mass, and body fat mass to assess children’s body composition and growth status. Calculation involves determining standard weight based on height and a target BMI,
adjusting for age- and sex-specific norms. Validation

Ekramzadeh et al. BMC Pediatrics(2024) 24:549

Page 3 of 9

Table 1 Cut-off points used in this study for each age group
Boys
5-7y
8-10y
11-13y14-16y
> 16y
Girls
5-7y
8-10y
11-13y14-16y> 16y

FFM, kg

aSMM, kg

12.2
17
21.8
34.9
46.1

2.7
4.2
7.2
12.3
16.2

11.2
15
22.2
32.6
35.8

3.3
4.1
6.8
11
10.7

Abbreviations = FFM, Fat Free Mass; aSMM, appendicular skeletal muscle mass

studies conducted by InBody demonstrate high agreement with Dual-Energy X-ray Absorptiometry for pediatric body composition measurements, ensuring reliability
in clinical settings [10].

Table 2 Etiologies of underlying disease, complications andoutcomes of children with chronic liver disease
Variable
Frequency
Etiology
Biliary cirrhosis
3
Biliary atresia
14
Progressive familial intrahepatic cholestasis
9
Wilson’s disease5
Tyrosinemia
2
GSD
1
Autoimmune hepatitis
6
Primary sclerosing cholangitis
3
Cryptogenic
13
Alagille syndrome1
Duration of end-stage liver disease, years (median [IQR])
Total
5.43
Survived

5.33

Deceased

6.62

Percent
5.3
24.6
15.8
8.8
3.5
1.8
10.5
5.3
22.8
1.8
3.32,
8.39
3.24,
7.85
3.85,
9.75

The SPSS statistical software version 26.0 (SPSS Inc.,
Chicago, IL, USA) was used to carry out the analyses.
Data were reported using the count (percentage) andthe median [interquartile range (IQR)]. To compare thesurvived children and deceased cirrhotic children for
demographic, clinical, laboratory, and body composition
characteristics, we applied Wilcoxon rank-sum, Fisher’s
exact, and Pearson’s Chi-square tests. A P-value of < 0.05
was considered statistically significant.

Complication
Esophageal varices
Ascites
Variceal bleeding
Cyanosis
Hepatic encephalopathy
Biliary leakage
Herniorrhaphy
SBP
Accident trauma
Mortality
Cause of deathHeart failure
Multiorgan failure
Rejection
ARDS
Infection
Unknown
Enrollment-to-death duration, days (median[IQR])

Results
Fifty-seven children with liver cirrhosis (42.1% girl, 57.9%
boy; median age at diagnosis of 10.9 [IQR = 7.8, 15.1]
years) were included in this study, with an average of 5.43
years [IQR: 3.32, 8.39] of living with end-stage liver disease prior the participation. The most common etiology
among children was biliary atresia 14 (24.6%), followedby cryptogenic 13 (22.8%). Overall, 27 children underwent liver transplantation (19 children from the surviving
group and 8 from the deceased group). The most common complications were esophageal varices (63.2%) andascites (28.1). Till April 2023, 11 children expired and
46 children survived, without any statistical differencein terms of years lived with end-stage liver disease (6.62[IQR: 3.85, 9.75] vs. 5.33 [IQR: 3.24, 7.85]; P = 0.542).
Additionally, heart failure, multi-organ failure and LT
rejection were responsible for 63.6% of deaths. Moreover,
the average days from enrollment to death were 163 days
[IQR: 105, 421] (Table 2).
The median BMI-for-age percentile was 39.0
[IQR = 11.0, 62.0] in the living children and it was 17.0
[IQR = 1.0, 66.0] among the deceased population; however, the difference was not statistically significant(P = 0.352). Also, the mean BMI for children with ascites was 15.44 ± 2.17SD and 17.64 ± 3.58SD for children
without ascites (P = 0.007). In addition, the waist circumference, arm circumference, and arm muscle circumference were lower and the waist-to-hip ratio was higher

Defining sarcopenia

We determined the cut-off points for sarcopenia by using
2SDs below the age-specific mean values using cutoff points, as shown in Table 1, based on the study by
McCarthy et al. [11], which provided reference values for
FFM and appendicular skeletal muscle mass (aSMM) in
Caucasian children and adolescents.
Statistical analysis

