Sarcopenia: modern approaches to solving diagnosis problems

Cover Image

Cite item


Although sarcopenia is a relatively new diagnosis for medical statistics and the healthcare system, it represents a social and economic burden on the healthcare due to the large number of possible adverse outcomes such as increased risk of falls, physical disability, longer hospital stays, and increased mortality. No specialized medical treatment is available for sarcopenia; however, prevention and timely nonpharmacological treatment can reduce the risk of potential adverse effects. To establish the diagnosis of sarcopenia, it is necessary to confirm the decrease in not only muscle strength but also muscle mass. Instrumental diagnostics includes methods such as dual-energy X-ray absorptiometry and bioimpedance analysis. These methods can be supplemented by artificial intelligence algorithms for the automatic segmentation of muscle and fat tissue on computed tomography and magnetic resonance images, followed by calculation of the skeletal muscle index at the level of the L3 vertebra (L3SMI). Such software, when used in systems such as the Unified Radiological Information Service of the Unified Medical Information and Analytical System of Moscow, opens up opportunities for opportunistic screening. However, despite the recognition of CT and MRI as the “gold standard” by the European Working Group on Sarcopenia in Older People, there are no generally accepted L3SMI cut-off values for CT and MR diagnostics of sarcopenia. Furthermore, there is the problem of unifying the term “skeletal muscle index.” If these problems could be solved through further population studies, it will be possible to obtain a new method for the instrumental diagnosis of sarcopenia with its subsequent use for opportunistic screening.

Full Text


Increasingly more attention is currently paid to systemic diseases and conditions associated with a general change in the composition of the human body, including those due to the population aging. One of these nosologies is sarcopenia (sarcopenia, from the Greek sarx―muscles, flesh and penia―lack). This disease is included in the syndrome of senile asthenia or frailty syndrome.

The ICD-10 code for sarcopenia defined in 2016 is retained in ICD-11; the first working definition of sarcopenia was published in 2010. Therefore, this is a relatively new diagnosis for medical statistics and the health care system.

Sarcopenia is a progressive generalized skeletal muscle disease associated with an increased risk of adverse outcomes, including falls, fractures, physical disability, and mortality [1]. In geriatrics, the term “sarcopenia” implies primary sarcopenia, which is a condition characterized by a progressive generalized loss of strength, mass, and function of skeletal muscles due to aging without other causes [2].

Sarcopenia entails many negative consequences and outcomes in addition to adverse effects on the body, such as an increased risk of falls and, consequently, fractures [3] and physical disability [4]. Additionally, sarcopenia is associated with longer hospital stay and more severe outcomes in many diseases [5].

Timely diagnostics of sarcopenia and prevention of its consequences are relevant given the potential burden on the economy and health care system.

Our review aimed to assess the real significance of sarcopenia on the scale of a large metropolis (Moscow), as well as to determine methods for optimizing of diagnostics of this condition.



Older patients are most susceptible for primary sarcopenia since it is associated with aging. The literature provides data on the onset of loss of muscle tissue function as early as 50 years of age [6]. However, in a clinical context, it is customary to consider age groups of 60 years and older.

According to a number of reviews [7, 8], the prevalence of sarcopenia in the general population is 10% (inhabitants older than 60 years). In addition to gender and attitude to a particular social group [individually living (community-dwelling) older people; population of nursing homes and specialized clinics], differences in prevalence depend on the method of diagnostics of this condition, namely, tests, bioimpedancemetry, dual-energy X-ray absorptiometry (DXA), computed tomography (CT), and magnetic resonance imaging (MRI).

At the time of writing this article, no epidemiological studies with a large sample had been conducted in Russia. A study [9] was performed with a sample of 230 outpatients over the age of 65, where the prevalence of sarcopenia was estimated at 30% according to the criteria of the European Working Group on Sarcopenia in Older People (EWGSOP) and the International Working Group on Sarcopenia (IWGS), which is inconsistent with the above reviews. The discrepancies may be possibly due to the small sample size and its imbalance (95.6% of women). Therefore, the data needs to be clarified.

Impact of sarcopenia on the quality of life and health care system

Sarcopenia significantly reduces the quality of life, leading to a forced restriction of physical activity [10], which further aggravates the patient’s situation. Both a healthy population and those with acute, chronic, and oncological diseases have a negative impact [11, 12]. Patients’ quality of life is assessed using the Short Form-36 (SF-36)1 and EuroQoL (EQ-5D)2, general-purpose questionnaires, as well as using the effective specialized questionnaire SarQoL (Sarcopenia and Quality of Life) [13–16] available in Russian language [17]. When assessing the quality of life according to these questionnaires, a hundred-point system is used. The SF-36 presents a scale of values from 0 to 100, where 0 is the worst and 100 is the best quality of life. In EQ-5D, the result is also represented by a 100-point scale, but 0 implies the worst, and 100 is the best health state.

Sarcopenia not only reduces quality of life, but it also causes significant economic expenditures to the health care system. Studies on the financial costs for patients with and without sarcopenia have been conducted (median total hospitalization expenditures, average health care expenditures per person for 3 months, and costs for postoperative care for 90 days). For patients with sarcopenia, the average increase in total hospitalization expenditures can reach up to $14,322 per person [18]. It should be borne in mind that the available studies mainly consider the costs associated with hospitalization for surgical interventions (e.g., oncosurgery), use different diagnostic methods, and lack unified threshold values for confirming the diagnosis of sarcopenia [19]. Further studies using a more homogeneous sample and supplementing with other clinical scenarios (especially the analysis of outpatient expenditures) are required for more accurate estimation of the total expenditures of the health care system for patients with sarcopenia.

