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Systematic Review
ARTICLE IN PRESS
doi:
10.25259/IJN_351_2025

Prevalence of Sarcopenia in Patients with Chronic Kidney Disease in Asian Population Using Asian Working Group for Sarcopenia 2019 Definition: A Systematic Review and Meta-Analysis

, , ,
1Department of Community Medicine, Shri M P Shah Government Medical College, Jamnagar, India
2Department of Faculty of Medicine, Zagazig University, Zagazig, Egypt

Corresponding author: Jay Nagda, Department of Community Medicine, Shri M P Shah Government Medical College, Jamnagar, India. E-mail: jay.nagda1999@gmail.com

Received: , Accepted: ,
© 2026 Indian Journal of Nephrology | Published by Scientific Scholar
Licence
This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, transform, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.

How to cite this article: Yogesh M, Parmar PA, Nagda J, Helal MM. Prevalence of Sarcopenia in Patients with Chronic Kidney Disease in Asian Population Using Asian Working Group for Sarcopenia 2019 Definition: A Systematic Review and Meta-Analysis. Indian J Nephrol. doi: 10.25259/IJN_351_2025

Abstract

Background

Sarcopenia in patients with chronic kidney disease (CKD) represents a significant health concern, particularly in Asian populations. This systematic review and meta-analysis aimed to determine the prevalence of sarcopenia using the Asian Working Group for Sarcopenia (AWGS) 2019 criteria and identify associated risk factors in Asian patients with CKD.

Materials and Methods

We systematically searched PubMed and Scopus databases (January 2019 – November 2024) following PRISMA guidelines. Studies using AWGS 2019 criteria for sarcopenia diagnosis in Asian patients with CKD were included. We screened records, extracted data, and assessed the risk of bias in duplicate. Random-effect models were used to calculate pooled prevalence and odds ratios. This study is registered with PROSPERO, Reg No. CRD42024606055.

Results

Analysis of 43 studies (15,832 patients) revealed an overall sarcopenia prevalence of 25% (95% CI: 20-30%). Prevalence varied significantly by region (Japan: 38%; Malaysia: 5%) and treatment modality (dialysis: 30%; non-dialysis: 14%). Patients undergoing peritoneal dialysis showed the highest prevalence (40%, 95% CI: 32-49%). Significant risk factors included age (OR: 1.06, 95% CI: 1.05-1.07), male sex (OR: 1.35, 95% CI: 1.09-1.68), hypertension (OR: 2.72, 95% CI: 2.24-3.32), and diabetes mellitus (OR: 2.29, 95% CI: 1.94-2.71). Higher BMI showed a protective effect (OR: 0.85, 95% CI: 0.82-0.88).

Conclusion

Sarcopenia affects approximately one-quarter of Asian patients with CKD, with a higher prevalence in dialysis populations. The identified risk factors and regional variations provide valuable insights for targeted screening and intervention strategies in clinical practice.

Keywords

Asian population
AWGS 2019
Chronic kidney disease
Meta-analysis
Sarcopenia
Prevalence

Introduction

CKD represents a significant global health burden, particularly in Asian countries where its prevalence continues to rise.1 Among the major complications of CKD, sarcopenia has emerged as a key concern due to its substantial impact on patient outcomes, morbidity, and quality of life.2 Sarcopenia, defined as a progressive decline in skeletal muscle mass, strength, and function, affects a considerable proportion of patients with CKD and is linked to increased mortality, cardiovascular events, and reduced physical performance.3,4

Recognizing the need for population-specific criteria, the Asian Working Group for Sarcopenia (AWGS) introduced updated guidelines in 2019 tailored to Asian populations.5 These criteria incorporate region-specific cut-offs for muscle mass, strength, and physical performance, acknowledging distinct anthropometric and physiological characteristics compared with Western populations.6 The AWGS 2019 framework has since facilitated uniform sarcopenia assessment and cross-study comparability across Asia.

