Cited by 25Chronic Obstructive Pulmonary Disease (COPD) ResearchMuscle Physiology and DisordersNutrition and Health in Aging

Skeletal Myosteatosis Is Associated with Systemic Inflammation and a Loss of Muscle Bioenergetics in Stable COPD

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Persson, Hans Lennart, Sioutas, Apostolos, Kentson, Magnus, Jacobson, Petra, Lundberg, Peter, Dahlqvist Leinhard, Olof, Forsgren, Mikael Fredrik·2022·Journal of Inflammation Research

Abstract

Background: Common features among patients with more advanced chronic obstructive pulmonary disease (COPD) are systemic inflammation and a loss of both muscle mass and normal muscle composition. In the present study, we investigated COPD subjects to better understand how thigh muscle fat infiltration (MFI) and energy metabolism relate to each other and to clinical features of COPD with emphasis on systemic inflammation. Methods: Thirty-two Caucasians with stable COPD were investigated using questionnaires, lung function tests, blood analysis and magnetic resonance imaging (MRI) for analysis of body- and thigh muscle composition. Bioenergetics in the resting thigh muscle, expressed as the PCr/Pi ratio, were analysed using 31 phosphorus magnetic resonance spectroscopy ( 31 P-MRS). Results: Based on the combination of the MFI adjusted for sex (MFI a ) and the thigh fat-tissue free muscle volume, expressed as the deviation from the expected muscle volume of a matched virtual control group (FFMV vcg ), all COPD subjects displayed abnormally composed thigh muscles. Clinical features of increased COPD severity, including a decrease of blood oxygenation (r = − 0.44, p < 0.05) and FEV 1 /FVC ratio, reflecting airway obstruction (r = − 0.53, p < 0.01) and an increase of COPD symptoms (r = 0.37, p < 0.05) and breathing frequency at rest (r = 0.41, p < 0.05), were all associated with a raise of the PCr/Pi ratio in the thigh muscle. Increased MFI a of the thigh muscle correlated positively with markers of systemic inflammation (white blood cell count, r = 0.41, p < 0.05; fibrinogen, r = 0.44, p < 0.05), and negatively with weekly physical activity (r = − 0.40, p < 0.05) and the PCr/Pi ratio in the resting thigh muscle (r = − 0.41, p < 0.05). Conclusion: The present study implies a link between systemic inflammation, excessive MFI and a loss of bioenergetics in subjects with stable COPD. Keywords: adiposity, body composition, muscle fat infiltration, magnetic resonance imaging, 31 P-MRS, biomarkers

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  • Adipose tissue infiltration has been reported in COPD muscle, particularly in the obese sarcopenic phenotype where adipose inflammation leads to redistribution of fat to the intra-abdominal area and skeletal muscle tissue.

    “Adipose tissue infiltration has also been reported in COPD muscle, in a similar way to senescent muscle in the aging population, particularly in the obese sarcopenic phenotype where adipose inflammation leads to the redistribution of fat to the intra-abdominal area and skeletal muscle tissue [16,17]”

    From Main Pathogenic Mechanisms and Recent Advances in COPD Peripheral Skeletal Muscle Wasting (2023) · cites this paper as [16]

    The Introduction defines muscle fat infiltration in COPD, and the Discussion reports excess thigh-muscle fat infiltration and visceral adipose tissue in the COPD cohort. The paper does not establish that infiltration occurs particularly in an obese sarcopenic phenotype or that adipose inflammation causes fat to redistribute into the abdomen and muscle. Instead, it describes those fat deposits as possible sources of systemic inflammation. (LLM, conf=0.88)

