Biomarkers of Body Composition

Connie Y. Chang; Leon Lenchik; Louis Blankemeier; Akshay S. Chaudhari; Robert D. Boutin

doi:10.1055/s-0043-1776430

Seminars in Musculoskeletal Radiology, Table of Contents

Semin Musculoskelet Radiol 2024; 28(01): 078-091
DOI: 10.1055/s-0043-1776430

Review Article

Biomarkers of Body Composition

Connie Y. Chang

¹Division of Musculoskeletal Imaging and Intervention, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts

,

Leon Lenchik

²Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina

,

Louis Blankemeier

³Department of Electrical Engineering, Stanford University, Stanford, California

,

Akshay S. Chaudhari

⁴Department of Radiology and of Biomedical Data Science, Stanford University School of Medicine, Stanford, California

,

Robert D. Boutin

⁵Department of Radiology, Stanford University School of Medicine, Stanford, California

› Author Affiliations

Abstract

The importance and impact of imaging biomarkers has been increasing over the past few decades. We review the relevant clinical and imaging terminology needed to understand the clinical and research applications of body composition. Imaging biomarkers of bone, muscle, and fat tissues obtained with dual-energy X-ray absorptiometry, computed tomography, magnetic resonance imaging, and ultrasonography are described.

Keywords

adipose tissue - osteoporosis - obesity - myosteatosis - sarcopenia

Full Text

References

References
1 Wright NC, Looker AC, Saag KG. et al. The recent prevalence of osteoporosis and low bone mass in the United States based on bone mineral density at the femoral neck or lumbar spine. J Bone Miner Res 2014; 29 (11) 2520-2526
2 Looker AC, Sarafrazi Isfahani N, Fan B, Shepherd JA. Trends in osteoporosis and low bone mass in older US adults, 2005-2006 through 2013-2014. Osteoporos Int 2017; 28 (06) 1979-1988
3 Fischer S, Kapinos KA, Mulcahy A, Pinto L, Hayden O, Barron R. Estimating the long-term functional burden of osteoporosis-related fractures. Osteoporos Int 2017; 28 (10) 2843-2851
4 Vidal M, Thibodaux RJ, Neira LFV, Messina OD. Osteoporosis: a clinical and pharmacological update. Clin Rheumatol 2019; 38 (02) 385-395
5 Burge R, Dawson-Hughes B, Solomon DH, Wong JB, King A, Tosteson A. Incidence and economic burden of osteoporosis-related fractures in the United States, 2005-2025. J Bone Miner Res 2007; 22 (03) 465-475
6 Vincent GK, Velkoff VA. Report Number P25–1138: The Next Four Decades: The Older Population in the United States: 2010 to 2050. Washington, DC: US Census Bureau; 2010
7 Williams SA, Daigle SG, Weiss R, Wang Y, Arora T, Curtis JR. Economic burden of osteoporosis-related fractures in the US Medicare population. Ann Pharmacother 2021; 55 (07) 821-829
8 Curry SJ, Krist AH, Owens DK. et al; US Preventive Services Task Force. Screening for Osteoporosis to prevent fractures: US Preventive Services Task Force recommendation statement. JAMA 2018; 319 (24) 2521-2531
9 Boutin RD, Houston DK, Chaudhari AS, Willis MH, Fausett CL, Lenchik L. Imaging of sarcopenia. Radiol Clin North Am 2022; 60 (04) 575-582
10 Papadopoulou SK, Tsintavis P, Potsaki P, Papandreou D. Differences in the prevalence of sarcopenia in community-dwelling, nursing home and hospitalized individuals. a systematic review and meta-analysis. J Nutr Health Aging 2020; 24 (01) 83-90
11 Pacifico J, Geerlings MAJ, Reijnierse EM, Phassouliotis C, Lim WK, Maier AB. Prevalence of sarcopenia as a comorbid disease: a systematic review and meta-analysis. Exp Gerontol 2020; 131: 110801
12 Cruz-Jentoft AJ, Bahat G, Bauer J. et al; Writing Group for the European Working Group on Sarcopenia in Older People 2 (EWGSOP2), and the Extended Group for EWGSOP2. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing 2019; 48 (01) 16-31
