Prevalence and Risk Factors of Sarcopenia in People Receiving Dialysis: A Systematic Review and Meta‐Analysis

Joanna Stockings; Susan Heaney; Ginger Chu; Peter Choi; Ritin Fernandez

doi:10.1111/sdi.70000

. 2025 Jul 8;38(4):237–249. doi: 10.1111/sdi.70000

Prevalence and Risk Factors of Sarcopenia in People Receiving Dialysis: A Systematic Review and Meta‐Analysis

Joanna Stockings ¹, Susan Heaney ^2,^3,^✉, Ginger Chu ^4,⁵, Peter Choi ¹, Ritin Fernandez ^4,⁶

PMCID: PMC12378093 PMID: 40625163

ABSTRACT

Background

People receiving dialysis treatment are at higher risk of sarcopenia. This review aimed to determine the global prevalence of sarcopenia in people on peritoneal dialysis and hemodialysis. We investigated whether the prevalence of sarcopenia differs based on assessment criteria, timing of assessment, and measurement tools used and explored the risk factors for sarcopenia in people on dialysis.

Methods

This review followed JBI and PRISMA guidelines and included studies assessing sarcopenia in adults aged 18 and older with chronic kidney disease undergoing dialysis. Five databases were searched from inception to November 2022. The JBI SUMARI software was used to perform the meta‐analysis. Publication bias and risk factor analysis were performed using STATA (Version 18).

Results

A meta‐analysis of 62 studies (15,382 participants) found the global prevalence of sarcopenia to be 30.1% (95% CI: 25.6%–39.9%) for hemodialysis and 20.5% (95% CI: 15.1%–26.4%) for peritoneal dialysis. Sarcopenia prevalence ranged between 23.1% and 30.3% in HD and between 6.1% and 26.9% in PD, based on the assessment criteria. Post‐dialysis sarcopenia prevalence was higher (33%) than pre‐dialysis (24.2%) in hemodialysis patients. Measuring muscle mass after dialysis using dual‐energy X‐ray assessment yielded a lower prevalence of sarcopenia (22.5%) than bioimpedance analysis or spectroscopy (33%). Risk factors in the HD population included age, sex, diabetes, inflammation markers, nutritional indices, and dialysis vintage, although heterogeneity between studies was high.

Conclusion

This study showed a high prevalence of sarcopenia among the dialysis population and identified many risk factors, emphasizing the need for early identification and intervention and standardized assessments.

Keywords: hemodialysis, meta‐analysis, peritoneal dialysis, sarcopenia

1. Introduction

Sarcopenia is a medical condition characterized by progressive muscle mass and function loss, which can lead to physical disability, poor quality of life, and death [1]. Although the natural aging process stands as a primary causative factor of sarcopenia [2], it is notable that chronic kidney disease (CKD) plays a significant role as one of the major causes and exacerbating factors of this condition [3]. An observational study of people with CKD reported that the risk of sarcopenia increases as kidney function deteriorates, and for each additional grade of CKD, the risk of sarcopenia increases by 45% [4]. The prevalence of sarcopenia in people with CKD ranges from 4% to 42%, depending on the stage of kidney disease [5], which is nearly four times higher than in healthy adults over 60 years old [6].

Several poor health outcomes are associated with sarcopenia, including mortality, hospitalization, functional decline, and falls [7]. A longitudinal study demonstrated that CKD people with sarcopenia had a higher number of cardiovascular diseases (56.9% vs. 12.6%, p < 0.001), hospitalization (93.8% vs. 49.5%, p < 0.001), and risk of mortality (hazard ratio: 3.3, 95% confidence interval: 1.6–6.9, p = 0.001) compared with CKD people without sarcopenia [8]. Therefore, assessing sarcopenia to ensure early detection and intervention is important in caring for people with CKD.