Treatment of sarcopenia

There is currently no unified and highly specific approach to the treatment of sarcopenia. Nevertheless, understanding the process pathophysiology enables to select possible options for therapy. For sarcopenia, there are both non-drug and drug treatments. The most effective non-drug methods are physical exercises, particularly strength exercises [20]. They currently represent the most cost-effective way not only for long-term prevention of sarcopenia, but also for maintaining and improving overall physical health [21]. Nutritional supplements, such as proteins (including whey), antioxidant agents, and long-chain polyunsaturated fatty acids, are used to improve and maintain muscle gain and strength [22]. For non-pharmacological therapy of sarcopenia, exercise is recommended because it has higher evidentiality than dietary changes [23]. Drugs, such as vitamin D [24], selective androgen receptor modulators, myostatin, and activin antagonists [25], are considered as drug therapy, but at the moment, their efficiency requires further confirmation due to more clinical studies.

Diagnostics of sarcopenia

Currently, the diagnosis of sarcopenia in clinical practice is often based on common anamnestic data, such as complaints about the symptoms and signs characteristic of the disease (falls, a feeling of weakness, slow walking speed, difficulty standing up from a sitting position) [26]. The EWGSOP recommends using the SARC-F (strength, assistance with walking, rising from a chair, climbing stairs, and falls) questionnaire [27] to objectify the diagnostics, and as screening, which considers both strength and assistance when walking, rising from a chair and stairs, and falling. However, due to the average sensitivity (25; 31.6; 50%) and high specificity (81.4; 82.4; 81.8%) of the test, according to the diagnosis criteria of EWGSOP, the Foundation for the National Institutes of Health, and the IWGS [30], respectively, the questionnaire in most cases tends to identify only severe cases of sarcopenia.

In Russia, sarcopenia is diagnosed not as a separate nosology but as a component of the senile asthenia syndrome; for primary screening, the “Age is not a problem” scale is used, and for further clarification of the diagnosis, a comprehensive geriatric assessment is used [2].

Instrumental diagnostic methods should be used for accurate diagnostics of sarcopenia. Measurement of two parameters is required, namely, muscle strength (including physical performance) and muscle mass [1]. Low muscle strength implies the possibility of sarcopenia; methods for assessing muscle mass are required to confirm the diagnosis [1].

The simplest and most accessible method to measure muscle strength is to assess grip strength using a hand dynamometer, followed by comparison of the data obtained with reference values in the population [1]. Although grip strength correlates well with overall muscle strength, different muscle groups should be considered for the most reliable results [29]. Standardized data for determining low muscle strength in carpal dynamometry are determined by gender and body mass index (BMI); for example, in men with a BMI of 24.1 to 28, the threshold value is the grip strength lower than 30 kg, and in women with a BMI of 23.1 up to 26, it is lower than 17.3 kg [2]. To assess performance, the chair rising test [2] is used, as well as the “Stand up and walk” tests, and the walking speed test included in the Short Physical Performance Battery (SPPB) can be used. When measuring walking speed, the patient is asked to walk 4 m at his usual speed, while the time of passing the distance is registered, and the speed is calculated (m/s) [30]. EWGSOP2 (2019) recommended a walking speed of 0.8 m/s or lower as the threshold for defining severe sarcopenia. In the “Stand up and walk” test, patients are asked to stand up from a chair, walk 3 m to the marker, turn around, walk back to the chair, and sit on it [31]. According to the EVGSOP2 recommendations, the threshold value of this test for the diagnosis of sarcopenia is 20 s or higher. The SPPB test is comprehensive and includes a walking speed assessment, a balance assessment, and a chair rising test. In this test, the maximum possible score is 12 points, with a score of 8 points or less indicating poor physical function [32].

To assess the human body composition and assess muscle mass, DXA, and bioimpedancemetry, bioelectrical impedance analysis are used. According to the EWGSOP, CT and MRI are the gold standard for assessing the mass and quality of muscle tissue [1]. EWGSOP recommends visualization at the level of the vertebra L3 using CT, measurement of muscle tissue in the middle third of the thigh, and assessment of muscle tissue using ultrasound [33]. Creatine dilution tests, a number of neuromuscular biomarkers, assessment of nonspecific inflammatory reactions, and hormonal and anabolic factors are used as methods for laboratory assessment of sarcopenia [1].

Methods of radiation diagnostics of sarcopenia

Radiation diagnostics of sarcopenia is based on an assessment of a decrease in the volume of muscle tissue and deterioration in its “quality.” DXA is the most commonly used method for these purposes, due to its low radiation exposure and ability to obtain reproducible results. However, it has some drawbacks and limitations, namely, the study involves the measurement of lean body mass and not isolated muscle mass, which could lead to inaccurate results in patients with increased fluid intake and/or with a large amount of fibrous tissue. It is also impossible to assess the quality of muscle tissue due to the projection type of images [32]. The method limitation is the need for studies in dynamics on a single densitometer with regular calibration [34], and studies cannot be performed opportunistically.