In CKD, sarcopenia develops through multifactorial mechanisms involving uremic toxin accumulation, chronic inflammation, metabolic acidosis, hormonal imbalance, and protein-energy wasting.7 Reported prevalence rates among patients with CKD range widely from 5% to 70%, primarily due to differences in population characteristics, diagnostic tools, and study designs.8 This variability underscores the need for standardized, region-specific evaluation using consistent diagnostic criteria, particularly in Asian populations where CKD epidemiology, dietary patterns, and body composition differ markedly from Western cohorts.9

Despite its clinical significance, no systematic review has comprehensively examined sarcopenia prevalence among Asian patients with CKD using the AWGS 2019 criteria. Prior meta-analyses relied on outdated definitions, heterogeneous populations, or non-CKD cohorts.10,11 Furthermore, the influence of CKD-related factors, such as dialysis modality, disease stage, and comorbidities, remains insufficiently defined in Asian settings.12

Therefore, this systematic review and meta-analysis aim to (1) determine the prevalence of sarcopenia in Asian patients with CKD using AWGS 2019 criteria, (2) evaluate variations by CKD stage and treatment modality, (3) identify key risk factors, and (4) analyze demographic and clinical determinants shaping sarcopenia prevalence.

Materials and Methods

This systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines.13 The protocol was prospectively registered with PROSPERO (Registration ID: CRD42024606055).

Studies were included if they met the following criteria: (i) population: Adults (≥18 years) with CKD stages III-V, including dialysis and kidney transplant recipients; (ii) Setting: conducted in Asian populations; (iii) Exposure: sarcopenia diagnosed using the AWGS 2019 criteria; (iv) Design: observational (cross-sectional or cohort); and (v) Outcome: reported prevalence or sufficient data to calculate prevalence.

Studies not applying AWGS 2019 criteria; inclusion of CKD stages I-II; non-Asian populations; case reports, reviews, conference abstracts; or studies without extractable prevalence data were excluded.

A systematic search was conducted in PubMed and Scopus from January 2019 to November 2024, with the final search on November 20, 2024. Reference lists of included articles were screened manually. Search terms combined Medical Subject Headings (MeSH) and free-text words for “sarcopenia,” “chronic kidney disease,” and “Asia.” The complete search syntax for each database has been provided in Supplementary Table 1.

Supplementary Table S1

Two independent reviewers (PP and JN) screened titles and abstracts using standardized forms. Full texts of potentially eligible studies were assessed independently, and disagreements were resolved by consensus or third-party adjudication (YM). No automation or AI tools were used. Reasons for exclusion were documented at the full-text stage.

Data extraction was performed independently by PP and JN using a standardized, pilot-tested sheet. Corresponding authors were contacted for missing information, with a reminder after 2 weeks. Discrepancies were resolved by consensus or third-party arbitration (YM). To avoid data duplication, studies sharing authors, institutions, or recruitment periods were cross-checked; the most comprehensive dataset was retained. A sensitivity analysis excluding potentially overlapping datasets was performed.

Extracted variables included study details like author, year, country, design, and setting; population characteristics like sample size, mean/median age, sex distribution, and CKD stage; sarcopenia assessment like diagnostic tools (bioelectrical impedance analysis [BIA] or dual-energy X-ray absorptiometry [DXA]), and timing relative to dialysis sessions, outcomes like prevalence of sarcopenia, severe, possible, and sarcopenic obesity; and associated factors like demographic and clinical risk variables.

Methodological quality was independently evaluated by two reviewers using the Joanna Briggs Institute (JBI) checklist for prevalence studies.14 Discrepancies were resolved by discussion; no studies were excluded based solely on quality.

The primary effect measure was sarcopenia prevalence, expressed as the proportion of affected individuals. For risk factor analysis, we extracted odds ratios with 95% confidence intervals from included studies. Where available, we prioritized adjusted ORs over unadjusted estimates. When studies reported both, we used the most fully adjusted model. The adjustment status (adjusted, unadjusted, or mixed) is reported for each pooled estimate.

Analyses were conducted using R software version 4.1.0,15 employing the “meta” and “metafor” packages.16 Random-effects models (DerSimonian-Laird method) with Freeman-Tukey double arcsine transformation were used to calculate pooled prevalence. Heterogeneity was quantified using I2 statistics (I2 >75% = substantial). Subgroup analyses were stratified by region, study design, CKD stage, dialysis modality, measurement tool, age, and sex. Meta-regression identified sources of heterogeneity.

Publication bias was assessed using funnel plots and Egger’s regression test (p<0.05 significant). Sensitivity analyses employed leave-one-out methods. Prediction intervals estimated expected ranges in future studies, and trim-and-fill analysis assessed small-study effects. Forest plots displayed individual and pooled prevalence estimates with 95% CIs. All p-values were two-sided, with p<0.05 indicating significance.