Currently, there is no global consensus to assess sarcopenia in people with CKD. Several criteria have been proposed by different professional bodies, including the European Working Group on Sarcopenia in Older People (EWGSOP) [9, 10], the Asian Working Group for Sarcopenia (AWGS) [11], the Foundation for the National Institute of Health (FNIH) sarcopenia project [12], and the International Working Group on Sarcopenia (IWGS) [13]. These criteria differ in their approaches to assessing lean muscle mass (LMM), lean muscle strength (LMS), and physical performance, as well as in establishing cutoffs for these parameters (Table S1). When measuring LMM, dual‐energy X‐ray (DXA) is regarded as the gold standard for the estimation of muscle mass; however, it is not always practical in the clinical setting due to its invasive nature and associated labor costs [14]. Alternatives such as bioimpedance analysis (BIA) or bioimpedance spectroscopy (BIS) are often used in clinical practice. A meta‐analysis involving both hemodialysis (HD) and peritoneal dialysis (PD) patients reported a higher prevalence of sarcopenia when LMM was measured by DXA (29.2%) vs. BIA (26.2%) and BIS (15.2%) [15]. However, the prevalence of sarcopenia using different tools to evaluate LMM has not been evaluated separately in the HD and PD population.

Although several studies have reported the prevalence of sarcopenia in the CKD and dialysis population [5], [14, 15, 16], there remains a significant lack of quantitative syntheses focusing on dialysis modality assessment criteria, timing of assessment, and the tools used for measuring muscle mass. This gap limits the ability to draw meaningful comparisons and inform clinical practice. To address this critical need, we conducted a detailed investigation of the prevalence of sarcopenia in PD and HD populations separately. We further systematically examined whether prevalence estimates differ based on diagnostic criteria employed, the timing of measurements, and the specific tools used to measure muscle mass. Finally, we explored the risk factors for sarcopenia in CKD patients on dialysis to inform targeted screening and early intervention strategies in this vulnerable population.

2. Methods

2.1. Design

This systematic review followed the JBI guidelines for undertaking prevalence reviews [16] and the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines [17]. The protocol for the review was registered in the Prospective Register of Systematic Reviews (PROSPERO).

2.2. Literature Search

We systematically searched the following electronic databases in November 2022: Ovid MEDLINE, Embase, Allied and Complementary Medicine Database, CINAHL, and Scopus. The search strategy was developed by the team in collaboration with a research librarian and tested in the MEDLINE database (Table S2). All retrieved studies were collected and transferred into reference management software (EndNote 20), and any duplicate entries were removed during this phase of the review process.

2.3. Inclusion and Exclusion Criteria

Studies were included if they (1) assessed sarcopenia using clearly defined assessment criteria, (2) investigated adults aged 18 years and older with CKD and receiving dialysis, and (3) reported prevalence of sarcopenia and/or risk factors of sarcopenia. We excluded review articles, conference abstracts, and studies without a clear definition of sarcopenia and/or did not report the prevalence or risk factor of sarcopenia in the dialysis population. To avoid duplication, if more than one study reported the same population, the study with the largest sample size and most informative data was included.

2.4. Study Selection and Data Extraction

The titles and abstracts were screened independently by two authors (JS, SH) using the Covidence systematic review software [18]. The same authors also independently reviewed full texts of studies. Any discrepancies were resolved through discussion with a third author (GC, RF). One author (JS) independently extracted data, including the study population, study methods, assessment criteria for sarcopenia, prevalence of sarcopenia, and risk factors of sarcopenia. All data extracted were checked by another author (GC), and during the data input, the data were checked by a third author (SH). We contacted the authors if there was an absence of data in the published paper; if no information was received, studies were excluded.

2.5. Quality Assessment

Four authors (JS, SH, GC, RF) independently assessed the methodological quality using the JBI critical appraisal tool for analytical cross‐sectional studies [17]. The JBI checklist consists of eight items, with each item rated as yes (2 points), unclear (1 point), or no (0 points). Conflicts were resolved through discussion and consultation. All papers were included in this review, irrespective of quality.

