The aim of this study was to evaluate the effect on disease severity of the quantitative measurements of pectoral muscle area (PMA), pectoral muscle index (PMI), pectoral muscle density (PMD), subcutaneous adipose tissue (SAT) and mediastinal adipose tissue (MAT) taken on thoracic computed tomography (CT) of patients with chronic obstructive pulmonary disease (COPD), according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification. A retrospective screening was made of patients diagnosed with COPD and applied with thoracic CT and respiratory function tests. For patients with obstructive findings, a record was made of height, weight, body mass index, and smoking history (packet/year). On thoracic axial CT images, the PMA, PMI, PMD, SAT, and MAT values at the aortic arch level were calculated quantitatively using OsiriX software (Pixmeo, Switzerland). The patients were grouped as A-B-C-D according to the GOLD 2018 guidelines. Then two groups were formed as mild-moderate COPD (GOLD A-B) and severe COPD (GOLD C-D). The relationship was evaluated between clinical severity and quantitative body composition values according to the GOLD classification. A total of 80 patients diagnosed with COPD were included in the study comprising 61 males and 19 females. The GOLD A-B group included 43 (53.75%) patients and the GOLD C-D group, 37 (46.25%) patients. No significant difference was determined between the two groups in respect of the PMA, PMI, and PMD values (p=0.001). A statistically significant difference was determined between the groups in respect of the SAT and MAT values (p=0.001, p=0.002, respectively). A cutoff value of <30.04 in PMD (0.964; 95%CI:0.928-1) showed the best performance in predicting the mild-moderate COPD patients (GOLD A-B) with 92% sensitivity and 93% specificity. The results of this study demonstrated that PMD showed the best quantitative body composition performance in the differentiation of mild-moderate and severe COPD disease.
Turner AM, Tamasi L, Schleich F, Hoxha M, Horvath I, Louis R, et al. Clinically relevant subgroups in COPD and asthma. Eur Respir Rev. 2015;24(136):283–298. https://doi.org/10.1183/16000617.00009014.
Hruban RH, Meziane MA, Zerhouni EA, Khouri NF, Fishman EK, Wheeler PS, et al. High resolution computed tomography of inflation-fixed lungs. Pathologic-radiologic correlation of centrilobular emphysema. Am Rev Respir Dis. 1987;136(4):935–940. https://doi.org/10.1164/ajrccm/136.4.935
Sava F, Laviolette L, Bernard S, Breton MJ, Bourbeau J, Maltais F. The impact of obesity on walking and cycling performance and response to pulmonary rehabilitation in COPD. BMC Pulm Med 2010;10:55. https://doi.org/10.1186/1471-2466-10-55
Rutten EP, Calverley PM, Casaburi R, Agusti A, Bakke P, Celli B, et al. Changes in body composition in patients with chronic obstructive Pulmonary disease: do they influence patient-related outcomes? Ann Nutr Metab. 2013;63:239–247. https://doi.org/10.1159/000353211
Schols AM, Broekhuizen R, Weling-Scheepers CA, Wouters EF. Body composition and mortality in chronic obstructive pulmonary disease. Am J Clin Nutr 2005;82:53–59. https://doi.org/10.1093/ajcn.82.1.53
Marquis K, Debigare R, Lacasse Y, LeBlanc P, Jobin J, Carrier G, et al. Midthigh muscle cross-sectional area is a better predictor of mortality than body mass index in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2002;166:809–813. https://doi.org/10.1164/rccm.2107031
Romme EA, Murchison JT, Edwards LD, van Beek E Jr, Murchison DM, Rutten EP,et al. CT-measured bone attenuation in patients with chronic obstructive pulmonary disease: relation to clinical features and outcomes. J Bone Miner Res. 2013;28:1369–1377. https://doi.org/10.1002/jbmr.1873
Diaz AA, Zhou L, Young TP, McDonald ML, Harmouche R, Ross JC, et al. Chest CT measures of muscle and adipose tissue in COPD: gender-based differences in content and in relationships with blood biomarkers. Acad Radiol. 2014;21(10):1255-1261. https://doi.org/10.1016/j.acra.2014.05.013
van den Borst B, Gosker HR, Wesseling G, de Jager W, Hellwig VA, Snepvangers FJ, et al. Low-grade adipose tissue inflammation in patients with mild-to-moderate chronic obstructive pulmonary disease. Am J Clin Nutr. 2011;94:1504–1512. https://doi.org/10.3945/ajcn.111.023911
Cho YH, Do KH, Chae EJ, Choi SH, Jo KW, Lee SO, et al. Association of Chest CT-Based Quantitative Measures of Muscle and Fat with Post-Lung Transplant Survival and Morbidity: A Single Institutional Retrospective Cohort Study in Korean Population. Korean J Radiol. 2019;20(3):522-530. https://doi.org/10.3348/kjr.2018.0241
Tanimura K, Sato S, Fuseya Y, Hasegawa K, Uemasu K, Sato A,et al. Quantitative Assessment of Erector Spinae Muscles in Patients with Chronic Obstructive Pulmonary Disease. Novel Chest Computed Tomography-derived Index for Prognosis. Ann Am Thorac Soc. 2016;13(3):334-341. https://doi.org/10.1513/AnnalsATS.201507-446OC
Ufuk F, Demirci M, Sagtas E, Akbudak IH, Ugurlu E, Sari T. The prognostic value of pneumonia severity score and pectoralis muscle Area on chest CT in adult COVID-19 patients. Eur J Radiol. 2020;131:109271. doi: 10.1016/j.ejrad.2020.109271
McDonald ML, Diaz AA, Ross JC, San Jose Estepar R, Zhou L, Regan EA, et al. Quantitative computed tomography measures of pectoralis muscle area and disease severity in chronic obstructive pulmonary disease. A cross-sectional study. Ann Am Thorac Soc. 2014;11:326–334. https://doi.org/10.1513/AnnalsATS.201307-229OC
Kim YS, Kim EY, Kang SM, Ahn HK, Kim HS. Single cross-sectional area of
pectoralis muscle by computed tomography-correlation with bioelectrical
impedance based skeletal muscle mass in healthy subjects. Clin Physiol
Funct Imaging. 2017;37:507– 511. https://doi.org/10.1111/cpf.12333
Park MJ, Cho JM, Jeon KN, Bae KS, Kim HC, Choi DS, et al. Mass and fat infiltration of intercostal muscles measured by CT histogram analysis and their correlations with COPD severity. Acad Radiol. 2014;21:711–717. https://doi.org/10.1016/j. acra.2014.02.003.
