Computed tomography based morphometry of muscle and adipose tissue in a generally healthy Moscow population: a cross-sectional study

Introduction: Alterations in body composition (muscle and adipose tissue) can influence clinical outcomes of underlying diseases. Comprehensive assessment of body composition via instrumental diagnostics includes bioelectrical impedance analysis, dual-energy X-ray absorptiometry, computed tomography (CT), and magnetic resonance imaging (MRI). Although CT and MRI are considered gold-standard methods, their use is limited by challenges in manual segmentation and the lack of universally accepted diagnostic thresholds. Artificial intelligence (AI) can fully automate this process and facilitate the calculation of diagnostic threshold values in cross-sectional population studies.

Objective: To obtain data on the sex and age distribution of morphometric parameters of muscle and adipose tissue, assessed using AI-based software on CT scans capturing the region of interest (L3 vertebra level), in a generally healthy population in Moscow.

Materials and methods: A retrospective, observational, cross-sectional descriptive study was conducted across 65 medical institutions of the Moscow Department of Healthcare. CT data from generally healthy patients were selected through a multistage process, including assessment of anamnestic data. Participants were grouped by sex and age. Native CT images were analyzed using AI-based software for segmentation and calculation of the following parameters: skeletal muscle area (SMA), skeletal muscle index (SMI), radiological density of skeletal muscle (RDSM), subcutaneous adipose tissue area (SATA), visceral adipose tissue area (VATA), intramuscular adipose tissue area (IMATA), and its proportion (PIMAT). For each parameter, quantile regression models were constructed (5th, 25th, 50th, 75th, and 95th percentiles) with 95% confidence intervals. Statistics: Cut-off values for morphometric parameters were determined using a quantile regression model. T-test, one-way ANOVA, and Mann-Whitney tests were used for comparative analysis. Statistical analysis was performed using jamovi version 2.3.28 and RStudio 2024.12.1.

Results: After multi-stage analysis of CT images, text of their corresponding reports and electronic medical records according to inclusion and exclusion criteria, the final sample consisted of 900 patients (579 women (64.3%) and 321 men (35.7%)). SMA and SMI showed similar trends: a gradual increase to peak values followed by a decline, with minima in the «80 years and older» group. SMA, SMI, and RDSM were statistically significantly higher in men (p<0.001) and differed across age groups (p<0.001). Median peak SMA values: men — 41 years (168.77 cm2), women — 41 years (115.17 cm2). Median peak SMI values: men — 47 years (53.66 cm2/m2), women — 48 years (41.95 cm2/m2). SATA and VATA increased to peak values, with minima in the «18–29 years» group. SATA, VATA, IMATA, and PIMAT differed statistically significantly by sex (p=0.001 for PIMAT, p<0.001 for others) and age (p<0.001). Median peak SATA values: men — 63 years (177.93 cm2), women — 62 years (226.97 cm2). Median peak VATA values: men — 71 years (233.97 cm2), women — 71 years (169.26 cm2).

Discussion: Representative population data on the sex and age distribution of body composition morphometric parameters were obtained for conditionally healthy men and women in Moscow using AI-based software. These results enable further research on body composition changes in various pathologies and the diagnosis of sarcopenia through opportunistic analysis of previously performed CT scans including the region of interest. The observed distribution patterns of both muscle and adipose tissue parameters align with data from similar population studies in the literature. However, SMI values in the Moscow population were lower than those reported for american and dutch cohorts (statistically significant for women). These identified differences underscore the value of the obtained results for the specific population.

Conclusion: This study presents the first population data on the sex and age distribution of body composition morphometric parameters from CT images for Moscow, obtained using AI-based software. All parameters showed statistically significant differences across sex and age groups, with higher values of muscle and visceral adipose tissue in men, and higher subcutaneous and intramuscular adipose tissue in women. The lower SMI in the Moscow population compared to American and Dutch cohorts highlights the relevance of these data for CT-based sarcopenia diagnostics in the European part of Russia.

1. Dixon C.B., Masteller B., Andreacci J.L. The effect of a meal on measures of impedance and percent body fat estimated using contact-electrode bioelectrical impedance technology // European Journal of Clinical Nutrition. 2013. Vol. 67, No. 9. P. 950–955. doi: https://doi.org/10.1038/ejcn.2013.118.

2. Mourtzakis M., Prado C.M.M., Lieffers J.R. et al. A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care // Applied Physiology, Nutrition, and Metabolism. 2008. Vol. 33, No. 5. P. 997–1006. doi: https://doi.org/10.1139/H08-075.

3. Cruz-Jentoft A.J., Bahat G., Bauer J. et al. Sarcopenia: revised European consensus on definition and diagnosis // Age and Ageing. 2019. Vol. 48. Sarcopenia, No. 1. P. 16–31. doi: https://doi.org/10.1093/ageing/afy169.