2.6. Data Analysis

A statistical meta‐analysis and a sensitive analysis were performed using the JBI SUMARI software. Publication bias and risk factor analysis were performed using STATA (Version 18) [19]. Effect sizes were expressed as proportions with 95% confidence intervals (CIs) around the summary estimate. Subgroup analyses were performed for PD and HD populations under different assessment criteria. In addition, the timing of LMM measurement (pre‐ or post‐dialysis) and measurement tool used (BIA/BIS vs. DXA) were analyzed separately for the HD group. Heterogeneity was quantified using the I ² statistic. A random‐effects model employing the Freeman–Tukey transformation approach was used to measure outcomes if I ² > 50%; otherwise, a fixed‐effect model was used. Pooled odds ratio (OR) and their 95% CI for the risk factors were calculated using Der Simonian and Laird random‐effects model in STATA. Adjusted ORs reported in the individual studies were included in the meta‐analysis where available. Only adjusted associations for confounding variables that were reported inconsistent values in greater than two studies were included.

A sensitivity analysis was conducted to test the robustness of the findings by sequentially removing one study at a time and altering the effects models. Additionally, funnel plots and Egger's test were used to assess publication bias. A p‐value of < 0.05 was considered statistically significant for all tests.

3. Results

3.1. Study Characteristics

Of the 174 articles retrieved, 62 studies met the inclusion criteria. The results of the study selection are outlined in Figure 1. These studies involved 15,382 participants (ranging from 20 to 3196 in each study), the majority of whom were receiving HD treatment (92%). All studies were published in the last 10 years, predominately in China [20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33] (22%), Japan [20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] (19%), Korea [32, 33, 34, 35, 36] (8%), and Taiwan [37, 38, 39, 40] (6%). Most were cross‐sectional studies (65%, n = 43), followed by cohort studies (28.5%, n = 18). The AWGS was the most commonly used assessment criterion for sarcopenia in 32 studies (51.5%), followed by EWGSOP‐1 (13 studies, 21%) and EWGSOP‐2 (12 studies, 19%). In terms of timing of assessment, LMM was commonly measured post‐HD in 35% of the studies. Three main instruments were employed to assess LMM: BIA in 33 studies (53%), DXA in 17 studies (27%), and BIS in 8 studies (13%). Table 1 summarizes the characteristics of the included studies.

Reference screening selection flowchart.

TABLE 1.

Characteristics of included studies.