13 Ahn H, Kim DW, Ko Y. et al. Updated systematic review and meta-analysis on diagnostic issues and the prognostic impact of myosteatosis: a new paradigm beyond sarcopenia. Ageing Res Rev 2021; 70: 101398
14 Stierman B, Afful J, Carroll M. National Health and Nutrition Examination Survey 2017—March 2020 prepandemic data files. Development of files and prevalence estimates for selected health outcomes. June 14, 2021. Available at: https://stacks.cdc.gov/view/cdc/106273 . Accessed February 5, 2023
15 Goossens GH. The metabolic phenotype in obesity: fat mass, body fat distribution, and adipose tissue function. Obes Facts 2017; 10 (03) 207-215
16 World Health Organization. Obesity and Overweight. June 9, 2021. Available at: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight . Accessed February 9, 2023
17 Centers for Disease Control and Prevention. Defining adult overweight & obesity. Available at: https://www.cdc.gov/obesity/basics/adult-defining.html . Accessed February 9, 2023
18 Shimizu Y, Nagaya N, Isobe T. et al. Increased plasma ghrelin level in lung cancer cachexia. Clin Cancer Res 2003; 9 (02) 774-778
19 Kanis JA. FRAX Fracture Risk Assessment Tool. Available at: https://frax.shef.ac.uk/FRAX/index.aspx . Accessed February 15, 2023
20 Lewiecki EM. Osteoporosis: Clinical Evaluation. MDText.com; 2000. Available at: http://www.ncbi.nlm.nih.gov/books/NBK279049/ . Accessed February 15, 2023
21 Stuck AK, Basile G, Freystaetter G, de Godoi Rezende Costa Molino C, Lang W, Bischoff-Ferrari HA. Predictive validity of current sarcopenia definitions (EWGSOP2, SDOC, and AWGS2) for clinical outcomes: a scoping review. J Cachexia Sarcopenia Muscle 2023; 14 (01) 71-83
22 Westbury LD, Beaudart C, Bruyère O. et al; International Musculoskeletal Ageing Network. Recent sarcopenia definitions—prevalence, agreement and mortality associations among men: findings from population-based cohorts. J Cachexia Sarcopenia Muscle 2023; 14 (01) 565-575
23 Lauretani F, Russo CR, Bandinelli S. et al. Age-associated changes in skeletal muscles and their effect on mobility: an operational diagnosis of sarcopenia. J Appl Physiol 2003; 95 (05) 1851-1860
24 Bassey EJ, Short AH. A new method for measuring power output in a single leg extension: feasibility, reliability and validity. Eur J Appl Physiol Occup Physiol 1990; 60 (05) 385-390
25 Guralnik JM, Ferrucci L, Pieper CF. et al. Lower extremity function and subsequent disability: consistency across studies, predictive models, and value of gait speed alone compared with the short physical performance battery. J Gerontol A Biol Sci Med Sci 2000; 55 (04) M221-M231
26 Cesari M, Kritchevsky SB, Newman AB. et al; Health, Aging and Body Composition Study. Added value of physical performance measures in predicting adverse health-related events: results from the Health, Aging And Body Composition Study. J Am Geriatr Soc 2009; 57 (02) 251-259
27 Blundell JE, Dulloo AG, Salvador J, Frühbeck G. EASO SAB Working Group on BMI. Beyond BMI—phenotyping the obesities. Obes Facts 2014; 7 (05) 322-328
28 Rolland Y, Czerwinski S, Abellan Van Kan G. et al. Sarcopenia: its assessment, etiology, pathogenesis, consequences and future perspectives. J Nutr Health Aging 2008; 12 (07) 433-450
29 Bennett JP, Liu YE, Kelly NN. et al. Next-generation smart watches to estimate whole-body composition using bioimpedance analysis: accuracy and precision in a diverse, multiethnic sample. Am J Clin Nutr 2022; 116 (05) 1418-1429
30 Brandner CF, Tinsley GM, Graybeal AJ. Smartwatch-based bioimpedance analysis for body composition estimation: precision and agreement with a 4-compartment model. Appl Physiol Nutr Metab 2023; 48 (02) 172-182