4. Khoruzhaya A.N., Arzamasov K.M., Kodenko M.R. et al. Diagnosis of Intracranial Hemorrhages Based on Brain Computed Tomography with Artificial Intelligence. Digital Diagnostics, 2025, Vol. 6, No. 2, рр. 214–228 (In Russ.). doi: https://doi.org/10.17816/DD645364.

5. Vasilev Y.A., Nanova O.G., Vladzymyrskyy A.V. et al. Use of Artificial Intelligence Technologies in Laboratory Medicine, their Effectiveness and Application Scenarios: A Systematic Review. Digital Diagnostics, 2025, Vol. 6, No. 2, рр. 251–267 (In Russ.). doi: https://doi.org/10.17816/DD635349.

6. Garrett J.W., Pickhardt P.J., Summers R.M. Methodology for a fully automated pipeline of AI-based body composition tools for abdominal CT // Abdominal Radiology. 2025. doi: https://doi.org/10.1007/s00261-025-04951-7.

7. Lee M.W., Jeon S.K., Paik W.H. et al. Prognostic value of initial and longitudinal changes in body composition in metastatic pancreatic cancer // Journal of Cachexia, Sarcopenia and Muscle. 2024. Vol. 15, No. 2. P. 735–745. doi: https://doi.org/10.1002/jcsm.13437

8. Lyadov V.K., Fedorinov D.S., Statsenko Y.A. et al. Cachexia and sarcopenia as predictive factors in patients with gastric cancer: observational study. Moscow Surgical Journal, 2024, No. 1, рр. 61–69 (In Russ.). doi: https://doi.org/10.17238/2072–3180–2024-1-61-69.

9. Neeshpapa A.G., Karetnikova V.N., Krivoshapova K.E. et al. Dynapenia and presarcopenia in patients with coronary atherosclerosis: clinical predictors and cardiac structural and functional features. Russian Journal of Cardiology, 2024, Vol. 30, No. 1, рр. 5967, рр. 5967 (In Russ.). doi: https://doi.org/10.15829/1560-4071-2025-5967.

10. Smorchkova A.K., Vasilev Yu.A., Shumskaya Yu.F. et al. Automated assessment of muscle and adipose tissue with computed tomography data for noninvasive determination of inflammatory bowel diseases activity: pilot study. Vestnik SurGU. Meditsina, 2025, Vol. 18, No. 3. P. 39–49 (In Russ.). doi: https://doi.org/10.35266/2949-3447-2025-3-5.

11. Jenkins D.G., Quintana-Ascencio P.F. A solution to minimum sample size for regressions // PLoS One. 2020. Vol. 15, No. 2. P. e0229345. doi: https://doi.org/10.1371/journal.pone.0229345.

12. Weston A.D., Grossardt B.R., Garner H.W. et al. Abdominal Body Composition Reference Ranges and Association With Chronic Conditions in an Age- and SexStratified Representative Sample of a Geographically Defined American Population // The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences. 2024. Vol. 79, No. 4. P. glae055. doi: https://doi.org/10.1093/gerona/glae055.

13. Tonnesen P.E., Mercaldo N.D., Tahir I. et al. Muscle Reference Values From Thoracic and Abdominal CT for Sarcopenia Assessment: The Framingham Heart Study // Investigative Radiology. 2024. Vol. 59, No. 3. P. 259–270. doi: https://doi.org/10.1097/RLI.0000000000001012.

14. Yoon J.K., Lee S., Kim K.W. et al. Reference Values for Skeletal Muscle Mass at the Third Lumbar Vertebral Level Measured by Computed Tomography in a Healthy Korean Population // Endocrinology and Metabolism. 2021. Vol. 36, No. 3. P. 672–677. doi: https://doi.org/10.3803/EnM.2021.1041.

15. Coelho-Júnior H.J., Marques F.L., Sousa C.V. et al. Age- and sex-specific normative values for muscle mass parameters in 18,625 Brazilian adults // Frontiers in Public Health. 2024. Vol. 11. P. 1287994. doi: https://doi.org/10.3389/fpubh.2023.1287994.

16. Raguso C.A., Kyle U., Kossovsky M.P. et al. A 3-year longitudinal study on body composition changes in the elderly: Role of physical exercise // Clinical Nutrition. 2006. Vol. 25, No. 4. P. 573–580. doi: https://doi.org/10.1016/j.clnu.2005.10.013.

17. Westenberg L.B., Zorgdrager M., Swaab T.D.A. et al. Reference values for low muscle mass and myosteatosis using tomographic muscle measurements in living kidney donors // Scientific Reports. 2023. Vol. 13, No. 1. P. 5835. doi: https://doi.org/10.1038/s41598-023-33041-1