The useful lean tissue and appendicular skeletal muscle mass indices related to physical performance in patients undergoing hemodialysis

Abstract

BACKGROUND:

Low muscle quantity commonly seen in patients undergoing hemodialysis (HD) is the key contributor of declined physical performance and increases the risk of morbidity and mortality. However, how to normalize muscle mass for operational criteria in this population remains unknown.

OBJECTIVE:

To identify the clinically useful whole body lean tissue mass (LTM) and appendicular skeletal muscle mass (ASM) indices pertinent to physical performance in patients undertaking HD.

METHODS:

Whole body LTM in 38 and ASM in 22 patients undergoing HD were measured by body composition monitor (BCM) and dual-energy X-ray absorptiometry (DXA), respectively. Physical performance was assessed by handgrip strength, the incremental shuttle walk test, sit-to-stand tests, gait speed, Short Physical Performance Battery and Duke Activity Status Index. Besides crude LTM and ASM, the other muscle indices were utilized normalizing for height, height squared, weight, body mass index (BMI), fat mass and body fat%.

RESULTS:

Regardless of BCM or DXA being used, the relationships between different muscle mass indices and physical function were not consistent. While the most useful LTM index which was strongly associated with physical function involved adjustment for height, the strongest (and most useful) ASM index was normalization for BMI.

CONCLUSION:

The superiority of adjustment for BMI or height (height²) recommended by international sarcopenia consensus is also suitable for patients undergoing HD. Patients’ BMI or fat mass should be considered in estimating prevalence of sarcopenia and evaluating relationship between muscle mass and physical performance.

Keywords

Sarcopenia skeletal muscle mass haemodialysis physical functional performance muscle strength

1. Introduction

Sarcopenia is a progressive and age-related skeletal muscle disorder, including low muscle quantity, strength and physical performance, in which muscle quantity and strength are crucial criteria for diagnosis of sarcopenia [1]. Patients undergoing hemodialysis (HD) are characterized by increased muscle degradation and decreased muscle synthesis due to a variety of conditions inherent to end-stage kidney disease, thereby accelerating the loss of muscle mass and leading to the high prevalence of sarcopenia [2, 3, 4]. In light of different methods and cutoff limit, the prevalence of muscle mass loss varied from 4.0 to 73.5% and sarcopenia from 4.0 to 63.0% in patients undergoing HD [4]. Reports showing that muscle loss lowers physical function, quality of life and contributes to the higher mortality risk in this population [5, 6].

In order to obtain a thorough understanding of sarcopenia, it is necessary to develop accurate measures against muscle mass. Anthropometric screening, Magnetic resonance imaging (MRI), computed tomography (CT), Bioelectrical Impedance Analysis (BIA) and Dual Energy X-Ray Absorptiometry (DXA) were commonly used methods providing assessments of body composition for patients undergoing HD. However, doubts about the accuracy of the results remain. Generally, DXA is reviewed as a reliable and precise instrument when determining muscle quantity. However, the cost of DXA is high and it is not widely available, especially in the community and at poverty areas in the low and middle-income countries. Another disadvantage is that the accuracy of DXA measurements can be influenced by the hydration status of patients undergoing HD [1, 7]. Although BIA is also influenced by hydration status of patients, as a multifrequency bioimpedance spectroscopy device, the body composition monitor (BCM) is regarded a convenient and safe method and frequently used for measuring body composition in patients on HD [8] by determining the quantitative amount of overhydration and lean tissue index [9, 10]. However, BCM is only able to quantitatively assess whole body lean tissue mass (LTM) rather than skeletal muscle mass (SMM) or appendicular skeletal muscle mass (ASM) both of which are recommended for diagnosing sarcopenia in clinical practice and in research [1, 11]. Notably, there are emerging tools that offer innovative ways to assess body composition in patients undergoing HD. A study conducted by Yuri Battaglia et al. employed B-mode ultrasound system to measure the thicknesses of quadriceps rectus femoris muscle (QRFM) and subcutaneous fat tissue (SFT), revealing a significant correlation between muscle ultrasound measurements and bioelectrical impedance vector analysis (BIVA) parameters. Furthermore, it was observed that chronic dialysis patients exhibited decreased thigh SFT thicknesses and ultrasound QRFM, highlighting the potential of ultrasound in assessing muscle mass and fat tissue across diverse clinical settings [12].

An intriguing result has been reported that the prevalence of low muscle mass showed a wide range of variation from 8.1 to 32.4%, even when the same muscle quantities were presented [13]. It follows then that the appropriate normalizing methods used for muscle mass is of great significance. Although the European Working Group on Sarcopenia in Older People (EWGSOP) keeps updating the definition and criteria for sarcopenia [1] and the Asian Working Group on Sarcopenia (AWGS) established criteria for Asian population in 2019 [11], many problems remain to be solved. European or Asian consensus, even Chinese guidelines for sarcopenia were specifically developed for the elderly [14]. There is an ongoing debate about the preferred adjustment for quantifying muscle mass and whether the normalizing method recommended to the older populations can be used for patients undergoing HD. Most studies with patients undergoing HD adopted the criteria recommended by the sarcopenia consensus for the older population [15, 16, 17]. In other words, it is still unknown what is the preferred adjustment for muscle quantity for patients undergoing HD.

Table 1
Recommendations for measuring muscle quantity in sarcopenia in different guidelines

Sarcopenia guidelines	Measurement tools	Muscle quantity measurement
IWGS	DXA	ALM/height² (kg/m²)
FNIH	DXA	ALM/height² (kg/m²)
EWGSOP2	DXA/BIA	ASM/height² (kg/m²)
AWGS	DXA/BIA	ASM/height² (kg/m²)
Chinese expert consensus on diagnosis and treatment for elderly with sarcopenia	DXA/BIA	ASM/height² (kg/m²), ASM/BMI (kg/kg/m²)

IWGS: The International Working Group on Sarcopenia; FNIH: The Foundation for the National Institutes of Health Sarcopenia; EWGSOP: European Working Group on Sarcopenia in Older People; AWGS: The Asian Working Group on Sarcopenia; DXA: Dual-energy X-ray absorptiometry; BIA: Bioelectrical Impedance Analysis; ASM: Appendicular skeletal muscle mass; ALM: Appendicular lean muscle mass.