According to the EWGSOP, CT and MRI represent the gold standard for noninvasive assessment of the amount of muscle tissue, although these methods are not widely used due to some reasons, such as high cost of research/equipment, lack of clear diagnostic thresholds, and the need for segmentation to identify muscles and other anatomical structures [1, 32]. In contrast to DXA, CT and MRI often do not allow scanning of the entire body due to dose load (CT) or time constraints (MRI), and the study is limited to one anatomical region.

The measurement of axial slices at the level of the vertebra L3 is currently one of the most reliable and effective methods of CT assessment of muscle mass [35]. For a given anatomical location, the ratio of muscle mass to adipose tissue correlates well with the ratio for the whole body [36]. This anatomical landmark can be used to assess sarcopenia in patients regardless of BMI, while the parenchymal organs of the abdominal cavity, bone structures are the least represented on this section and make segmentation of muscle and adipose tissues less difficult [37, 38].

Diagnostics of sarcopenic obesity

Sarcopenic obesity is one of the most serious comorbidities in sarcopenia. The loss of muscle mass in sarcopenia is often (but not always) accompanied by an increase in adipose tissue. This process affects directly the quality of muscle tissue and, consequently, physical performance [39].

Similar to obesity, sarcopenia is characterized by subacute chronic proinflammation that impairs the function of muscle and adipose tissue [40]. Additional links of pathogenesis have also been identified, which determine the relationship between muscle and fat metabolism in sarcopenic obesity [41]. Thus, this comorbid condition in a patient increases the risks of adverse consequences [42].

The diagnostic capabilities of medical imaging used in sarcopenia are well suited for sarcopenic obesity; for example, DXA has been successfully used to determine body composition and tissue mass ratio (Fig. 1), while CT and MRI can visualize the area and distribution pattern of excess adipose tissues in the visceral organs and as part of the subcutaneous fat.


Fig. 1. An example of diagnostic images obtained using dual-energy X-ray absorptiometry (according to D.J. Tomlinson et al. [43]) at various body mass index (BMI) values in young (a−d) and elderly (e−h) women. Bone tissue is highlighted in blue, lean muscle tissue is highlighted in red, and adipose tissue is highlighted in yellow.


Quantitative criteria for diagnostics of sarcopenia

Total skeletal muscle mass and appendicular skeletal muscle mass (AMM) are the two parameters most commonly used to diagnose muscle loss. AMM is determined using DXA (the entire body of the patient is scanned, the upper and lower limbs, which are anatomical regions that do not contain parenchymal organs, are evaluated; Figure 1), while skeletal muscle mass is determined by bioimpedancemetry.

As a derived parameter, the skeletal muscle index (SMI) is determined. There is an issue for terminology unification, since in works by various authors, different calculation equations are used for the same concept of “musculoskeletal index” [44]. For determining it, the ratio of AMM to height (AMM/m2), the ratio of AMM to weight (AMM/kg), and the ratio of AMM to BMI (AMM/BMI) are used, depending on the adjustment for individual parameters. Each method has its advantages and disadvantages. Thus, SMI that has been adjusted for patient height (AMM/m2) has a less reliable correlation with asthenia in women and does not take into account the distribution of adipose tissue [45]. There is evidence that the detection rate of severe sarcopenia when using BMI-adjusted SMI is twice as high as that of height-adjusted SMI [44]. The ratio of AMM to height (AMM/m2) is the only SMI option with generally accepted threshold values for establishing a diagnosis of sarcopenia. They are enshrined in the consensus of the European (EWGSOP) and Asian (Asian Working Group on Sarcopenia) working groups on sarcopenia. These data, as well as quantitative criteria for establishing a diagnosis based on muscle strength, are presented in Tables 1 and 2. For other SMI options using other parameters for adjustment (BMI, weight), there are no generally accepted threshold values.


Table 1. EWGSOP2 consensus threshold quantitative indices for establishing the diagnosis of sarcopenia

Study method

Threshold for men

Threshold for women

EWGSOP2 quantitative criteria for low muscle strength

Grip strength

<27 kg

<16 kg

Chair rising test

>15 s for 5 risings

EWGSOP2 quantitative criteria for low muscle strength

Appendicular muscle mass (AMM)

<20 kg

<15 kg

AMM/height2, kg/m2

<7,0 kg/m2

<5,5 kg/m2


Table 2. AWGS consensus threshold quantitative indices for establishing the diagnosis of sarcopenia

Study method

Threshold for men

Threshold for women

AWGS quantitative criteria for low muscle strength

Grip strength

<28 kg

<18 kg

Chair rising test

≥12 s for 5 risings

AWGS quantitative criteria for low muscle mass

AMM/height2, kg/m2

<7,0 kg/m2

<5,4 kg/m2


A variant of SMI for cross-sectional instrumental diagnostics using CT and MRI is the calculation of L3 SMI according to the equation L3SMI=sh2, where L3 of SMI is the skeletal muscle index at the level of the L3 vertebra (cm2/m); S is the area of all muscle groups in the section (cm2); and h is human height (m).

Fig. 2 presents a variant of measuring the area of muscle tissue, subcutaneous adipose, and visceral adipose tissue in the section slice at the L3 level, using an artificial intelligence algorithm.


Fig. 2. An example of measuring the area (in cm2) of muscle tissue, subcutaneous adipose and visceral adipose tissue that fell into the slice at the L3 level, using the L3SEG-net artificial intelligence algorithm from the work of J. Ha et al. [46]. From left to right, subcutaneous adipose tissue is highlighted in red, skeletal muscle mass in purple, and visceral adipose tissue in green.