Author, year	Country	Sample size	Diagnostic criteria	Timing	Measurement of muscle mass	Methodological quality score (x/16)
HD population
Abdala, 2021 [41]	Argentina	100	EWGSOP2	Post	DXA	12
Bae, 2022 [32]	S. Korea	191	Other	Post	BIA	12
Bataille, 2017 [42]	France	111	EWGSOP1	During	BIA	12
Cai, 2022 [43]	China	615	AWGS2019	Post	BIA	12
Chen, R 2022 [44]	China	142	AWGS 2019	Post	BIA	12
Chen, X 2022 [45]	China	849	AWGS 2019	ND	BIA	12
Cheng, 2021 [46]	China	238	AWGS 2019	Post	BIA	12
De Luca Correa, 2022 [8]	Brazil	247	EWGSOP2	ND	DXA	12
Dierkes, 2018 [47]	Norway	24	EWGSOP1	Post	BIA	12
Ding, 2022 [48]	China	346	AWGS 2019	Post	BIA	16
Dong, 2019 [49]	China	341	AWGS 2014	Post	BIA	12
Du, 2022 [50]	China	589	AWGS 2019	Post	BIA	16
Fan, 2022 [51]	China	105	AWGS 2019	ND	DXA	16
Ferreira, 2022 [52]	Brazil	127	EWGSOP1, EWGSOP2	NA	Other	16
Furtado, 2020 [53]	Brazil	209	EWGSOP2	Post	DXA	16
Giglio, 2018 [54]	Brazil	170	EWGSOP1	ND	DXA	12
Hayashi, 2022 [21]	Japan	342	AWGS 2019	ND	DXA	12
Hotta, 2015 [22]	Japan	33	EWGSOP1	ND	BIA	10
Imamura, 2022 [23]	Japan	179	AWGS 2019	Pre	BIA	12
Ishimura, 2021 [24]	Japan	308	AWGS 2019	ND	DXA	12
Isoyama, 2014 [55]	Sweden	330	EWGSOP1	Post	DXA	16
Kakita, 2022 [31]	Japan	356	AWGS 2019	ND	BIA	16
Kim, 2019 [33]	Korea	142	EWGSOP1	ND	BIS	12
Kittiskulnam, 2017 [56]	USA	643	FNIH	Pre	BIS	12
Kurajoh, 2022 [25]	Japan	296	AWGS 2019	Pre	DXA	12
Lamarca, 2014 [57]	Brazil	102	Other	ND	DXA and BIA	12
Lee, 2020 [34]	Korea	131	AWGS 2014	ND	BIA	16
Li, 2022 [58]	China	112	AWGS 2019	ND	BIA	12
Lin, 2021 [37]	Taiwan	259	AWGS 2019	Pre	DXA	12
Lin, 2020 [38]	Taiwan	126	AWGS 2014, EWGSOP1	Pre	BIA	12
Macedo, 2021 [59]	Brazil	170	EWGSOP2	NA	Other	12
Matsuzawa, 2021 [26]	Japan	58	AWGS 2019	Post	BIA	16
Mattera, 2021 [60]	Italy	77	Other	ND	DXA	12
Medeiros, 2020 [61]	Brazil	92	Other	ND	BIA	16
Miyazaki, 2021 [27]	Japan	20	AWGS 2019	ND	DXA	12
Mori, 2019 [28]	Japan	308	AWGS 2014	Post	DXA	12
Moriyama, 2019 [29]	Japan	306	Other	NA	Other	12
Ren, 2016 [62]	China	131	EWGSOP1	ND	BIA	12
Rosa, 2022 [63]	Brazil	67	EWGSOP 1, EWGSOP2, AWGS, FNIH	Post	DXA	12
Rymarz, 2018 [64]	Poland	49	OTHER	Pre	BIS	12
Sanchez‐Tocino, 2022 [65]	Spain	60	EWGSOP2	Post	BIA	12
Saitoh, 2019 [30]	Japan	117	AWGS 2014	During	BIA	16
Ulgen, 2022 [66]	Turkey	79	EWGSOP2	Pre	BIA	12
Wang, 2021 [67]	China	87	AWGS 2014	Post	BIA	12
Widajanti, 2022 [68]	Indonesia	40	AWGS 2019	ND	BIS	12
Xie, 2023 [69]	China	757	AWGS 2019	Pre	BIA	12
Yoowannakul, 2018 [70]	England	600	FNIH, EWGSOP1, AWGS 2014	Post	BIA	8
Yuenyongchaiwat, 2021 [71]	Thailand	104	AWGS 2019	ND	BIA	12
Zhou, 2022 [72]	China	3196	AWGS 2019	Pre	BIS	16
PD population
Chen Y, 2022 [73]	China	87	AWGS 2019	NA	BIA	12
DaSilva, 2019 [74]	Brazil	50	EWGSOP 1, EWGSOP2	NA	DXA	12
Do, 2021 [36]	S. Korea	199	AWGS 2014	NA	DXA	12
Kamijo, 2018 [20]	Japan	119	AWGS 2014	NA	BIA	16
Kim 2021 [35]	Korea	160	Other	NA	BIS	12
Lin, 2022 [39]	Taiwan	186	AWGS, EWGSOP2, FNIH, IWGS	NA	BIS	12
Wu, 2022 [40]	Taiwan	158	Other	NA	Other	12
Leal‐Alegre, 2021 [75]	Mexico	51	Other	NA	BIA	12
As'Habi, 2018 [76]	Iran	79	Other	NA	BIA	12
HD and PD populations
Baltaci, 2022 [77]	Turkey	HD 70, PD 36	Other	Post	BIA	16
Bellafronte, 2020 [78]	Italy	HD 73, PD 23	EWGSOP2	ND	DXA	16
Umakanthan, 2021 [79]	Australia	HD 24, PD 15	EWGSOP1	Pre	BIS	12
Yasar, 2022 [80]	Turkey	HD 37, PD 28	EWGSOP 2	Pre	BIA	16

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Abbreviations: Asian Working Group for Sarcopenia (AWGS), bioelectrical impedance analysis (BIA), bioimpedance spectroscopy (BIS), dual‐energy X‐ray absorptiometry (DXA), European Working Group on Sarcopenia in Older People (EWGSOP), Foundation for the National Institutes of Health (FNIH), hemodialysis (HD), not applicable (NA), not described (ND), peritoneal dialysis (PD).