The absolute level of SMM or ASM adjusting for body size is the commonly employed metric of muscle quantity. While SMM or ASM divided by height, often height squared are supported as the most frequently employed metric of relative muscle mass [18, 19] in older people by the EWGSOP, AWGS and the international Working Group on Sarcopenia (IWGS) [20], Chinese expert consensus on diagnosis and treatment for elderly with sarcopenia (2021) [14] and the Foundation for the National Institutes of Health Sarcopenia Project suggest adjusting muscle mass for BMI in the diagnosis of sarcopenia [21] (Table 1 shows the recommendations for measuring muscle quantity in sarcopenia from different guidelines). However, seldom studies showed that which normalizing method for muscle mass is suitable for the evaluation of muscle quantity when diagnosing sarcopenia in patients undergoing HD. It is well known that BMI is unable to differentiate between fat and muscle mass, the prevalence of sarcopenia may be overestimated when patients with a greater BMI due to a larger amount of fat mass [13, 22]. Obviously, height squared is positively correlated with BMI, and this index may have the same limitation with the one adjusted for BMI [23]. Due to the negative effect of excessive adipose on physical function [24], muscle mass adjusted for fat mass may be a more useful index for diagnosing low muscle quantity. Wilkinson’s study involving 72 chronic kidney disease patients including 11 patients undergoing HD identified that the most useful muscle mass index pertinent to physical performance may be ASM normalized for adiposity [22]. As the key contributor of the declined physical function [25, 26], the muscle mass indices showing the strong association with physical performance may be the useful operational criteria for predicting clinical states. Obviously, it is important to note that the differences in muscle quantity index may cause inconsistent and erroneous associations between muscle mass and physical function [22], leading to misunderstanding of clinical states depending on evaluation of low muscle mass.

The study was aiming to identify the most useful ASM indices pertinent to physical performance for patients undergoing HD. In light of the widespread use of BCM with this population, the purpose of the study was also to obtain the preferred whole body LTM indices related to physical performance in this population.

2. Method

This is an exploratory secondary-analysis of body composition data from a study conducted to explore the relationship between physical condition and associated factors in patients receiving HD.

2.1 Participants

Patients receiving maintenance HD (receiving treatment for $>$ 3 months) aged $\geqslant$ 18 years who were willing and able to take part were recruited. Patients who: (1) were unable to walk e.g. amputation, unable to mobilize/wheelchair dependent; (2) were pregnant; (3) had any element of study assessment protocol considered by own clinician to be contraindicated due to severe co-morbidity or any other reason; (4) had clinically overt infection in the last 6 weeks; (5) were unable to understand the patient information sheet and give consent for any reason were excluded.

Eligible patients at a HD unit from the affiliated hospital of xx University in China were identified by unit staff. The patients who expressed an interest in participating in the study were provided with the patient information sheets and discussed the study in more details with the researcher. Informed consent was obtained from each participant at the screening prior to any study-related activities being performed. Participants were recruited between September 2016 and March 2018, and September to December in 2021. All aspects of the study were approved by the Ethical committee of the Affiliated Hospital of Nantong University (Ref. 2015-12) and conducted in accordance with the Declaration of Helsinki

2.2 Data collection

Participants were asked to complete a series of outcome measure assessments once only. Based on the underlying fatigue or discomfort after the HD session and and the potential impact of water overload on physical performance, four physical performance tests including the Shuttle Walking Test (SWT), Sit to Stand (STS) 60, Handgrip Strength (HGS) and Short Physical Performance Battery (SPPB), were conducted for evaluating physical function with patients undergoing HD before a HD session, which was with one non-HD day interval between two consecutive HD sessions. Participants were given a chance to become familiar with the tests before they were conducted. During the period, patients completed the questionnaire of the Duke Activity Status Index (DASI). Patients’ body composition was assessed by the Fresenius Body Composition Monitor (BCM) before the dialysis session. Patients were requested to lie flat on a bed or a couch and electrodes of the BCM were placed on one hand and foot. The procedure usually took around two minutes. Additionally, patients were scanned on a GE Healthcare Lunar iDXA scanner to obtain body composition at any time based on their own convenience or preference. They had the option to separate all the assessments into several visits and the total assessments took about 3 hours altogether.

2.3 Outcome measures

2.3.1 Demographic and clinical parameters

Demographics and clinical parameters were obtained from their medical records with the patient’s consent, including age, gender, vintage (the duration between the initiation of dialysis and the implementation of the study), comorbidities, medication, bicarbonate (mmol/L) and hemoglobin (g/L). The data was collected on the same day as the investigation of the questionnaire in the study. The clinical parameters were recorded with the pre-dialysis results, which was recorded with the average of three available dialysis days that are closest to the survey day. When there was no data available for the required date, the closest date available was accepted. The results available with $>$ 3 months old were omitted from the data collection records.

2.3.2 Questionnaire

As a surrogate of peak oxygen uptake (VO₂peak) for patients undergoing HD, the Duke Activity Status Index (DASI) has been documented as being a powerful marker of physical function [27]. It consists of two responses ‘yes’ and ‘no’ which refer to 12 activities of personal care, ambulation, household tasks, sexual function and recreational activities. A response of ‘no’ indicates a score of zero, and a response of ‘yes’ to each item is marked as a value based on metabolic cost by calculating Metabolic Equivalent of Task (METs), which means the Energy Expenditure (EE) of a specific physical activity. The question is weighted depending on the level of activity and the values of each specific activity are summed up to produce a numerical total score (0.00-58.2 METs).

2.3.3 Physical performance tests

The Shuttle Walking Test (SWT) is a validated test of maximal exercise capacity in patients receiving HD and has been used as a vital assessment tool for physical function [28, 29]. The SWT includes the Incremental Shuttle Walking Test (ISWT) which was conducted in the study. Participants were asked to walk a 10-meter shuttle course at a speed controlled by an external audible bleep signal. For the ISWT, participants walked back and forth continuously at a progressively increasing pace. The test was terminated when participants failed to complete the shuttle course in the allowed time. The distance in ISWT’s performance was recorded.

The Sit to Stand (STS) 60 has been confirmed as a reliable and valid measure of functional outcome in patients receiving HD [30, 31]. The participants started from a seated position on a hard, upright chair, with arms folded across the chest, the feet flat on the floor and the knees bent at 90^∘. For the test, the subject simply stood up fully and then sat down again to the starting position, without using their arms. For the STS60, this is the number of STS cycles achieved in 60 seconds.

The Short Physical Performance Battery (SPPB) has been validated [32] and widely used across diverse populations of elderly people by providing objective function measurements [33, 34, 35]. The SPPB includes a gait speed test, muscle strength test (STS5) and a balance test. Participants were asked to walk a 5 m course twice at their usual walking speed with a walking aid if it was normally used. For increased accuracy in measuring patients’ typical walking speed, the duration time was recorded when they completed a 4-meter section of the course. The average time was obtained from the participant’s two performances. The time taken for the participant to complete the STS cycles over 5 times as quickly as possible was then recorded for the measurement of the STS5. In terms of the balance test, participant attempted to stand with 3 different movements for 10 seconds in each one, including standing still with the feet together side by side (side-by-side), the side of the heel of one foot touching the big toe of the other foot (semi-tandem position), and with the heel of one foot in front of and touching the big toe of the other foot (full tandem position). These three separate tests had their own scoring system, ranging from 0 to 4. The scores from the separate tests were used to calculate the overall SPPB score.

