Sarcopenia: modern approaches to solving diagnosis problems

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Abstract

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.

About the authors

Anastasia K. Smorchkova

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: a.smorchkova@npcmr.ru
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

Email: alexeypetraikin@gmail.com
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

Email: d.semenov@npcmr.ru
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.
Email: d.sharova@npcmr.ru
ORCID iD: 0000-0001-5792-3912
SPIN-code: 1811-7595
Russian Federation, Moscow

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2. 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.

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3. 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.

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4. 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.

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