Contemporary sarcopenia diagnosis: evolving definitions, screening, and instrumental assessment – a narrative review

The relevance of sarcopenia research is determined by its direct impact on disability, hospitalizations, and mortality. Sarcopenia increases the risk of falls, fractures, and loss of independence in activities of daily living and is also associated with cardiovascular and respiratory diseases, as well as cognitive impairment [1][2]. According to the United Nations, the proportion of people aged ≥65 years increased from 6% in 1990 to 9% in 2019 and is projected to reach 16% by 2050¹. The global prevalence of sarcopenia ranges from 10% to 27% [3], whereas in the Russian Federation it ranges from 30% to 37% [4][5].

The aim of this review is to analyze the evolution of international and Russian approaches to sarcopenia diagnosis, assess the diagnostic value of available screening and instrumental methods, identify their limitations, and formulate proposals for optimizing diagnostic strategies in real-world clinical practice in the Russian Federation.

MATERIALS AND METHODS

For this narrative review, a literature search was conducted in the electronic databases PubMed/MEDLINE, Scopus, Google Scholar, and eLibrary.ru for the period from January 2006 to June 2026. The search strategy included combinations of the following terms in Russian and English: “sarcopenia”, “diagnosis”, “screening”, “muscle mass”, “muscle strength”, “physical performance”, “bioimpedance analysis”, “dual-energy X-ray absorptiometry”, “dynamometry”, “older adults”, and “frailty.”

The inclusion criteria comprised systematic reviews, meta-analyses, randomized controlled trials, large observational cohort studies, consensus documents, and clinical guidelines issued by the European Working Group on Sarcopenia in Older People (EWGSOP and EWGSOP2), Asian Working Group for Sarcopenia (AWGS), Global Leadership Initiative in Sarcopenia (GLIS), Foundation for the National Institutes of Health Sarcopenia Project (FNIH), Sarcopenia Definition and Outcomes Consortium (SDOC), and the Russian Association of Gerontologists and Geriatricians (RAGG), as well as publications addressing the diagnosis, epidemiology, or pathogenesis of sarcopenia. The exclusion criteria were clinical case reports, animal studies, and publications not related to the diagnosis of sarcopenia.

Data extraction was performed independently by five authors, with disagreements resolved by consensus. For each included source, the following information was recorded: authors, year of publication, study design, study population, methods used for sarcopenia diagnosis, main findings, and limitations.

Evolution of sarcopenia diagnostic criteria: from muscle mass to function

The first step toward standardizing sarcopenia diagnosis was the EWGSOP consensus (2010), which defined sarcopenia as a syndrome characterized by progressive loss of skeletal muscle mass and strength [6]. In 2014, the FNIH, based on data from large cohort studies, proposed optimized cutoff values for muscle strength and muscle mass [7].

In 2016, sarcopenia received a separate M62.84 code in the International Classification of Diseases, Tenth Revision, Clinical Modification, used in the United States (ICD-10-CM); the code became effective on October 1, 2016, formally recognizing sarcopenia as a distinct disease entity rather than merely a syndrome [8]. This step not only emphasized the need for standardized diagnosis and treatment but also promoted convergence of diagnostic approaches at the global level. It should be noted that this refers specifically to the national clinical modification: in the core ICD-10 version of the World Health Organization, which is also used in the Russian Federation, there is no separate category for sarcopenia, while code M62.8 denotes “Other specified disorders of muscle.”

In 2018, EWGSOP2 defined sarcopenia as a progressive and generalized skeletal muscle disorder [2]. In 2019, AWGS proposed ethnically adapted cutoff values [9]. SDOC emphasized the need to distinguish sarcopenia from cachexia and malnutrition [10], while GLIS (2024) separated diagnostic parameters into components and outcomes, including impaired physical performance, falls, and disability [11]. In 2025, AWGS published an updated consensus, extending the age range to 50–64 years, simplifying the diagnostic algorithm, and abandoning the category of “severe sarcopenia” in favor of “probable sarcopenia” [12].

In 2026, the clinical guidelines of the Russian Association of Gerontologists and Geriatricians defined sarcopenia as a progressive, age-associated, generalized, potentially reversible skeletal muscle disease associated with a high risk of falls, fractures, disability, and death².

Challenges in sarcopenia diagnosis

Despite substantial progress in understanding the pathogenetic mechanisms and clinical consequences of sarcopenia, its diagnosis remains challenging. This is due to the lack of universal diagnostic criteria, standardized cutoff values, and widely available screening tools.