The Handgrip strength (HGS) has been reported as being a good simple measure of muscle strength in arms and often used in the diagnosis of sarcopenia [1]. A hand-held dynamometer was used to measure HGS three times and an average reading was obtained by averaging the three readings. Participants were asked to remain seated with the shoulder adducted elbow flexed at 90^∘ and forearm in a neutral position [36, 37, 38]. The dynamometer was held with the side opposite to the vascular access arm by the patients. Participants were asked to squeeze the handle as hard as they could. 30 seconds was allowed for the interval between measurements.

2.3.4 Body composition

As a tool for distinguishing over-hydration, lean and adipose tissue mass, the BCM has been extensively used for understanding the body composition of dialysis patients [39, 40], and has played an important role in evaluating nutritional status [41] and predicting protein-energy wasting [42, 43]. However, the BCM only enables to obtain quantitative assessment of whole-body LTM, instead of ASM or SMM. DXA is viewed the gold standard for estimating three principal components including fat mass, fat free mass and bone mineral content by using X-rays passing through bone and soft tissue. However, its measurements can be influenced by overhydration of the patients.

The following are the different LTM or ASM normalization indices.

LTM or ASM/height (m) $=$ LTM or ASM divided by height in meters LTM or ASM/height-squared (m²) $=$ LTM or ASM divided by height in meters squared LTM or ASM/weight (kg) $=$ LTM or ASM divided by pre-dialysis weight (taken from patients’ medical record) LTM or ASM/BMI (kg/m²) $=$ LTM or ASM divided by BMI (pre-dialysis weight divided by height in meters squared) LTM /body fat % $=$ LTM divided by total body fat % (fat mass/weight taken from BCM) ASM /body fat % $=$ ASM divided by tissue fat % (fat mass/tissue taken from DXA) LTM or ASM/fat mass (kg) $=$ LTM or ASM divided by fat mass (taken from BCM and DXA, respectively)

2.4 Statistical analysis

Continuous data were checked for normality of distribution using Shapiro-Wilk tests. Whilst variables with normally distributed were presented as mean $\pm$ standard deviation (SD), non-normally distributed variables were shown as median [interquartile range (IQR)]. Pearson’s and Spearman’s correlation coefficient (Pearson’s $r$ and Spearman’s $r$ ) were used to assess the relationship between muscle mass indices and physical function variables. Pearson’s correlation coefficient was performed to evaluate the relationship between continuous variables but only when both variables were normally distributed; otherwise, Spearman’s correlation coefficient was used. The strength of correlation was interpreted based on Cohen’s guidelines with $r$ value. When the $r$ value $\geqslant$ 0.10, the correlation was defined as ‘small’, $\geqslant$ 0.30 as ‘medium’, and $\geqslant$ 0.50 as ‘large’ [44]. In order to identify the best overall muscle normalization index, each individual $r$ value between the ASM or the LTM index and different physical performance tests were averaged to determine a mean r for ASM and LTM indices, respectively.

All statistical analyses were performed using the statistical package of Social Sciences (IBM SPSS Statistics v.26, New York, USA.) and GraphPad Prism (v.7 Graphpad Software Inc, CA, USA). Statistical significance was taken as $P<$ 0.05.

3. Results

43 patients undergoing HD were approached. 2 refused to take part and 1 withdrew due to a serious medical condition which developed during the study. 38 patients completed body composition assessment conducted with the BCM and physical function tests. 22 patients completed the DXA assessment due to the far distance between the DXA assessment centre and their HD unit.

Table 2
Demographics and characteristics of the patients undergoing HD using BCM and DXA

Characteristics	Mean $\pm$ SD; Median (IQR); $n$ (%)
	BCM group: $n=$ 38	DXA group: $n=$ 22
Age (years)	48.6 $\pm$ 9.6	47.0 $\pm$ 8.2
Males: Females	22 (55%): 18 (45%)	12 (54.5%): 10 (45.5%)
HD vintage (Months)	73.7 $\pm$ 41.3	93.4 $\pm$ 47.3
Co-morbidities
Diabetes	3 (7.9%)	3 (13.6%)
Hypertension	25 (65.8%)	12 (54.5%)
Ischaemic Heart Disease	9 (23.9%)	4 (18.1%)
Medication
Antihypertensives	33 (86.8%)	12 (54.5%)
Oral Bicarbonate	9 (23.7%)	7 (31.8%)
Blood Results
Bicarbonate (mmol/L)	21.1 $\pm$ 2.7	21.8 $\pm$ 1.7
Haemoglobin (g/dL)	111.1 $\pm$ 17.1	113.9 $\pm$ 13.5
Physical function
HGS (kg)	29.0 $\pm$ 9.2	28.6 $\pm$ 8.7
ISWT (m)	411.5 $\pm$ 117.7	426.0 $\pm$ 114.2
STS-60 (repetitions)	30.7 $\pm$ 10.9	32.5 $\pm$ 9.4
STS-5 (s)	7.7 (7.1–9.8)	7.5 (7.2–10.3)
Gait speed (m/s)	2.4 $\pm$ 0.4	2.4 $\pm$ 0.4
SPPB, total/12	12.0 (11.0–12.0)	12.0 (11.0–12.0)
DASI, total/58.2	37.1 $\pm$ 11.8	36.8 $\pm$ 11.6
Body composition
Height (m)	161.9 $\pm$ 8.5	162.4 $\pm$ 7.9
Weight (kg)	60.9 $\pm$ 11.2	58.4 $\pm$ 7.5
BMI (kg/m²)	23.0 $\pm$ 3.1	22.0 $\pm$ 2.4
LTM(kg)/ASM(kg)	LTM: 35.0 (31.5–41.4)	ASM: 15.8 $\pm$ 3.2

HGS: Handgrip strength; ISWT: Incremental shuttle walk test; STS: Sit-to-stand; SPPB:Short physical performance battery; DASI: Duke activity status index; BMI: Body mass index; ASM: Appendicular skeletal muscle; LTM: Lean Tissue Mass.

3.1 Demographics and characteristics of the patients undergoing HD assessed by BCM and DXA

The median ages of patients who completed the BCM and DXA were 48.6 and 47.0 years, ranging from 30 to 65 and 35 to 65 years old, respectively. The most frequently reported comorbidities of the two groups were hypertension (BCM: 65.8%; DXA: 54.5%) and ischaemic heart disease (BCM: 23.9% and DXA: 18.1%). The main medications patients administered were anti-hypertensives (BCM: 86.8%; DXA: 54.5%) and oral bicarbonate (BCM: 23.7%; DXA: 31.8%). The means of bicarbonate in the BCM and DXA groups were 21.1 mmol/L and 21.8 mmol/L, both of which were lower than the normal range. Table 2 shows the demographics, clinical parameters, physical function and body composition in patients undergoing HD assessed by the BCM and DXA.