36
16
5
5
2
2
1
1
1
11

63.2
28.1
8.8
8.8
3.5
3.5
1.8
1.8
1.8
19.3

3
2
2
1
1
2
163

27.3
18.2
18.2
9.1
9.1
18.2
105,
421

Ekramzadeh et al. BMC Pediatrics(2024) 24:549

in deceased children; however, these differences did notreach the significance level (P = 0.635, 0.332, 0.262, 0.598,
respectively). Survival was also not correlated with LFT
components, GGT (P = 0.270), white blood cell count(P = 0.233), and electrolyte levels (P Na = 0.654, P K =0.095). Specifically, the median serum albumin level was
3.9 [IQR = 3.2, 4.2], and 3.5 [IQR = 3.1, 4.0] (P = 0.224);
the total protein level was 6.8 [IQR = 6.4, 7.4] and 6.8[IQR = 6.3, 7.4] (P = 0.750) in the survived and deceased
cirrhotic children, respectively. Furthermore, survived
and deceased cirrhotic children showed no significant
difference in terms of using various medications (P > 0.05)(Table 3).
Generally, the total body, the limb-specified and theextracellular and intracellular water content were lower
in deceased cirrhotic children though there were no significant differences observed between them. The totalmineral and bone mineral content were lower in deceased
children; however, the total mineral content was normal
in more than 90% of children in both living and deceased
children (P = 1.000). The total protein content was also
lower in deceased children; but this was deficient in morethan 50% of the living and deceased children (58.5% vs.
54.5%; P = 1.000). In addition, several fat content indiceswere measured, which showed lower values in deceased
children overall; however, only the water-to-FFM percentage was statistically different (72.5 [IQR = 72.2, 72.8]
vs. 73.1 [IQR = 72.8, 73.4]; P = 0.009). Moreover, the total
body and the limb-specified lean mass were lower indeceased cirrhotic children though this did not reach thesignificance level either. Furthermore, the basal metabolic rate was lower (771.0 [IQR = 723.0, 1018.0] vs. 934.0[IQR = 741.2, 1170.2]; P = 0.166) and the growth score
was significantly lower (65.0 [IQR = 43.0, 75.0] vs. 80.5
[IQR = 69.0, 88.0] P = 0.005) in the deceased. Finally, theupper and lower body was balanced in 85.4% and 72.7%(P = 0.424) and 95.1% and 81.8% (P = 0.125) in living anddeceased children, respectively. Tables 4 and 5 shows several body composition findings and height and weightpercentiles of 57 children with cirrhosis, respectively.
Using the FFM and aSMM cutoff points for childhood
sarcopenia [11], we calculated the prevalence of sarcopenia in children with ESLD as 37% and 63% based on FFM
and aSMM, respectively. Interestingly, a significantly
higher prevalence of sarcopenia was observed among
the deceased children with ESLD compared to living
patients, as indicated by both the FFM (prevalence difference = 33.4% [95% CI: 1.47, 57.59]; P = 0.041) and aSMM
(prevalence difference = 35.1% [95% CI: 3.32, 51.60];
P = 0.031) indices (Fig. 1).

