Biomarkers for sarcopenia, muscle mass, muscle strength, and physical performance: an umbrella review

Abstract

Background

Biomarkers in sarcopenia have garnered increasing interest. This study aims to systematically identify biomarkers with potential diagnostic significance for sarcopenia, as well as those exhibiting correlations with sarcopenia, particularly biomarkers reporting quantitative pooled data.

Methods

This umbrella review adhered to the PRISMA 2020 guidelines. Data sources including PubMed, the Cochrane Library, Embase, Scopus, and Web of Science were searched from the inception of data to April 7, 2025. The AMSTAR 2 tool and QUADAS-2 tool was utilized to assess the methodological quality of including studies, while the GRADE approach was used to evaluate the quality of evidence in these studies.

Results

A total of 22 studies were included. The identified biomarkers encompass inflammatory, metabolic, hormonal, amino acid-related, genetic, and other categories. The evidence quality for most biomarkers was rated as very low. The creatinine to cystatin C (Cr/CysC) ratio emerged as the most frequently utilized diagnostic biomarker, demonstrating moderate diagnostic accuracy. However, its diagnostic performance exhibited variability across different diagnostic criteria. Biomarkers associated with sarcopenia predominantly demonstrated weak or negligible inverse correlations. Inflammatory biomarkers underwent the most comprehensive investigation. Patients with sarcopenia exhibited elevated interleukin-6 (IL-6), C-reactive protein (CRP), and tumor necrosis factor-alpha (TNF-α) levels compared to controls, with these markers showing post-intervention improvement. Some metabolic and amino acid biomarkers were found to be lower in patients with sarcopenia than in the control group.

Conclusion

The Cr/CysC ratio demonstrates moderate diagnostic accuracy and represents the most frequently utilized diagnostic biomarker for sarcopenia. Inflammatory biomarkers constitute the predominant biomarkers and exhibit general elevation in sarcopenia patients. Following sarcopenia interventions, alterations in biomarker expression levels are observed, suggesting novel therapeutic applications for these biomarkers.

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Introduction

Sarcopenia is an age-related degenerative condition characterized by progressive loss of muscle mass and strength, accompanied by diminished physical performance [1, 2]. The prevalence of sarcopenia ranges from 5.5 to 25.7%, with higher rates observed in males (5.1-21.0%) versus females (4.1-16.3%), and is projected to affect over 200 million individuals globally within the next four decades [2, 3]. The progressive impairment of muscle function significantly increases the risk of fragility fractures, disability, and functional decline [4]. Utilizing data from a 2014 survey in the United States, the projected total hospitalization costs for sarcopenia were estimated at 40.4 billion USD, with an average cost per patient of 260 USD [5]. Specifically, hospitalization expenditures for sarcopenia patients aged ≥ 65 years accounted for $19.12 billion, imposing a substantial financial burden on healthcare systems [6]. To date, there is no specific targeted treatment for sarcopenia, with dietary modification, exercise, or a combination of both being the most common intervention strategies [7].

The identification and diagnosis of sarcopenia in its early stages is critical for preventive purposes, potentially mitigating the risk of subsequent severe complications [8]. Diagnostic criteria for sarcopenia are guided by international standards such as the European Working Group on Sarcopenia in Older People (EWGSOP) 1, EWGSOP 2 guidelines, the Asian Working Group for Sarcopenia (AWGS) 2014, AWGS 2019 criteria and so on [9, 10–11]. Assessment of sarcopenia necessitates the evaluation of muscle mass, muscle strength, and physical performance. Techniques including dual-energy X-ray absorptiometry (DXA), computed tomography (CT), and bioelectrical impedance analysis (BIA) are utilized for the precise measurement of muscle mass [12]. Handgrip strength is typically assessed with a spring-type dynamometer, and physical performance is evaluated using metrics such as usual gait speed and the 6-minute walk test [13]. Current screening and diagnostic methods face limitations in scenarios such as large-scale screening, inadequate hardware facilities, or uncooperative patients. Therefore, there is a need for simple, validated, and cost-effective biomarkers for sarcopenia screening and even diagnosis.

A multitude of biomarkers associated with sarcopenia have been identified, reflecting its complex pathophysiology involving impaired protein metabolism, chronic systemic inflammation, oxidative stress, hormonal dysregulation, neuromuscular junction degradation, and mitochondrial dysfunction [14, 15]. For example, Kashani et al. [16] proposed the ratio of serum creatinine to cystatin C (Cr/CysC) as an indicator of muscle mass. Liu et al. [17] further demonstrated that Cr/CysC serves as a pivotal diagnostic biomarker for sarcopenia and myosteatosis. Whereas Lien et al. [18] have suggested the Cr×eGFRCysC (serum creatinine multiplied by the cystatin C-based estimated glomerular filtration rate) as a potential diagnostic biomarker for sarcopenia. However, clinical validation studies have yielded inconsistent diagnostic accuracy across populations [19, 20]. Furthermore, biomarkers such as indices of chronic inflammation, hormone metabolites, and non-coding RNAs have been noted in the related studies. Lin et al. [9] and Lian et al. [10] evaluated the diagnostic performance of Cr/CysC, Cr×eGFRCysC, and other indices using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Additional biomarkers, including brain natriuretic peptide (BNP), amino acid profiles, and growth differentiation factor-15 (GDF15), have not been extensively characterized [21, 22].In conclusion, the assessment of biomarkers is a prompt and effective approach relative to conventional diagnostic criteria [23]. However, results from two meta-analyses indicated that the diagnostic accuracy and level of clinical evidence of these biomarkers still failed to meet the requirements for clinical application [9, 10]. This umbrella review aims to synthesize biomarkers demonstrating potential diagnostic value for sarcopenia and those exhibiting correlations with sarcopenia, particularly biomarkers reporting quantitative pooled data, thereby providing evidence-based references for early screening, diagnosis, and intervention assessment of sarcopenia.

Methods

Protocol registration

We conducted a systematic search of systematic reviews and meta-analyses on biomarkers for muscle mass and sarcopenia. The 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines guided the conduct of this umbrella review [24, 25] (Supplementary Material 1). This study was registered in PROSPERO (CRD42024561371) on June 23, 2024 (Supplementary Material 2).

Search strategy and study selection

A comprehensive search was conducted in PubMed, Embase, the Cochrane Library, and Web of Science to identify pertinent studies using a combination of Medical Subject Headings terms and keywords for literature searching: ((muscle mass OR sarcopenia) AND (biomarkers)) AND (systematic review OR meta-analysis). We systematically searched for sarcopenia and systematic reviews or meta-analysis from the inception of these databases until April 7, 2025. The detailed search strategy was shown in the Table S1.

Two investigators independently screened the retrieved literature based on titles and abstracts to determine which relevant literature to be included in our study. A full-text search of the literature was conducted whenever one of the investigators considered it potentially relevant. Final eligibility for inclusion of the retrieved full-text articles was assessed independently by the two investigators. Any disagreements were resolved through discussion. If there existed disagreement, a third investigator stepped in and made the final decision. We hand searched meta-analysis and systematic reviews from the reference lists of all included articles to identify studies that might have been missed.

Inclusion and exclusion criteria

The inclusion criteria were as follows: (1) No restrictions on the type of English studies for systematic reviews and meta-analyses (such as systematic reviews and meta-analyses related to diagnostic test accuracy); (2) Primary studies included could be of any design type; (3) Demographic characteristics of the included studies: included individuals diagnosed with sarcopenia across all age groups and with any underlying comorbidity; (4) Diagnostic criteria for sarcopenia included in the studies: EWGSOP 1, EWGSOP 2, AWGS 2014, AWGS 2019, Foundation for the National Institutes of Health (FNIH) Sarcopenia Project, low skeletal muscle mass and handgrip weakness, or other diagnostic criteria; (5) Outcomes: relevant diagnostic test accuracy (DTA) indicators of biomarkers, including sensitivity (SEN), specificity (SPE), and area under the receiver operating characteristic curve (AUC), and the area under the curve of the summary receiver operating characteristic (SROC-AUC). Other indicators of biomarkers included mean differences (MD), standardized mean differences (SMD), weighted mean differences (WMD), relative risks (RR), odds ratios (OR), and hazard ratios (HR), along with corresponding 95% confidence intervals (CI).

The exclusion criteria included: (1) Primary studies that encompassed preclinical or basic research; (2) net-work meta-analyses, narrative reviews, surveys, letters, editorials, case reports, reviews, conference abstracts, and expert opinions; (3) duplicated publications and unavailable full text.