Table 3
The association between LTM normalization indices and physical function

$n=$ 38	LTM (kg)	LTM/weight (kg)	LTM/height (m)	LTM/height2 (m2)	LTM/BMI (kg/m2)	LTM/fat mass (kg)	LTM/body fat% (kg)
Upper extremities function
HGS (Kg)	$r=$ 0.722	$r=$ 0.481	$r=$ 0.764	$r=$ 0.689	$r=$ 0.677	$r=$ 0.519	$r=$ 0.720
	$P<$ 0.001^***	$P=$ 0.002^**	$P<$ 0.001^***	$P<$ 0.001^***	$P<$ 0.001^***	$P=$ 0.001^**	$P<$ 0.001^***
Lower extremities function
ISWT (m)	$r=$ 0.607	$r=$ 0.564	$r=$ 0.638	$r=$ 0.625	$r=$ 0.634	$r=$ 0.536	$r=$ 0.620
	$P<$ 0.001^***	$P=$ 0.001^**	$P<$ 0.001^***	$P<$ 0.001^***	$P<$ 0.001^***	$P=$ 0.001^**	$P<$ 0.001^***
STS60 (reps)	$r=$ 0.455	$r=$ 0.474	$r=$ 0.492	$r=$ 0.518	$r=$ 0.471	$r=$ 0.363	$r=$ 0.409
	$P=$ 0.005^**	$P=$ 0.003^**	$P=$ 0.002^**	$P=$ 0.001^**	$P=$ 0.004^**	$P=$ 0.032^*	$P=$ 0.015^*
SPPB	$r=$ 0.349	$r=$ 0.273	$r=$ 0.365	$r=$ 0.361	$r=$ 0.276	$r=$ 0.081	$r=$ 0.190
	$P=$ 0.034^*	$P=$ 0.103	$P=$ 0.026^*	$P=$ 0.028^*	$P=$ 0.099	$P=$ 0.638	$P=$ 0.266
STS5 (secs)	$r=-$ 0.183	$r=-$ 0.396	$r=-$ 0.178	$r=-$ 0.212	$r=-$ 0.309	$r=-$ 0.319	$r=-$ 0.285
	$P=$ 0.284	$P=$ 0.017^*	$P=$ 0.300	$P=$ 0.214	$P=$ 0.607	$P=$ 0.062	$P=$ 0.097
GS (m/s)	$r=$ 0.426	$r=$ 0.132	$r=$ 0.315	$r=$ 0.326	$r=$ 0.180	$r=$ 0.030	$r=$ 0.228
	$P=$ 0.009^**	$P=$ 0.436	$P=$ 0.058	$P=$ 0.049^*	$P=$ 0.287	$P=$ 0.864	$P=$ 0.181
Self-reported function
	$r=$ 0.205	$r=$ 0.114	$r=$ 0.267	$r=$ 0.248	$r=$ 0.186	$r=$ 0.012	$r=$ 0.120
DASI	$P=$ 0.217	$P=$ 0.496	$P=$ 0.105	$P=$ 0.133	$P=$ 0.263	$P=$ 0.945	$P=$ 0.481

Table 4

Averaged correlation coefficient value for each LTM index and their association with physical performance tests

$n=$ 38	LTM(kg)	LTM/weight (kg)	LTM/height (m)	LTM/height² (m²)	LTM/BMI (kg/m²)	LTM/fat mass (kg)	LTM/body fat% (kg)
Upper extremities function	0.722	0.481	0.764	0.689	0.677	0.519	0.72
Lower extremities function	0.404	0.368	0.398	0.408	0.374	0.266	0.346
All tests	0.421	0.348	0.431	0.426	0.390	0.266	0.367

3.2 Association between LTM normalization indices and physical function

All indices of LTM were positively and significantly correlated with HGS of the non-fistula hand. LTM/height, crude LTM and LTM/body fat% were the top three indices strongly related with HGS ( $r$ ’s $=$ 0.764 to 0.720). In regard to lower extremity function, all LTM normalization indices were significantly and strongly associated with ISWT. The top strongest indices correlated with ISWT were LTM/height, LTM/BMI and LTM/body fat% ( $r$ ’s $=$ 0.638 to 0.620). Besides ISWT, the only strong correlation was shown between LTM adjusted for height squared and STS60 ( $r=$ 0.518, $p=$ 0.001). None of LTM indices was significantly associated with self-reported physical function (DASI). The detailed association between LTM normalization indices and physical function was shown in Table 4.

3.3 The strongest LTM normalization index with physical performance tests

LTM/height was observed having the overall strongest association with physical function ( $r=$ 0.431), followed by LTM adjusted for height squared ( $r=$ 0.426). LTM/body fat showed the poorest associations with physical function ( $r=$ 0.266). Concerning HGS, LTM/height still showed the strongest association with upper body function ( $r=$ 0.764). LTM/height² was the strongest index correlated with lower extremity physical function ( $r=$ 0.408). LTM/fat mass (kg) showed the poorest association with overall ( $r=$ 0.266) and lower extremity physical function ( $r=$ 0.266). The averaged correlation coefficient value for each LTM index and their association with physical performance tests was shown in Table 4.

Table 5
The association between ASM normalization indices and physical function

$n=$ 22	ASM (kg)	ASM/weight (kg)	ASM/height (m)	ASM/height² (m²)	ASM/BMI (kg/m²)	ASM/fat mass (kg)	ASM/body fat % (kg)
Upper extremities function
HGS	$r=$ 0.857	$r=$ 0.666	$r=$ 0.831	$r=$ 0.759	$r=$ 0.791	$r=$ 0.706	$r=$ 0.616
	$P<$ 0.001^***	$P=$ 0.005^**	$P<$ 0.001^***	$P=$ 0.001^**	$P<$ 0.001^***	$P=$ 0.002^**	$P=$ 0.011^*
Lower extremities function
ISWT	$r=$ 0.708	$r=$ 0.667	$r=$ 0.668	$r=$ 0.582	$r=$ 0.768	$r=$ 0.589	$r=$ 0.474
	$P=$ 0.003^**	$P=$ 0.007^**	$P=$ 0.007^**	$P=$ 0.023^*	$P=$ 0.001^**	$P=$ 0.021^*	$P=$ 0.074
STS60	$r=$ 0.168	$r=$ 0.166	$r=$ 0.102	$r=$ 0.124	$r=$ 0.278	$r=$ 0.279	$r=-$ 0.046
	$P=$ 0.548	$P=$ 0.554	$P=$ 0.717	$P=$ 0.659	$P=$ 0.315	$P=$ 0.313	$P=$ 0.871
SPPB	$r=$ 0.208	$r=-$ 0.002	$r=$ 0.141	$r=$ 0.022	$r=$ 0.249	$r=$ 0.051	$r=-$ 0.041
	$P=$ 0.457	$P=$ 0.994	$P=$ 0.617	$P=$ 0.937	$P=$ 0.371	$P=$ 0.857	$P=$ 0.885
STS5	$r=-$ 0.021	$r=-$ 0.068	$r=$ 0.057	$r=$ 0.166	$r=-$ 0.24	$r=-$ 0.023	$r=-$ 0.132
	$P=$ 0.939	$P=$ 0.810	$P=$ 0.839	$P=$ 0.553	$P=$ 0.39	$P=$ 0.934	$P=$ 0.638
GS	$r=$ 0.379	$r=$ 0.245	$r=$ 0.341	$r=$ 0.27	$r=$ 0.34	$r=$ 0.311	$r=-$ 0.015
	$P=$ 0.164	$P=$ 0.379	$P=$ 0.213	$P=$ 0.331	$P=$ 0.215	$P=$ 0.260	$P=$ 0.958
Self-reported function
	$r=$ 0.195	$r=-$ 0.197	$r=$ 0.116	$r=-$ 0.002	$r=$ 0.030	$r=$ 0.012	$r=-$ 0.280
DASI	$P=$ 0.469	$P=$ 0.464	$P=$ 0.670	$P=$ 0.993	$P=$ 0.911	$P=$ 0.965	$P=$ 0.293