According to the EWGSOP2, AWGS, and FNIH consensus recommendations, diagnosis follows a three-step approach: identification of reduced muscle strength, confirmation of low muscle mass, and, at the final stage, assessment of physical performance.

However, differences in the cutoff values used complicate interpretation of the results and comparison of data across populations.

Another major challenge is the lack of universal standards and readily accessible tools for diagnosing sarcopenia. Accurate methods for assessing muscle mass, including dual-energy X-ray absorptiometry (DXA), computed tomography (CT), and magnetic resonance imaging (MRI), are limited in routine use by their cost and technical requirements. Screening tools such as SARC-F (strength, assistance in walking, rise from a chair, climb stairs, falls) have low sensitivity at early stages.

Sarcopenia screening in primary care: opportunities and limitations

No universal screening test combines high sensitivity, specificity, and reproducibility [2][13]. The SARC-F questionnaire is the most widely used screening tool; a score of ≥4 points indicates a risk of sarcopenia [14]. However, its low sensitivity (21–61%, with a specificity of 86–91%) limits its use [15–20].

To improve diagnostic accuracy, several modifications incorporating anthropometric parameters have been developed. The most extensively studied is SARC-CalF (SARC-F with calf circumference), which includes measurement of calf circumference (cutoff values <34 cm in men and <33 cm in women; 10 additional points, with a total cutoff of ≥11) [21]. According to E.J. Kim et al. [22], the sensitivity of SARC-CalF is 53.3%, specificity is 87.3%, and the area under the ROC curve (AUC) is 0.78. Other modifications include SARC-F + MUAC (mid-upper arm circumference), SARC-F + EBM (elderly body mass) where age ≥75 years and body mass index (BMI) ≤21 kg/m², SARC-CalF + MUAC, and SARC-CalF + MUAC adjusted for BMI. Adding MUAC increases sensitivity while reducing specificity [23], whereas SARC-F + EBM improves identification of older patients with low body mass [24][25]. These combined versions require further validation [23][26].

Among non-questionnaire-based methods, the Ishii test, with cutoff values of ≥105 points for men and ≥120 points for women [27], showed the highest diagnostic accuracy in the study by A.B. de Lima et al. [23]: in men, sensitivity was 69.2%, specificity 97.1%, and AUC 0.831; in women, the corresponding values were 52.5%, 100%, and 0.762.

The MSRA (Mini Sarcopenia Risk Assessment) questionnaire, available in MSRA-7 and MSRA-5 versions, assesses age, hospitalizations, physical activity, nutritional status, and weight loss [28]. Cutoff values of ≤30 points for MSRA-7 and ≤45 points for MSRA-5 are associated with an increased risk of sarcopenia [28][29]. MSRA has high sensitivity (80%) but comparatively lower specificity (50–60%); these diagnostic characteristics have been confirmed in validation studies conducted in different
populations [28–31].

The choice of screening tool depends on the clinical setting and available resources. Amongst the Russian population, validation data for the tools discussed above remain limited: SARC-F has been studied mainly in selected disease-specific groups and showed a low diagnostic value [19], whereas data on SARC-CalF, the Ishii test, and MSRA are lacking. Moreover, the diagnostic accuracy of screening tools varies substantially across studies and populations [32], underscoring the need for their separate validation before widespread use in Russian clinical practice.

Table 1 presents a comparative overview of the main screening tools used to identify sarcopenia.

Assessment of muscle strength: dynamometry as a key criterion

Handgrip dynamometry is a simple, inexpensive, and non-invasive method for assessing muscle strength and is widely used in geriatrics, rehabilitation, and epidemiological research. Low muscle strength is associated with an increased risk of functional limitations, hospitalization, cardiovascular events, and mortality [33][34], which supports its inclusion in diagnostic algorithms for sarcopenia and frailty.

EWGSOP2 and AWGS 2025 identified low muscle strength as a central diagnostic parameter alongside muscle mass, with reduced strength regarded as a key early sign [2][12]. However, the use of handgrip dynamometry is limited by variability in cutoff values, anthropometric differences, heterogeneity of measurement protocols, and differences between dynamometers [2][35].

Historically, dynamometry has been developing since the nineteenth century, while the hydraulic Jamar dynamometer became a reference instrument in the 1950s [36][37]. Analysis of the literature showed that even at that time measurement results were influenced by hand position, elbow flexion angle, number of attempts, and type of device [35][37]. The standardized protocol of the American Society of Hand Therapists recommends measurements in a seated position, with the shoulders adducted and the elbows flexed at 90° [38]. V. Mathiowetz et al. [39] established early normative values and confirmed an age-related pattern, with peak strength around the age of 30 years followed by a gradual decline. L.P. Fried et al. (2001) [40] included weakness in the frailty phenotype, considering handgrip strength a marker of vulnerability.