Results

The study selection process, illustrated in Figure 1‘s PRISMA flow diagram, began with 348 records. After removing duplicates and applying inclusion criteria, 43 studies were included in the final analysis.

Flowchart of the selection of studies for the systematic review and meta-analysis on the prevalence of sarcopenia in patients with chronic kidney disease in the Asian population using the Asian working group for sarcopenia 2019 definition.
Figure 1:
Flowchart of the selection of studies for the systematic review and meta-analysis on the prevalence of sarcopenia in patients with chronic kidney disease in the Asian population using the Asian working group for sarcopenia 2019 definition.

This systematic review and meta-analysis evaluated the prevalence of sarcopenia in patients with CKD across Asian populations using the AWGS 2019 definition. Table 1 summarizes the characteristics of the included studies published between 2019-2024. Most were conducted in China (21 studies), followed by Taiwan, Japan, and Korea, encompassing a total of 15,832 participants. Sample sizes ranged from 20 to 3,196, with mean ages spanning 47.8-79.7 years.

Table 1: Basic characteristics of the included study
Study ID Region/Country Study type Sample size Age mean (years) Age group distribution (years) CKD staging Number of cases Prevalence of sarcopenia (% per 100) JBI quality score
Zheng et al.17, 2024 China Cross-sectional 701 59.23 <65 III to V 116 16.55 5
Hsu et al.18, 2024 Taiwan Cross-sectional 420 69 ≥65 III to V 102 24.50 8
Inoshita et al.19, 2023 Japan Cross-sectional 441 79.7 ≥65 III to V 100 22.70 7
Xuan et al.20, 2023 China Cross-sectional 101 57 <65 III to V 19 19.00 8
Huang et al.21, 2024 Taiwan Cross-sectional 481 71.9 ≥65 III to V 14 3.00 8
An et al.22, 2021 Korea Cross-sectional 892 66 ≥65 III to V 251 28.10 7
Chen et al.23, 2022 China Cross-sectional 233 69 ≥65 III 43 18.45 5
Lee et al.24, 2022 Korea Cross-sectional 150 60 <65 III to V 14 9.33 7
Rao et al.25, 2022 India Cross-sectional 117 58 <65 III to V 34 29.00 8
Yogesh et al.26, 2024 India Cross-sectional 442 67.8 ≥65 III to V 132 29.90 8
Zeng et al.27, 2024 China Cross-sectional 244 52 <65 V 24 9.80 7
Wang et al.28, 2024 China Prospective cohort 220 58 <65 V 119 54.10 8
Lin et al.29, 2022 Taiwan Cross-sectional 297 68.8 ≥65 III to V 59 20.02 5
Tsai et al.30, 2021 Taiwan Cross-sectional 134 65.34 ≥65 III to V 9 6.70 7
Matsuzawa et al.31, 2021 Japan Cross-sectional 58 77.5 ≥65 V 39 68.00 6
Do et al.32, 2022 Korea Cross-sectional 199 55.7 <65 V 75 37.70 5
Xie et al.33, 2023 China Cross-sectional 757 60.4 <65 V 124 16.40 8
Song et al.34, 2022 China Cross-sectional 598 70.4 ≥65 III to V 179 30.00 7
Kang et al.35, 2022 Korea Cross-sectional 147 65 ≥65 III to V 14 9.52 6
Hou et al.36, 2023 Taiwan Cross-sectional 58 58.1 <65 III to V 11 19.50 7
Wang et al.37, 2023 China Cross-sectional 130 54.1 <65 V 36 27.70 5
Nie et al.38, 2024 China Cross-sectional 272 60 <65 V 89 32.72 6
Miyasato et al.39, 2024 Japan Prospective cohort 201 69.8 ≥65 V 48 24.00 5
Kusunoki et al.40, 2021 Japan Cross-sectional 393 73.2 ≥65 III to IV 75 33.30 6
Yang et al.41, 2024 China Cross-sectional 220 58.6 ≥18 V 31 64.1 6
Moorthy et al.42, 2023 Malaysia Cross-sectional 250 58.5 ≥18 III-V 13 5.2 8
Chen et al.43, 2024 Taiwan Prospective Cohort 101 61 ≥20 III to V 28 28.7 5
Li et al.44, 2024 China Cross-sectional 142 66.3 ≥18 V 12 15.5 5
Shirai et al.45, 2024 Japan Prospective cohort 65 74.5 ≥20 V 36 55.4 7
Xing et al.46, 2024 China Cross-sectional 147 61.8 18-80 III to V 66 39 8
Yang et al.47, 2023 China Prospective longitudinal 1117 56.8 ≥18 V 414 37.1 5
Dai et al.48, 2024 China Prospective clinical pilot 259 ≥18 V 89 34.4 6
Ishimura et al.49, 2022 Japan Retrospective cohort 308 58 <65 V 82 26.9 8
Zhou et al.50, 2023 China Cross-sectional 3169 55 <65 V 1156 36.2 7
Jauwerissa et al.51, 2023 Indonesia Cross-sectional 96 50.82 ≥18 V 52 54.2 6
Cai et al.52, 2022 China Cross-sectional 615 60.07 ≥18 V 102 16.6 5
Du et al.53, 2022 China Cross-sectional 589 53.8 ≥18 V 140 17.1 7
Wu et al.54, 2023 China Cross-sectional 105 54.2 ≥18 V 32 31.4 8
Miyazaki et al.55, 2021 Japan Cross-sectional 20 76.5 ≥65 V 11 55 6
Cheng et al.56, 2021 China Cross-sectional 238 60.9 23-87 V 117 49.2 5
Liao et al.57, 2023 China Cross-sectional 242 47.8 ≥18 V 21 50.8 8
Yuan et al.58, 2024 China Cross-sectional 202 57 ≥18 V 107 53 8
Hsu et al.59, 2024 Taiwan Cross-sectional 186 57.5 <65 V 71 38.2 7