Table 6

Averaged correlation coefficient value for each ASM index and their association with physical performance tests

$n=$ 22	ASM (kg)	ASM/weight (kg)	ASM/height (m)	ASM/height² (m²)	ASM/BMI (kg/m²)	ASM/fat mass (kg)	ASM/body fat% (kg)
Upper extremities function	0.857	0.666	0.831	0.759	0.791	0.706	0.616
Lower extremities function	0.297	0.230	0.262	0.208	0.375	0.251	0.142
All tests	0.362	0.287	0.322	0.257	0.385	0.282	0.229

3.4 Association between ASM normalization indices and physical function

HGS was significantly and strongly associated with all indices of ASM in patients who completed DXA assessment. In particular, ASM/BMI, ASM/height and crude ASM showed the top three strongest correlation with HGS ( $r$ ’s $=$ 0.791 to 0.857). Except for ASM normalized for tissue fat%, all the other muscle mass indices were strongly and significantly associated with ISWT. Similarly, the top three indices showing the strong association with ISWT were ASM/height, crude ASM and ASM/BMI ( $r$ ’s $=$ 0.668 to 0.768). No significant associations were observed between the other lower extremity physical performance tests and any of muscle mass indices in the study, neither was the self-reported physical function. The detailed association between ASM normalization indices and physical function was shown in Table 6.

3.5 The strongest ASM normalization index with physical performance tests

ASM/BMI was identified as the strongest index associated with overall physical performance tests, followed by crude ASM ( $r=$ 0.362). Crude ASM was the strongest index ( $r=$ 0.857) when observing the association with upper extremity physical function. In regard to lower extremity physical function, the strongest index was ASM/BMI ( $r=$ 0.375). ASM/tissue fat% showed the poorest association with overall ( $r=$ 0.229), upper extremity ( $r=$ 0.616) and lower extremity physical function ( $r=$ 0.142). The averaged correlation coefficient value for each ASM index and their association with physical performance tests was shown in Table 6.

4. Discussion

Muscle mass loss is a crucial hallmark of sarcopenia in patients receiving HD, contributing to declines in physical performance, poor quality of life and high mortality risk [5, 6, 45]. The loss of muscle mass results in poor outcomes mainly through physical impairment. Therefore, it is clinically useful to identify the muscle mass index that is associated with physical function for patients on HD. In light of the different muscle mass measured by DXA and BCM, the aim of the study was to identify the most useful or the strongest normalization methods in regard to physical function performance for whole body LTM (taken from BCM) and ASM (taken from DXA). The main finding is that different relationship was shown between different LTM or ASM normalization indices and physical function. While LTM adjusted for height (either height or height squared) is the strongest LTM normalization index associated with physical function, the strongest ASM adjustment index is ASM adjusted for BMI.

Fundamentally, individual’s muscle mass is influenced by body size. The useful and well-established normalization of muscle mass is crucial for patients undergoing HD to predict physiological status or negative outcomes. It is well-known that the EWGSOP [1], the AWGS [11] and IWGS [20]support skeletal muscle mass or ASM adjusted for height, particularly height-squared as the most frequently used indexing metric of muscle mass. ASM normalized for height squared have also been commonly used in patients undergoing HD [15, 16, 17]. An observational, longitudinal and multi-center study including 170 older adults on HD revealed that the group with sarcopenia showed a hazard ratio 2.65 (95% CI: 0.86–7.05) for mortality when compared to a group no-sarcopenia and no-malnutrition [16]. Sarcopenia in the patients was diagnosed based on low muscle quantity which was defined by ASM adjusted for height squared and adopted the cutoffs recommended in the 2019 EWGSOP [1]. A Korean study using the cut-off points suggested by the AWGSOP showed that ASM/height² may be the most valuable indices for predicting physical performance or strength compared with the other ones in male patients on HD [46].

Superiority of ASM/height² is consistent with our finding in the present study. The difference lies in whole-body LTM which was obtained instead of ASM in the study. The result is in agreement with a previous study by Kittiskulnam et al., which revealed that whole-body LTM taken from pre-dialysis bioimpedance spectroscopy adjusted for height² was a stronger predictor of mortality compared to alternative indexing metrics, such as body weight, body surface area, and body mass index [13]. The whole body LTM is not recommended as a criterion for diagnosing sarcopenia by international sarcopenia working groups. However, numerous studies have shown that BCM is a robust tool for measuring total body fat and lean body mass in patients undergoing HD [47, 48, 49]. It is also extensively applied in patients as it is a reproducible, more feasible and lower cost method comparing with DXA [50, 51, 52]. The most prominent reason behind its wide application in patients undergoing HD is that its measurement is not affected by overhydration. New research further revealed that body composition parameters obtained using BCM have been confirmed as useful predictors of mortality in this population [48, 53, 54].

There is no doubt that height or height squared as indexing metric of muscle mass is recommended by major consensus panels. However, muscle mass relative to height may be unable to clearly identify patients’ sarcopenia if they are obese or overweight with low muscle quantity [13, 23]. Increasing studies have shown that adjusting for height would not recognize patients with higher fat body mass index and lower muscle mass, a condition that is called “obese sarcopenia” [55]. Wilkinson et al. noted that the strongest and most clinically useful muscle mass index in regard to physical function was ASM adjusted for fat mass [22]. The demographics of all 72 patients with renal disease in the study revealed that their mean BMI was up to 27.6 kg/m². In terms of patients undergoing HD ( $n=$ 11), the mean BMI of them was 26.1kg/m². Obviously, Chinese patients in the present study have much lower mean BMI (LTM group: SD $=$ 23.0 kg/m²; ASM group: SD $=$ 22.0 kg/m²) than the UK cohort in Thomas’s study. The superior of height or height squared showing the strongest association with functional status may be ascribed to the low or normal BMI in the most of patients in the study, possibly appearing the low proportion of obese sarcopenia. Similarly, when ASM was utilized as the muscle mass criteria, adjustment for body fat has not been shown the strongest association with physical function either. To the contrary, ASM adjusted for adiposity showed physiologically and clinically least relevant in the patients. Our finding is in agree with an analysis based on the data from the Health Aging and Body Composition (Health ABC) Study. When only accounting for fat was associated with low extremity function, sarcopenia diagnosed based on ASM adjusted for height and fat mass showed a stronger association with physical performance scores than the method of ASM for height² [23].