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  1. [2]Maltais F, Decramer M, Casaburi R, et al. An official American Thoracic Society/European Respiratory Society statement: update on limb muscle dysfunction in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2014; 189(9): e15– 62. doi: 10.1164/rccm.201402-0373ST 24787074 PMC4098112 · DOI
  2. [3]Marquis K, Debigaré R, Lacasse Y, 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(6): 809– 813. doi: 10.1164/rccm.2107031 12231489 · DOI
  3. [5]McDonald MN, Wouters EFM, Rutten E, et al. It’s more than low BMI: prevalence of cachexia and associated mortality in COPD. Respir Res. 2019; 20(1): 100. doi: 10.1186/s12931-019-1073-3 31118043 PMC6532157 · DOI
  4. [6]Pishgar F, Shabani M, Silva TQ, et al. Quantitative analysis of adipose depots by using chest CT and associations with all-cause mortality in Chronic Obstructive Pulmonary Disease: longitudinal analysis from MESArthritis ancillary study. Radiology. 2021; 299(3): 703– 711. doi: 10.1148/radiol.2021203959 33825508 PMC8165946 · DOI
  5. [7]Vivodtzev I, Moncharmont L, Tamisier R, et al. Quadriceps muscle fat infiltration is associated with cardiometabolic risk in COPD. Clin Physiol Funct Imaging. 2018; 38(5): 788– 797. doi: 10.1111/cpf.12481 29105276 · DOI · Paper page
  6. [8]Niklasson E, Borga M, Dahlqvist Leinhard O, et al. Assessment of anterior thigh muscle size and fat infiltration using single-slice CT imaging versus automated MRI analysis in adults. Br J Radiol. 2022; 23(1133): 20211094. doi: 10.1259/bjr.20211094 PMC10993966 35195445 · DOI
  7. [9]Karlsson A, Peolsson A, Romu T, et al. The effect on precision and T1 bias comparing two flip angles when estimating muscle fat infiltration using fat-referenced chemical shift-encoded imaging. NMR Biomed. 2021; 34(11): e4581. doi: 10.1002/nbm.4581 34232549 · DOI
  8. [10]Dalle S, Koppo K. Is inflammatory signaling involved in disease-related muscle wasting? Evidence from osteoarthritis, chronic obstructive pulmonary disease and type II diabetes. Exp Gerontol. 2020; 137: 110964. doi: 10.1016/j.exger.2020.110964 32407865 · DOI
  9. [11]Jakobsson P, Jorfeldt L, Henriksson J. Metabolic enzyme activity in the quadriceps femoris muscle in patients with severe chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 1995; 151(2): 374– 377. doi: 10.1164/ajrccm.151.2.7842194 7842194 · DOI
  10. [12]Gosker HR, Zeegers MP, Wouters EF, Schols AM. Muscle fiber type shifting in the vastus lateralis of patients with COPD is associated with disease severity: a systematic review and meta-analysis. Thorax. 2007; 62(11): 944– 949. doi: 10.1136/thx.2007.078980 17526675 PMC2117111 · DOI
  11. [13]Linge J, Heymsfield SB, Dahlqvist Leinhard O. On the definition of sarcopenia in the presence of aging and obesity - initial results from UK Biobank. J Gerontol A Biol Sci Med Sci. 2020; 75(7): 1309– 1316. doi: 10.1093/gerona/glz229 31642894 PMC7302181 · DOI · Paper page
  12. [15]Lee K, Shin Y, Huh J, et al. Recent issues on body composition imaging for sarcopenia evaluation. Korean J Radiol. 2019; 20(2): 205– 217. doi: 10.3348/kjr.2018.0479 30672160 PMC6342757 · DOI
  13. [16]Coats V, Després J-P, Alméras N, et al. Ectopic adiposity and cardiometabolic health in COPD. Int J Chron Obstruct Pulmon Dis. 2018; 13: 3331– 3340. doi: 10.2147/COPD.S168963 30410322 PMC6197246 · DOI
  14. [17]Jeon Y-J, Han S, Park G-M, et al. Intramuscular and intermuscular abdominal fat infiltration in COPD. A propensity score matched study. Int J Chron Obstruct Pulmon Dis. 2021; 16: 1989– 1999. doi: 10.2147/COPD.S312888 34262269 PMC8275100 · DOI