Subgroup analyses [Table 2] demonstrated marked geographic variation in sarcopenia prevalence, highest in Japan (38%, 95% CI: 27-52%) and lowest in Malaysia (5%, 95% CI: 3-9%). Study design influenced outcomes, with follow-up studies showing a higher prevalence (36%, 95% CI: 28-45%) than cross-sectional studies (23%, 95% CI: 18-28%). Disease severity also affected prevalence, with CKD stage V showing higher rates (31%, 95% CI: 25-38%) than stages III-V combined (17%, 95% CI: 12-23%). Dialysis status significantly impacted prevalence: patients undergoing dialysis exhibited higher rates than non-dialysis patients, and patients undergoing peritoneal dialysis had the highest prevalence (40%, 95% CI: 32-49%). Among non-dialysis patients, stage-specific data were scarce; only one study each analyzed stage III (18%, 95% CI: 13–24%) and stages III-IV (33%, 95% CI: 27-38%), while 17 studies reported combined stages III-V (17%, 95% CI: 12-23%). Most stage V data were derived from dialysis cohorts (24 studies, 31%, 95% CI: 25-38%), limiting separate analysis of pre-dialysis stage V cases.

Table 2: Summary of subgroup analysis for sarcopenia prevalence in patients with CKD
Subgroup category Number of studies (k) Heterogeneity I2 (%) Effect model Prevalence % [95% CI]
Geographic region
 China 21 96.8 Random 25 [20-32]
 Taiwan 7 94.8 Random 16 [08-28]
 Japan 7 91.7 Random 38 [27-52]
 Korea 4 94.5 Random 18 [09-33]
 India 2 0.00 Fixed 30 [26-33]
 Malaysia 1 - 05 [03-09]
 Indonesia 1 - 54 [44-64]
Study design
 Cross-sectional 36 95.8 Random 23 [18-28]
 Follow-up study 7 91.1 Random 36 [28-45]
CKD stage
 3 1 - 0.18 [0.13-0.24]
 5 24 96.2 Random 31 [25-38]
 3 to 4 1 - 33 [27-38]
 3 to 5 17 93 Random 17 [12-23]
Dialysis status
 Non-dialysis 14 93.9 Random 14 [10-21]
 On dialysis 29 95.3 Random 30 [25-36]
Dialysis type
 Non-dialysis 14 93.5 Random 14 [10-21]
 Hemodialysis (HD) 19 96.6 Random 29 [22-38]
 Peritoneal dialysis (PD) 4 85.8 Random 40 [32-49]
 Both (HD and PD) 6 65.8 Random 28 [23-33]
Measurement tool
 BIA 37 96.2 Random 24 [19-30]
 DXA 6 79.4 Random 29 [23-36]
Muscle mass Measure time
 Before 12 96.6 Random 25 [17-36]
 After 13 93.3 Random 28 [22-36]
Study period
 2020-2021 6 94.9 Random 36 [19-57]
 2022-2023 22 96 Random 21 [17-26]
 2024 15 95.6 Random 27 [18-37]
Age groups
 <65 years 27 96.3 Random 25 [20-31]
 ≥ 65 years 17 93.9 Random 24 [16-35]
Gender
 Male 24 89.2 Random 24 [19-29]
 Female 24 89.5 Random 22 [17-28]