The Foundation for the National Institutes of Health (FNIH) Sarcopenia Project recently derived from nine sources of community-dwelling older persons and proposed the definition of sarcopenia in which ASM was adjusted for BMI [21]. Additionally, Chinese expert consensus on diagnosis and treatment for elderly with sarcopenia (2021) also clearly recommends to identify the absolute level of muscle mass by using ASM indexed to BMI [14]. In patients undergoing HD, our study has consistently shown that ASM normalized for BMI had the strongest (and most useful muscle mass index) association with physical performance. To our knowledge, limited previous studies involving patients undergoing HD diagnosed low muscle mass using the ASM/BMI metric, as the overwhelming researchers adopted ASM/height² as the optimal diagnostic criteria of sarcopenia. In a study of data from 100 patients with chronic kidney disease not yet on dialysis, the inverse correlation was shown between ASM adjusted for BMI and the serum levels of high-sensitivity C-reactive protein (hsCRP) ( $p=$ 0.007) [56]. Additionally, Viviane viewed ASM normalized for BMI as the most appropriate muscle mass criteria in clinical research due to its significant association with walking speed. Considering the influence of obesity on the relationship between muscle mass and strength, McLean also concluded muscle mass adjusted for BMI would be a more appropriate indexing metric [57]. Normalization by BMI may overestimate the prevalence of low muscle mass when being applied on obese patients who maintained muscle mass. However, most patients in the present study could not be classified into “obese group”, which is different from the phenomenon that obesity has been increasingly confirmed as an nontraditional risk factor in CKD worldwide [58].

4.1 Strengths and limitations

As far as we know, few studies focused on the useful muscle mass indices for predicting physical function in patients receiving HD [46]. Moreover, physical performances and muscle strength tests conducted in the current study are all well-established recommended by the various international sarcopenia consensus [1, 11, 14]. Additionally, whilst DXA was used for identifying low muscle mass, BCM which is commonly used for assessing body composition in patients on HD was applied indexing metric of whole-body lean mass as well. A limitation of the study is the restricted sample size. Additionally, participants in this study were not derived from multiple centers and did not represent a diverse sample of Chinese patients undergoing HD. However, this paper only presents an exploratory secondary analysis of body composition data, providing support for the clinical utility of measuring lean body mass or skeletal muscle mass as a predictor of physical functional and performance. Future studies with a larger sample from multi-centers or regions are warranted to confirm the association between muscle mass indices and physical function performance.

5. Conclusion

Regardless of DAX or BCM being applied for assessing muscle mass in the current study, inconsistent relationship has been appeared between different muscle mass indices and physical function performance. Our study corroborates the evidence to identify the optimal adjustment indices for predicting low muscle strength or physical performance in patients receiving HD. While the most useful LTM index was adjustment for height, ASM index should normalize for BMI. Consequently, superiority of adjustment for height (height²) or BMI recommended by various international sarcopenia working groups have been consistently shown in patients undergoing HD. However, it is required to take into account the influence of individual’s BMI or obesity on prevalence of low muscle mass.

5.1 Relevance to clinical practice

Patients undergoing HD suffer from muscle mass loss that leads to high risk of morbidity and mortality. It is clinically useful to identify the preferred muscle mass normalization method by examining its association with physical function. The exploratory study indicates that ASM indexed for BMI may be clinically and physiologically more relevant in patients undergoing HD. Additionally, although BCM only provides whole body LTM which is probably not pertinent to the definition of sarcopenia, it has been widely used to assess body composition in the patients. Obviously, it is equally imperative in clinical practice to figure out the optimal whole body LTM indices. The study suggests that the most clinically useful LTM index involves adjustment for height. The preferred adjustment method recommended in international sarcopenia consensus can be used for patients undergoing HD. However, it is essential for healthcare professionals or researchers to consider patients’ BMI or obesity when understanding their clinical states based on estimation of low muscle mass.

Footnotes

Acknowledgments

We would like to extend our sincere gratitude to our patients who were willing to participate in our study, conduct a variety of physical performance tests and accept different body composition assessments. The study is supported by the Social Livelihood Science and Technology Fund of Nantong (MS22022064) and the National Social Science Fund of China (21BGL302).

Conflict of interest

The authors declare no competing interests.

Author contributions

Each of the authors has made a significant contribution to the work. The individual contributions of each co-author were as follows: Conception; YS. Data acquisition; YS XRZ JXL. Data analysis and interpretation; YS XRZ. Statistical analysis; YS XRZ. Manuscript preparation; YS.

References

Cruz-Jentoft

Bahat

Bauer

Boirie

Bruyère

Cederholm

, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019; 48(1): 16-31.

Zhou

Hellberg

Svensson

Höglund

Clyne

. Sarcopenia and relationships between muscle mass, measured glomerular filtration rate and physical function in patients with chronic kidney disease stages 3–5. Nephrol Dial Transplant. 2018; 33(2): 342-8.

Xie

Zhu

Niu

, et al. Development and validation of a diagnostic nomogram for sarcopenia in Chinese hemodialysis patients. Nephrol Dial Transplant. 2023; 38(4): 1017-1026.

Lamarca

Carrero

Rodrigues

Bigogno

Fetter

Avesani

. Prevalence of sarcopenia in elderly maintenance hemodialysis patients: the impact of different diagnostic criteria. J Nutr Health Aging. 2014; 18(7): 710-7.

McKenna

Salvador

Hendriks

Harris

APY

van Loon

LJC

Burd

. Exercising to offset muscle mass loss in hemodialysis patients: The disconnect between intention and intervention. Semin Dial. 2019; 32(4): 379-85.

Baltacı

Atmis

Metin

Aktar

Eren

Sengul

, et al. Sarcopenia and cardiovascular risk indices: Its impact on cardiovascular events and mortality in dialysis patients. Semin Dial. 2023; 36(3): 221-230.

Bellafronte

Diani

Vega-Piris

Cuadrado

Chiarello

. Comparison between dual-energy x-ray absorptiometry and bioelectrical impedance for body composition measurements in adults with chronic kidney disease: A cross-sectional, longitudinal, multi-treatment analysis. Nutrition. 2021; 82: 111059.