  15. [18]Roig M, Eng JJ, MacIntyre DL, et al. Deficits in muscle strength, mass, quality, and mobility in people with chronic obstructive pulmonary disease. J Cardiopulm Rehabil Prev. 2011; 31(2): 120– 124. doi: 10.1097/HCR.0b013e3181f68ae4 21037481 PMC3326068 · DOI
  16. [19]Maddocks M, Shrikrishna D, Vitoriano S, et al. Skeletal muscle adiposity is associated with physical activity, exercise capacity and fibre shift in COPD. Eur Respir J. 2014; 44(5): 1188– 1198. doi: 10.1183/09031936.00066414 24993908 PMC4216454 · DOI
  17. [20]Robles PG, Sussman MS, Naraghi A, et al. Intramuscular fat infiltration contributes to impaired muscle function in COPD. Med Sci Sports Exerc. 2015; 47(7): 1334– 1341. doi: 10.1249/MSS.0000000000000556 25373483 · DOI
  18. [21]Martin M, Almeras N, Després J-P, et al. Ectopic fat accumulation in patients with COPD: an ECLIPSE substudy. Int J Chron Obstruct Pulmon Dis. 2017; 12: 451– 460. doi: 10.2147/COPD.S124750 28203068 PMC5293362 · DOI
  19. [22]Shields GS, Coissi GS, Jimenez-Royo P, et al. Bioenergetics and intermuscular fat in chronic obstructive pulmonary disease-associated quadriceps weakness. Muscle Nerve. 2015; 51(2): 214– 221. doi: 10.1002/mus.24289 24831173 · DOI
  20. [23]Valkovič L, Chmelík M, Krššák M. In-vivo 31P-MRS of skeletal muscle and liver: a way for non-invasive assessment of their metabolism. Anal Biochem. 2017; 529: 193– 215. doi: 10.1016/j.ab.2017.01.018 28119063 PMC5478074 · DOI
  21. [24]Wuyam B, Payen JF, Levy P, et al. Metabolism and aerobic capacity of skeletal muscle in chronic respiratory failure related to chronic obstructive pulmonary disease. Eur Respir J. 1992; 5(2): 157– 162. 1559579
  22. [25]Mannix ET, Boska MD, Galassetti P, et al. Modulation of ATP production by oxygen in obstructive lung disease as assessed by 31P-MRS. J Appl Physiol. 1995; 78(6): 2218– 2227. doi: 10.1152/jappl.1995.78.6.2218 7665421 · DOI
  23. [26]Lévy P, Wuyam B, Pépin JL, et al. Skeletal muscle abnormalities in chronic obstructive lung disease with respiratory insufficiency. Value of P31 magnetic resonance spectroscopy. Rev Mal Respir. 1997; 14(3): 183– 191. 9411595
  24. [27]van den Borst B, Gosker HR, Koster A, et al. The influence of abdominal visceral fat on inflammatory pathways and mortality risk in obstructive lung disease. Am J Clin Nutr. 2012; 96(3): 516– 526. doi: 10.3945/ajcn.112.040774 22811442 PMC3417214 · DOI
  25. [28]Addison O, Drummond MJ, LaStayo PC, et al. Intramuscular fat and inflammation differ in older adults: the impact of frailty and inactivity. J Nutr Health Aging. 2014; 18(5): 532– 538. doi: 10.1007/s12603-014-0019-1 24886741 PMC6639009 · DOI
  26. [29]Yende S, Waterer GW, Tolley EA, et al. Inflammatory markers are associated with ventilatory limitation and muscle dysfunction in obstructive lung disease in well functioning elderly subjects. Thorax. 2006; 61(1): 10– 16. doi: 10.1136/thx.2004.034181 16284220 PMC2080698 · DOI · Paper page
  27. [30]Ferrari R, Caram LMO, Faganello MM, et al. Relation between systemic inflammatory markers, peripheral muscle mass, and strength in limb muscles in stable COPD patients. Int J Chron Obstruct Pulmon Dis. 2015; 10: 1553– 1558. doi: 10.2147/COPD.S85954 26345641 PMC4531022 · DOI
  28. [31]Byun MK, Cho EN, Chang J, et al. Sarcopenia correlates with systemic inflammation in COPD. Int J Chron Obstruct Pulmon Dis. 2017; 12: 669– 675. doi: 10.2147/COPD.S130790 28255238 PMC5325093 · DOI
  29. [32]Borga M, Ahlgren A, Romu T, et al. Reproducibility and repeatability of MRI-based body composition analysis. Magn Reson Med. 2020; 10. doi: 10.1002/mrm.28360 32519807 · DOI · Paper page