3.2. Methodological Quality of Included Studies

The methodological quality of the included studies ranged from 12 to 16 (average 13) out of a possible 16 points. All studies reported on the criteria for inclusion, provided detailed descriptions of the study setting, and measured the exposure and outcomes in a valid and reliable manner using objective standard criteria for the measurement of the condition, as well as appropriate statistical analysis. Areas of concern include the identification of confounding factors and the strategies used to address them, with only 14 studies (23%) reported on these criteria.

3.3. Prevalence of Sarcopenia

The global prevalence of sarcopenia was 30.1% (95% CI: 25.6%–39.9%; I ²: 97.1%) among people receiving HD (Figure 2) and was 20.5% (95% CI: 15.1–26.4%; I ²: 80.0%) among people receiving PD (Figure 3).

Prevalence of sarcopenia in hemodialysis patients.

Prevalence of sarcopenia in peritoneal dialysis patients.

3.4. Prevalence by Different Assessment Criteria

Subgroup analyses were performed for HD and PD populations based on different assessment criteria. For HD, the prevalence of sarcopenia ranged between 23.1% and 30.3% (I ²: 87% and 98.3%), as determined by EWGSOP‐2 and AWGS, respectively (see Figures S7–S10). In PD patients, the prevalence of sarcopenia ranged between 6.1% and 26.9% (I ²: 36.7% and 91.2%) for EWGSOP‐1 and AWGS, respectively (see Figures S3–S6) (Table 2).

TABLE 2.

Subgroup analysis of prevalence across dialysis modalities and different diagnostic criteria and timing of measurement.

	No. studies	No. participants	Prevalence (%)	95% CI (%)	Heterogenicity (I ²)
Peritoneal dialysis	13 ^a	1191	20.5	15.1–26.4	80
AWGS	4	591	26.9	15.5–40	91.2
EWGSOP1	2	65	6.1	0.1–17.7	36.7
EWGSOP2	4	287	18.8	8.7–31.4	75.7
Other	6	641	21.1	16.2–26.5	57.4
Hemodialysis	52 ^a	14,112	30.1	25.6–34.9	97.1
AWGS	28	10,680	30.3	23.8–37.3	98.1
EWGSOP1	13	1977	28.7	21.8–36.2	90.9
EWGSOP2	10	1174	23.1	16.5–30.4	87.0
Other	9	2105	30.7	16–47.8	98.3
Timing and type of muscle mass measurements
Pre‐dialysis
BIA	10	5177	24.2	14.8–35.0	97.8
Post‐dialysis
BIA/BIS	14	3502	33.0	24.7–41.7	96.3
DXA	5	1014	22.5	12.9–33.8	93.3

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Note: Totals of subgroup analysis may not add up to total of studies as some studies looked at multiple components or diagnostic criteria.

^{^a}

Studies that presented both HD and PD are used separately for each group.

3.5. Prevalence by Different Timing of Assessment

The timing of the LMM assessment was analyzed only in the HD group. To assess the effect of the timing of LMM assessment, studies using BIA/BIS were categorized into pre‐ and post‐dialysis. A total of 10 studies (n = 5177) investigated the LMM pre‐dialysis with a sarcopenia prevalence of 24.2% (CI: 14.8%–35%, I ²: 97.8%) (see Figure S11). When the same instrument was used post‐dialysis (14 studies, n = 3502), the prevalence of sarcopenia was 33% (CI: 24.7%–41.7%, I ²: 96.3%) (see Figure S12) (Table 2).

3.6. Prevalence by Different Measurement Tools

Studies that reported post‐dialysis LMM measurements were categorized into DXA and BIA/BIS measurements. The prevalence of sarcopenia was 22.5% (CI: 12.9–33.8, I ²: 93.3%) using DXA, compared to 33% (CI: 24.7–41.7, I ²: 93.3%) using BIA/BIS (see Figure S13) (Table 2).