CKD: Chronic Kidney Disease, BIA: Bioelectrical Impedance Analysis, DXA: Dual-Energy X-ray Absorptiometry

Figures 2-4 show pooled estimates for overall, severe, and sarcopenic obesity, while Supplementary Figure 1 displays possible sarcopenia prevalence. Table 3 identifies key risk factors: age (OR 1.06, 95% CI: 1.05-1.07), male sex (OR 1.35, 95% CI: 1.09-1.68), hypertension (OR 2.72, 95% CI: 2.24-3.32), diabetes mellitus (OR 2.29, 95% CI: 1.94-2.71), and cardiovascular disease (OR 1.70, 95% CI: 1.36-2.16). Higher BMI was protective (OR 0.85, 95% CI: 0.82-0.88), and each 1 mL/min/1.73 m2 increase in eGFR reduced sarcopenia odds by 2% (OR 0.98, 95% CI: 0.97-0.99), indicating that declining kidney function correlates with increased sarcopenia risk.

Supplementary Figure S1
Overall pooled prevalence of sarcopenia.
Figure 2:
Overall pooled prevalence of sarcopenia.
Overall pooled prevalence of severe sarcopenia.
Figure 3
Overall pooled prevalence of severe sarcopenia.
Overall pooled prevalence of sarcopenic obesity.
Figure 4:
Overall pooled prevalence of sarcopenic obesity.
Table 3: Meta-analysis of risk factors of sarcopenia in patients with CKD
Variable Number of included literature I2 (%) Effect model Pooled OR 95% CI p-value
Age (continuous) 10 68 Random 1.06 (1.05, 1.07) 0.000
Male 5 65 Random 1.35 (1.09, 1.68) 0.011
eGFR (per 1 mL/min/1.73m2 increase) 5 85.7 Random 0.98 (0.97, 0.99) 0.003
DM (Yes vs. No) 5 86 Random 2.29 (1.94, 2.71) 0.000
Hypertension (Yes vs. No) 3 89 Random 2.72 (2.24, 3.32) 0.000
Cardiovascular disease (CVD) (Yes vs. No) 3 19 Fixed 1.70 (1.36, 2.16) <0.001
BMI (continuous) 9 93 Random 0.85 (0.82, 0.88) 0.000

CKD: Chronic Kidney Disease, OR: Odds Ratio, eGFR: Estimated Glomerular Filtration Rate, DM: Diabetes Mellitus, BMI: Body Mass Index

This aligns with the known pathophysiology of CKD-related muscle wasting, where progressive renal dysfunction leads to the accumulation of uremic toxins, metabolic acidosis, and chronic inflammation, all contributing to muscle catabolism.

Sensitivity analysis [Supplementary Figure 2] and funnel plot inspection [Supplementary Figure 3] supported the robustness of findings. Egger’s regression test (p = 0.12) indicated publication bias. The comprehensive search strategy [Supplementary Table 1] confirmed thorough identification of relevant studies across PubMed and Scopus.

Supplementary Figure S2

Supplementary Figure S3

Meta-regression analysis

Meta-regression [Table 4] explored sources of heterogeneity (I2 = 95.7%). Univariate analysis identified significant moderators: mean age (p = 0.002, R2 = 12.3%), study design (p = 0.003, R2 = 8.9%), dialysis status (p < 0.001, R2 = 18.4%), geographic region (p = 0.001), and sample size (p = 0.042, R2 = 5.6%). In multivariate analysis, dialysis status (coefficient = 0.14, 95% CI: 0.07–0.21, p < 0.001), mean age (coefficient = 0.006, 95% CI: 0.001–0.011, p = 0.018), and region remained significant, explaining 34.7% of between-study variance. Residual heterogeneity (I2 = 89.2%) suggested additional unmeasured contributors.