Liu

Sun

Chen

Yuan

Shen

, et al. The effect of BCM guided dry weight assessment on short-term survival in Chinese hemodialysis patients: Primary results of a randomized trial – BOdy COmposition MOnitor (BOCOMO) study. BMC Nephrol. 2020; 21(1): 135.

Mussnig

Schmiedecker

Waller

Niknam

Paschen

Schneditz

, et al. Differences in bioimpedance-derived fluid status between two versions of the Body Composition Monitor. Nutrition. 2023; 114: 112131.

10.

Yang

Yuan

Zha

. One-year change in sarcopenia was associated with cognitive impairment among haemodialysis patients. J Cachexia Sarcopenia Muscle. 2023; 14(5): 2264-2274.

11.

Chen

Woo

Assantachai

Auyeung

Chou

Iijima

, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc. 2020; 21(3): 300-7e2..

12.

Battaglia

Ullo

Massarenti

Esposito

Prencipe

Ciancio

, et al. Ultrasonography of Quadriceps Femoris Muscle and Subcutaneous Fat Tissue and Body Composition by BIVA in Chronic Dialysis Patients. Nutrients. 2020; 12(5).

13.

Kittiskulnam

Chertow

Carrero

Delgado

Kaysen

Johansen

. Sarcopenia and its individual criteria are associated, in part, with mortality among patients on hemodialysis. Kidney Int. 2017; 92(1): 238-47.

14.

Liu

Qingqing

Baiyu

Xiang

Jingmin

Yongming

, et al. Chinese expert consensus on diagnosis and treatment for elderly with sarcopenia (2021). Chin J Geriatr. 2021; 40(08): 943-52.

15.

Hortegal

EVF

Alves

Santos

EJF

Nunes

LCR

Galvão

Nunes

, et al. Sarcopenia and inflammation in patients undergoing hemodialysis. Nutr Hosp. 2020; 37(4): 855-62.

16.

Macedo

Amaral

Rodrigues

Santin

Avesani

. Malnutrition and Sarcopenia Combined Increases the Risk for Mortality in Older Adults on Hemodialysis. Front Nutr. 2021; 8: 721941.

17.

Oliveira

Silva

RPD

Lemos

Burgos

Costa

DMN

Maio

. [Frequency of sarcopenia, cachexia, and associated factors in patients with chronic kidney disease in dialysis treatment]. Nutr Hosp. 2020; 37(6): 1157-65.

18.

Darroch

O’Brien

Mazahery

Wham

. Sarcopenia Prevalence and Risk Factors among Residents in Aged CareNutrients. 2022; 14(9): 1837.

19.

Purcell

MacKenzie

Barbosa-Silva

Dionne

Ghosh

Olobatuyi

, et al. Sarcopenia Prevalence Using Different Definitions in Older Community-Dwelling Canadians. J Nutr Health Aging. 2020; 24(7): 783-790.

20.

Fielding

Vellas

Evans

Bhasin

Morley

Newman

, et al. Sarcopenia: an undiagnosed condition in older adults. Current consensus definition: prevalence, etiology, and consequences. International working group on sarcopenia. J Am Med Dir Assoc. 2011; 12(4): 249-56.

21.

Studenski

Peters

Alley

Cawthon

McLean

Harris

, et al. The FNIH sarcopenia project: rationale, study description, conference recommendations, and final estimates. J Gerontol A Biol Sci Med Sci. 2014; 69(5): 547-58.

22.

Wilkinson

Nixon

DGD

Richler-Potts

Neale

Song

Smith

. Identification of the most clinically useful skeletal muscle mass indices pertinent to sarcopenia and physical performance in chronic kidney disease. Nephrology (Carlton). 2020; 25(6): 467-74.

23.

Newman

Kupelian

Visser

Simonsick

Goodpaster

Nevitt

, et al. Sarcopenia: alternative definitions and associations with lower extremity function. J Am Geriatr Soc. 2003; 51(11): 1602-9.

24.

Tanaka

Ikezoe

Ichihashi

Tabara

Nakayama

Takahashi

, et al. Relationship of low muscle mass and obesity with physical function in community dwelling older adults: Results from the Nagahama study. Arch Gerontol Geriatr. 2020; 88: 103987.

25.

Carrero

Johansen

Lindholm

Stenvinkel

Cuppari

Avesani

. Screening for muscle wasting and dysfunction in patients with chronic kidney disease. Kidney Int. 2016; 90(1): 53-66.

26.

Wilkinson

Gould

Nixon

DGD

Watson

Smith

. Quality over quantity? Association of skeletal muscle myosteatosis and myofibrosis on physical function in chronic kidney disease. Nephrol Dial Transplant. 2019; 34(8): 1344-53.

27.

Maynard

de Menezes

Lião

de Jesus

Andrade

NLS

Santos

JCD

, et al. Effects of Exercise Training Combined with Virtual Reality in Functionality and Health-Related Quality of Life of Patients on Hemodialysis. Games Health J. 2019; 8(5): 339-348.

28.

Figueiredo

PHS

Veloso

LRS

Lima

MMO

Vieira

CFD

Alves

Lacerda

ACR

, et al. The reliability and validity of the 30-seconds sit-to-stand test and its capacity for assessment of the functional status of hemodialysis patients. J Bodyw Mov Ther. 2021; 27: 157-164.

29.

Figueiredo

Lima

Costa

Gomes

Neves

Oliveira

, et al. The role of the inspiratory muscle weakness in functional capacity in hemodialysis patients. PLoS One. 2017 9; 12(3): e0173159.

30.

March

Wilkinson

Burnell

Billany

Jackson

Baker

, et al. The Effect of Non-Pharmacological and Pharmacological Interventions on Measures Associated with Sarcopenia in End-Stage Kidney Disease: A Systematic Review and Meta-Analysis. Nutrients. 2022; 14(9): 1817.

31.

Martínez-Olmos

Gómez-Conesa

García-Testal

Ortega-Pérez-de-Villar

Valtueña-Gimeno

Gil-Gómez

, et al. An intradialytic non-immersive virtual reality exercise programme: a crossover randomized controlled trial. Nephrol Dial Transplant. 2022; 37(7): 1366-1374.

32.

Santamaría-Peláez

González-Bernal

Da Silva-González

Medina-Pascual

Gentil-Gutiérrez

Fernández-Solana

, et al. Validity and Reliability of the Short Physical Performance Battery Tool in Institutionalized Spanish Older Adults. Nurs Rep. 2023; 13(4): 1354-1367.

33.

Fuentes-Abolafio

Stubbs

Pérez-Belmonte

Bernal-López

Gómez-Huelgas

Cuesta-Vargas

. Physical functional performance and prognosis in patients with heart failure: a systematic review and meta-analysis. BMC Cardiovasc Disord. 2020; 20(1): 512.

34.