3.7. Risk Factors Associated With Sarcopenia

The association between risk factors and sarcopenia in HD patients was explored in 21 studies. A pooled OR was calculated only for the HD group due to the small number of PD studies. The results demonstrated a significant association with sarcopenia for the following factors: older age, sex, diabetes, high c‐reactive protein, low body mass index, low albumin, and low calf circumference. However, there was a high heterogeneity among studies (I ²: 67%–95.6%). Longer time on dialysis (dialysis vintage) was the only significant risk factor for sarcopenia in the HD population (OR: 1.06, CI: 1.03–1.08), with lower heterogeneity (I ²: 45.1%). A list of risk factors and the pooled effects of their association with sarcopenia in HD patients is presented in Table 3.

TABLE 3.

Pooled odds ratio for risk factors of sarcopenia in dialysis patients.

Exposure	No. of studies	No. of participants	Pooled OR (95% CI)	Heterogeneity (I ², %)
Older age	11	N = 3384	1.06 (1.05–1.07)	82.3
BMI	11	N = 2447	0.80 (0.77–0.83)	87.3
Gender, male	7	N = 1659	2.09 (1.52–2.89)	67.0
Diabetes	8	N = 1817	1.65 (1.30–2.10)	73.9
CRP	6	N = 1900	1.07 (1.03–1.12)	71.5
Dialysis vintage	4	N = 1043	1.06 (1.03–1.08)	45.1
Albumin	4	N = 1065	0.46 (0.28–0.77)	73.9
Gender, female	3	N = 972	5.93 (3.21–11.05)	94.6
Calf circumference	3	N = 1312	0.80 (0.72–0.89)	95.6

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Abbreviations: body mass index (BMI), c‐reactive protein (CRP), geriatric nutrition risk index (GNRI), mid upper arm circumference (MUAC), subjective global assessment (SGA).

3.8. Publication Bias/Sensitivity Analysis

The funnel plots showed that the studies were evenly distributed, suggesting no evidence of publication bias (Figures S1 and S2). Egger's tests for HD studies (p = 0.12) and PD studies (p = 0.27) showed no significant publication bias (p > 0.05). The robustness of the meta‐analysis was tested by sequentially removing studies and changing the effect models. There was no significant difference between the adjusted values and the original values of the total pooled estimates.

4. Discussion

This review investigated the prevalence of sarcopenia in people receiving different dialysis modalities and highlighted variations in prevalence based on different assessment criteria and the timing of assessment. We found that people receiving HD have a higher prevalence of sarcopenia compared to PD. Using AWGS criteria to determine sarcopenia in patients on dialysis yielded a higher prevalence compared to EWGSOP criteria. This review further investigated the timing of LMM measurement and found that measuring LMM post‐dialysis presented a higher prevalence than pre‐dialysis. When LMM was measured post‐dialysis, those using BIA/BIS presented a higher prevalence of sarcopenia than those using DXA. While several factors have been reported as risk factors associated with sarcopenia, the overall results were inconclusive due to the high heterogeneity among studies.

This review found that the prevalence of sarcopenia is high in people receiving dialysis and higher in those undergoing HD than in those undergoing PD. Therefore, routine screening for sarcopenia across the dialysis population is warranted in the clinical setting due to the high prevalence demonstrated in this review, particularly for those receiving HD who experience sarcopenia at a younger age [81]. The choice of assessment criteria however remains less clear as they are not developed specifically for the CKD population [15, 81]. The EWGSOP [10] and AWGS [11] criteria were initially developed for the general elderly population and did not consider sarcopenia resulting from chronic disease. Despite this, they remain the most used assessment criteria for sarcopenia in patients on dialysis.

The choice of assessment criteria, however, remains less clear as they are not developed specifically for the CKD population [15, 81]. The EWGSOP [10] and AWGS [11] criteria were both updated in 2019 and developed for the general elderly population and did not consider sarcopenia resulting from chronic disease. Despite this, they remain the most used assessment criteria for sarcopenia in patients on dialysis.

Our study demonstrated a higher prevalence of sarcopenia using AWGS criteria compared to the European criteria. This difference in prevalence is likely explained by the AWGS criteria's use of a higher cutoff for handgrip strength (HGS) in both males and females and slightly higher muscle mass cutoffs for females when indexing for height compared to other criteria [10, 11]. Furthermore, the AWGS criteria call for different cutoffs for muscle mass depending on the use of DXA or BIA and specify that muscle mass should be indexed for height (kg/m²) [11]. However, individual studies comparing the various criteria in patients on HD routinely found a higher prevalence of sarcopenia using the European criteria [9, 10]. Therefore, while the assessment criteria may influence the prevalence of sarcopenia, other factors, including timing and measurement tools used for muscle measurements, may also play a role.