Table 4: Meta-regression
Variable Coefficient 95% CI p-value R2 (%)
Mean age (per year increase) 0.008 (0.003, 0.013) 0.002 12.3
Male proportion (%) 0.002 (-0.001, 0.005) 0.156 2.8
Study design (follow-up vs. cross-sectional) 0.13 (0.05, 0.21) 0.003 8.9
Geographic region (reference: China)
 Japan 0.15 (0.06, 0.24) 0.001
 Taiwan -0.09 (-0.18, 0.00) 0.051
 Korea -0.07 (-0.17, 0.03) 0.168
Dialysis status (dialysis vs. non-dialysis) 0.16 (0.09, 0.23) <0.001 18.4
Measurement tool (DXA vs BIA) 0.05 (-0.04, 0.14) 0.268 1.2
Publication year -0.02 (-0.05, 0.01) 0.187 3.1
Sample size (log-transformed) -0.03 (-0.06, 0.00) 0.042 5.6

DXA: Dual-Energy X-ray Absorptiometry, BIA: Bioelectrical Impedance Analysis

Discussion

This systematic review and meta-analysis included 43 studies with 15,832 participants; the pooled prevalence was 25% (95% CI: 20–30%) with substantial heterogeneity (I2 = 95.7%).17-59 This is notably higher than the 10-15% reported in the general Asian elderly population,5 reflecting the additional muscle-wasting burden imposed by CKD.

CKD-specific mechanisms contribute significantly to this high prevalence. Uremic toxins such as indoxyl sulfate and p-cresyl sulfate impair muscle protein turnover via the ubiquitin-proteasome pathway.60 Metabolic acidosis accelerates protein degradation and reduces amino acid utilization.61 Chronic inflammation and oxidative stress activate catabolic cytokines (IL-6, TNF-α), while resistance to anabolic hormones like growth hormone and IGF-1 further worsens muscle loss.62,63 These overlapping mechanisms explain the multidimensional pathogenesis of sarcopenia in CKD.

Marked regional differences were observed, with prevalence ranging from 5% in Malaysia to 38% in Japan, influenced by genetic, dietary, demographic, and healthcare disparities.64,65 Japan’s higher rates may reflect its older population, genetic predispositions, and better screening systems.64-67

Patients undergoing dialysis showed higher sarcopenia prevalence (30%, 95% CI: 25–36%) than non-dialysis patients (14%, 95% CI: 10–21%), consistent with associations between dialysis, protein-energy wasting, and chronic inflammation.67,68 Among modalities, peritoneal dialysis had the highest prevalence (40%, 95% CI: 32–49%) due to daily protein losses (5-15 g/day),69,70 reduced mobility from abdominal distension,71 glucose absorption–related metabolic shifts,72 and nutritional variations.73

Age was a major risk factor (OR: 1.06, 95% CI: 1.05–1.07), supporting the concept of accelerated aging in CKD.74 The combined impact of age and CKD amplifies sarcopenic decline. Male gender (OR: 1.35, 95% CI: 1.09–1.68) showed a higher risk, possibly related to hormonal differences, greater baseline muscle mass, and lifestyle factors.75-77 Comorbidities such as hypertension (OR: 2.72, 95% CI: 2.24–3.32) and diabetes (OR: 2.29, 95% CI: 1.94–2.71) also increased risk, highlighting overlapping metabolic and inflammatory mechanisms.10,78 Higher BMI appeared protective (OR: 0.85, 95% CI: 0.82–0.88), consistent with the “obesity paradox,” though BMI’s inability to differentiate fat and muscle limits interpretation.79

Methodological quality varied (JBI scores 4–8), contributing to heterogeneity, though sensitivity analyses confirmed the robustness of results. The diagnostic method (BIA vs. DXA) did not significantly alter prevalence estimates, supporting the clinical practicality of BIA in resource-limited settings.80

The high heterogeneity (I2 = 95.7%) reflects the complex and multifactorial nature of sarcopenia in CKD. Meta-regression identified dialysis status as the strongest source, explaining 18.4% of between-study variance. Mean age accounted for 12.3%, with each additional year increasing prevalence by 0.8%. Geographic differences, especially between Japan and China, contributed further, likely due to demographic aging, dietary practices, and diagnostic approaches. Study design also influenced findings, with prospective studies showing higher prevalence, possibly from longitudinal follow-up or high-risk cohorts. Smaller studies tended to report higher prevalence, suggesting publication bias.