  30. [33]Kentson M, Leanderson P, Jacobson P, et al. The influence of disease severity and lifestyle factors on the peak annual 25(OH)D value of COPD patients. Int J Chron Obstruct Pulmon Dis. 2018; 13: 1389– 1398. doi: 10.2147/COPD.S156121 29731626 PMC5927355 · DOI
  31. [34]Hedenström H, Malmberg P, Agarwal K. Reference values for lung function tests in females. regression equations with smoking variables. Bull Eur Physiopathol Respir. 1985; 21(6): 551– 557. 4074961
  32. [35]Hedenström H, Malmberg P, Fridriksson H. Reference values for lung function tests in men: regression equations with smoking variables. Ups J Med Sci. 1986; 91(3): 299– 310. doi: 10.3109/03009738609178670 3811032 · DOI
  33. [36]Ekelund U, Sepp H, Brage S, et al. Criterion‐related validity of the last 7‐day, short form of the international physical activity questionnaire in Swedish adults. Public Health Nutr. 2006; 9(2): 258– 265. doi: 10.1079/phn2005840 16571181 · DOI
  34. [37]Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987; 40(5): 373– 383. doi: 10.1016/0021-9681(87)90171-8 3558716 · DOI · Paper page
  35. [39]Linge J, Borga M, West J, et al. Body composition profiling in the UK Biobank Imaging Study. Obesity. 2018; 26(11): 1785– 1795. doi: 10.1002/oby.22210 29785727 PMC6220857 · DOI · Paper page
  36. [40]Borga M, Thomas EL, Romu T, et al. Validation of a fast method for quantification of intra‐abdominal and subcutaneous adipose tissue for large scale human studies. NMR Biomed. 2015; 28(12): 1747– 1753. doi: 10.1002/nbm.3432 26768490 · DOI
  37. [41]Karlsson A, Rosander J, Romu T, et al. Automatic and quantitative assessment of regional muscle volume by multi‐atlas segmentation using whole‐body water-fat MRI. J Magn Reson Imaging. 2015; 41(6): 1558– 1569. doi: 10.1002/jmri.24726 25111561 · DOI
  38. [42]West J, Dahlqvist Leinhard O, Romu T, et al. Feasibility of MR-based body composition analysis in large scale population studies. PLoS One. 2016; 11(9): e0163332. doi: 10.1371/journal.pone.0163332 27662190 PMC5035023 · DOI
  39. [43]West J, Romu T, Thorell S, et al. Precision of MRI‐based body composition measurements of postmenopausal women. PLoS One. 2018; 13(2): e0192495. doi: 10.1371/journal.pone.0192495 29415060 PMC5802932 · DOI
  40. [44]Gerdle B, Forsgren MF, Bengtsson A, et al. Decreased muscle concentrations of ATP and PCr in the quadriceps muscle of fibromyalgia patients–a 31P-MRS study. Eur J Pain. 2013; 17: 1205– 1215. doi: 10.1002/j.1532-2149.2013.00284.x 23364928 · DOI
  41. [45]Naressi A, Couturier C, Devos JM, et al. Java-based graphical user interface for the MRUI quantitation package. MAGMA. 2001; 12(2–3): 141– 152. doi: 10.1007/BF02668096 11390270 · DOI
  42. [46]Vanhamme L, van den Boogaart A, Van Huffel S. Improved method for accurate and efficient quantification of MRS data with use of prior knowledge. J Magn Reson. 1997; 129(1): 35– 43. doi: 10.1006/jmre.1997.1244 9405214 · DOI
  43. [47]Weibel ER, Hoppeler H. Exercise-induced maximal metabolic rate scales with muscle aerobic capacity. J Exp Biol. 2005; 208(9): 1635– 1644. doi: 10.1242/jeb.01548 15855395 · DOI
  44. [48]Guthrie BM, Frostick SP, Mikulis J, et al. Endurance-trained and untrained skeletal muscle bioenergetics observed with magnetic resonance spectroscopy. Can J Appl Physiol. 1996; 21: 251– 263. doi: 10.1139/h96-022 8853467 · DOI
  45. [49]Goodpaster BH, Chomentowski P, Ward BK, et al. Effects of physical activity on strength and skeletal muscle fat infiltration in older adults: a randomized controlled trial. J Appl Physiol. 2008; 105(5): 1498– 1503. doi: 10.1152/japplphysiol.90425.2008 18818386 PMC2584841 · DOI · Paper page