In contrast to the existing literature, which identified a higher prevalence of sarcopenia when LMM was measured by DXA [15, 82], our review found that studies on the HD population using DXA reported a lower prevalence of sarcopenia compared to those using BIA/BIS. This discrepancy may be related to differences in subgroup analysis, with previous studies grouping PD and HD together (despite clinical differences between these groups) and not considering the timing of measurements. In this review, we included only studies focusing on HD and compared measurements taken exclusively after a dialysis session. The discrepancy could also be influenced by ethnicity as most studies included in our review were from Asian countries. East Asians tend to have lower muscle mass and higher fat percentage compared to Europeans for the same BMI [83]. Consequently, the difference between BIA‐measured LMM and DXA could be more pronounced [84]. This finding highlights the need for population‐specific calibration of body composition measurement tools to ensure accuracy and reliability across diverse ethnic groups. Furthermore, the potential impact of other factors such as diet, physical activity levels, and genetic predispositions should be explored to better understand and address these discrepancies.

Although DXA is considered the gold standard for estimating muscle mass, it is not always practical in the clinical setting due to its invasive nature and associated labor costs [14]. Therefore, BIA and BIS are more commonly used and are well supported by the literature and current guidelines. In dialysis patients, BIA has shown good correlation and agreement with DXA in assessing body composition [82]. Furthermore, lean body mass index assessed via BIA has been found to be a good surrogate for DXA in elderly dialysis patients [57]. The 2020 Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines for nutrition in CKD recommend the use of BIA in patients on dialysis [14].

However, questions remain regarding the timing of measurements. In this study, a higher prevalence of sarcopenia was found when BIA/BIS measures were performed post‐dialysis compared to pre‐dialysis, potentially due to changes in fluid status. Wang et al. reported that nearly half of all patients with sarcopenia were missed when using pre‐dialysis estimation of body stores compared to post‐dialysis (13.8% vs. 23%) [67], suggesting that for the best estimation of muscle mass and measurement consistency, BIA should be taken in a dry state, preferably 15–20 min after the second weekly dialysis session [81, 85]. Future studies should assess the feasibility of this method as it may not always be practical due to patients' ability or willingness to stay after treatment and staff availability.

In this study, we aimed to identify associations for sarcopenia in dialysis patients. However, the high heterogeneity and small number of studies investigating each potential risk factor limited the interpretation and application to the clinical setting. Surprisingly, both male and female genders were identified as risk factors for sarcopenia across different studies. However, for females, despite an OR approaching 6, this result should be interpreted with caution due to the high heterogeneity and a sample size of only three studies. Older age was strongly associated with sarcopenia, based on data from 11 studies. This aligns with the well‐documented age‐related decline in muscle mass and function, exacerbated by the physiological stress of HD. Dialysis vintage, representing the duration of time on dialysis, was the only factor with relatively low heterogeneity. Diabetes and elevated CRP were also linked to sarcopenia. This finding suggests that prolonged dialysis contributes to sarcopenia, likely due to cumulative catabolic effects, chronic inflammation, and nutrient losses associated with the procedure. Low albumin levels, indicative of poor nutritional status and/or inflammation (commonly found in patients on dialysis), were another significant factor. While the results highlight important associations, the high heterogeneity limits the generalizability of the findings. Variations in study design, participant characteristics, diagnostic criteria for sarcopenia, and measurement methods contribute to this variability. Standardizing these parameters in future research is essential to strengthen evidence and guide clinical practice. Furthermore, few studies reported risk factors for sarcopenia in patients on PD; thus, no pooled OR could be performed for PD patients given the low number of studies. Additional caution should be taken when applying results to PD patients.

4.1. Strengths and Limitations

Our study had several limitations. Firstly, most of the included studies were cohort or observational, so causation cannot be determined from these associations. Secondly, the considerable variation in assessment criteria for sarcopenia and the use of different muscle measurement tools may limit the overall results. Despite using pooled OR to determine the association, high heterogeneity between studies restricted the number of meaningful results. Thirdly, many studies were conducted in Asian countries, so these findings may not be generalizable to other parts of the world.