When multiple published meta-analyses on the same biomarkers were identified, we selected only one to avoid the inclusion of duplicate studies. In such cases, the typically the most recent study with the largest number of included studies was chosen. If the number of included studies was equal, the study with the higher Assessing the Methodological Quality of Systematic Reviews (AMSTAR) 2 score or the higher Quality Assessment of Diagnostic Accuracy Studies 2 quality assessment tool (QUADAS-2) was included [26, 27–28]. When necessary, we will extract data from the primary studies for pooled analysis.

Data extraction

Two investigators independently extracted all relevant information related to the study characteristics using a standardized form. The basic elements of data extraction included first author, year of publication, sample size, mean age, repair methods of cartilage defect. The main outcomes of data extraction included relevant indicators of biomarkers (see the inclusion criteria). Furthermore, we extracted the model of effect (random and fixed), heterogeneity (I² statistic and Cochran’s Q test P value), and publication bias assessment (P value of Egger’s test or funnel plot). If subgroup analysis were conducted, we also extracted the results of subgroup analysis in meta-analysis. Disagreements, if any, would be resolved by a third investigator.

Quality assessment

To grade the methodological quality of each included studies, we used the revised AMSTAR 2 tool to assess the methodological quality of systematic reviews of randomized and non-randomized studies [27, 29], and we used the QUADAS-2 tool to assess the DTA studies [28]. In addition, we assessed confidence in effect estimates (quality of evidence) according to the Grading of Recommendations Assessment, Development and Evaluation criteria (GRADE) criteria and graded it as “high,” “moderate,” “low,” or “very low” quality to draw conclusions [30, 31]. Quality level can be downgraded for each of four factors: limitations in study design, inconsistency of results, imprecision, and publication bias. Should the studies present factors that necessitate downgrading the quality of evidence, such downgrades must be made in accordance with the specific reasons associated with each factor. And the factors rating up the quality of evidence were plausible confounding, magnitude of effect, and dose-response gradient. We graded the quality of evidence for each association derived from meta-analyses by applying criteria consistent with previously published umbrella reviews [32] (Table S2).

Statistical analysis

Count data are presented as numbers, ratios or percentages. Measurement data are expressed as mean, mean ± SD, and range. Further calculations were conducted by the authors when raw data were available and did not conform to the specifications of descriptive statistics. We extracted outcome data and estimated the summary effect with 95% CI reported in each meta-analysis where applicable. When the interested outcomes in the included meta-analyses did not have pooled results, we extracted the raw data for pooling. AUC is a global measure of test performance. It tells us nothing about individual parameters, such as sensitivity and specificity. Diagnostic accuracy was classified as low (AUC < 0.7), moderate (0.7 ≤ AUC < 0.9), or high (AUC ≥ 0.9) [33]. We performed the I² statistic and the Cochran’s Q test as estimates of heterogeneity between studies. The I² value ranges from 0 to 100% and represents the percentage of the total variance across studies that can be attributed to heterogeneity [34]. Publication bias and small study effects were assessed for each meta-analysis by graphical and statistical tests, namely the funnel plot and Egger’s test. A P value < 0.10 was considered significant for Egger’s test and heterogeneity [35]. All calculations required for outcomes were conducted using IBM SPSS Statistics version 26.0 and Review Manager version 5.4.

Results

Literature selection

A comprehensive and systematic search for biomarkers of sarcopenia was conducted twice across the following databases: PubMed (n = 222), Web of Science (n = 439), Embase (n = 258), Scopus (n = 301) and the Cochrane Library (n = 162), yielding a total of 1382 articles (Table S1). After the exclusion of duplicates and a preliminary screening process, 39 studies were selected for full-text review. Of these, 36 studies were eligible for qualitative assessment. However, 14 studies were subsequently excluded due to duplication of content, lack of reporting on sarcopenia biomarkers, or exclusive focus on biomarkers of frailty (Table S3). Finally, 22 systematic reviews and meta-analyses were included in this umbrella review. (Fig. 1)

[See PDF for image]

Fig. 1

The PRISMA flow diagram for the umbrella review

General information and characteristics of included studies

In the subset of 22 studies, 3 were only included randomized controlled trials (RCTs) [36, 37–38], with the remainder predominantly comprising cross-sectional studies (Table 1). The populations enrolled in these studies comprised elderly community-dwelling individuals or hospitalized patients with underlying conditions such as cardiovascular, respiratory diseases, and cancer. The primary outcomes of the included studies involved the diagnostic test accuracy of biomarkers for sarcopenia [9, 10], as well as the correlation and expression levels of biomarkers in sarcopenia, muscle mass, muscle strength, and physical performance [21, 22, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52–53]. 8 studies reported inflammatory biomarkers, including interleukin-6 (IL-6), C-reactive protein (CRP), and tumor necrosis factor-alpha (TNF-α) [22, 36, 37–38, 41, 42, 45, 48] (Table 1). The diagnostic criteria used for diagnosing sarcopenia in the included studies were highly varied, ranging from the AWGS 2014, AWGS 2019, EWGSOP 1, EWGSOP 2, FNIH, the Japanese Society of Hypertension Guidelines (JSHG), and the European Society of Parenteral and Enteral Nutrition (ESPEN), among others (Table 1).