31 Tinsley GM, Stratton MT, Harty PS. et al. Influence of acute water ingestion and prolonged standing on raw bioimpedance and subsequent body fluid and composition estimates. J Electr Bioimpedance 2022; 13 (01) 10-20
32 Camacho PM, Petak SM, Binkley N. et al. American Association of Clinical Endocrinologists/American College of Endocrinology Clinical Practice Guidelines for the diagnosis and treatment of postmenopausal osteoporosis—2020 update. Endocr Pract 2020; 26 (Suppl. 01) 1-46
33 Mazess RB, Barden HS, Bisek JP, Hanson J. Dual-energy x-ray absorptiometry for total-body and regional bone-mineral and soft-tissue composition. Am J Clin Nutr 1990; 51 (06) 1106-1112
34 Abdalla PP, Silva AM, Venturini ACR. et al. Cut-off points of appendicular lean soft tissue for identifying sarcopenia in older adults in Brazil: a cross-sectional study. Nutr Hosp 2020; 37 (02) 306-312
35 Cawthon PM, Travison TG, Manini TM. et al. Establishing the link between lean mass and grip strength cut points with mobility disability and other health outcomes: Proceedings of the Sarcopenia Definition and Outcomes Consortium Conference. J Gerontol A Biol Sci Med Sci 2020; 75 (07) 1317-1323
36 Genant HK, Cann CE, Ettinger B. et al. Quantitative computed tomography for spinal mineral assessment: current status. J Comput Assist Tomogr 1985; 9 (03) 602-604
37 Heuck AF, Block J, Glueer CC, Steiger P, Genant HK. Mild versus definite osteoporosis: comparison of bone densitometry techniques using different statistical models. J Bone Miner Res 1989; 4 (06) 891-900
38 Bolotin HH. DXA in vivo BMD methodology: an erroneous and misleading research and clinical gauge of bone mineral status, bone fragility, and bone remodelling. Bone 2007; 41 (01) 138-154
39 Mueller DK, Kutscherenko A, Bartel H, Vlassenbroek A, Ourednicek P, Erckenbrecht J. Phantom-less QCT BMD system as screening tool for osteoporosis without additional radiation. Eur J Radiol 2011; 79 (03) 375-381
40 Lenchik L, Weaver AA, Ward RJ, Boone JM, Boutin RD. Opportunistic screening for osteoporosis using computed tomography: state of the art and argument for paradigm shift. Curr Rheumatol Rep 2018; 20 (12) 74
41 Pickhardt PJ, Lee LJ, del Rio AM. et al. Simultaneous screening for osteoporosis at CT colonography: bone mineral density assessment using MDCT attenuation techniques compared with the DXA reference standard. J Bone Miner Res 2011; 26 (09) 2194-2203
42 Pan Y, Shi D, Wang H. et al. Automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening. Eur Radiol 2020; 30 (07) 4107-4116
43 Lee SJ, Binkley N, Lubner MG, Bruce RJ, Ziemlewicz TJ, Pickhardt PJ. Opportunistic screening for osteoporosis using the sagittal reconstruction from routine abdominal CT for combined assessment of vertebral fractures and density. Osteoporos Int 2016; 27 (03) 1131-1136
44 Graffy PM, Lee SJ, Ziemlewicz TJ, Pickhardt PJ. Prevalence of vertebral compression fractures on routine CT scans according to L1 trabecular attenuation: determining relevant thresholds for opportunistic osteoporosis screening. AJR Am J Roentgenol 2017; 209 (03) 491-496
45 Boutin RD, Lenchik L. Value-added opportunistic CT: insights into osteoporosis and sarcopenia. AJR Am J Roentgenol 2020; 215 (03) 582-594
46 Shepherd JA, Schousboe JT, Broy SB, Engelke K, Leslie WD. Executive Summary of the 2015 ISCD Position Development Conference on Advanced Measures from DXA and QCT: Fracture Prediction Beyond BMD. J Clin Densitom 2015; 18 (03) 274-286
47 Gausden EB, Nwachukwu BU, Schreiber JJ, Lorich DG, Lane JM. Opportunistic use of CT imaging for osteoporosis screening and bone density assessment: a qualitative systematic review. J Bone Joint Surg Am 2017; 99 (18) 1580-1590
48 Johannesdottir F, Allaire B, Bouxsein ML. Fracture prediction by computed tomography and finite element analysis: current and future perspectives. Curr Osteoporos Rep 2018; 16 (04) 411-422