Page 4 of 9

Discussion
Assessment of body composition compartments andsarcopenia in children with CLD is not commonly performed as a standard procedure. Nonetheless, a numberof recently conducted studies have indicated a significant occurrence of sarcopenia, along with a connection
between sarcopenia and various outcomes, notably
post-transplant outcomes [12, 13]. In our study, we useda sarcopenia cut-off point based on a study [11], which
provided reference values for FFM and aSMM in Caucasian children and adolescents using a BIA device.
Our findings revealed a sarcopenia prevalence of 37%
and 63% based on FFM and aSMM, respectively, inchildren with End-Stage Liver Disease (ESLD). Intriguingly, a significantly higher prevalence of sarcopenia wasobserved among deceased children compared to their living counterparts. These results are consistent with thoseof a previous study by Rezende et al. on children and adolescents with CLD using dual-energy X-ray absorptiometry (DEXA); the frequency of sarcopenia was identified in
40% (34/85) of the patients. When assessing the variables
that characterize sarcopenia individually, 50.6% (43/85)
of the patients had muscle mass deficit [14].
The present study demonstrated that the BMR was
lower, and the growth score was significantly lower indeceased children than those measured in living children
with ESLD. These results are consistent with a study byMager et al. on the pediatric population after LT aged
0.5–17 years, which concluded that sarcopenia occurredin 41% of these patients. Persistent sarcopenia was associated with poorer growth and recurrent hospitalizationin children post-LT [15].
Also, a study focused on non-obese children and adolescents, aged 9 to 15 years, who were diagnosed with
non-alcoholic fatty liver disease (NAFLD) using the BIA
method; it was revealed that the Mean Muscle to Fat
Ratio (MFR) was noticeably lower in individuals with
NAFLD compared to those without this condition. The
researchers concluded that NAFLD was significantly
linked to relatively reduced skeletal muscle mass (SMM)
in non-obese children and adolescents. They suggested
that increasing SMM, through methods like weight training, could be considered as one of the treatment strategies for pediatric NAFLD in the absence of obesity [16].
Consistent with these prior studies, our results underscore the prevalence of either sarcopenia or myopenia
among the pediatric population with ESLD, highlighting
their negative impact on outcomes both pre- and postLT [17].
In a previous study by Mangus et al. sarcopenia wasevaluated using pre-transplant computed tomography(CT) on children younger than 19 years of age with endstage liver, kidney, or intestine failure. They concluded
that significant sarcopenia and increased fat stores were

Ekramzadeh et al. BMC Pediatrics(2024) 24:549

Page 5 of 9

Table 3 Demographic, clinical and laboratory characteristics of children with chronic liver disease
Variable
Sex
Boy
Girl
Age
Medications
Mineral supplement
Vitamin supplement
Ursodeoxycholic acid
Antibiotics
Antacids
Propranolol
Spironolactone
Furosemide
Prednisolone
Azathioprine
Mycophenolic acidSulfasalazine
Pack cell transfusion
Platelet transfusion
FFP transfusion
Albumin
BMI
BMI-for-age percentile
Waist circumference†
Waist-to-hip ratio†
Arm circumference†
Arm muscle circumference†
Liver transplantation
Yes
No
AST
ALT
ALP
Albumin
Total protein
GGT
WBC
Na
K

Total
(n = 57)

Survived(n = 46)

Deceased(n = 11)P3

24 (42.1) 1
33 (57.9)
10.9 [7.8, 15.1] 2

22 (47.8)
24 (52.2)
11.3 [7.5, 15.9]

2 (18.2)
9 (81.8)
10.9 [9.0, 14.5]

0.097 40.984

19 (33.9)
47 (83.9)
44 (78.6)
9 (16.1)
12 (21.4)
24 (42.9)
12 (21.4)
1 (1.8)8 (14.3)
5 (8.9)2 (3.6)
1 (1.8)10 (17.9)
4 (7.1)1 (1.8)2 (3.6)16.2 [15.0, 19.2]
37.5 [6.5, 62.5]
60.0 [53.1, 68.5]
0.78 [0.73, 0.82]
20.6 [19.1, 24.6]
18.2 [15.5, 21.0]

16 (35.6)
39 (86.7)
36 (80.0)
6 (13.3)
9 (20.0)
21 (46.7)
8 (17.8)07 (15.6)
5 (11.1)
2 (4.4)0
8 (17.8)4 (8.9)1 (2.2)1 (2.2)16.5 [15.1, 20.0]
39.0 [11.0, 62.0]
60.1 [53.2, 70.6]
0.77 [0.73, 0.82]
21.1 [19.1, 24.7]
18.7 [15.5, 21.8]

3 (27.3)
8 (72.7)
8 (72.7)
3 (27.3)
3 (27.3)
3 (27.3)
4 (36.4)
1 (9.1)1 (9.1)00
1 (9.1)2 (18.2)
00
1 (9.1)15.8 [13.0, 18.8]
17.0 [1.0, 66.0]
56.3 [53.0, 65.6]
0.79 [0.74, 0.82]
19.6 [18.5, 22.9]
16.2 [15.1, 19.1]