EWGSOP 2010 incorporated handgrip dynamometry into the diagnosis of sarcopenia, whereas EWGSOP2 substantially increased its diagnostic importance by defining low muscle strength as the primary criterion: values <27 kg in men and <16 kg in women indicate probable sarcopenia [2][6]. AWGS 2019 proposed cutoff values of <28 kg for men and <18 kg for women in Asian populations [9]. AWGS 2025 requires concurrent reductions in muscle strength and muscle mass for the diagnosis of sarcopenia; the cutoff values are <28 and <18 kg for individuals aged ≥ 65 years and < 34 and < 20 kg for those aged 50–64 years, respectively [12].

There is no universal cutoff value for muscle strength. EWGSOP2 relies on the study by R.M. Dodds et al. (60,803 observations; a T-score approach using a value 2.5 standard deviations below peak strength) [41]. However, muscle strength depends on height, body mass, ethnicity, physical activity, and the measurement protocol. Historically, higher cutoff values have been used, including approximately 30 kg in men and 20 kg in women [42], as well as 37 kg in men and 21 kg in women for identifying the risk of mobility limitation [43], which highlights the dependence of cutoff values on the selected clinical outcome and population characteristics.

International differences are evident: in the PURE study (125,462 participants), handgrip strength was higher in Europe and North America and lower in South Asia and Africa [44]. A 2024 systematic review including 2.4 million adults from 69 countries showed that peak handgrip strength values were 49.7 kg in men and 29.7 kg in women aged 30–39 years, followed by a subsequent decline [45]. Comparison of Russian and Norwegian populations aged 40–69 years revealed persistent between-country differences in strength that could not be explained by protocol differences [46]. Russian data showed that muscle strength in individuals older than 65 years was at the lower end of European reference ranges; subsequently, Russian cutoff values of 24 kg for men and 17 kg for women were proposed, although the sample was limited to individuals up to 74 years of age [47][48]. At the same time, the 2026 clinical guidelines for Russian practice adopted the EWGSOP2 cutoff values, making their validation in representative Russian cohorts a priority.

BMI-adjusted cutoff values are not intended for the diagnosis of sarcopenia [40]. A 2007 study found that muscle strength in Taiwanese individuals was 25–27% lower than the “consolidated norms,” which led to the development of regional reference values [49].

Methodological challenges include variability in measurement protocols, including patient position, number of attempts, and choice of hand [35], as well as differences between dynamometers.

Thus, muscle strength is not an independent diagnostic criterion for sarcopenia. Cutoff values depend on the population, age, sex, and the clinical outcome being assessed. Dynamometry requires standardization of the device, measurement protocol, and cutoff values. Otherwise, methodological variability may compromise the reproducibility of results.

Assessment of muscle mass: instrumental methods and their limitations

Bioelectrical impedance analysis (BIA) is an accessible method for assessing body composition based on the electrical resistance of tissues, allowing estimation of muscle mass, fat mass, and body water content. The main parameter used for diagnosing sarcopenia is the appendicular skeletal muscle mass index (ASMI; appendicular muscle mass / height²). According to AWGS 2019, the BIA cutoff values for ASMI are <7.0 kg/m² in men and <5.7 kg/m² in women [9].

However, not all devices are capable of separately estimating muscle mass in the arms and legs, which limits calculation of ASMI. In such cases, alternative indices are used, including the skeletal muscle index (total muscle mass / height²) or lean mass index, although their cutoff values are less standardized and require population-specific validation.

BIA also provides the phase angle as a marker of soft-tissue quality, reflecting metabolic characteristics and hydration status. Higher values are assumed to indicate greater cell membrane integrity [50][51]. Results may be distorted in the presence of metal or silicone implants, cardiac pacemakers, and the use of certain medications. No universally accepted phase-angle cutoff values for sarcopenia diagnosis have been established, highlighting the need for further research.

DXA in whole-body mode is a key method for assessing muscle mass and is based on differential X-ray absorption by soft tissues. Particular importance is placed on the assessment of appendicular muscle mass (AMM; the sum of muscle mass in the limbs) and ASMI. EWGSOP2 defines low muscle mass as an ASMI <7.0 kg/m² in men and <5.5 kg/m² in women, corresponding to AMM values of ≤20 kg and ≤15 kg, respectively [2].

In obese patients, alternative indices are used, including AMM/BMI (<0.789 in men and <0.512 in women according to FNIH) or AMM/body weight, which correlate more closely with physical performance [7]. AWGS 2025 proposes age-specific ASMI cutoff values: <7.2 kg/m² in men and <5.5 kg/m² in women aged 50–64 years, and <7.0 kg/m² and <5.4 kg/m², respectively, in individuals aged ≥65 years [12].

A meta-analysis in professional athletes showed that BIA systematically overestimates lean mass compared with DXA, by an average of 2.78 kg. However, these findings cannot be directly extrapolated to the general population because of differences in body composition [52]. In addition, DXA cannot distinguish skeletal muscle tissue from other components of lean mass, such as fluid, glycogen, and connective tissue, which may lead to overestimation of muscle mass in the presence of edema. The method is also subject to projection-related errors, including tissue overlap, positioning artifacts, and the presence of implants. DXA provides primarily quantitative assessment and does not directly characterize muscle quality, architecture, or fatty infiltration, which limits its prognostic value.

Table 2 summarizes the key parameters of BIA, DXA, and imaging-based methods.

Specific considerations in defining reference values for muscle mass

One of the fundamental studies that established reference values for muscle mass in European populations was the Rosetta Study, a body composition research project. Its findings were used by R.N. Baumgartner et al. (1998) [53], who developed and validated an anthropometric equation for predicting AMM, using DXA as the reference method. The equation demonstrated high accuracy (R² = 0.91; standard error of the estimate = 1.58 kg), allowing its use in epidemiological studies when instrumental methods were unavailable. An operational definition of sarcopenia was proposed based on the AMM/height² index, with sarcopenia defined as a value more than 2 standard deviations below the mean of a young reference population. The cutoff values were 7.26 kg/m² for men and 5.45 kg/m² for women [53]. This approach subsequently formed the basis for later consensus definitions, including EWGSOP and FNIH. The Health ABC Study confirmed the applicability of these reference values and contributed to the development of the EWGSOP 2010 criteria [54].

Asian studies that subsequently informed the AWGS 2014 consensus demonstrated poor agreement with the Baumgartner equation for identifying low muscle mass [55]. The limitations of height-adjusted muscle mass assessment were particularly evident in women, as the index did not adequately reflect age-related loss of muscle mass because of the substantial decline in height with age. A 2011 study in a Chinese population reported similar findings: the AMM/height² cutoff values for Chinese adults (5.85 kg/m² for men and 4.23 kg/m² for women) were substantially lower than those reported for the US population (7.26 and 5.45 kg/m², respectively), while the cutoff value for women was even lower than that reported in Hong Kong (4.82 kg/m²) [56]. These findings called into question the universality of this approach and highlighted the need for ethnicity-specific criteria in Asian populations.

Computed tomography and magnetic resonance imaging

CT and MRI are among the most reliable methods for assessing muscle mass because they provide three-dimensional measurements and avoid the projection-related distortions inherent to DXA. CT allows assessment of muscle density in Hounsfield units and segmentation of muscle tissue on axial images, distinguishing it from adipose and connective tissue and enabling calculation of the skeletal muscle index. The conventional approach involves measuring the cross-sectional muscle area at the level of the third lumbar vertebra and normalizing it to the patient’s height, which correlates with total body muscle mass [57][58]. The routine use of CT and MRI for screening is limited by high cost, radiation exposure with CT, and the labor-intensive nature of manual image segmentation.

Assessment of physical performance: functional tests as indicators of sarcopenia severity

In current diagnostic algorithms, physical performance tests are used to stratify disease severity rather than to replace muscle mass assessment. According to EWGSOP2, severe sarcopenia is diagnosed when reduced muscle strength, low muscle mass, and impaired physical performance are present simultaneously. The most commonly used tests are the Timed Up and Go test, the 4-m gait speed test, and the Short Physical Performance Battery (SPPB), which assess mobility, balance, endurance, and lower-extremity function [2][59].

A Timed Up and Go result of ≥20 s indicates poor physical performance and, when accompanied by reduced muscle strength and muscle mass, severe sarcopenia [2][60]. Diagnostic accuracy varies across populations: in hospitalized older patients, a cutoff of ≥10.85 s showed a sensitivity of 67.0%, specificity of 88.7%, and AUC of 0.80 [61][62]; in older women, the AUC was 0.703, while a cutoff of 9.8 s yielded a sensitivity of 89.8% and specificity of 41.7% [63]. The advantages of the test include rapid administration and integrated assessment, whereas its limitations include the influence of neurological, vestibular, and cognitive impairment and the absence of a universally accepted cutoff [64].

The 4-m gait speed test, with a cutoff of ≤0.8 m/s, indicates impaired physical performance [2][65]. According to a meta-analysis, each 0.1 m/s decrease in gait speed is associated with a 12% increase in mortality risk [66]. However, the test has limited diagnostic value for sarcopenia [64]. A five-repetition chair stand time >15 s is interpreted as reduced lower-extremity strength and may be used as an alternative to handgrip dynamometry [2][59]. In the study by L.A. da Costa Teixeira et al. [63], the test demonstrated moderate diagnostic accuracy (AUC <0.7). Its limitations include the influence of joint pain, chair height, and performance technique.

The SPPB includes three components: balance tests, 4-m gait speed, and the five-repetition chair stand test; each component is scored from 0 to 4 points, with a total score ranging from 0 to 12 [67]. A score of ≤8 points indicates poor physical performance and can be used to confirm severe sarcopenia. In the study by S. Phu et al. [68], the SPPB demonstrated moderate diagnostic performance (AUC 0.644–0.770); a cutoff of ≤8 points showed high sensitivity (82–100%) but low specificity (36–41%). The limitations of the SPPB include the relatively longer administration time and moderate specificity.

Directions for future research in the Russian Federation

To advance toward personalized, evidence-based diagnosis of sarcopenia in Russia, a number of coordinated scientific and organizational measures are required:

1. Establishment of multicenter population-based cohorts to determine reference values for muscle strength and muscle mass, taking into account sex, age, ethnicity, region of residence, level of physical activity, and comorbidities. Studies should include not only older adults (≥65 years) but also middle-aged individuals (50–64 years), in line with the current emphasis on early detection and prevention.
2. Development and validation of Russian screening tools or adaptation of existing instruments, with determination of optimal cutoff values for the Russian population. Particular interest lies in creating simple questionnaire-based tools that do not require specialized equipment and account for Russian sociocultural characteristics.
3. Standardization of dynamometry protocols at the national level, including mandatory specification of the device type, patient position, number of attempts, and method used to calculate the final result, as well as cross-calibration studies between different dynamometer models to ensure comparability of results.
4. Evaluation of the comparative performance of BIA and DXA in the Russian population and development of algorithms to correct systematic BIA measurement error for use in healthcare settings where DXA is unavailable.
5. Investigation of the prognostic value of diagnostic criteria in relation to clinically significant outcomes, including falls, fractures, hospitalizations, loss of independence, and mortality, in Russian cohorts in order to identify clinically meaningful cutoff values.

CONCLUSION

Sarcopenia represents a major global challenge for healthcare systems in the context of population ageing. However, its effective detection and management require population-specific epidemiological and clinical data. Direct application of international criteria to Russian clinical practice without appropriate adaptation and validation may reduce diagnostic accuracy and increase the risk of unnecessary healthcare expenditures.

An integrated diagnostic algorithm is needed that combines accessible screening tools, standardized dynamometry, and stepwise instrumental confirmation. Integration of international experience with national research will facilitate the development of a sarcopenia diagnostic system consistent with the principles of evidence-based medicine and the needs of the Russian healthcare system.

AUTHOR CONTRIBUTIONS

Olga N. Tkacheva: conceptualization, manuscript editing, approval of the final version. Ekaterina N. Dudinskaya: conceptualization and design, analysis and interpretation of literature data, writing and editing of the manuscript. Yulia V. Kotovskaya, Anton V. Naumov, Natalia O. Khovasova: literature search and collection, analysis and interpretation of literature data, manuscript preparation. Kristina O. Chepygova, Bagzhat I. Isaeva: literature search and analysis. All authors approved the final version of the article.

Ethics statement. Ethics committee approval and informed consent were not required because this article is a narrative review of previously published studies and did not involve direct participation of humans or animals.

Conflict of interests. All authors declare no conflicts of interest.

Financing. The article was prepared within the framework of the state assignment “AI-based method for diagnosing sarcopenia and presarcopenia”, registration number 1025101700006-3.

Use of artificial intelligence. The AI-based tool Consensus (2026) was used for preliminary literature search and source systematization. All AI-generated outputs were reviewed and edited by the authors. AI was not used for text writing, conclusion formulation, or graphic creation. The authors are responsible for the manuscript content.