Table 1. Demographic information and characteristics of the included studies

Author, year	Study Design	The number of studies(T/R/C/Others)^a	Regions on included studies	Total of participants (T/M)^b	Commonalities	Age (years)	Diagnostic criteria of Sarcopenia/ others criteria	Primary outcomes	Metric
Lin 2024 [9]	SRMA (DTA)	16/0/7/9	China, Japan, South Korea, Indian, France	5566/2795	Cancer, respiratory diseases, T2DM, CKD, healthy older adults	51.0–78.4	AWGS 2019, AWGS 2014, EWGSOP2, WGSOP1, JSHG	Cr, CysC, Cr/CysC, Cr×eGFRcys, CysC/Cr, eGFRcys/eGFRcre ratio, eGFR ratio, TBMM index	SEN, SPE, SROC-AUC
Lian 2024 [10]	SRMA (DTA)	32/NR	China, Japan, Korea, Ireland, Turkey, Uskudar Turkey	23,840/NR	cancer, respiratory diseases, T2DM, CKD, CVD, AD, healthy older adults	44.5–78.7	AWGS 2019, AWGS 2014, EWGSOP2, WGSOP1, PMI, SMI, QcCSA/BW	Cr, CysC, Cr/CysC, Cr×eGFRcys, AST/ALT, insulin, PLR, irisin, GDF15, NLR, eGFRcysC/eGFRcre, myostatin, albumin	SEN, SPE, SROC-AUC
Prokopidis 2024 [39]	SRMA	16/NR	China, Japan, France, Brazil, Germany, USA	6051/4169	T2DM, CKD, CVD, AD, cancer, respiratory diseases, healthy older adults	53.8–85.6	AWGS 2019, AWGS 2014, EWGSOP1, Ishii index, Japanese Geriatrics Society,	BNP, NT-proBNP	MD
Liu 2023 [40]	SRMA	5/NR	China, Korea, Turkey	625/NR	NR	57.5–80.3	EWGSOP 2, ESPEN	FGF21	SMD
Byrne 2023 [36]	SRMA	16/13/3/0	Malaysia, China, Brazil, Portugal, Netherlands, USA, Mexico, Iran, Canada, Italy, Thailand	1031/NR	NR	NR	SMI, HGS, AMMI, FFMI, 6-m walk speed, sarcopenic index, body fat mass, appendicular lean soft tissue, relative muscle mass, total lean body mass	CRP, IL-6, TNF-α	SMD (after intervention)
Picca 2022 [41]	SRMA	80/0/9/71 (SP: 22/0/0/22)	Brazil, China, Holland, Italy, Japan, Korea, Singapore, Spain, Turkey	All: 38,064/NR SP: 4904/NR	Community-dwelling, Hospitalized patients	68–87.6	Muscle mass plus IHG or WS, ASMI plus IHG, muscle mass and SPPB	Glucose, cholesterol, triglycerides, LDL, HDL, albumin, CRP, IL-6, TNF-α, vitamin D, IGF-1, hemoglobin	SMD
Jones 2022 [42]	SR	20/5/0/15	China, Japan, Brazil, Germany, USA, UK, Singapore, Iran, Italy, Korea, Austria	3306/498	NR	53-92Y	ASMM, AWG, BMD, SMMI, HGS, EWGSOP, Body fat, SMI, Maximal knee extension torque	MicroRNAs, oxidative stress, energy metabolism, inflammation, enzyme, hormone, bone, vitamin, and cytokine	R², r
Shokri-Mashhadi 2021 [43]	SRMA	19/0/1/18	USA, UK, China, Brazil, Korea, Sweden, Japan, Finland, Lebanon, Germany, Netherlands, Slovenia	14,650/NR	Respiratory diseases, arthritis, cancer, hemodialysis, depression, healthy olders	≥ 50	HGS, SPPB score, lean body mass, walking speed test	CRP, hs-CRP, muscle mass, and muscle strength	ES^c
Dai 2021 [21]	SRMA	6/0/0/6	Japan, Singapore, Norway, Italy	1120/560	NR	54.8–85.1	AWGS, EWGSOP, FNIH	Leucine, isoleucine, tryptophan, lysine, methionine, arginine, asparagine, tyrosine, and glutamine, valine, phenylalanine, threonine, and histidine	SMD
Amarasekera 2021 [44]	SR	16/0/2/14	USA, Japan, Spain, Brazil, Ecuador, Italy, Germany, Korea, China, India	20,889/NR	NR	NR	SMM, muscular power, muscular strength, physical exercises	ADMA, circulating CD34-positive cells	R²
Tuttle 2020 [45]	SRMA	168/0/19/149	NR	76,899/NR	Multimorbid, metabolic, immune, CVD, kidney, respiratory, depression, cancer, community dwelling, healthy	20–90	HGS, KE, KF	CRP, IL-6, TNF-α	R²
Margutti 2017 [22]	SR	4/0/1/3	Austria, Japan, Korea, Brasil	2582/NR	Community dwelling older, post-menopausal women	> 60	EWGSOP, HGS, isokinetic knee extension force, gait speed test, 6-minute walk test, chair stand test	GDF-15, IGF-1, follistatin, eHsp72, ferritin, CRP	r
Dowling 2022 [46]	SR	42/NR (6 only SP)	Korea, China, Singapore, New Zealand, USA, Spain, UK	NR (340 only SP)	NR	71–89 (Only SP)	Fried Frailty Phenotype, AWGS, EWGSOP	MicroRNAs	Up or down
Thompson 2024 [47]	SRMA	119/NR	USA, Norway, Japan, China, Korea, France, Egypt, Netherlands, Italy, Turkey, Poland, Texas, UK, Greece, Canada, Germany, Romania, Austria, Croatia, Denmark, Singapore, Cyprus, Greece, Spain	42,653/NR (muscle mass: 758)	Cancer cachexia (lung, lymphoma, pancreatic, urothelial cell)	NR	Cancer cachexia, muscle mass	LDH	SMD
Ding 2024 [48]	SRMA	21/0/0/21	Germany, Singapore, China, South Korea, Brazil, Japan, Italy, Saudi Arabia, Brazil, Poland	3902/NR	Chronic heart failure, community-dwelling, peritoneal dialysis, hemodialysis, COPD, CKD, T2DM	65.3–84.0	AWGS 2019, AWGS 2014, EWGSOP 2, EWGSOP 1, FNIH 2014, SSCWD 2011	IL-6	SMD, Fisher’s Z value^d, Pearson’s correlation coefficients
Xue 2024 [37]	SRMA	6/6/0/0	China, Korea, Thailand, Japan	278/68	NR	56–81	ASMI, KE, ASMI, low muscle mass, grip strength	CRP, IL-6, IL-10, TNF-α	WMD, MD (after intervention)
Sun 2024 [38]	SRMA	22/22/0/0	NR	959	NR	61.8–94.3	AWGS, EWGSOP, SMI, ASM, BMI	CRP, IL-6, IL-10, TNF-α, IGF-1, follistatin, myostatin	SMD (after intervention)
Shin 2024 [49]	SR	15/NR	NR	NR	Community-dwelling, hospital	> 65	AWGS 2014, EWGSOP 2010, SPPB, chair stand test	MicroRNAs	Up or down
Zhang 2025 [50]	SRMA	12/0/0/12	Arab, Iran, China	794/398	Liver cirrhosis, T2DM, dialysis patients, outpatient, colorectal cancer	53-79.4	AWGS 2014, AGWS 2019, EWGSOP	Irisin	SMD, r
Fatima 2025 [51]	SRMA	17/0/10/7	Italy, UAE, Spain, Ireland, Switzerland, China, Iraq, Japan, Sharjah, India, Germany, UK	1837/NR	Community dwelling, chronic heart failure, hip fracture, Parkinson, stroke, asthma, T2DM, COPD, COVID	48.5–87.6	AGWS 2019, EWGSOP, EWGSOP 2, SPPB score	CAF	ROM
Wang 2024 [52]	SRMA	16/0/0/16	China, Korea, Istanbul, USA	10,836/NR	Community dwelling, hospital	> 18	AWGS, EWGSOP, SMI-L3, SMI	hemoglobin	MD
Salamanna 2023 [53]	SR	58/NR (5 only SP)	China, Slovenia, USA	604/NR (only SP)	Hypertension, T2DM, community dwelling	55–86 (only SP)	HGS, ASM, ASMI, grip strength, gait speed	MicroRNAs	Up or down

Abbreviations: SR, systematic review; SRMA, systematic review and meta-analysis; USA, the United States of America; UK, the United Kingdom; NR, not reported; MD, mean difference; SMD, standardized mean difference; WMD, weighted mean difference; RR, relative risk; OR, odds ratio, AD, Alzheimer’s Disease; CVD, cardiovascular diseases; CKD, chronic kidney disease; COPD: chronic obstructive pulmonary disease; T2DM: type 2 diabetes mellitus; SEN, sensitivity; SPE specificity, ROC-AUC, the area under the receiver operating characteristic curve; AWGS: the Asian Working Group on Sarcopenia; EWGSOP: the European Working Group on Sarcopenia; FNIH: the Foundation for the National Institutes of Health; SPPB: the Short Physical Performance Battery; SSCWD: the Society of Sarcopenia, Cachexia and Wasting Disorders; ESPEN, European Society of Parenteral and Enteral Nutrition; SMI, skeletal mass index; ASM, appendicular skeletal muscle; WS, walking speed; SMM, skeletal muscle mass; GS grip strength; ASMI, appendicular skeletal muscle index; BMI, body mass index; AMMI, appendicular muscle mass index; ALM: appendicular lean mass; AST/ALT, aspartate aminotransferase to alanine aminotransferase ratio; Cr, creatinine; CysC, serum cystatin C; Cr/CysC, Cr to CysC ratio; eGFRcre, creatinine-based GFR; eGFRCysC, eGFRCysC = 86 ×CysC-1.13; KE, knee extension strength; HGS, handgrip strength; ADMA, asymmetric dimethylarginine; CRP, C-reactive protein, GDF, growth/differentiation factor, IL, interleukin, TNF, tumor necrosis factor; Hsp, heat shock proteins; LDH, lactate dehydrogenase; PLR, platelet–lymphocyte ratio; NLR, neutrophil-lymphocyte ratio; FGF, fibroblast growth factor; BNP, brain natriuretic peptide; UAE, United Arab Emirates; CAF, C-terminal agrin fragment; ROM, ratio of mean, which means the ratio of (mean CAF concentration of sarcopenic group)/ (mean CAF concentration of non-sarcopenic group)

^a T/R/C/Others means total studies/ randomized controlled trials/ cohort studies/ other study styles

^b T/M means total participants and the number of males

^c ES: correlation coefficients between these two variables were converted to a standardized unit using Fisher r to z transformation. Correlation coefficient (r) was converted to the effect size: (ES (z) = ½ ln [(1 + r)/(1 − r)])

^d Correlation coefficients were standardized using Fisher’s r-to-Z transformation to evaluate the association between serum IL-6 levels and the muscle mass, muscle strength, and physical performance

Methodological quality and grading of the evidence

The methodological quality of the included studies was assessed using the validated AMSTAR 2 tool. The main limitations of the included studies were the absence of a predefined review protocol prior to conducting the review, the lack of a list of excluded studies provided by the review authors, and the failure to evaluate publication bias. The overall and detailed AMSTAR 2 scores for each included study were presented in Table S4. The QUADAS-2 tool was used for assess DTA studies, and detailed condition can be found in Table S5. The quality of evidence within some biomarkers was assessed using the GRADE approach (Table S6). Only 3 studies investigating changes in inflammatory biomarkers post-intervention were assessed with low, while the remaining studies were classified as very low quality of evidence or not evaluable [36, 37–38]. According to the evidence quality assessment based on previously published studies, only inflammatory biomarkers, BNP, and hemoglobin were rated as suggestive (class III) [39, 43, 48, 52], while the other studies were classified as weak (class IV) or nonsignificant (Table 2).