Despite the prospect of using and recognizing as the gold standard the assessment of sarcopenia according to CT data [1], there are still no unified threshold values for this indicator [44].

Various criteria are used to define sarcopenia by SMI defined for L3. Threshold values for sarcopenia are less than 55 cm2/m2 for men and less than 39 cm2/m2 for women [47], less than 53 cm2/m2 for BMI more than 25 and less than 43 cm2/m2 for BMI less than 25 for men, and less than 41 cm2/m2 regardless of BMI for women [48]. The lack of unification emphasizes again the need for further population studies to determine the correct L3 SMI values.

New approaches in radiation diagnostics of sarcopenia

The problem of using CT for a reliable assessment of the degree of muscle mass reduction is the need for manual segmentation of muscle tissue on a series of CT images, which is difficult with a large patient stream. However, the existing semi-automatic morphometric computer software for segmentation based on radiological density of muscle tissue (often in range from –29 to +150 Hounsfield units for muscle and from –30 to –190 Hounsfield units for fat) using masks has not yet been widely adopted. Examples of such software include Slice-O-Matic [49], AsanJ-Morphometry [50], and 3D Slicer [51].

It became possible to create software that more accurately segments various structures on CT and MRI images and has the ability to self-learn with the widespread advent of artificial intelligence technologies in medical imaging, in particular, machine learning, neural networks, and deep learning (deep learning). Despite the difficulties associated with the development and use of the technique, the literature presents relevant scientific papers covering the development and validation of machine learning segmentation algorithms, in particular, segmentation of skeletal muscles in CT studies of the abdominal organs in cancer patients [52–55]. U-Net, which was initially created for visualization in cell biology but is now successfully used in radiation diagnostics, is the most popular architecture used for model training [56]. The second most frequently used architecture is the fully connected neural network (FCNN) [57], which is applied for more general purposes and was originally created for semantic segmentation. The main parameter by which the performance of neural network segmenters is evaluated is the Dice coefficient, which indicates the percentage of correspondence of one data array to another, and in a given case, compliance of the predicted markup with the standard specified by the neural network developers. The literature now offers examples of algorithms that Dice coefficient exceeds 0.97 [58].

More and more often, researchers create complex models based on several architectures for several different subtasks, as seen, for example, in the work of J. Ha et al. [46], where the finished software uses two architectures, YOLOv3 and FCNN, where the former performs the task of searching for a specific object (in this case, the vertebra L3), and the latter performs direct segmentation. By utilizing deep learning, it is also possible to create maps that display the quality of skeletal muscle tissue based on the amount of intramuscular fat. This is presented in the work by D.W. Kim et al. [59], where the authors, based on a previously created algorithm with the FCNN architecture, created a web application that can be used to create labeled maps from CT slices at the vertebra L3 level, visually displaying the ratio of muscle tissue and intramuscular fat (Fig. 3). Currently, the main practical disadvantage of the work is the lack of appropriate boundary values or a special index that would enable to evaluate the quality of muscle tissue objectively rather than not subjectively. Nevertheless, given the importance of not only assessment of the strength and quantity, but also the quality of muscle tissue for a full-fledged diagnostics and prognosis of the course of sarcopenia, this direction is quite promising for future research.


Fig. 3. Muscle tissue quality maps obtained using an automated web-based tool (according to D.W. Kim et al. [59]). IMAT: area between-/intramuscular adipose tissue; LAMA: low density muscle tissue zone; NAMA: zone of muscle tissue of normal density; SMA: skeletal muscle tissue area; TAMA: General area of abdominal muscle tissue.


It is noteworthy that the availability of specialized software based on deep learning technologies enables not only to solve the clinical and practical task of clarifying the diagnosis of sarcopenia, but also provides the possibilities for opportunistic screening of this condition.

Possibilities of opportunistic screening

As was mentioned earlier, the specificity of calculation of the L3 SMI indicator and obtaining it through instrumental diagnostics using X-ray methods enables to perform opportunistic CT screening using automatic segmentation software. This anatomical landmark is interesting because of its frequent occurrence in CT examinations, which is partly due to the standards of providing medical care, such as in oncological diseases when CT is required to search for distant metastases. As one of the evidence, L3 SMI calculation is often available in cancer patients and is used to assess cancer cachexia and its association with the disease outcomes [47, 48, 60]. The publication of P.M. Graffy et al. can be cited as an example of successful operation of the muscle tissue segmentation algorithm on a large retrospective screening sample [61]. The authors used a sample of 8,037 asymptomatic patients who underwent abdominal CT without contrast enhancement from April 2004 and December 2016. Given the fact that healthy patients constituted the majority of the sample, in addition to validating the algorithm, the authors also managed to obtain average values for a healthy population. According to the authors, the study limitations are the use of CT images of patients from only one medical institution, as well as the unsatisfactory performance of the algorithm on CT images with artifacts from hardware, motion artifacts, as well as on CT images obtained using low-dose scanning protocols. The last limitation is typical for any segmenter algorithms. Nevertheless, despite the disadvantages, it is difficult to overestimate one of the main advantages of opportunistic screening, which is the absence of the need to perform additional X-ray studies and, consequently, the reduction in the hypothetical dose of X-ray radiation received by the patient.