0.732 40.358 40.686 40.358 40.686 40.319 40.224 40.196 41.000 40.571 41.000 40.196 41.000 40.575 41.000 40.357 4
0.293
0.352
0.635
0.598
0.332
0.262

27 (47.4)
30 (52.6)
66.0 [32.5, 137.5]
56.0 [33.8, 115.5]
776.5 [459.5, 1005.2]
3.8 [3.2, 4.2]
6.8 [6.5, 7.4]
119.5 [40.0, 195.7]
4.6 [3.1, 6.8]
139.5 [136.25, 141.75]
4.2 [4.07, 4.47]

19 (41.3)
27 (58.7)
64.0 [33.0, 129.0]
57.5 [32.0, 115.2]
778.0 [463.0, 1032.5]
3.9 [3.2, 4.2]
6.8 [6.4, 7.4]
107.5 [37.0, 183.5]
4.5 [3.0, 6.4]
139.9 [136.25, 141]
4.2 [4.06, 4.34]

8 (72.7)
3 (52.6)
72.0 [32.0, 196.0]
54.0 [39.0, 122.0]
648.0 [377.0, 975.0]
3.5 [3.1, 4.0]
6.8 [6.3, 7.4]
163.5 [61.5, 342.7]
6.2 [3.9, 7.4]
138 [136.25, 149.5]
4.4 [4.2, 6.32]

0.093 4
0.592
0.976
0.893
0.224
0.75
0.27
0.233
0.654
0.095

Abbreviations BMI, body mass index; AST, aspartate aminotransferase; ALT, alanine transaminase; ALP, Alkaline phosphatase; GGT, gamma-glutamyl transferase; WBC,
white blood cell†

Measured by body composition device

1

count (%)

2

median [interquartile range]

3

Wilcoxon rank-sum test

4

Fisher’s exact test

present in end-stage organ failure patients, supporting the notion of an active physiological mechanism
to store fat while losing muscle mass. Sarcopenia may
be related to total protein loss from a catabolic state, orfrom decreased synthesis (liver), wasting (kidney), or
malabsorption (intestine). In our study, we compared

two groups of children both living with ESLD. We were
not able to compare the result with normal population;
however, several fat content indices were measured,demonstrating that lower values in deceased children
are generally seen compared to the living children. Also,
the total protein content was lower in deceased children.

Ekramzadeh et al. BMC Pediatrics(2024) 24:549

Page 6 of 9

Table 4 Body composition findings of children with chronic liver disease
Variable
Total water, L
Right arm water, L
Left arm water, L
Trunk water, L
Right leg water, L
Left leg water, L
Intracellular water, L
Extracellular water, L
Total mineral, kg
NormalDeficientBone mineral, kg
Total protein, kg
NormalDeficient
Protein-to-fat mass, %Fat mass, kg
NormalDeficient
Excessive
Fat free mass, kg
Fat free mass index, kg/m2
Fat mass index, kg/m2
Body fat percentage
NormalSlightly overfat
OverfatVisceral fat area, cm2
Water-to-fat free mass, %Skeletal muscle mass, kg
Soft lean mass, kg
Right arm lean mass, kg
Left arm lean mass, kg
Trunk lean mass, L
Right leg lean mass, kg
Left leg lean mass, kg
Leg lean mass, kg
Growth score
Basal metabolic rate, kcal
Child obesity degree, %Cell mass, kg
Whole body phase angle
Upper body
Balanced
Slightly unbalanced
Extremely unbalanced
Lower body
Balanced
Slightly unbalanced
Extremely unbalanced
Upper/Lower body
BalancedTotal(n = 57)
17.9 [12.0, 25.6] 1
0.6 [0.4, 1.1]
0.6 [0.4, 1.1]
7.8 [4.8, 11.2]
2.4 [1.2, 4.2]
2.4 [1.2, 4.2]
10.7 [7.3, 15.3]
6.9 [4.7, 10.2]
1.9 [1.4, 2.7]
48 (92.3)
4 (7.7)
1.7 [1.2, 2.3]
4.6 [3.2, 6.6]
22 (42.3) 2
30 (57.7)
21.8 [16.9, 29.2]
5.5 [4.4, 11.4]
28 (53.8)
9 (17.3)
15 (28.8)
24.2 [16.6, 35.1]
12.5 [11.6, 14.2]
3.5 [2.9, 5.2]