Table 2. Associations between biomarkers and sarcopenia, muscle mass, muscle strength and physical performance

Biomarkers	Included studies	N/No.^a	Outcomes	Metric^b	Effect (95% CI)	p value	Effect model (random and fixed)	Heterogeneity (I² statistic)	Cochran’s Q test P value	Begg’s test/ Egger’s test P value	GRADE	Quality of Evidence Classification
*Assessment of causal factors: inflammatory biomarkers*
CRP	Picca 2022 [41]	10/NR	SP	SMD	0.141 (-0.445, 0.726)	0.637	Random	NR	NR	NR	Very Low	Nonsignificant
TNF-α	Picca 2022 [41]	6/NR	SP	SMD	1.033 (0.164, 1.903)	0.02	Random	NR	NR	NR	Very Low	IV
IL-6	Ding 2024 [48]	21/3738	SP	SMD	0.31 (0.18, 0.44)	< 0.001	Random	50%	0.006	0.144/ 0.901	Very Low	III
CRP	Shokri-Mashhadi 2021 [43]	7/5893	MM	ES^c	0.03 (− 0.09, 0.15)	> 0.05	Random	83.0%	NR	0.45/ 0.67	Very Low	Nonsignificant
IL-6	Ding 2024 [48]	7/1879	MM	Fisher’s Z^d	-0.18 (-0.31, -0.06)	0.004	Fixed	0	0.82	1.000/ 0.381	Very low	IV
CRP	Shokri-Mashhadi 2021 [43]	9/5728	Muscle strength	ES^c	-0.22 (− 0.34, − 0.09)	< 0.001	Random	95.4%	NR	0.53/ 0.17	Very Low	III
hs-CRP	Shokri-Mashhadi 2021 [43]	5/2850	Muscle strength	ES^c	−0.22 (− 0.34, − 0.09)	< 0.001	Random	54.6%	NR	0.62/ 0.08	Very Low	III
IL-6	Ding 2024 [48]	7/1780	HGS	Fisher’s Z^d	-0.10 (-0.26, 0.05)	0.2	Fixed	0	0.93	0.707/ 0.737	Very Low	Nonsignificant
IL-6	Ding 2024 [48]	5/1383	Gait speed	Fisher’s Z^d	-0.09 (-0.25, 0.07)	0.28	Fixed	0	0.79	0.462/ 0.563	Very Low	Nonsignificant
*Assessment of causal factors: inflammatory biomarkers after intervention*
CRP	Byrne 2023 [36]	5/315	SP^e	SMD	-0.50 (− 0.73, − 0.28)	< 0.01	Fixed	0%	NR	NR	Low	IV
TNF-α	Byrne 2023 [36]	4/184	SP^e	SMD	-0.28 (− 0.93, 0.37)	0.40	Random	78%	NR	NR	Low	Nonsignificant
IL-6	Byrne 2023 [36]	4/184	SP^e	SMD	-0.44 (− 0.94, 0.07)	0.09	Random	63%	NR	NR	Low	Nonsignificant
CRP	Xue 2024 [37]	3/136	SP^f	WMD	-0.45 (-1.14, 0.23)	0.194	Fixed	0	0.604	0.602/ 0.286	Low	Nonsignificant
TNF-α	Xue 2024 [37]	5/209	SP^f	WMD	-1.00 (-2.47, 0.46)	0.141	Random	88.2%	< 0.001	0.462/ 0.186	Low	Nonsignificant
TNF-α	Xue 2024 [37]	4/177	SP^g	MD	-1.64 (-2.60, -0.46)	0.001	Fixed	6.3%	0.362	NR	Low	IV
TNF-α	Xue 2024 [37]	2/81	SP^h	MD	-1.65 (-2.26, -1.05)	< 0.001	Fixed	0	0.724	NR	Very low	IV
IL-6	Xue 2024 [37]	5/244	SP^f	WMD	-0.73 (-1.02, -0.44)	< 0.001	Fixed	21.5%	0.278	1/ 0.851	Low	IV
IL-6	Xue 2024 [37]	4/206	SP^g	MD	-0.91 (-1.25, -0.57)	< 0.001	Fixed	0	0.836	NR	Low	IV
IL-6	Xue 2024 [37]	3/149	SP^h	MD	-0.92 (-1.27, -0.58)	< 0.001	Fixed	0	0.688	NR	Very low	IV
IL-6	Xue 2024 [37]	3/141	SPⁱ	MD	-0.73 (-1.02, -0.43)	< 0.001	Random	59.2%	0.086	NR	Very low	IV
IL-10	Sun 2024 [38]	4/149	SP^e	SMD	0.61 (0.09, 1.13)	0.069	Random	57.6%	NR	NR	Low	Nonsignificant
*Assessment of causal factors: hormonal biomarkers*
Vitamin D	Picca 2022 [41]	5/NR	SP	SMD	-0.101 (-0.352, 0.149)	0.427	Random	NR	NR	NR	Very Low	Nonsignificant
IGF-1	Picca 2022 [41]	3/NR	SP	SMD	-0.035 (-0.606, 0.536)	0.904	Random	NR	NR	NR	Very Low	Nonsignificant
IGF-1	Sun 2024 [38]	5/118	SP^f	SMD	0.70 (0.10, 1.30)	0.05	Random	57.2%	NR	NR	Low	IV
Assessment of musculoskeletal status
Myostatin	Sun 2025 [38]	4/79	SP^f	SMD	-0.59 (-1.22, 0.04)	0.135	Fixed	46.1%	NR	NR	Low	Nonsignificant
Follistatin	Sun 2024 [38]	5/101	SP^f	SMD	0.56 (0.16, 0.96)	0.97	Fixed	0	NR	NR	Low	Nonsignificant
Irisin	Zhang 2025 [50]	12/794	SP	SMD	– 1.28 (– 1.65, –0.90)	< 0.00001	Random	92%	< 0.00001	0.131/0.205	Very Low	IV
CAF	Fatima 2025 [51]	7/445	SP	ROM	1.93 (1.49, 2.36)	< 0.001	Random	98.82%	< 0.001	0.37/NR	Very Low	IV
CAF	Fatima 2025 [51]	3/118	HGS	ROM	1.09 (1.05, 1.13)	< 0.001	Random	0.01%	0.51	NR	Very Low	IV
CAF	Fatima 2025 [51]	2/87	SMI	ROM	1.10 (1.05, 1.14)	< 0.001	Random	0.01%	0.52	NR	Very Low	IV
*Metabolic biomarkers*
Glucose	Picca 2022 [41]	4/NR	SP	SMD	-0.298 (-1.404, 0.808)	0.597	Random	NR	NR	NR	Very Low	Nonsignificant
Cholesterol	Picca 2022 [41]	6/NR	SP	SMD	0.159 (-0.793, 1.111)	0.743	Random	NR	NR	NR	Very Low	Nonsignificant
Triglycerides	Picca 2022 [41]	6/NR	SP	SMD	-0.604 (-1.022, 0.187)	0.005	Random	NR	NR	NR	Very Low	IV
LDL	Picca 2022 [41]	7/NR	SP	SMD	0.073 (-0.414, 0.559)	0.770	Random	NR	NR	NR	Very Low	Nonsignificant
HDL	Picca 2022 [41]	4/NR	SP	SMD	0.593 (-0.282, 1.469)	0.184	Random	NR	NR	NR	Very Low	Nonsignificant
Albumin	Picca 2022 [41]	10/NR	SP	SMD	-0.452 (-0.726, − 0.179)	0.001	Random	NR	NR	NR	Very Low	IV
*Amino acids biomarkers*
Leucine	Dai 2021 [21]	5/1052	SP	SMD	-1.249 (-2.275, -0.223)	0.04	Random	97.8%	NR	NR	Very Low	IV
Leucine^j	Dai 2021 [21]	3/722	SP	SMD	-0.465 (-0.641, -0.288)	< 0.01	Random	0	NR	NR	Very Low	IV
Isoleucine	Dai 2021 [21]	5/1052	SP	SMD	-1.077 (-2.106, -0.049)	0.02	Random	97.7%	NR	NR	Very Low	IV
Isoleucine^j	Dai 2021 [21]	3/722	SP	SMD	-0.329 (-0.505, -0.513)	< 0.01	Random	0	NR	NR	Very Low	IV
Tryptophan	Dai 2021 [21]	3/492	SP	SMD	-0.923 (-1.580, -0.265)	0.01	Random	89.9%	NR	NR	Very Low	IV
Tryptophan^j	Dai 2021 [21]	2/303	SP	SMD	-0.615 (-1.023, -0.208)	< 0.01	Random	55.7%	NR	NR	Very Low	IV
Valine^f	Dai 2021 [21]	3/722	SP	SMD	-0.294 (-0.469, -0.118)	0.01	Random	< 0.01	NR	NR	Very Low	IV
Phenylalanine^j	Dai 2021 [21]	3/722	SP	SMD	-0.215 (-0.390, -0.040)	0.02	Random	0	NR	NR	Very Low	IV
Threonine^j	Dai 2021 [21]	2/303	SP	SMD	-0.278 (-0.543, -0.014)	0.04	Random	0	NR	NR	Very Low	IV
Histidine^j	Dai 2021 [21]	3/722	SP	SMD	-0.285 (-0.460, -0.110)	< 0.01	Random	0	NR	NR	Very Low	IV
*Other biomarkers*
BNP	Prokopidis 2024 [39]	5/1188	SP	MD	87.76 (20.74, 154.78)	0.01	Random	61%	0.04	NR	Very Low	III
NT-proBNP	Prokopidis 2024 [39]	5/1352	SP	MD	947.45 (98.97, 1795.93)	0.03	Random	35%	0.19	NR	Very Low	IV
FGF-21	Liu 2023 [40]	5/625	SP	SMD	0.31 (-0.42, 1.04)	0.41	Random	94%	< 0.00001	NR	Very Low	Nonsignificant
Hemoglobin	Wang 2024 [52]	16/10,836	SP	MD	-0.47 ( −0.69, − 0.24)	< 0.0001	Random	97%	< 0.0001	0.259	Low	III
BNP	Prokopidis 2024 [39]	5/3026	ASM	MD	118.95 (46.91, 191.00)	0.001	Random	93%	< 0.00001	NR	Very Low	III
NT-proBNP	Prokopidis 2024 [39]	2/807	ASM	MD	672.01 (383.72, 960.30)	< 0.0001	Random	2%	0.31	NR	Very Low	IV
eHsp72	Margutti 2017 [22]	1/652	MM	OR	2.72 (1.21, 6.16)	< 0.01	NR	NR	NR	NR	NA	IV
Elevated LDH	Thompson 2024 [47]	4/527	Reduced MM	OR	1.31 (0.82, 2.11)	0.26	Random	21	0.28	NR	Very Low	Nonsignificant
eHsp72	Margutti 2017 [22]	1/652	HGS	OR	2.60 (1.17, 5.81)	< 0.01	NR	NR	NR	NR	NA	IV
eHsp72	Margutti 2017 [22]	1/652	Walking speed	OR	1.815 (1.029, 3.202)	0.04	NR	NR	NR	NR	NA	IV