An example of the successful implementation of the opportunistic screening system in the Unified Radiological Information Service of the Unified Medical Information and Analytical System of Moscow (URIS UMIAS) is the project of opportunistic screening of osteoporosis [62] and a number of other diseases and conditions (aortic aneurysms, signs of pulmonary hypertension with determination of the pulmonary trunk diameter, lung cancer, signs of stroke, etc.) as part of an experiment on the use of innovative technologies in the field of computer vision for the analysis of medical images and further application in the health care system of Moscow3 [63].

By analogy with the already implemented solution, as well as considering the experience of colleagues from other countries, we propose a possible option for organizing opportunistic screening of sarcopenia (including retrospective) with subsequent implementation in URIS UMIAS. The U-Net++ architecture can be used as a prototype for developing a tool for automatic segmentation of muscle and adipose tissue on CT images at the level of the vertebra L3 [64]. The advantage of this approach is the high values of the Dice coefficient for the neural network even with a very small size of the training sample (subcutaneous fat, 0.9706; muscles, 0.9312; intramuscular fat, 0.6465; visceral fat, 0.9609; training sample, 15 patients). The prospect of this direction includes the possibility of a rapid opportunistic assessment of the amount of muscle tissue, clarification of the boundary values for diagnosing sarcopenia, which will allow timely detection of the pathological condition, prediction and reduction of adverse outcomes in various invasive interventions, prevention of a progressive decline in the quality of life of this category of patients, and reduction the cost of health care system resources.


Sarcopenia is a disease that represents a social and economic burden on the health care system. Although sarcopenia does not have a highly specialized medical treatment, prevention and timely non-drug treatment through strength exercise can help reduce the risk of potential adverse effects. Timely diagnostics of a decrease in both muscle strength and muscle mass is required. A good addition to the already used methods of instrumental diagnostics (DXA and bioimpedancemetry) can be artificial intelligence algorithms for automatic segmentation of muscle and adipose tissue on CT and MR images with subsequent calculation of L3 SMI. Such software, when used in systems such as URIS UMIAS, provides possibility for opportunistic screening.

There are no generally accepted quantitative L3 SMI values for CT and MR diagnostics of sarcopenia, despite the recognition of EWGSOP as the gold standard for CT and MRI techniques. In addition, there is a problem of unifying the term “skeletal muscle index.” When solving these problems by means of further population studies, it will be possible to obtain a new method for instrumental diagnostics of sarcopenia with its subsequent use for screening this condition.


Funding source. This article was prepared by a group of authors as part of research (No. in the Unified State Information System for Accounting of Research, Development, and Technological Works (EGISU): АААА-А21-121012290079-2) under the Program of the Moscow Healthcare Department “Scientific Support of the Capital’s Healthcare” for 2020–2022.

Competing interests. The authors declare that they have no competing interests.

Authors’ contribution. All authors made a substantial contribution to the conception of the work, acquisition, analysis, interpretation of data for the work, drafting and revising the work, final approval of the version to be published and agree to be accountable for all aspects of the work. A.S. Smorchkova — data sources collection and analysis, manuscript preparation; A.V. Petraikin — manuscript preparation, manuscript revision; D.S. Semenov, D.E. Sharova — review concept, manuscript preparation, final editing.

1 36-Item Short Form Survey Instrument (SF-36) [Electronic resource]. Access mode: Reference date: 06/06/2022.

2 EuroQol Research Foundation. EQ-5D-5L User Guide, 2019 [Electronic resource]. Access mode: Reference date: 06/06/2022.

3 Center for Diagnostics and Telemedicine [Internet]. Catalog of AI services. AI services in radiation diagnostics. Access mode: Date of access: 08/26/2022.


About the authors

Anastasia K. Smorchkova

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

ORCID iD: 0000-0002-9766-3390
SPIN-code: 4345-8568
Scopus Author ID: 57213145638
Russian Federation, Moscow

Alexey V. Petraikin

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

ORCID iD: 0000-0003-1694-4682
SPIN-code: 6193-1656

MD, Dr. Sci. (Med.)

Russian Federation, Moscow

Dmitry S. Semenov

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

ORCID iD: 0000-0002-4293-2514
SPIN-code: 2278-7290
Scopus Author ID: 57213154475
ResearcherId: P-5228-2017
Russian Federation, Moscow

Daria E. Sharova

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Author for correspondence.
ORCID iD: 0000-0001-5792-3912
SPIN-code: 1811-7595
Russian Federation, Moscow