Przkora

Kinsky

Fisher

Babl

Heyde

Vasilopoulos

, et al. Functional Improvements Utilizing the Short Physical Performance Battery (SPPB) in the Elderly after Epidural Steroid Injections. Curr Pain Headache Rep. 2019; 23(2): 14.

35.

Bohm

Storsley

Hiebert

Nelko

Tangri

Cheskin

, et al. Impact of Exercise Counseling on Physical Function in Chronic Kidney Disease: An Observational Study. Can J Kidney Health Dis. 2018; 5: 2054358117753615.

36.

Luo

Yao

Zhang

. Exploring the bidirectional associations between handgrip strength and depression in middle and older Americans. J Psychosom Res. 2021; 152: 110678.

37.

Lee

Chiang

Chen

Lin

, et al. Validating the Capability for Measuring Age-Related Changes in Grip-Force Strength Using a Digital Hand-Held Dynamometer in Healthy Young and Elderly Adults. Biomed Res Int. 2020; 2020: 6936879.

38.

McGrath

Vincent

Hackney

Robinson-Lane

Downer

Clark

. The Longitudinal Associations of Handgrip Strength and Cognitive Function in Aging Americans. J Am Med Dir Assoc. 2020; 21(5): 634-639e1.

39.

Parthasarathy

Oei

Fan

. Clinical value of body composition monitor to evaluate lean and fat tissue mass in peritoneal dialysis. Eur J Clin Nutr. 2019; 73(11): 1520-1528.

40.

Mathilakath

Selvaraj

Parameswaran

Viswanathan

Pillai

. Prevalence of Overhydration in Patients on Maintenance Haemodialysis As Determined by Body Composition Monitor and Effects of Attaining Target Dry Weight. Cureus. 2022; 14(9): e29509.

41.

Zhang

Tao

Shi

Jiang

Yang

. Evaluation of body composition monitoring for assessment of nutritional status in hemodialysis patients. Ren Fail. 2019; 41(1): 377-383.

42.

Magnard

Deschamps

Cornu

Paris

Hristea

. Effects of a six-month intradialytic physical ACTIvity program and adequate NUTritional support on protein-energy wasting, physical functioning and quality of life in chronic hemodialysis patients: ACTINUT study protocol for a randomised controlled trial. BMC Nephrol. 2013; 14: 259.

43.

Broers

Martens

Cornelis

Diederen

Wabel

van der Sande

, et al. Body composition in dialysis patients: a functional assessment of bioimpedance using different prediction models. J Ren Nutr. 2015; 25(2): 121-8.

44.

Hemphill

. Interpreting the magnitudes of correlation coefficients. Am Psychol. 2003; 58(1): 78-9.

45.

Kitamura

Takazono

Yamaguchi

Notomi

Sawase

Harada

, et al. The impact of muscle mass loss and deteriorating physical function on prognosis in patients receiving hemodialysis. Sci Rep. 2021; 11(1): 22290.

46.

Kim

Cho

Kang

. Comparison of appendicular lean mass indices for predicting physical performance in Korean hemodialysis patients: A cross-sectional study. Medicine (Baltimore). 2021; 100(49): e28168.

47.

Liu

Sun

Chen

Yuan

Shen

, et al. The effect of BCM guided dry weight assessment on short-term survival in Chinese hemodialysis patients: Primary results of a randomized trial - BOdy COmposition MOnitor (BOCOMO) study. BMC Nephrol. 2020; 21(1): 135.

48.

Hwang

Lee

Kim

Song

. Risk of overhydration and low lean tissue index as measured using a body composition monitor in patients on hemodialysis: a systemic review and meta-analysis. Ren Fail. 2018; 40(1): 51-9.

49.

Tian

Zha

Qie

Lin

Yuan

. Association of Body Composition and Intradialytic Hypotension in Hemodialysis Patients. Blood Purif. 2020; 49(3): 334-40.

50.

Slouma

Sahli

Bahlous

Laadhar

Smaoui

Rekik

, et al. Mineral bone disorder and osteoporosis in hemodialysis patients. Adv Rheumatol. 2020; 60(1): 15.

51.

Lin

Chen

Hung

Lim

. Development and validation of a multifrequency bioimpedance spectroscopy equation to predict appendicular skeletal muscle mass in hemodialysis patients. Clin Nutr. 2021; 40(5): 3288-3295.

52.

Gonzalez

Heymsfield

. Bioelectrical impedance analysis for diagnosing sarcopenia and cachexia: what are we really estimating? J Cachexia Sarcopenia Muscle. 2017; 8(2): 187-9.

53.

Caetano

Valente

Oliveira

Garagarza

. Body Composition and Mortality Predictors in Hemodialysis Patients. J Ren Nutr. 2016; 26(2): 81-6.

54.

Lin

Peng

Hung

Tarng

. Body composition is associated with clinical outcomes in patients with non-dialysis-dependent chronic kidney disease. Kidney Int. 2018; 93(3): 733-40.

55.

Honda

Qureshi

Axelsson

Heimburger

Suliman

Barany

, et al. Obese sarcopenia in patients with end-stage renal disease is associated with inflammation and increased mortality. Am J Clin Nutr. 2007; 86(3): 633-8.

56.

Souza

Oliveira

Barbosa

Corrêa

Colugnati

FAB

Mansur

, et al. Sarcopenia in patients with chronic kidney disease not yet on dialysis: Analysis of the prevalence and associated factors. PLoS One. 2017; 12(4): e0176230.

57.

McLean

Kiel

. Developing consensus criteria for sarcopenia: an update. J Bone Miner Res. 2015; 30(4): 588-92.

58.

Pommer

. Preventive Nephrology: The Role of Obesity in Different Stages of Chronic Kidney Disease. Kidney Dis (Basel). 2018; 4(4): 199-204.

The useful lean tissue and appendicular skeletal muscle mass indices related to physical performance in patients undergoing hemodialysis

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

Table 1 Recommendations for measuring muscle quantity in sarcopenia in different guidelines

2.1 Participants

2.2 Data collection

2.3 Outcome measures

2.3.1 Demographic and clinical parameters

2.3.2 Questionnaire

2.3.3 Physical performance tests

2.3.4 Body composition

2.4 Statistical analysis

3. Results

Table 2 Demographics and characteristics of the patients undergoing HD using BCM and DXA

Table 3 The association between LTM normalization indices and physical function

3.3 The strongest LTM normalization index with physical performance tests

Table 5 The association between ASM normalization indices and physical function

3.5 The strongest ASM normalization index with physical performance tests

4. Discussion

4.1 Strengths and limitations

5. Conclusion

5.1 Relevance to clinical practice

Footnotes

Acknowledgments

Conflict of interest

Author contributions

References

Table 1
Recommendations for measuring muscle quantity in sarcopenia in different guidelines

Table 2
Demographics and characteristics of the patients undergoing HD using BCM and DXA

Table 3
The association between LTM normalization indices and physical function

Table 5
The association between ASM normalization indices and physical function