Abbreviations: NR, not reported; NA, not applicable; N/No., number of studies/patients; MM, muscle mass; HGS, handgrip strength; SP, sarcopenia, ASM, appendicular skeletal muscle; CRP, C-reactive protein; GDF, growth/differentiation factor; IL, Interleukin; TNF, tumor necrosis factor; Hsp, heat shock proteins; LDH, lactate dehydrogenase; FGF, fibroblast growth factor; BNP, brain natriuretic peptide; MD, mean difference; SMD, standardized mean difference; WMD, weighted mean difference; RR, relative risk; OR, odds ratio; CAF, C-terminal agrin fragment; ROM, ratio of mean; SMI, skeletal mass index

^a N/No. means number of studies/patients

^b Metric means MD, SMD, RR, OR, HR, ES and, Fisher’s Z

^e Intervention methods included dietary supplement, exercise intervention, and exercise combined with dietary supplements

^f Intervention methods included resistance training

^g Subgroup analysis: Intervention period > 12week

^h Subgroup analysis: Intervention Frequency ≥ 3 sessions

ⁱ Subgroup analysis: Intervention Duration ≤ 60 min

^j Studies that only used the bioelectrical impedance analysis to measure muscle mass were included

Diagnostic test accuracy indicators of biomarkers for sarcopenia

Two studies have reported on the diagnostic test accuracy of biomarkers for sarcopenia. The SROC-AUC was derived by pooling the corresponding AUC data using a bivariate random-effects model in the included studies [9, 10]. The most extensively investigated biomarkers were based on serum Cr and CysC, including the serum Cr/CysC ratio, the serum creatinine × CysC-based glomerular filtration rate (Cr × eGFRcys) index, and other indices. Additional biomarkers studied include aspartate aminotransferase (AST)/alanine aminotransferase (ALT), irisin, insulin, and the platelet-to-lymphocyte ratio (PLR) [9, 10]. (Figures 2 and 3) The Cr/CysC ratio for diagnosing sarcopenia demonstrated a sensitivity of 0.65 (95% CI 0.52, 0.76), specificity of 0.76 (95% CI 0.67, 0.83), and AUC of 0.78 (95% CI 0.78, 0.82). The diagnostic accuracy of the Cr/CysC ratio varied with different sarcopenia diagnostic criteria, with a predominance of studies using the AWGS 2019 criteria. When using the AWGS 2019 criteria, the Cr/CysC ratio demonstrated the highest sensitivity, specificity, and AUC in males, with values of 0.73 (95% CI 0.59, 0.84), 0.75 (95% CI 0.61, 0.86), and 0.620 to 0.880, respectively [9, 10]. (Figures 2 and 3) The diagnostic accuracy of the Cr/CysC ratio appeared to be poorer when using the EWGSOP 1 or EWGSOP 2 criteria. Using the FNIH criteria, the Cr/CysC ratio exhibited the highest sensitivity (0.86, 95% CI: 0.70–0.95) but lower specificity and SROC-AUC [9, 10]. (Figures 2 and 3)

[See PDF for image]

Fig. 2

The sensitivity and specificity of biomarkers for diagnosing sarcopenia. Abbreviations: N/P, number of studies/number of participants; Cr/CysC ratio, creatinine/cystatin C ratio; eGFRCysC, eGFRCysC = 86 ×CysC-1.13; AWGS: the Asian Working Group on Sarcopenia; EWGSOP: the European Working Group on Sarcopenia; FNIH: the Foundation for the National Institutes of Health; JSHG, Japan Society of Hepatology guidelines; LMM, low muscle mass; HGS, hand grip strength; AST/ALT, aspartate aminotransferase to alanine aminotransferase ratio; PLR, platelet–lymphocyte ratio

[See PDF for image]

Fig. 3

The area under the receiver operating characteristic curve (ROC) of biomarkers for diagnosing sarcopenia. Abbreviations: N/P, number of studies/number of participants; Cr/CysC ratio, creatinine/cystatin C ratio; eGFRCysC, eGFRCysC = 86 ×CysC-1.13; AWGS: the Asian Working Group on Sarcopenia; EWGSOP: the European Working Group on Sarcopenia; FNIH: the Foundation for the National Institutes of Health; JSHG, Japan Society of Hepatology guidelines; LMM, low muscle mass; HGS, hand grip strength; AST/ALT, aspartate aminotransferase to alanine aminotransferase ratio; PLR, platelet–lymphocyte ratio

Additional studies have employed AST/ALT, irisin, insulin, and PLR as diagnostic biomarkers for sarcopenia, with detailed sensitivity, specificity, and AUC data provided in Figs. 2 and 3.

Correlation and expression levels of biomarkers in sarcopenia, muscle mass, muscle strength, and physical performance

Biomarkers in sarcopenia can be categorized into non-muscle-specific pathophysiological mechanisms (e.g., inflammatory biomarkers, hormonal biomarkers, and adipokines), musculoskeletal status assessments (muscle mass, myokines, follistatin, and neuromuscular junction biomarkers), metabolic biomarkers, genetic markers, and other emerging composite biomarkers [21, 22, 36, 39, 40, 41, 42, 43, 44, 45, 46, 47–48, 54].