The strengths of this review include its status as the largest review of studies examining the prevalence of sarcopenia in CKD patients undergoing dialysis. While the prevalence may be influenced by factors such as gender and age, this analysis was beyond the scope of the review. The inclusion of various assessment criteria and measurement tools provides better insight into clinically applicable and currently used measurements. This is the first review to examine pooled ORs for association with sarcopenia in dialysis patients, aiming to offer insight into patients at risk of sarcopenia.

4.2. Clinical Implications

Given the discrepancies in sarcopenia prevalence when applying different diagnostic criteria, the AWGS criteria appear to be more appropriate for dialysis cohorts, particularly in Asian populations. The higher sensitivity of AWGS, reflected in stricter cutoffs for muscle mass and strength, allows for earlier identification of sarcopenia, which is crucial in preventing further decline in physical function in dialysis patients. Conversely, the EWGSOP criteria may remain applicable in Western population, given their alignment with regional practices and validated cutoffs. Clinicians should tailor their selection of diagnostic criteria based on the demographic and clinical contexts of their patients, while advocating for modification to both AWGS and EWGSOP guidelines to better address the specific needs of the CKD patients.

5. Conclusion

In our systematic review, we have shown that sarcopenia is an issue affecting 20.5% of PD patients and 30.1% of HD patients, highlighting it as a significant issue among patients on dialysis. The large variability in criteria used, muscle measurement tools, and timing affect the strength of results and the ability to define risk factors and associations. There is a need for additional research to assess the practicality of various assessment tools and the role of other measurements or predictors of low muscle mass, strength, and sarcopenia. Global research would benefit from the development of a single criterion to be applied to dialysis patients and standardize screening tools for this population. Further research is required in regions such as Australasia and North America, where minimal studies have been conducted to ascertain the prevalence of sarcopenia.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Table S1 Commonly used diagnostic criteria for sarcopenia.

Table S2 Search terms for literature search.

Figure S1 Funnel plot of sarcopenia prevalence studies in hemodialysis patients.

Figure S2 Funnel plot of sarcopenia prevalence studies in peritoneal dialysis patients.

Figure S3 Subgroup analysis of the prevalence of sarcopenia in PD by AWGS criteria.

Figure S4 Subgroup analysis of the prevalence of sarcopenia in PD by EWGSOP1 criteria.

Figure S5 Subgroup analysis of the prevalence of sarcopenia in PD by the EWGSOP2 criteria.

Figure S6 Subgroup analysis prevalence of sarcopenia in PD by other criteria.

Figure S7 Subgroup analysis prevalence of sarcopenia in HD by AWGS criteria.

Figure S8 Subgroup analysis of sarcopenia in HD by EWGSOP1 criteria.

Figure S9 Subgroup analysis of sarcopenia prevalence in HD by EWGSOP2 criteria.

Figure S10 Subgroup analysis of sarcopenia prevalence in HD by other criteria.

Figure 11 Subgroup analysis of prevalence of sarcopenia in HD with muscle mass measured via BIA/BIS pre‐dialysis.

Figure 12 Subgroup analysis of prevalence of sarcopenia in HD with muscle mass measured via BIA/BIS post‐dialysis.

Figure 13 Subgroup analysis of prevalence of sarcopenia in HD, with muscle mass measured using DXA post‐dialysis.

SDI-38-237-s001.docx^{(651KB, docx)}

Acknowledgments

S.H. is employed under the Rural Health Multidisciplinary Training Program funded through the Australian Commonwealth Department of Health. Open access publishing facilitated by The University of Newcastle, as part of the Wiley ‐ The University of Newcastle agreement via the Council of Australian University Librarians.

Stockings J., Heaney S., Chu G., Choi P., and Fernandez R., “Prevalence and Risk Factors of Sarcopenia in People Receiving Dialysis: A Systematic Review and Meta‐Analysis,” Seminars in Dialysis 38, no. 4 (2025): 237–249, 10.1111/sdi.70000.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

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