Together, these factors explained only 34.7% of the total heterogeneity (residual I2 = 89.2%), implying that nutritional status, inflammation, comorbidities, activity levels, fluid status, and diagnostic inconsistencies may contribute further. Persistent heterogeneity emphasizes the need for standardized diagnostic criteria and consistent study reporting.

This review has several limitations. First, high unexplained heterogeneity limits the precision and generalizability of pooled estimates. Second, restricting the search to PubMed and Scopus may have excluded studies from EMBASE, Web of Science, CNKI, J-STAGE, and KoreaMed. Third, overlapping cohorts, especially among Chinese studies, may have inflated certain data despite sensitivity checks. Fourth, risk factor analyses combined adjusted and unadjusted odds ratios, introducing possible confounding. Fifth, limited stage-specific data among non-dialysis patients undergoing CKD prevented detailed trend analyses. Additionally, the variable quality (JBI 4-8/9) and predominance of cross-sectional hospital-based designs hindered causal inference and community-level extrapolation.

Nevertheless, this review represents the most comprehensive synthesis to date of sarcopenia prevalence in Asian CKD populations using AWGS 2019 criteria. It identifies dialysis status, age, and regional variation as major moderators and underscores the need for multicenter, longitudinal, stage-specific studies employing standardized diagnostic tools.

Given the high prevalence, especially among elderly and patients undergoing dialysis, clinical management should integrate muscle health into CKD care. Key strategies include routine sarcopenia screening for high-risk groups, early interventions combining resistance exercise and nutritional therapy, dialysis prescription modifications to minimize protein loss and integration of muscle assessments in CKD management.81

Resistance exercise combined with adequate protein intake (1.2-1.4 g/kg/day) helps preserve muscle mass,82 while controlling metabolic acidosis and inflammation may mitigate progression.83

Sarcopenia in CKD imposes a major economic burden due to prolonged hospitalizations and complications.84 Early screening and preventive measures can reduce costs and improve outcomes.85 Integration of sarcopenia screening in nephrology care pathways represents a practical, cost-effective approach for improving prognosis.86

Future studies should use longitudinal designs to track sarcopenia progression across CKD stages and identify optimal intervention windows. Trials combining exercise, nutrition, and metabolic correction are warranted, particularly in elderly and patients undergoing dialysis. Exploration of novel biomarkers (e.g., uremic toxins, myokines, metabolomics) may enable early detection and targeted therapies.

Persistent challenges include resource constraints, fluid-related errors in assessment, limited interdisciplinary coordination, and poor adherence to exercise or nutrition plans. Region-specific, integrated care models involving nephrology, nutrition, and rehabilitation are essential. Collaborative efforts among clinicians, researchers, and policymakers are crucial to establishing cost-effective, culturally adapted, and evidence-based approaches for sarcopenia prevention and management in CKD.

Our systematic review and meta-analysis found that sarcopenia affects nearly one in four Asian patients with CKD, with the highest burden observed among those on dialysis, especially peritoneal dialysis, where 2/5 patients are affected. The pronounced loss of muscle strength and severe sarcopenia in patients undergoing dialysis highlights the profound impact of renal replacement therapy on muscle health. These findings emphasize the urgent need for routine sarcopenia screening and early, targeted interventions, including individualized nutrition and exercise programs, to preserve muscle mass and function, particularly among patients undergoing dialysis, where sarcopenia is most prevalent and clinically consequential.

Author contributions

YM, PAP, JN: Concepts, design, definition of intellectual content, literature search, clinical studies, experimental studies, data acquisition, data analysis, statistical analysis, manuscript preparation, manuscript editing and review; MMH: Literature search, clinical studies, experimental studies, data acquisition, data analysis, statistical analysis, manuscript preparation, manuscript editing and review.

Conflicts of interest

There are no conflicts of interest.

Use of artificial intelligence (AI)-assisted technology for manuscript preparation

The authors confirm that they have used artificial intelligence (AI)-assisted technology solely for language refinement and to improve the clarity of writing. No AI assistance was employed in the generation of scientific content, data analysis or interpretation.

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