In studies investigating the correlation between biomarkers and sarcopenia, muscle mass, muscle strength, and physical performance, the majority of inflammatory markers, hormonal biomarkers, genetic markers, and other biomarkers have shown negative correlations [22, 42, 44, 45]. Only a few biomarkers, such as insulin-like growth factor (IGF)-1, GDF-15, follistatin, free testosterone, circulating CD34-positive cells, and microRNA-125b-5p, have shown positive correlations [22, 42, 44]. Although the correlations of most biomarkers are statistically significant, the majority exhibit weak or even negligible correlations (Table 3).

Table 3. The correlation between biomarkers and muscle mass, muscle strength and physical performance

Conditions	Author, year	Biomarkers	N/No.	Metric^a	P value	I²%
*Muscle mass*
NR	Tuttle 2020 [45]	CRP	43/NR	-0.12	< 0.01	68.9
NR	Tuttle 2020 [45]	IL-6	42/NR	-0.11	< 0.01	67.1
NR	Tuttle 2020 [45]	TNF-α	31/NR	-0.16	< 0.01	73.8
NR	Tuttle 2020 [45]	GDF15	4/NR	-0.33	< 0.01	36.0
NR	Tuttle 2020 [45]	TNFsRII	5/NR	-0.11	< 0.01	52.9
NR	Tuttle 2020 [45]	IL8	4/NR	-0.28	< 0.01	75.0
NR	Tuttle 2020 [45]	Overall inflammatory marker^b	149/NR	-0.10	< 0.01	71.4
NR	Margutti 2017 [22]	IGF-1	1/98	0.365	< 0.01	NR
SMI	Margutti 2017 [22]	Follistatin	1/98	0.112	< 0.05	NR
SMI	Margutti 2017 [22]	Hsp72	1/652	-0.138	0.001	NR
SMI	Zhang 2025 [50]	Irisin	5/471	0.62 (0.31, 0.81)	0.0005	NR
Muscle volume	Jones 2022 [42]	Phosphodiester	1/21	-0.625	0.001	NR
Muscle volume	Jones 2022 [42]	Phosphatidylcholine	1/23	-0.529	0.011	NR
Muscle volume	Jones 2022 [42]	Phosphatidylethanolamine	1/23	-0.522	0.008	NR
Muscle volume	Jones 2022 [42]	Phosphatidylglycerol	1/23	-0.435	0.043	NR
*Muscle strength*
HGS	Tuttle 2020 [45]	Overall inflammatory marker^b	184/NR	-0.11	< 0.01	75.5
HGS	Tuttle 2020 [45]	CRP	46/NR	-0.10	< 0.01	66.7
HGS	Tuttle 2020 [45]	IL-6	57/NR	-0.14	< 0.01	70.8
HGS	Tuttle 2020 [45]	TNF-α	35/NR	-0.09	< 0.01	81.7
HGS	Tuttle 2020 [45]	GDF15	6/NR	-0.32	< 0.01	59.6
HGS	Tuttle 2020 [45]	IL-8	7/NR	-0.33	< 0.01	82.1
Grip strength	Amarasekera 2021 [44]	ADMA	1/550	-1.257	0.001	NR
Grip strength	Amarasekera 2021 [44]	Circulating CD34-positive cells	1/262	0.22	0.021	NR
Grip strength	Zhang 2025 [50]	Irisin	5//280	0.47 (0.23, 0.66)	0.0003	NR
KE	Tuttle 2020 [45]	Overall inflammatory marker^b	65/NR	-0.17	< 0.01	71.2
KE	Tuttle 2020 [45]	CRP	9/NR	-0.33	< 0.01	87.4
KE	Tuttle 2020 [45]	IL-6	17/NR	-0.16	< 0.01	66.2
KE	Tuttle 2020 [45]	TNF-α	15/NR	-0.21	< 0.01	79.8
KE	Tuttle 2020 [45]	GDF15	4/NR	-0.31	< 0.01	6.04
KE	Jones 2022 [42]	Free testosterone	1/46	0.40	0.01	NR
Knee extensor peak power	Jones 2022 [42]	Phosphatidylethanolamine	1/23	-0.433	0.044	NR
Quadriceps strength	Amarasekera 2021 [44]	ADMA	1/550	-11.730	0.012	NR
*Physical performance*
Chair lift test	Margutti 2017 [22]	Follistatin	1/98	0.220	< 0.05	NR
6-minute walk test	Margutti 2017 [22]	GDF-15	1/98	0.261	< 0.05	NR
Gait speed	Amarasekera 2021 [44]	ADMA	1/550	-0.065	0.003	NR
Gait speed	Amarasekera 2021 [44]	Circulating CD34-positive cells	1/117	0.126	0.003	NR
Gait speed	Zhang 2025 [50]	Irisin	3/142	0.11 (-0.26, 0.45)	0.5523	NR
Chair-stand test	Zhang 2025 [50]	Irisin	2/79	-0.1 (-0.24, 0.04)	0.1467	NR
6-min walk test	Amarasekera 2021 [44]	Circulating CD34-positive cells	1/117	70.1	0.028	NR
Velocity	Jones 2022 [42]	MicroRNA-125b‐5p	1/56	0.263	0.05	NR

Abbreviations: NR, not reported; N/No., number of studies/patients; SMI, skeletal mass index; KE, knee extension strength; HGS, handgrip strength; ADMA, asymmetric dimethylarginine; CRP, C-reactive protein, GDF, Growth/differentiation factor, IL, Interleukin, TNF, Tumor Necrosis Factor; Hsp, heat shock proteins

^a Correlation coefficient (R², ρ or r) through from Pearson’s correlation, multivariate linear regression, and spearman correlation. Presenting with mean (95% confidence interval)

^b Overall inflammatory marker including IL-6, IL-8, CRP, TNF-α et al. More detailed data were provided by Tuttle et al

Among inflammatory biomarkers, those significantly associated with sarcopenia include IL-6 (SMD 0.31, 95% CI 0.18, 0.44, p < 0.001) and TNF-α (SMD 1.033, 95% CI 0.164, 1.903, p < 0.02) [41, 48]. IL-6 demonstrated significantly associated with muscle mass (Fisher’s Z -0.18, 95% CI -0.31, -0.06, p = 0.004), and CRP was significantly associated with muscle strength (ES -0.22, 95% CI − 0.34, − 0.09, p < 0.001) [43, 48]. Following interventions in patients with sarcopenia, including dietary supplements, exercise intervention, and a combination of exercise with dietary supplements, had shown significant reductions in inflammatory biomarker expression. The changes in CRP (SMD − 0.50, 95% CI − 0.73, − 0.28, p < 0.01) and IL-6 (WMD − 0.73, 95% CI -1.02, -0.44, p < 0.001) were statistically significant [36, 37–38]. Subgroup analyses stratified by intervention type confirmed significant reductions in TNF-α and IL-6 expression across all therapeutic modalities (p < 0.05 for all comparisons [37, 38] (Table 2).

For the hormonal biomarkers, patients with sarcopenia exhibited reduced IGF-1 levels compared to controls (SMD − 0.035, 95% CI -0.606, 0.536, p = 0.904) [41]. Following resistance training interventions, a statistically significant enhancement in IGF-1 levels was observed (SMD 0.70, 95% CI 0.10, 1.30, p = 0.05) [38].This study evaluated biomarkers of musculoskeletal status, including myostatin, follistatin, irisin, and C-terminal agrin fragments (CAF) [50, 51]. Patients with sarcopenia exhibited significantly decreased irisin levels and increased CAF concentrations, with both findings demonstrating statistical significance [50, 51] (Table 2).

For the metabolic biomarkers, sarcopenia patients demonstrated significantly reduced levels of albumin (SMD − 0.452, 95% CI -0.726, -0.179, p < 0.001) and triglycerides (SMD − 0.604, 95% CI -1.022, 0.187, p < 0.005) when compared to the control group [41].

Within the amino acid biomarker profile, individuals with sarcopenia demonstrated significantly reduced circulating levels of three essential amino acids compared to healthy controls: leucine (SMD − 1.249, 95% CI -2.275, -0.223, p = 0.04), isoleucine (SMD − 1.077, 95% CI -2.106, -0.049, p = 0.02), and tryptophan (SMD − 0.923, 95% CI -1.580, -0.265, p = 0.01) [21]. A stratified analyses focusing on BIA-assessed lean body mass revealed significantly lower concentrations of seven amino acids in sarcopenic subjects versus non-sarcopenic controls: leucine, isoleucine, tryptophan, valine, phenylalanine, threonine, and histidine (p < 0.05 for all comparisons) [21] (Table 2).