49 Budoff MJ, Malpeso JM, Zeb I. et al. Measurement of phantomless thoracic bone mineral density on coronary artery calcium CT scans acquired with various CT scanner models. Radiology 2013; 267 (03) 830-836
50 Adams AL, Fischer H, Kopperdahl DL. et al. Osteoporosis and hip fracture risk from routine computed tomography scans: the Fracture, Osteoporosis, and CT Utilization Study (FOCUS). J Bone Miner Res 2018; 33 (07) 1291-1301
51 Kutleša Z, Jerković K, Ordulj I, Budimir Mršić D. The effect of contrast media on CT measures of bone mineral density: a systematic review. Skeletal Radiol 2023; 52 (04) 687-694
52 Overman RA, Farley JF, Curtis JR, Zhang J, Gourlay ML, Deal CL. DXA utilization between 2006 and 2012 in commercially insured younger postmenopausal women. J Clin Densitom 2015; 18 (02) 145-149
53 Maurovich-Horvat P, Massaro J, Fox CS, Moselewski F, O'Donnell CJ, Hoffmann U. Comparison of anthropometric, area- and volume-based assessment of abdominal subcutaneous and visceral adipose tissue volumes using multi-detector computed tomography. Int J Obes 2007; 31 (03) 500-506
54 Anjana M, Sandeep S, Deepa R, Vimaleswaran KS, Farooq S, Mohan V. Visceral and central abdominal fat and anthropometry in relation to diabetes in Asian Indians. Diabetes Care 2004; 27 (12) 2948-2953
55 Larsson B, Svärdsudd K, Welin L, Wilhelmsen L, Björntorp P, Tibblin G. Abdominal adipose tissue distribution, obesity, and risk of cardiovascular disease and death: 13 year follow up of participants in the study of men born in 1913. Br Med J (Clin Res Ed) 1984; 288 (6428) 1401-1404
56 Lapidus L, Bengtsson C, Larsson B, Pennert K, Rybo E, Sjöström L. Distribution of adipose tissue and risk of cardiovascular disease and death: a 12 year follow up of participants in the population study of women in Gothenburg, Sweden. Br Med J (Clin Res Ed) 1984; 289 (6454) 1257-1261
57 Peiris AN, Sothmann MS, Hoffmann RG. et al. Adiposity, fat distribution, and cardiovascular risk. Ann Intern Med 1989; 110 (11) 867-872
58 Veld J, Vossen JA, De Amorim Bernstein K, Halpern EF, Torriani M, Bredella MA. Adipose tissue and muscle attenuation as novel biomarkers predicting mortality in patients with extremity sarcomas. Eur Radiol 2016; 26 (12) 4649-4655
59 De Amorim Bernstein K, Bos SA, Veld J, Lozano-Calderon SA, Torriani M, Bredella MA. Body composition predictors of therapy response in patients with primary extremity soft tissue sarcomas. Acta Radiol 2018; 59 (04) 478-484
60 Neeland IJ, Ross R, Després JP. et al; International Atherosclerosis Society, International Chair on Cardiometabolic Risk Working Group on Visceral Obesity. Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement. Lancet Diabetes Endocrinol 2019; 7 (09) 715-725
61 Engelke K, Museyko O, Wang L, Laredo JD. Quantitative analysis of skeletal muscle by computed tomography imaging—state of the art. J Orthop Translat 2018; 15: 91-103
62 O'Leary MF, Wallace GR, Davis ET. et al. Obese subcutaneous adipose tissue impairs human myogenesis, particularly in old skeletal muscle, via resistin-mediated activation of NFκB. Sci Rep 2018; 8 (01) 15360
63 Lowell BB, Shulman GI. Mitochondrial dysfunction and type 2 diabetes. Science 2005; 307 (5708) 384-387
64 Hamrick MW, McGee-Lawrence ME, Frechette DM. Fatty infiltration of skeletal muscle: mechanisms and comparisons with bone marrow adiposity. Front Endocrinol (Lausanne) 2016; 7: 69
65 Penton CM, Thomas-Ahner JM, Johnson EK, McAllister C, Montanaro F. Muscle side population cells from dystrophic or injured muscle adopt a fibro-adipogenic fate. PLoS One 2013; 8 (01) e54553
66 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
67 Amini B, Boyle SP, Boutin RD, Lenchik L. Approaches to assessment of muscle mass and myosteatosis on computed tomography: a systematic review. J Gerontol A Biol Sci Med Sci 2019; 74 (10) 1671-1678