Tzeng YH, Wei J, Tsao TP, Lee YT, Lee KC, Liou HR, et al. Computed tomography-determined muscle quality rather than muscle quantity is a better determinant of prolonged hospital length of stay in patients undergoing transcatheter aortic valve implantation. Acad Radiol 2020; 27:381–388. https://doi.org/ 10.1016/j.acra.2019.05.007.
Fragala MS, Kenny AM, Kuchel GA. Muscle quality in aging: a multi-dimensional approach to muscle functioning with applications for treatment. Sport Med 2015; 45:641–658. https://doi.org/10.1007/s40279-015-0305-z.
Sjøblom B, Grønberg BH, Wentzel-Larsen T, Baracos VE, Hjermstad MJ, Aass N, et al. Skeletal muscle radiodensity is prognostic for survival in patients with advanced non-small cell lung cancer. Clin Nutr. 2016;35:1386–1393. https://doi.org/10.1016/j. clnu.2016.03.010.
Hayashi N, Ando Y, Gyawali B, Shimokata T, Maeda O, Fukaya M, et al. Low skeletal muscle density is associated with poor survival in patients who receive chemotherapy for metastatic gastric cancer. Oncol Rep. 2016;35:1727–1731. https://doi.org/ 10.3892/or.2015.4475.
Wang CW, Feng S, Covinsky KE, Hayssen H, Zhou LQ, Yeh BM, et al. A comparison of muscle function, mass, and quality in liver transplant candidates: results from the functional assessment in liver transplantation study. Transplantation. 2016;100:1692–1698. https://doi.org/10.1097/TP.0000000000001232.
Namm JP, Thakrar KH, Wang CH, Stocker SJ, Sur MD, Berlin J, et al. A semi-automated assessment of sarcopenia using psoas area and density predicts outcomes after pancreaticoduodenectomy for pancreatic malignancy. J Gastrointest Oncol. 2017;8:936–944. https://doi.org/10.21037/jgo.2017.08.09.
Kalafateli M, Karatzas A, Tsiaoussis G, Koutroumpakis E, Tselekouni P, Koukias N, et al. Muscle fat infiltration assessed by total psoas density on computed tomography predicts mortality in cirrhosis. Ann Gastroenterol. 2018;31:491–498. https://doi.org/ 10.20524/aog.2018.0256.
Ufuk F, Herek D, Yüksel D. Diagnosis of Sarcopenia in Head and Neck Computed Tomography: Cervical Muscle Mass as a Strong Indicator of Sarcopenia. Clin Exp Otorhinolaryngol. 2019;12(3):317-324. https://doi.org/10.21053/ceo.2018.01613
Kim JH, Lim S, Choi SH, Kim KM, Yoon JW, Kim KW, et al. Sarcopenia: an independent predictor of mortality in community-dwelling older Korean men. J Gerontol - Ser A Biol Sci Med Sci. 2014;69:1244–1252. https://doi.org/10.1093/gerona/glu050.
Batsis JA, Mackenzie TA, Barre LK, Lopez-Jimenez F, Bartels SJ. Sarcopenia, sarcopenic obesity and mortality in older adults: results from the National Health and Nutrition Examination Survey III. Eur J Clin Nutr. 2014;68:1001–1007. https://doi. org/10.1038/ejcn.2014.117.
Psutka SP, Carrasco A, Schmit GD, Moynagh MR, Boorjian SA, Frank I, et al. Sarcopenia in patients with bladder cancer undergoing radical cystectomy: impact on cancer-specific and all-cause mortality. Cancer. 2014;120:2910–2918. https://doi. org/10.1002/cncr.28798.
. Yoo T, Lo WD, Evans DC. Computed tomography measured psoas density predicts outcomes in trauma. Surgery. 2017;162(2):377-384. https://doi.org/10.1016/j.surg.2017.03.014
Kramer HR, Fontaine KR, Bathon JM, Giles JT. Muscle density in rheumatoid arthritis: associations with disease features and functional outcomes. Arthritis Rheum. 2012;64:2438– 2450. https://doi.org/10.1002/art.34464
Bak SH, Kwon SO, Han SS, Kim WJ. Computed tomography-derived area and density of pectoralis muscle associated disease severity and longitudinal changes in chronic obstructive pulmonary disease: a case control study. Respir Res. 2019;20:1–12. https://doi.org/10.1186/s12931-019-1191-y
Furutate R, Ishii T, Wakabayashi R, Motegi T, Yamada K, Gemma A, et al. Excessive visceral fat accumulation in advanced chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis. 2011;6:423-430. https://doi.org/10.2147/COPD.S22885
Grace J, Leader JK, Nouraie SM, Pu J, Chandra D, Zhang Y,et al. Mediastinal and Subcutaneous Chest Fat Are Differentially Associated with Emphysema Progression and Clinical Outcomes in Smokers. Respiration. 2017;94(6):501-509. https://doi.org/10.1159/000479886
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2021 Holistence Publications