Three studies investigated microRNAs, one in the context of sarcopenia and obesity, and the other in sarcopenia and frailty. These studies identified common target genes for microRNAs, including IGF-1, forkhead box O (FOXO), phosphatase and tensin homolog deleted on chromosome 10 (PTEN), and transforming growth factor beta (TGF-β). The main shared functions involve cellular synthesis, differentiation, oxidative stress, and mitochondrial dynamics [46, 49, 53] (Table S7).

Among additional biomarkers, brain natriuretic peptide (BNP) and NT-proBNP levels were significantly elevated in sarcopenia patients compared to the control group, while hemoglobin levels were significantly lower [39, 41, 52]. A high expression of heat shock proteins 72 (eHsp72) was correlated with reduced muscle mass, muscle strength, and walking speed [22] (Table 2).

Discussion

The current study demonstrated moderate diagnostic accuracy of the Cr/CysC ratio and Cr×eGFRcys index for sarcopenia identification, with observed variability attributable to diverse diagnostic criteria and regional differences. Biomarkers associated with sarcopenia, muscle mass, muscle strength, and physical performance include inflammatory markers, metabolic biomarkers, amino acids biomarkers, hormonal biomarkers, genetic biomarkers, and other markers. Inflammatory biomarkers have been the most extensively studied, and the expression levels of some inflammatory markers decrease following intervention. In metabolic biomarkers, albumin is decreased in patients with sarcopenia. Similarly, among amino acids biomarkers, leucine, isoleucine, and tryptophan are also decreased in patients with sarcopenia. Regarding hormonal biomarkers, the expression of IGF-1 improves after resistance training intervention. Within the domain of genetic biomarkers, microRNAs exhibit some shared targets and biological roles.

Several biomarkers demonstrate diagnostic potential for sarcopenia, including the creatinine Cr/CysC ratio, the Cr ×eGFRcys index, AST/ALT ratio, irisin, insulin, and PLR. The Cr/CysC ratio has been the subject of extensive research and exhibits moderate diagnostic accuracy for sarcopenia in terms of sensitivity, specificity, and the AUC [9, 10]. Cr is a byproduct of phosphocreatine metabolism in skeletal muscle, whereas CysC is produced by endogenous nucleated cells and is freely filtered by the glomerulus without being secreted or reabsorbed by renal tubular epithelial cells [55, 56]. First proposed by Kashani et al. [16] for lean mass assessment, the Cr/CysC ratio was subsequently validated in multiple sarcopenia diagnostic studies. Our meta-analyses revealed an overall AUC of 0.78 (95% CI 0.74–0.82) for Cr/CysC in sarcopenia detection, aligning with the 0.518–0.88 range reported by Lian et al. [10]. Using the AWGS 2019 criteria across 6,275 participants from eight cohorts, Lian et al. [10] reported a pooled AUC of 0.77 (95% CI 0.74–0.81), consistent with Lin et al.‘s [9] findings of 0.81 (95% CI 0.77–0.84). When accounting for gender, the range of AUCs for diagnosing sarcopenia in males and females using the AWGS 2019 criteria were 0.620–0.880 and 0.620–0.808, respectively, which do not significantly diverge from the previously mentioned AUCs [10]. However, when the EWGSOP 1 and EWGSOP 2 criteria were applied, the range of AUCs for the Cr/CysC ratio were 0.555–0.660 and 0.582–0.752, respectively [9, 10]. The pooled sensitivity and specificity of the Cr/CysC ratio using the AWGS 2019 criteria were generally higher compared to those using the EWGSOP 1 and EWGSOP 2 criteria, indicating that the diagnostic accuracy of EWGSOP 1 or EWGSOP 2 may be inferior to that of AWGS 2019 [9, 10].

The key factors contributing to the observed comparability and discrepancies may stem from regional regional differences, individual variation, gender-specific characteristics, physical health status, and the diversity of diagnostic criteria [9, 10]. The metabolism of Cr and CysC in East Asian versus Western populations is influenced by regional dietary patterns and individual physiological conditions. The diagnostic accuracy of the Cr/CysC ratio may be influenced by the variability within study populations, including those with chronic kidney disease, malignant neoplasms, community-dwelling seniors, and elderly hospital patients [23, 57]. Additionally, the clinical cutoffs for diagnosing sarcopenia defined by the AWGS and the EWGSOP are inconsistent, which may contribute to these discrepancies. Moreover, some studies included in the study did not strictly adhere to the diagnostic criteria established by AWGS and EWGSOP, thereby potentially reducing the diagnostic utility of the Cr/CysC ratio [19, 20]. Nevertheless, the Cr/CysC ratio and its related indices, such as the Cr × eGFRcys index, are considered potential biomarkers for sarcopenia; however, achieving high diagnostic accuracy may not be attainable with this indicator alone. The development of clinical prediction models that account for a range of potential confounders, including body mass index (BMI), chronic kidney disease, malignant tumors, gender, and others, may offer greater clinical relevance [20, 58]. For example, the Hospital Italiano de Buenos Aires (HIBA) score for sarcopenia, developed by Mauro et al. [59] incorporating BMI and the Cr/CysC ratio, yielded an AUC of 0.862 for diagnosing sarcopenia. Furthermore, after model adjustment, the HIBA score emerged as an independent predictor of mortality in patients awaiting liver transplantation [59].

Inflammatory biomarkers in sarcopenia, including IL-6, TNF-α, and CRP, have garnered significant interest. IL-6 levels in individuals with sarcopenia were elevated compared to controls, in contrast to the findings reported by Picca et al. [41] (SMD 0.198, 95% CI -0.192, 0.589, p = 0.319) and Bano et al. [60] (SMD 0.35, 95% CI -0.19, 0.89, p = 0.21), but consistent with the subgroup analysis of individuals under 75 years old by Picca et al. [41] (SMD 1.670, 95% CI 1.314, 2.026, p < 0.001). Similarly, TNF-α levels in sarcopenic individuals were higher than the control group, which contradicts the results from Bano et al. [60] (SMD 0.28, 95% CI -0.26, 0.83, p = 0.31). Although CRP levels were elevated in sarcopenic patients compared to controls, the difference was not statistically significant. In contrast, Bano et al.‘s study demonstrated a statistically significant increase in CRP levels in the sarcopenic group compared to controls (SMD 0.51, 95% CI 0.26, 0.77, p < 0.0001) [60]. Despite the variability across studies, the cumulative evidence suggested that IL-6, TNF-α, and CRP elevated in sarcopenic patients compared to non-sarcopenic individuals, implicating chronic low-grade inflammation as a pivotal factor in muscle loss and a component of aging. These discrepancies may arise from differences in study populations, age distribution, and diagnostic criteria employed [45, 60, 61–62].After interventions, the expression levels of IL-6, TNF-α, and CRP demonstrated statistically significant reductions in sarcopenic patients, although the extent of decrease varied across studies [36, 37–38]. Intervention strategies by Byrne et al. [36] consisted predominantly of dietary supplementation and exercise interventions, either individually or in combination. Studies by Xue et al. [37] and Sun et al. [38] focused principally on resistance training. Regardless of the intervention modality, a downregulation of inflammatory markers was noted. This finding was similarly observed in healthy older adults, where resistance training enhanced leg extension strength and reduced CRP concentrations [63]. However, it is important to recognize that different intervention programs may yield varying effects. Regrettably, despite numerous studies on interventions for sarcopenia, there is currently no standardized protocol for exercise and dietary interventions, which largely depends on the clinical experience of practitioners. Future research should aim to establish intervention standards based on regional and demographic characteristics [7, 64, 65–66].

Amino acid biomarkers represent a category within metabolic markers, in which serum levels of albumin, leucine, isoleucine, and tryptophan are reduced in individuals with sarcopenia compared to those without the condition. Petermann-Rocha et al. [67] conducted an analyses on 396,707 participants (68.8% female, aged 38 to 73 years) from the UK Biobank and found that low serum albumin concentrations were associated with sarcopenia, with no difference observed between genders. Earlier research had also demonstrated that sarcopenic individuals with low albumin levels had a 3.73-fold higher risk of disability after adjusting for confounding variables, reinforcing the association between low albumin and reduced skeletal muscle mass in the elderly population [68].