  1. Cruz-Jentoft AJ, Bahat G, Bauer J, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48(1):16−31. doi: 10.1093/ageing/afy169
  2. Tkacheva ON, Kotovskaya YV, Runikhina NK, et al. Clinical guidelines on Frailty. Russ J Geriatric Med. 2020;(1):11−46. (In Russ). doi: 10.37586/2686-8636-1-2020-11-46
  3. Bischoff-Ferrari HA, Orav JE, Kanis JA, et al. Comparative performance of current definitions of sarcopenia against the prospective incidence of falls among community-dwelling seniors age 65 and older. Osteoporos Int. 2015;26(12):2793–2802 doi: 10.1007/s00198-015-3194-y
  4. da Silva Alexandre T, de Oliveira Duarte YA, Ferreira Santos JL, et al. Sarcopenia according to the European working group on sarcopenia in older people (EWGSOP) versus Dynapenia as a risk factor for disability in the elderly. J Nutr Health Aging. 2014;18(5):547−553. doi: 10.1007/s12603-014-0465-9
  5. Sousa AS, Guerra RS, Fonseca I, et al. Sarcopenia and length of hospital stay. Eur J Clin Nutr. 2016;70(5):595−601. doi: 10.1038/ejcn.2015.207
  6. Faulkner JA, Larkin LM, Claflin DR, Brooks SV. Age-related changes in the structure and function of skeletal muscles. Clin Exp Pharmacol Physiol. 2007;34(11):1091−1096. doi: 10.1111/j.1440-1681.2007.04752.x
  7. Shafiee G, Keshtkar A, Soltani A, et al. Prevalence of sarcopenia in the world: a systematic review and meta-analysis of general population studies. J Diabetes Metab Disord. 2017;16:21. doi: 10.1186/s40200-017-0302-x
  8. Petermann-Rocha F, Balntzi V, Gray SR, et al. Global prevalence of sarcopenia and severe sarcopenia: a systematic review and meta-analysis. J Cachexia Sarcopenia Muscle. 2022;13(1):86−99. doi: 10.1002/jcsm.12783
  9. Safonova YA, Zotkin EG. Sarcopenia in older patients with osteoarthritis of large joints. Sci Pract Rheumatol. 2019;57(2):154−159. (In Russ). doi: 10.14412/1995-4484-2019-154-159
  10. Tsekoura M, Kastrinis A, Katsoulaki M, et al. Sarcopenia and its impact on quality of life. Adv Exp Med Biol. 2017;987:213−218. doi: 10.1007/978-3-319-57379-3_19
  11. Sepúlveda-Loyola W, Osadnik C, Phu S, et al. Diagnosis, prevalence, and clinical impact of sarcopenia in COPD: a systematic review and meta-analysis. J Cachexia Sarcopenia Muscle. 2020;11(5):1164−1176. doi: 10.1002/jcsm.12600
  12. Nipp RD, Fuchs G, El-Jawahri A, et al. Sarcopenia is associated with quality of life and depression in patients with advanced cancer. Oncologist. 2018;23(1):97−104. doi: 10.1634/theoncologist.2017-0255
  13. Beaudart C, Biver E, Reginster JY, et al. Development of a self-administrated quality of life questionnaire for sarcopenia in elderly subjects: the SarQoL. Age Ageing. 2015;44(6):960−966. doi: 10.1093/ageing/afv133
  14. Geerinck A, Bruyère O, Locquet M, et al. Evaluation of the responsiveness of the SarQoL questionnaire, a patient-reported outcome measure specific to sarcopenia. Adv Ther. 2018;35(11):1842−1858. doi: 10.1007/s12325-018-0820-z
  15. Geerinck A, Locquet M, Bruyère O, et al. Evaluating quality of life in frailty: applicability and clinimetric properties of the SarQoL questionnaire. J Cachexia Sarcopenia Muscle. 2021;12(2):319−330. doi: 10.1002/jcsm.12687
  16. Witham MD, Heslop P, Dodds RM, et al. Performance of the SarQoL quality of life tool in a UK population of older people with probable sarcopenia and implications for use in clinical trials: findings from the SarcNet registry. BMC Geriatr. 2022;22(1):368. doi: 10.1186/s12877-022-03077-5
  17. Russian translation and validation of SarQoL ― quality of life questionnaire for patients with sarcopenia. Sci Pract Rheumatol. 2019;57(1):38−45. (In Russ). doi: 10.14412/1995-4484-2019-38-45
  18. Gani F, Buettner S, Margonis GA, et al. Sarcopenia predicts costs among patients undergoing major abdominal operations. Surgery. 2016;160(5):1162−1171. doi: 10.1016/j.surg.2016.05.002
  19. Bruyère O, Beaudart C, Ethgen O, et al. The health economics burden of sarcopenia: a systematic review. Maturitas. 2019;119:61−69. doi: 10.1016/j.maturitas.2018.11.003
  20. Peterson MD, Rhea MR, Sen A, Gordon PM. Resistance exercise for muscular strength in older adults: a meta-analysis. Ageing Res Rev. 2010;9(3):226−237. doi: 10.1016/j.arr.2010.03.004
  21. McKendry J, Currier BS, Lim C, et al. Nutritional supplements to support resistance exercise in countering the sarcopenia of aging. Nutrients. 2020;12(7):2057. doi: 10.3390/nu12072057
  22. Robinson SM, Reginster JY, Rizzoli R, et al. Does nutrition play a role in the prevention and management of sarcopenia? Clin Nutr. 2018;37(4):1121−1132. doi: 10.1016/j.clnu.2017.08.016
  23. Lozano-Montoya I, Correa-Pérez A, Abraha I, et al. Nonpharmacological interventions to treat physical frailty and sarcopenia in older patients: a systematic overview ― the SENATOR Project ONTOP Series. Clin Interv Aging. 2017;12:721−740. doi: 10.2147/CIA.S132496
  24. Lappe JM, Binkley N. Vitamin D and sarcopenia/falls. J Clin Densitom. 2015;18(4):478−482. doi: 10.1016/j.jocd.2015.04.015
  25. Rooks D, Roubenoff R. Development of pharmacotherapies for the treatment of sarcopenia. J Frailty Aging. 2019;8(3):120−130. doi: 10.14283/jfa.2019.11
  26. Morley JE, Abbatecola AM, Argiles JM, et al. Sarcopenia with limited mobility: an international consensus. J Am Med Dir Assoc. 2011;12(6):403−409. doi: 10.1016/j.jamda.2011.04.014
  27. Malmstrom TK, Miller DK, Simonsick EM, et al. SARC-F: a symptom score to predict persons with sarcopenia at risk for poor functional outcomes. J Cachexia Sarcopenia Muscle. 2016;7(1):28−36. doi: 10.1002/jcsm.12048
  28. Bahat G, Yilmaz O, Kiliç C, et al. Performance of SARC-F in regard to sarcopenia definitions, muscle mass and functional measures. J Nutr Health Aging. 2018;22(8):898−903. doi: 10.1007/s12603-018-1067-8
  29. Porto JM, Nakaishi AP, Cangussu-Oliveira LM, et al. Relationship between grip strength and global muscle strength in community-dwelling older people. Arch Gerontol Geriatr. 2019;82:273−278. doi: 10.1016/j.archger.2019.03.005
  30. Maggio M, Ceda GP, Ticinesi A, et al. Instrumental and non-instrumental evaluation of 4-meter walking speed in older individuals. PLoS One. 2016;11(4):e0153583. doi: 10.1371/journal.pone.0153583
  31. Podsiadlo D, Richardson S. The timed “Up & go”: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc. 1991;39(2):142−148. doi: 10.1111/j.1532-5415.1991.tb01616.x
  32. Beaudart C, McCloskey E, Bruyère O, et al. Sarcopenia in daily practice: assessment and management. BMC Geriatr. 2016;16(1):170. doi: 10.1186/s12877-016-0349-4
  33. Stringer HJ, Wilson D. The role of ultrasound as a diagnostic tool for sarcopenia. J Frailty Aging. 2018;7(4):258−261. doi: 10.14283/jfa.2018.24