68 Morsbach F, Zhang YH, Martin L, Lindqvist C, Brismar T. Body composition evaluation with computed tomography: contrast media and slice thickness cause methodological errors. Nutrition 2019; 59: 50-55
69 Boutin RD, Katz JR, Chaudhari AJ. et al. Association of adipose tissue and skeletal muscle metrics with overall survival and postoperative complications in soft tissue sarcoma patients: an opportunistic study using computed tomography. Quant Imaging Med Surg 2020; 10 (08) 1580-1589
70 Boutin RD, Bamrungchart S, Bateni CP. et al. CT of patients with hip fracture: muscle size and attenuation help predict mortality. AJR Am J Roentgenol 2017; 208 (06) W208-W215
71 Lenchik L, Barnard R, Boutin RD. et al. Automated muscle measurement on chest CT predicts all-cause mortality in older adults from the National Lung Screening Trial. J Gerontol A Biol Sci Med Sci 2021; 76 (02) 277-285
72 Phan EN, Thorpe SW, Wong FS. et al. Opportunistic muscle measurements on staging chest CT for extremity and truncal soft tissue sarcoma are associated with survival. J Surg Oncol 2020; 122 (05) 869-876
73 Koitka S, Kroll L, Malamutmann E, Oezcelik A, Nensa F. Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks. Eur Radiol 2021; 31 (04) 1795-1804
74 Boutin R, Barnard R, Tan J, Kim J, Chaudhari A, Lenchik L. Opportunistic CT assessment of biological aging: comparing 2D vs. 3D metrics for muscle and adipose tissue. Paper presented at: Radiological Society of North America; November 28–December 2, 2021; Chicago, IL
75 Anyene I, Caan B, Williams GR. et al. Body composition from single versus multi-slice abdominal computed tomography: concordance and associations with colorectal cancer survival. J Cachexia Sarcopenia Muscle 2022; 13 (06) 2974-2984
76 Bedrikovetski S, Seow W, Kroon HM, Traeger L, Moore JW, Sammour T. Artificial intelligence for body composition and sarcopenia evaluation on computed tomography: a systematic review and meta-analysis. Eur J Radiol 2022; 149: 110218
77 Summers RM, Baecher N, Yao J. et al. Feasibility of simultaneous computed tomographic colonography and fully automated bone mineral densitometry in a single examination. J Comput Assist Tomogr 2011; 35 (02) 212-216
78 Jang S, Graffy PM, Ziemlewicz TJ, Lee SJ, Summers RM, Pickhardt PJ. Opportunistic osteoporosis screening at routine abdominal and thoracic CT: normative L1 trabecular attenuation values in more than 20 000 adults. Radiology 2019; 291 (02) 360-367
79 Pickhardt PJ, Nguyen T, Perez AA. et al. Improved CT-based osteoporosis assessment with a fully automated deep learning tool. Radiol Artif Intell 2022; 4 (05) e220042
80 Bridge CP, Best TD, Wrobel MM. et al. A fully automated deep learning pipeline for multi-vertebral level quantification and characterization of muscle and adipose tissue on chest CT scans. Radiol Artif Intell 2022; 4 (01) e210080
81 Pu L, Gezer NS, Ashraf SF, Ocak I, Dresser DE, Dhupar R. Automated segmentation of five different body tissues on computed tomography using deep learning. Med Phys 2023; 50 (01) 178-191
82 Perez AA, Pickhardt PJ, Elton DC, Sandfort V, Summers RM. Fully automated CT imaging biomarkers of bone, muscle, and fat: correcting for the effect of intravenous contrast. Abdom Radiol (NY) 2021; 46 (03) 1229-1235
83 Wasserthal J, Breit HC, Meyer MT. et al. TotalSegmentator: robust segmentation of 104 anatomic structures in CT images. Radiol Artif Intell 2023; 5 (05) e230024
84 Blankemeier L, Desai A, Chaves JM. et al. Comp2comp: Open-source body composition assessment on computed tomography. arXiv:2302.06568. Abstract available at: https://arxiv.org/abs/2302.06568 . Accessed October 13, 2023