Survived(n = 46)
19.1 [12.3, 27.1]
0.8 [0.4, 1.8]
0.7 [0.4, 1.2]
8.7 [4.9, 12.4]
3.1 [1.2, 4.4]
3.1 [1.2, 4.3]
11.7 [7.4, 16.6]
7.4 [4.9, 10.5]
2 [1.4, 2.9]
38 (92.7)
3 (7.3)
1.7 [1.2, 2.4]
5.1 [3.2, 7.2]
17 (41.5)
24 (58.5)
21.5 [16.1, 29.3]
6.6 [4.7, 11.2]
20 (48.8)
8 (19.5)
13 (31.7)
26.1 [17.3, 37.1]
12.8 [11.7, 14.6]
3.5 [2.9, 5.2]

Deceased(n = 11)13.4 [11.8, 21.8]
0.5 [0.3, 0.8]
0.4 [0.3, 0.9]
5.8 [4.8, 9.7]
1.4 [1.1, 2.8]
1.3 [1.2, 2.8]
8.2 [7.1, 13.5]
5.2 [4.6, 8.5]
1.5 [1.4, 2.3]
10 (90.9)
1 (9.1)
1.3 [1.3, 1.9]
3.6 [3.1, 5.6]
5 (45.5)
6 (54.5)
24 [18.7, 29.0]
5.2 [4.2, 12.2]
8 (72.7)
1 (9.1)2 (18.2)
18.5 [16.3, 30.0]
12.2 [11.3, 13.3]
3.2 [3.0, 5.4]

P3

27 (51.9)
10 (19.2)
15 (28.8)
30.8 [23.3, 52.2]
73.1 [72.5, 73.3]
12.0 [7.5, 18.1]
22.9 [15.4, 32.6]
0.7 [0.5, 1.4]
0.7 [0.5, 1.3]
8.8 [6.0, 14.1]
2.9 [1.5, 5.2]
2.8 [1.5, 4.9]
6.1 [3.1, 10.7]
78.0 [65.5, 85.0]
892.0 [728.0, 1127.5]
99.0 [87.5, 105.5]
15.3 [10.4, 22.0]
3.7 [3.1, 4.4]

22 (53.7)
6 (14.6)
13 (31.7)
33.0 [22.8, 52.6]
73.1 [72.8, 73.4]
13.3 [7.6, 19.6]
24.4 [15.7, 34.7]
0.8 [0.5, 1.6]
0.8 [0.5, 1.4]
9.5 [6.0, 15.1]
3.0 [1.5, 5.5]
2.9 [1.5, 5.5]
8.0 [3.1, 11.1]
80.5 [69.0, 88.0]
934.0 [741.2, 1170.2]
99.0 [87.7, 106.2]
16.8 [10.5, 23.7]
3.9 [3.4, 4.4]

5 (45.5)
4 (36.4)
2 (18.2)
30.2 [23.5, 49.1]
72.5 [72.2, 72.8]
8.7 [7.2, 15.6]
17.2 [15.0, 28.0]
0.6 [0.4, 1.1]
0.6 [0.4, 1.2]
7.4 [5.9, 13.2]
1.8 [1.4, 4.1]
1.7 [1.4, 3.9]
3.5 [2.9, 7.2]
65.0 [43.0, 75.0]
771.0 [723.0, 1018.0]
99.0 [82.0, 105.0]
11.7 [10.2, 19.4]
3.2 [2.9, 3.8]

0.248 543 (82.7)
8 (15.4)
1 (1.9)35 (85.4)
5 (12.2)
1 (2.4)8 (72.7)
3 (27.3)-

0.424 548 (92.3)
3 (5.8)1 (1.9)39 (95.1)
2 (4.9)-9 (81.8)
1 (9.1)1 (9.1)0.125 524 (46.2)

22 (53.7)

2 (18.2)

0.110 5

0.16
0.143
0.127
0.189
0.086
0.093
0.163
0.148
0.288
1.000 40.368
0.175
1.000 40.613
0.571
0.365 5