  46. [50]Trajanoska K, Schoufour JD, Darweesh SK, et al. Sarcopenia and its clinical correlates in the general population: the Rotterdam study. J Bone Miner Res. 2018; 33(7): 1209– 1218. doi: 10.1002/jbmr.3416 29502340 · DOI
  47. [51]Benz E, Trajanoska K, Lahousse L, et al. Sarcopenia in COPD: a systematic review and meta-analysis. Eur Respir Rev. 2019; 28(154): 190049. doi: 10.1183/16000617.0049-20195 31722892 PMC9488535 · DOI
  48. [52]Sepulveda-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 32862514 PMC7567149 · DOI
  49. [53]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 30312372 PMC6322506 · DOI · Paper page
  50. [54]Perrot L, Greil A, Boirie Y, et al. Prevalence of sarcopenia and malnutrition during acute exacerbation of COPD and after 6 months recovery. Eur J Clin Nutr. 2020; 74(11): 1556– 1564. doi: 10.1038/s41430-020-0623-636 32296123 · DOI
  51. [55]Steiner MC. Sarcopaenia in chronic obstructive pulmonary disease. Thorax. 2007; 62(2): 101– 103. doi: 10.1136/thx.2006.067009 17287301 PMC2111238 · DOI
  52. [57]Debevec T, Ganse B, Mittag U, et al. Hypoxia aggravates inactivity-related muscle wasting. Front Physiol. 2018; 9: 494. doi: 10.3389/fphys.2018.00494 29867545 PMC5962751 · DOI
  53. [58]Decramer M, de Bock V, Dom R. Functional and histologic picture of steroid-induced myopathy in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 1996; 153(6): 1958– 1964. doi: 10.1164/ajrccm.153.6.8665061 8665061 · DOI
  54. [59]Haberecht-Müller S, Krüger E, Fielitz J. Out of control: the role of the ubiquitin proteasome system in skeletal muscle during inflammation. Biomolecules. 2021; 11(9): 1327. doi: 10.3390/biom11091327 34572540 PMC8468834 · DOI · Paper page
  55. [60]Sioutas A, Vainikka LK, Kentson M, et al. Oxidant-induced autophagy and ferritin degradation contribute to epithelial-mesenchymal transition through lysosomal iron. J Inflamm Res. 2017; 10: 29– 39. doi: 10.2147/JIR.S128292 28405169 PMC5378460 · DOI
  56. [61]Persson HL, Sioutas A, Jacobson P, et al. Human lung macrophages challenged to oxidants ex vivo: lysosomal membrane sensitization is associated with inflammation and chronic airflow limitation. J Inflamm Res. 2020; 13: 925– 932. doi: 10.2147/JIR.S280419 33235481 PMC7678820 · DOI
  57. [62]Constantin-Teodosiu D, Constantin D. Molecular mechanisms of muscle fatigue. Int J Mol Sci. 2021; 22(21): 11587. doi: 10.3390/ijms222111587 34769017 PMC8584022 · DOI
  58. [63]Leermakers PA, Gosker HR. Skeletal muscle mitophagy in chronic disease: implications for muscle oxidative capacity? Curr Opin Clin Nutr Metab Care. 2016; 19(6): 427– 433. doi: 10.1097/MCO.0000000000000319 27537277 · DOI
  59. [64]van de Bool C, Gosker HR, van den Borst B, et al. Muscle quality is more impaired in sarcopenic patients with chronic obstructive pulmonary disease. J Am Med Dir Assoc. 2016; 17(5): 415– 420. doi: 10.1016/j.jamda.2015.12.094 26848065 · DOI
  60. [65]Goodpaster BH, Thaete FL, Simoneau JA, et al. Subcutaneous abdominal fat and thigh muscle composition predict insulin sensitivity independently of visceral fat. Diabetes. 1997; 46(10): 1579– 1585. doi: 10.2337/diacare.46.10.1579 9313753 · DOI · Paper page
  61. [66]Tejani S, McCoy C, Ayers CR, et al. Cardiometabolic health outcomes associated with discordant visceral and liver fat phenotypes: insights from the Dallas Heart Study and UK Biobank. Mayo Clin Proc. 2022; 97(2): 225– 237. doi: 10.1016/j.mayocp.2021.08.021 34598789 PMC8818017 · DOI · Paper page
  62. [67]Viglino D, Martin M, Piché M-E, et al. Metabolic profiles among COPD and controls in the CanCOLD population-based cohort. PLoS One. 2020; 154(4): e0231072. doi: 10.1371/journal.pone.0231072 PMC7147771 32275684 · DOI

Cited by

Sorted by citations

  • Main Pathogenic Mechanisms and Recent Advances in COPD Peripheral Skeletal Muscle Wasting

    Henrot, P., Dupin, I., Schilfarth, P., Esteves, P., Blervaque, L., Zysman, M., Gouzi, F., Hayot, M., Pomiès, P., & Berger, P. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24076454 · Cited by 85 · see citing works

    “Adipose tissue infiltration has also been reported in COPD muscle, in a similar way to senescent muscle in the aging population, particularly in the obese sarcopenic phenotype where adipose inflammation leads to the redistribution of fat to the intra-abdominal area and skeletal muscle tissue [16,17] (Figure 1).”
    Background

Authors
Persson, H. L., Sioutas, A., Kentson, M., Jacobson, P., Lundberg, P., Dahlqvist Leinhard, O., & Forsgren, M. F.
Affiliations
Linköping University (SE)
Corresponding author
Persson, Hans Lennart
Venue
Journal of Inflammation Research
Published
2022-08
Volume
Volume 15
Pages
4367-4384
ISSN
1178-7031 (ISSN-L)
OpenAlex
W4289201586
Cited by
25
References
3
Citations by year
2026: 12 · 2025: 4 · 2024: 5 · 2023: 4
FWCI
2.10
Type
journal article
Language
en
Open access
Locations
  • Journal of Inflammation Research (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • Dove Medical Press (Taylor and Francis Group) (submittedVersion, cc-by-nc)
Funders
  • Forskningsrådet i Sydöstra Sverige
  • Region Östergötland
  • Fondation pour la Recherche Médicale
  • Linköpings Universitet
  • Svenska Läkaresällskapet (SLS-368431)
  • Medical Research Council
  • Futurum - Akademin för Hälsa och Vård, Region Jönköpings läns
Topics
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Chronic Obstructive Pulmonary Disease (COPD) Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Muscle Physiology and Disorders
  • Health Sciences › Medicine › Physiology › Nutrition and Health in Aging
Keywords
chronic obstructive pulmonary disease, muscle fat infiltration, systemic inflammation, muscle energy metabolism, phosphocreatine to inorganic phosphate ratio, fibrinogen, physical activity, body composition, adiposity

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