85 Jones BC, Lee H, Cheng CC. et al. MRI quantification of cortical bone porosity, mineralization, and morphologic structure in postmenopausal osteoporosis. Radiology 2023; 307 (02) e221810
86 Chang G, Honig S, Brown R. et al. Finite element analysis applied to 3-T MR imaging of proximal femur microarchitecture: lower bone strength in patients with fragility fractures compared with control subjects. Radiology 2014; 272 (02) 464-474
87 Bae WC. Advances and shortfalls in MRI evaluation of osteoporosis. Radiology 2023; 307 (02) e223144
88 Bao H, Moal B, Vira S. et al. Spino-femoral muscles affect sagittal alignment and compensatory recruitment: a new look into soft tissues in adult spinal deformity. Eur Spine J 2020; 29 (12) 2998-3005
89 Medina G, Buckless CG, Thomasson E, Oh LS, Torriani M. Deep learning method for segmentation of rotator cuff muscles on MR images. Skeletal Radiol 2021; 50 (04) 683-692
90 Codari M, Zanardo M, di Sabato ME. et al. MRI-derived biomarkers related to sarcopenia: a systematic review. J Magn Reson Imaging 2020; 51 (04) 1117-1127
91 Shenvi SD, Taber DJ, Hardie AD, Botstein JO, McGillicuddy JW. Assessment of magnetic resonance imaging derived fat fraction as a sensitive and reliable predictor of myosteatosis in liver transplant recipients. HPB (Oxford) 2020; 22 (01) 102-108
92 van Dijk DPJ, Bakers FCH, Sanduleanu S. et al. Myosteatosis predicts survival after surgery for periampullary cancer: a novel method using MRI. HPB (Oxford) 2018; 20 (08) 715-720
93 Spooren CEGM, Lodewick TM, Beelen EMJ. et al. The reproducibility of skeletal muscle signal intensity on routine magnetic resonance imaging in Crohn's disease. J Gastroenterol Hepatol 2020; 35 (11) 1902-1908
94 Dai L, Huang XY, Lu YQ. et al. Defining reference values for body composition indices by magnetic resonance imaging in UK Biobank. J Cachexia Sarcopenia Muscle 2023; 14 (02) 992-1002
95 Lee SY, Gallagher D. Assessment methods in human body composition. Curr Opin Clin Nutr Metab Care 2008; 11 (05) 566-572
96 Baum T, Cordes C, Dieckmeyer M. et al. MR-based assessment of body fat distribution and characteristics. Eur J Radiol 2016; 85 (08) 1512-1518
97 Moal B, Bronsard N, Raya JG. et al. Volume and fat infiltration of spino-pelvic musculature in adults with spinal deformity. World J Orthop 2015; 6 (09) 727-737
98 Ogunleye OA, Raviprakash H, Simmons AM. et al. A combined region- and pixel-based deep learning approach for quantifying abdominal adipose tissue in adolescents using Dixon magnetic resonance imaging. Tomography 2023; 9 (01) 139-149
99 Wang JC, Wu WT, Chang KV. et al. Ultrasound imaging for the diagnosis and evaluation of sarcopenia: an umbrella review. Life (Basel) 2021; 12 (01) 9
100 Mah JK, van Alfen N. Neuromuscular ultrasound: clinical applications and diagnostic values. Can J Neurol Sci 2018; 45 (06) 605-619
101 van Alfen N, Mah JK. Neuromuscular ultrasound: a new tool in your toolbox. Can J Neurol Sci 2018; 45 (05) 504-515
102 Abe T, Patterson KM, Stover CD. et al. Site-specific thigh muscle loss as an independent phenomenon for age-related muscle loss in middle-aged and older men and women. Age (Dordr) 2014; 36 (03) 9634
103 Perkisas S, Bastijns S, Baudry S. et al. Application of ultrasound for muscle assessment in sarcopenia: 2020 SARCUS update. Eur Geriatr Med 2021; 12 (01) 45-59
104 Ticinesi A, Meschi T, Narici MV, Lauretani F, Maggio M. Muscle ultrasound and sarcopenia in older individuals: a clinical perspective. J Am Med Dir Assoc 2017; 18 (04) 290-300
105 Fu H, Wang L, Zhang W, Lu J, Yang M. Diagnostic test accuracy of ultrasound for sarcopenia diagnosis: a systematic review and meta-analysis. J Cachexia Sarcopenia Muscle 2023; 14 (01) 57-70
106 Khanna I, Pietro R, Ali Y. What has dual energy CT taught us about gout?. Curr Rheumatol Rep 2021; 23 (09) 71