0.169
0.087
0.992

0.944
0.009
0.16
0.163
0.219
0.184
0.38
0.157
0.188
0.087
0.005
0.166
0.976
0.163
0.06

Ekramzadeh et al. BMC Pediatrics(2024) 24:549

Page 7 of 9

Table 4 (continued)
VariableTotal(n = 57)
16 (30.8)
12 (23.1)

Slightly unbalanced
Extremely unbalanced
1

median [interquartile range]

2

count (%)

3

Wilcoxon rank-sum test

4

Fisher’s exact test

5

Pearson’s Chi square

Survived(n = 46)
11 (26.8)
8 (19.5)

Table 5 Weight and height percentiles of children with chronic
liver disease
Variables
Weight percentile
less than 10%
10-25%
25-50%
50-75%
75-90%Height percentile
less than 10%
10-25%
25-50%
50-75%
75-90%
more than 90%

Survived (n=11)
Frequency (percent)5 (45.5)
4 (36.4)
2 (18.2)-

Deceased (n=46)
Frequency (percent)13(28.3)
9 (19.6)
9 (19.6)
10 (21.7)
5 (10.9)9 (81.8)
1 (9.1)1 (9.1)

13 (28.3)
8 (17.4)
8 (17.4)
7 (15.2)
5 (10.9)
5 (10.9)

However, it is worth noting that protein content was
overall deficient in more than 50% of both deceased children and those who survived, which is consistent withthe study of Mangus et al. [18]. The observed patterns in
fat content indices and total protein content in children

Deceased(n = 11)5 (45.5)
4 (36.4)

P3

with ESLD, especially those who did not survive, may be
influenced by the complex interplay of liver dysfunction,
altered metabolic priorities, and the catabolic state associated with end-stage organ failure. These factors could
collectively contribute to a scenario where fat storage isfavored over muscle maintenance, and protein synthesis
is compromised, leading to the observed lower values inthe studied parameters.
In the current study, although the median BMI-for-age
percentile was lower in deceased children compared tosurvivors, this difference did not reach statistical significance. Similarly, measurements such as waist circumference, arm circumference, and arm muscle circumference
were observed to be lower in deceased children, but
again, these differences were not statistically significant.
The lack of statistical significance in these differences
could be attributed to the complexity of assessing nutritional status in children with ESLD. ESLD is often associated with fluid retention, indicated by conditions such asascites and edema. These factors can significantly influence weight and BMI measurements, potentially masking

Fig. 1 Prevalence of sarcopenia according to the BIA indices of FFM and aSMM in children with liver cirrhosis. Positive means patients with sarcopenia.
Abbreviations = BIA, bioimpedance analysis; aSMM, appendicular skeletal muscle mass; FMM, free fat mass. Three children aged < 5y were excluded fromthis analysis since cut-off points could not be calculated

Ekramzadeh et al. BMC Pediatrics(2024) 24:549

the true impact of nutritional deficits. Since BMI is acomposite measure of weight and height, any alterationsin fluid balance can obscure the interpretation of these
metrics. [19].
In our study, the total body and the limb-specified leanmass were lower in deceased cirrhotic children; however,these differences did not attain statistical significance.
This aligns with findings from a previous study, indicating that a majority of sarcopenic patients had a normal weight, while only a small number of them had low
weight as indicated by the BMI/A index. Nevertheless,this index is not an effective parameter for assessing theanthropometric status since it solely evaluates the overall
body mass without taking the distribution of body compartments into account. This limitation may fail to accurately reflect the true anthropometric status of pediatricpatients with CLD [14].
It’s noteworthy that muscle dysfunction can serve as asignificant indicator of the severity of liver disease. As aresult, future studies in this population should consider
exploring the potential inclusion of sarcopenia as part ofconventional prognostic criteria. Establishing sarcopenia
as a relevant marker for evaluating nutritional status in
individuals with CLD could enhance our understandingof the impact of muscle mass on outcomes in this patient
population [14]. Therefore, there is a need for further
research to validate and integrate sarcopenia assessments
into the broader framework of prognostic criteria forpediatric patients with CLD.
In the present study, the total mineral and the bonemineral content were lower in deceased children; however, the total mineral content was normal in more than
90% of children in both groups. A previous systematic
review by Jeong et al. concluded that sarcopenia was aserious risk factor for bone diseases in the background of
CLD due to alteration of metabolism and bone remodeling, strongly correlating with osteoporosis. They suggestthat sarcopenia should be screened in patients with livercirrhosis and further studies should be conducted on thereference cut-off of muscle mass for diagnosis in various
populations [20].
Due to the perception of sarcopenia primarily affecting
adults, there is a lack of specific age and sex-related normative data for assessing SMM in children. Furthermore,
the absence of a universally accepted tool to evaluate
muscle function poses a challenge in defining sarcopenia
in childhood. Previous studies addressing childhood sarcopenia have utilized various body composition assessment methods such as DEXA, BIA, CT scan, or magnetic
resonance imaging. These studies aimed to identify
potential indicators of sarcopenia, such as psoas muscle
area, SMM z-score, and MFR [21].
Another limitation arises from the lack of established cut-off points to classify muscle mass and muscle