In addition to albumin, serum levels of branched-chain amino acids (leucine and isoleucine) and aromatic amino acids (tryptophan) are also reduced in patients with sarcopenia compared to those without. Amino acids play a critical role in muscle protein synthesis and the maintenance of skeletal muscle function. Leucine and its metabolic derivatives had been demonstrated to activate protein synthesis [69]. The decline in muscle protein synthesis observed in the elderly can be reversed through leucine supplementation and leucine-enriched essential amino acid mixtures. Studies had also correlated higher branched-chain amino acids levels with increased thigh muscle cross-sectional area and fat-free mass index [70]. A systematic review and meta-analyses of 35 studies revealed that BCAA-enriched supplementation significantly enhanced muscle strength and mass in sarcopenic patients, athough no substantial improvement in physical performance was noted [71]. An umbrella review by Gielen et al. [66] on nutritional supplementation for sarcopenia highlighted the significant impact of leucine on muscle mass in the elderly with muscle atrophy. Additionally, research indicated that tryptophan significantly influences muscle mass through its metabolite serotonin, as tryptophan-deficient animals exhibited reduced growth hormone levels and marked muscle wasting, thereby contributing to sarcopenia [72]. However, there was a notable lack of research on the use of aromatic amino acids alone as a supplement to treat sarcopenia, with these typically being part of multi-nutrient supplementation [66, 73, 74]. In conclusion, protein supplementation is effective and essential, but it is most effective when administered in conjunction with exercise intervention to achieve optimal therapeutic outcomes.

Among hormonal biomarkers, IGF-1 is particularly significant, especially following resistance training, where an increase in IGF-1 expression levels has been observed. Petermann-Rocha et al. [67]reported lower IGF-1 concentrations were associated with sarcopenia in both males and females. Similarly, serum IGF-1 levels were found to be lower in sarcopenic individuals (n = 96, aged ≥ 60 years) from a community-dwelling population in Seoul compared to controls [75]. Additionally, the expression of microRNAs in sarcopenia is one of the hot topics in research [76]. Age-related alterations in microRNA expression within muscle tissue may adversely affect muscle mass and function [77]. This manuscript focuses on microRNAs co-expressed in sarcopenia, obesity, and frailty, acknowledging the intricate and extensive landscape of the microRNA profile. Similarly, Yin et al. [78] had demonstrated how microRNAs mediated the pathophysiological alterations of sarcopenia through the regulation of satellite cell function, protein homeostasis, mitochondrial dysfunction, reactive oxygen species imbalance, neurodegeneration, and the transformation of muscle fiber types, as well as fat infiltration. Overall, the microRNA expression spectrum implicated in the regulation of muscle quantity, quality, and homeostasis requires a comprehensive perspective, and the potential for precision therapy targeting microRNAs for sarcopenia remains a significant challenge [79].

In conclusion, this study demonstrates the potential of biomarkers for early screening and diagnosis of sarcopenia. However, currently available biomarkers are not yet optimal for sarcopenia screening or diagnosis, highlighting the need to explore and validate novel sarcopenia biomarkers [9, 10]. The diagnosis of sarcopenia still requires comprehensive assessment of muscle mass, muscle strength, and physical performance [1, 2]. Biomarkers such as D3-creatine, testosterone, DHEA-S, leptin, N-terminal propeptide of type III procollagen (P3NP), asymmetric dimethylarginine (ADMA), macrophage inflammatory protein 1β (MIP-1β), and gut microbial composition were not investigated in this study. These findings emphasize that sarcopenia is a complex multisystem disorder involving inflammatory, immune, metabolic, endocrine, and genetic pathways, and is influenced by multiple factors including regional differences, underlying comorbidities, and others. Reliance on any single biomarker is insufficient for establishing diagnostic criteria for sarcopenia. Machine learning or deep learning approaches may represent a promising future direction for sarcopenia biomarker research, enabling the development of clinical diagnostic or predictive models through integration of metabolic, inflammatory, hormonal, and neuromuscular junction biomarkers. Following adjustment for potential confounders (e.g., age, sex, ethnicity, comorbidities), the optimal multi-biomarker models should undergo rigorous clinical validation. Another possible application is the use of biomarkers for screening and even early diagnosis of pre-sarcopenia, thereby alerting individuals to the need for early intervention, which may hold greater significance for the prevention and treatment of sarcopenia. Ultimately, biomarkers might serve as surrogate endpoints for evaluating the impact of interventions aimed at sarcopenia [80]. While subjective improvements in physical status following dietary supplementation and exercise may not be immediately measurable, changes or improvements in biomarker levels could be utilized as an objective metric to assess the effectiveness of intervention [80]. However, current research on sarcopenia biomarkers predominantly comprises observational studies with limited sample sizes. Regional differences, variations in healthcare systems, or population demographics may substantially amplify heterogeneity across studies. Consequently, the clinical implementation of biomarkers for sarcopenia remains challenging, and future large-scale, high-level RCTs are needed to establish high-quality clinical evidence.

This investigation has several limitations. Initially, the exclusion of grey literature from the search strategy may result in an incomplete representation of available studies. Secondly, the heterogeneity of the included studies cannot be ignored due to the large inclusion scope. Moreover, some biomarkers yielding statistically insignificant results were not extracted, and subgroup analyses for certain biomarkers with potential clinical relevance were not delineated within the manuscript. Thirdly, data were extracted and combined from the primary publications of a subset of studies, concentrating on a select number of outcome measures, which might not align with the original findings reported in those studies. Fourthly, the level of evidence from the primary research is modest, thereby constraining the overall quality of the evidence. Fifthly, the results require cautious interpretation due to unresolved heterogeneity across studies, attributable to variations in the number of primary studies and differences in intervention protocols. Ultimately, despite the selection of the most recent and extensive studies for this analysis, the disparate diagnostic criteria for sarcopenia across different geographical regions may give rise to conflicting clinical interpretations of the biomarkers assessed.

Conclusions

In sarcopenia, a multitude of biomarkers are investigated, with the Cr/CysC ratio demonstrating moderate diagnostic accuracy and being the most frequently used diagnostic indicator. Inflammatory biomarkers are the most common biomarkers and are generally elevated in patients with sarcopenia. Post-intervention for sarcopenia, some biomarkers expression levels are decreased. The application of biomarkers for the diagnosis of sarcopenia is a prompt and efficient method compared to traditional criteria. However, the diagnostic accuracy of these biomarkers does not appear to meet the necessary standards for clinical implementation. Employing biomarkers to screen for individuals at risk of sarcopenia, or as an evaluative metric for the efficacy of interventions following diagnosis, while considering both clinical utility and cost-effectiveness, is likely to represent the future direction of research on biomarkers for sarcopenia.

Acknowledgements

This work was supported in part by the Bioinformatics Center, Furong Laboratory and Bioinformatics Center, Xiangya Hospital, Central South University.

Author contributions

Conceptualization: Gaoming Liu, Yusheng Li, Shide Jiang, Wenfeng Xiao; Methodology: Gaoming Liu, Wenhao Lu, Wenqing Xie; Statistic analysis and investigation: Gaoming Liu, Guang Yang, Hengzhen Li, Xu Liu, Zhi Liu; Writing - original draft preparation: Gaoming Liu, Shide Jiang, Xu Liu, Wenfeng Xiao; Writing - review and editing: Wenqing Xie, Wenfeng Xiao, Yusheng Li. All authors have read and approved the final version of manuscript.

Funding

This work was supported by the National Key R&D Program of China (2023YFC3603400), the National Natural Science Foundation of China (82272611, 82472522, 92268115), Projects of International Cooperation and Exchanges NSFC (No. W2421123), the Natural Science Foundation of Hunan Province (2023JJ30949), the Science and Technology Innovation Program of Hunan Province (2023SK2024), the Hunan Provincial Science Fund for Distinguished Young Scholars (2024JJ2089), and the Independent Exploration and Innovation Project for Postgraduate Students of Central South University (2024ZZTS0276).

Data availability

All data relevant to the study are included in the article or uploaded as supplementary information.

Declarations

Ethics approval and consent to participate

This article does not contain any studies with human participants or animals performed by any of the authors.

Consent for publication

Not applicable.

Competing interests

The authors have no competing interests to declare that are relevant to the content of this article.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Biomarkers for sarcopenia, muscle mass, muscle strength, and physical performance: an umbrella review

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