  34. Petraikin AV, Smoliarchuk MY, Petryaykin FA, et al. Assessment the accuracy of densitometry measurements using DMA PP2 Phantom. Traumatol Orthopedics Russ. 2019;25(3):124−134. (In Russ). doi: 10.21823/2311-2905-2019-25-3-124-134
  35. Shen W, Punyanitya M, Wang Z, et al. Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image. J Appl Physiol (1985). 2004;97(6):2333−2338. doi: 10.1152/japplphysiol.00744.2004
  36. Mourtzakis M, Prado CM, Lieffers JR, et al. A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care. Appl Physiol Nutr Metab. 2008;33(5):997−1006. doi: 10.1139/H08-075
  37. Kim EY, Kim YS, Park I, et al. Prognostic significance of CT-determined sarcopenia in patients with small-cell lung cancer. J Thorac Oncol. 2015;10(12):1795−1799. doi: 10.1097/JTO.0000000000000690
  38. Baracos V, Kazemi-Bajestani SM. Clinical outcomes related to muscle mass in humans with cancer and catabolic illnesses. Int J Biochem Cell Biol. 2013;45(10):2302−2308. doi: 10.1016/j.biocel.2013.06.016
  39. Franceschi C, Garagnani P, Morsiani C, et al. The continuum of aging and age-related diseases: common mechanisms but different rates. Front Med (Lausanne). 2018;5:61. doi: 10.3389/fmed.2018.00061
  40. Ferrucci L, Fabbri E. Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty. Nat Rev Cardiol. 2018;15(9):505−522. doi: 10.1038/s41569-018-0064-2
  41. Zamboni M, Rubele S, Rossi AP. Sarcopenia and obesity. Curr Opin Clin Nutr Metab Care. 2019;22(1):13−19. doi: 10.1097/MCO.0000000000000519
  42. Batsis JA, Villareal DT. Sarcopenic obesity in older adults: etiology, epidemiology and treatment strategies. Nat Rev Endocrinol. 2018;14(9):513−537. doi: 10.1038/s41574-018-0062-9
  43. Tomlinson DJ, Erskine RM, Winwood K, et al. Obesity decreases both whole muscle and fascicle strength in young females but only exacerbates the aging-related whole muscle level asthenia Physiol Rep. 2014;2(6):e12030. doi: 10.14814/phy2.12030
  44. Kim KM, Jang HC, Lim S. Differences among skeletal muscle mass indices derived from height-, weight-, and body mass index-adjusted models in assessing sarcopenia. Korean J Intern Med. 2016;31(4):643−650. doi: 10.3904/kjim.2016.015
  45. Newman AB, Kupelian V, Visser M, et al. Sarcopenia: alternative definitions and associations with lower extremity function. J Am Geriatr Soc. 2003;51(11):1602−1609. doi: 10.1046/j.1532-5415.2003.51534.x
  46. Ha J, Park T, Kim HK, et al. Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography. Sci Rep. 2021;11(1):21656. doi: 10.1038/s41598-021-00161-5
  47. Prado CM, Lieffers JR, McCargar LJ, et al. Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study. Lancet Oncol. 2008;9(7):629−635. doi: 10.1016/S1470-2045(08)70153-0
  48. Martin L, Birdsell L, Macdonald N, et al. Cancer cachexia in the age of obesity: skeletal muscle depletion is a powerful prognostic factor, independent of body mass index. J Clin Oncol. 2013;31(12):1539−1547. doi: 10.1200/JCO.2012.45.2722
  49. Popuri K, Cobzas D, Esfandiari N, et al. Body composition assessment in axial ct images using fem-based automatic segmentation of skeletal muscle. IEEE Trans Med Imaging. 2016;35(2):512−520. doi: 10.1109/TMI.2015.2479252
  50. Park J, Gil JR, Shin Y, et al. Reliable and robust method for abdominal muscle mass quantification using CT/MRI: an explorative study in healthy subjects. PLoS One. 2019;14(9):e0222042. doi: 10.1371/journal.pone.0222042
  51. Fedorov A, Beichel R, Kalpathy-Cramer J, et al. 3D Slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging. 2012;30(9):1323−1341. doi: 10.1016/j.mri.2012.05.001
  52. Burns JE, Yao J, Chalhoub D, et al. A machine learning algorithm to estimate sarcopenia on abdominal CT. Acad Radiol. 2020;27(3):311−320. doi: 10.1016/j.acra.2019.03.011
  53. Blanc-Durand P, Schiratti JB, Schutte K, et al. Abdominal musculature segmentation and surface prediction from CT using deep learning for sarcopenia assessment. Diagn Interv Imaging. 2020;101(12):789−794. doi: 10.1016/j.diii.2020.04.011
  54. Graffy PM, Liu J, Pickhardt PJ, et al. Deep learning-based muscle segmentation and quantification at abdominal CT: application to a longitudinal adult screening cohort for sarcopenia assessment. Br J Radiol. 2019;92(1100):20190327. doi: 10.1259/bjr.20190327
  55. Ackermans LL, Volmer L, Wee L, et al. Deep learning automated segmentation for muscle and adipose tissue from abdominal computed tomography in polytrauma patients. Sensors (Basel). 2021;21(6):2083. doi: 10.3390/s21062083
  56. Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. Lecture Notes Computer Sci. 2015:234−241. doi: 10.1007/978-3-319-24574-4_28
  57. Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell. 2017;39(4):640−651. doi: 10.1109/tpami.2016.2572683
  58. Kanavati F, Islam S, Arain Z, et al. Fully-automated deep learning slice-based muscle estimation from CT images for sarcopenia assessment. Clin Radiol. 2022;77(5):e363−e371. doi: 10.1016/j.crad.2022.01.036
  59. Kim DW, Kim KW, Ko Y, et al. Assessment of myosteatosis on computed tomography by automatic generation of a muscle quality map using a web-based toolkit: feasibility study. JMIR Med Inform. 2020;8(10):e23049. doi: 10.2196/23049
  60. Dong X, Dan X, Yawen A, et al. Identifying sarcopenia in advanced non-small cell lung cancer patients using skeletal muscle CT radiomics and machine learning. Thorac Cancer. 2020;11(9):2650−2659. doi: 10.1111/1759-7714.13598
  61. Graffy PM, Liu J, Pickhardt PJ, et al. Deep learning-based muscle segmentation and quantification at abdominal CT: Application to a longitudinal adult screening cohort for sarcopenia assessment. Br J Radiol. 2019;92(1100):20190327. doi: 10.1259/bjr.20190327
  62. Petraikin AV, Artyukova ZR, Nizovtsova LA, et al. Analysis of the effectiveness of implementing screening of osteoporosis. Health Care Manager. 2021;(2):31−39. (In Russ). doi: 10.21045/1811-0185-2021-2-31-39
  63. Morozov SP, Vladzymyrsky AV, Ledikhova NV, et al. Moscow experiment on Computer Vision in radiology: Involvement and participation of Radiologists. Doctor Inform Tech. 2020;(4):14−23. (In Russ). doi: 10.37690/1811-0193-2020-4-14-23
  64. Senyukova OV, Pyatkovskiy SA, Petraikin AV, et al. Automatic segmentation of muscle and adipose tissue on CT images for assessing human body composition and diagnosing sarcopenia. In: Conference “Information Technologies for personalized medicine” with a block of the summer school for young scientists, November 4, 2021: collection of abstracts. Moscow; 2021. P. 41. (In Russ). doi: 10.14341/cbaipm-2021-41