Page 8 of 9

strength deficits in pediatric patients with CLD. To overcome this, our study relied on the research conducted by
McCarthy et al. [11], which presents skeletal muscle massreference curves for children and adolescents using a BIA
device. Although BIA may have limited validity whencompared to DEXA for measuring muscle mass, recent
studies have demonstrated a strong correlation between
the two methods in evaluating body composition. BIA ispreferred in the pediatric population due to its simplicity,
speed, non-invasiveness, and applicability to children [11,21]. Despite this, the absence of direct comparisons withDEXA or similar gold standard methods in our study limits the generalizability of our findings. Incorporating suchcomparisons in future research would further enhance
the understanding of BIA’s utility in assessing sarcopenia
in pediatric ESLD patients and strengthen the evidencebase for its clinical use in this population.

Conclusion
In conclusion, this study sheds light on the clinical characteristics and body composition of children with livercirrhosis, emphasizing the prevalence of sarcopenia
as a significant concern in this population. The analysis revealed no significant differences in various clinicalparameters between surviving and deceased children,
underscoring the complexity of end-stage liver diseases
in pediatric population. However, the higher prevalence of sarcopenia among deceased children highlights
the importance of early detection and intervention to
improve outcomes in this vulnerable population. Besides,with emerging evidence suggesting sarcopenia as a significant factor warranting further investigation; employing methods like bioimpedance analysis (BIA) overCT or X-ray due to its non-invasiveness is particularly
promising.
Abbreviations
ESLD	End-stage liver disease
CT	Computed Tomography
ALT	Alanine transaminase
AST	Aspartate aminotransferase
GGT	Gamma-glutamyl transferase
BIA	Bioelectrical impedance analysis
CBC	Complete blood count
BUN	Blood urea nitrogen
LFT	Liver function test
SMM	Skeletal muscle mass
BFM	Body fat mass
FFM	Free mass
BMI	Body mass index
TBW	Total body water
BMR	Basal metabolic rate
BCM	Body cell massaSMM	Appendicular Skeletal muscle mass
NAFLD	Non-alcoholic fatty liver disease
MFR	Muscle to Fat Ratio
DEXA	Dual-energy X-ray absorptiometry

Ekramzadeh et al. BMC Pediatrics(2024) 24:549

Page 9 of 9

Author contributions
ME, NM, SMD, HI, AM, AH designed the project and revised the final
manuscript. SAM, AM, AG were involved in the data extraction and drafting of
the manuscript. AM and AH analyzed the data and contributed in drafting themanuscript. All authors read and approved the final manuscript.

7.

Funding
Not applicable.

9.

Data availability
I do not have any research data outside the submitted manuscript file.

10.
11.

Declarations

12.

Ethics approval and consent to participate
This study was conducted allied with according to Helsinki Declaration andapproved by the Research Ethical Committee of Shiraz University of Medical
Sciences (code: IR.SUMS.REC.1398.922). Written informed consent were takenfrom participant’s parent or legal guardian.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Received: 6 August 2023 / Accepted: 26 July 2024

8.

13.

14.
15.

16.
17.

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