Supplementary files

Supplementary Files
1. Fig. 1. An example of diagnostic images obtained using dual-energy X-ray absorptiometry (according to D.J. Tomlinson et al. [43]) at various body mass index (BMI) values in young (a−d) and elderly (e−h) women. Bone tissue is highlighted in blue, lean muscle tissue is highlighted in red, and adipose tissue is highlighted in yellow.

Download (366KB)
2. Fig. 2. An example of measuring the area (in cm2) of muscle tissue, subcutaneous adipose and visceral adipose tissue that fell into the slice at the L3 level, using the L3SEG-net artificial intelligence algorithm from the work of J. Ha et al. [46]. From left to right, subcutaneous adipose tissue is highlighted in red, skeletal muscle mass in purple, and visceral adipose tissue in green.

Download (168KB)
3. Fig. 3. Muscle tissue quality maps obtained using an automated web-based tool (according to D.W. Kim et al. [59]). IMAT: area between-/intramuscular adipose tissue; LAMA: low density muscle tissue zone; NAMA: zone of muscle tissue of normal density; SMA: skeletal muscle tissue area; TAMA: General area of abdominal muscle tissue.

Download (476KB)

Copyright (c) 2022 Eco-vector

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

СМИ зарегистрировано Федеральной службой по надзору в сфере связи, информационных технологий и массовых коммуникаций (Роскомнадзор).
Регистрационный номер и дата принятия решения о регистрации СМИ: серия ПИ № ФС 77 - 79539 от 09 ноября 2020 г.

This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies