Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Sociodemographic and clinical factors associated with low muscle mass and composition in people treated with (chemo)radiotherapy for lung cancer

European Journal of Clinical Nutrition volume 79, pages 369–378 (2025)Cite this article

Subjects

Abstract

Background

This study examined (1) associations between sociodemographic and clinical variables with low muscle mass and radiodensity and their loss relative to treatment commencement in patients with lung cancer; and (2) the magnitude of change in muscle mass and association with treatment outcomes and survival.

Methods

Prospective study in patients planned for curative (chemo)radiotherapy for lung cancer. Low skeletal muscle mass and radiodensity and muscle loss were determined from pre- and post-treatment computed tomography images. Sociodemographic, clinical, functional, nutritional, physical activity and alternate body composition were assessed pre-treatment. Logistic and linear regression and Fisher’s exact tests were used to assess associations between variables and study outcomes. Cox proportional hazards models were fitted to examine associations with survival.

Results

Overall, 53 patients (62.3% male) with a mean age of 69 ± 9.3 years and 54.8% with stage III disease were included. Pre-treatment low calf circumference was associated with pre-treatment low muscle mass (p = 0.006). Higher comorbidity scores pre-treatment were associated with normal muscle radiodensity pre- and post-treatment (p = 0.015, p = 0.027, respectively). Pre-treatment low energy and protein intake were associated with low muscle radiodensity post-treatment. Muscle mass and radiodensity were not associated with survival or treatment outcomes.

Conclusions

In patients with lung cancer, there is some evidence anthropometric measures of muscle mass are suggestive of low muscle mass pre-radiotherapy, while low energy intake pre-treatment may indicate low muscle radiodensity after treatment. However, these findings are limited by the small sample size and further prospective studies with larger samples are required.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Candidate predictor variables for the outcomes pre-treatment low skeletal muscle index (SMI) or low skeletal muscle density (SMD), post-treatment low SMI or low SMD and muscle loss relative to baseline in people treated with radiotherapy with or without chemotherapy for lung cancer.
Fig. 2

Similar content being viewed by others

Data availability

The data generated and analysed during this study can be found within the published article and its supplementary files.

References

  1. International Agency for Research on Cancer. Globocan World Fact Sheet: World Health Organisation; 2021 Available from: https://www.google.com/search?client=firefox-b-d&q=globocan.

  2. Baracos V, Reiman T, Mourtzakis M, Gioulbasanis I, Antoun S. Body composition in patients with non-small cell lung cancer: a contemporary view of cancer cachexia with the use of computed tomography image analysis. Am J Clin Nutr. 2010;91:1133S–7S.

    Article  CAS  PubMed  Google Scholar 

  3. Cederholm T, Jensen GL, Correia M, Gonzalez MC, Fukushima R, Higashiguchi T, et al. GLIM criteria for the diagnosis of malnutrition—a consensus report from the global clinical nutrition community. J Cachexia Sarcopenia Muscle. 2019;10:207–17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Lin T-Y, Chen Y-F, Wu W-T, Han D-S, Tsai IC, Chang K-V, et al. Impact of sarcopenia on the prognosis and treatment of lung cancer: an umbrella review. Discov Oncol. 2022;13:115.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Kiss NK, Denehy L, Edbrooke L, Prado CM, Ball D, Siva S, et al. Predicting muscle loss during lung cancer treatment (PREDICT): protocol for a mixed methods prospective study. BMJ Open. 2021;11:e051665.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373–83.

    Article  CAS  PubMed  Google Scholar 

  7. Prado CMM, Lieffers JR, McCargar LJ, Reiman T, Sawyer MB, Martin L, et al. Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study. Lancet Oncol. 2008;9:629–35.

    Article  PubMed  Google Scholar 

  8. Martin L, Birdsell L, MacDonald N, Reiman T, Clandinin MT, McCargar LJ, et al. Cancer cachexia in the age of obesity: skeletal muscle depletion is a powerful prognostic factor, independent of body mass index. J Clin Oncol. 2013;31:1539–47.

    Article  PubMed  Google Scholar 

  9. Ottery F. Definition of standardized nutritional assessment and interventional pathways in oncology. Nutrition. 1996;12:S15–S9.

    Article  CAS  PubMed  Google Scholar 

  10. Malmstrom TK, Morley JE. SARC-F: a simple questionnaire to rapidly diagnose sarcopenia. J Am Med Dir Assoc. 2013;14:531–2.

    Article  PubMed  Google Scholar 

  11. Kaysen GA, Zhu F, Sarkar S, Heymsfield SB, Wong J, Kaitwatcharachai C, et al. Estimation of total-body and limb muscle mass in hemodialysis patients by using multifrequency bioimpedance spectroscopy2. Am J Clin Nutr. 2005;82:988–95.

    Article  CAS  PubMed  Google Scholar 

  12. Schectman O, Sindhu BS. Grip assessment. In: American Society of Hand Therapists, editor. Clinical assessment recommendations. Third ed. Mount Laurel, NJ: American Society of Hand Therapists; 2015.

  13. Simonsick EM, Guralnik JM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG, et al. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol. 1994;49:M85–M94.

    Article  PubMed  Google Scholar 

  14. Gonzalez MC, Mehrnezhad A, Razaviarab N, Barbosa-Silva TG, Heymsfield SB. Calf circumference: cutoff values from the NHANES 1999-2006. Am J Clin Nutr. 2021;113:1679–87.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. National Cancer Institute. Dietary Assessment Primer: National Cancer Institute; Available from: https://dietassessmentprimer.cancer.gov/profiles/table.html.

  16. Thompson FE, Subar AF. Dietary assessment methodology. In: Coulston AM, Boushey CJ, Ferruzzi MG, Delahanty LM, editors. Nutrition in the prevention and treatment of disease (Fourth Edition): Academic Press; 2017.

  17. National Institutes of Health, National Cancer Institute. Division of Cancer Control & Population Sciences ASA24-Australia [August 2023]. Available from: https://epi.grants.cancer.gov/asa24/respondent/australia.html.

  18. Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35:1381–95.

    Article  PubMed  Google Scholar 

  19. Butt Z, Webster K, Eisenstein A, Beaumont J, Eton DT, Masters G, et al. Quality of life in lung cancer: the validity and cross-cultural applicability of the functional assessment of cancer therapy—lung scale. Hematol Oncol Clin North Am. 2005;19:389–420.

    Article  PubMed  Google Scholar 

  20. Cella DF, Tulsky DS, Gray G, Sarafian B, Linn E, Bonomi A, et al. The functional assessment of cancer therapy scale: development and validation of the general measure. J Clin Oncol. 1993;11:570–9.

    Article  CAS  PubMed  Google Scholar 

  21. Asaad M, Habibullah NK, Butler CE. The impact of COVID-19 on clinical trials. Ann Surg. 2020;272:e222–e3.

    Article  PubMed  Google Scholar 

  22. Shen W, Punyanitya M, Wang Z, Gallagher D, St-Onge MP, Albu J, et al. Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image. J Appl Physiol 2004;97:2333–8.

    Article  PubMed  Google Scholar 

  23. Ganesh S, Cave V. P-values, p-values everywhere! NZ Vet J. 2018;66:55–6.

    Article  Google Scholar 

  24. Oflazoglu U, Alacacioglu A, Varol U, Kucukzeybek Y, Salman T, Taskaynatan H, et al. Prevalence and related factors of sarcopenia in newly diagnosed cancer patients. Support Care Cancer. 2020;28:837–43.

    Article  PubMed  Google Scholar 

  25. Xiao J, Caan BJ, Cespedes Feliciano EM, Meyerhardt JA, Kroenke CH, Baracos VE, et al. The association of medical and demographic characteristics with sarcopenia and low muscle radiodensity in patients with nonmetastatic colorectal cancer. Am J Clin Nutr. 2019;109:615–25.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Xiao J, Caan BJ, Weltzien E, Cespedes Feliciano EM, Kroenke CH, Meyerhardt JA, et al. Associations of pre-existing co-morbidities with skeletal muscle mass and radiodensity in patients with non-metastatic colorectal cancer. J Cachexia Sarcopenia Muscle. 2018;9:654–63.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Barazzoni R, Jensen GL, Correia M, Gonzalez MC, Higashiguchi T, Shi HP, et al. Guidance for assessment of the muscle mass phenotypic criterion for the Global Leadership Initiative on Malnutrition (GLIM) diagnosis of malnutrition. Clin Nutr. 2022;41:1425–33.

    Article  PubMed  Google Scholar 

  28. Arends J, Bachmann P, Baracos V, Barthelemy N, Bertz H, Bozzetti F, et al. ESPEN guidelines on nutrition in cancer patients. Clin Nutr. 2017;36:11–48.

    Article  PubMed  Google Scholar 

  29. Kiss N, Loeliger J, Findlay M, Isenring E, Baguley BJ, Boltong A, et al. Clinical oncology society of Australia: position statement on cancer-related malnutrition and sarcopenia. Nutr Diet. 2020;77:416–25.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Erul E, Guven DC, Onur MR, Yazici G, Aksoy S. Role of sarcopenia on survival and treatment-related toxicity in head and neck cancer: a narrative review of current evidence and future perspectives. Eur Arch Otorhinolaryngol. 2023;280:3541–56.

    Article  PubMed  Google Scholar 

  31. Erul E, Guven DC, Ozbay Y, Altunbulak AY, Kahvecioglu A, Ercan F, et al. Evaluation of sarcopenia as a prognostic biomarker in locally advanced head and neck squamous cell carcinoma. Biomark Med. 2023;17:87–99.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the research assistants and clinicians who supported the recruitment of participants to the PREDICT study and access to the CT images.

Funding

NK and this study are supported by a Victorian Cancer Agency Nursing and Allied Health Clinical Research Fellowship (grant no: CRFNAH18001).

Author information

Authors and Affiliations

  1. Institute for Physical Activity and Nutrition, Deakin University, Geelong, VIC, Australia

    Nicole Kiss, Gavin Abbott, Annie R. Curtis, Steve F. Fraser & Robin M. Daly

  2. Department of Health Services Research, Peter MacCallum Cancer Centre, Parkville, VIC, Australia

    Nicole Kiss, Lara Edbrooke & Linda Denehy

  3. Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada

    Carla M. Prado

  4. Physiotherapy Department, University of Melbourne, Parkville, VIC, Australia

    Lara Edbrooke & Linda Denehy

  5. Lung Service, Peter MacCallum Cancer Centre, Parkville, VIC, Australia

    Shankar Siva, David Ball, Andrew Wirth & Greg Wheeler

  6. Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC, Australia

    Shankar Siva, David Ball & Nicholas Hardcastle

  7. Physical Sciences, Peter MacCallum Cancer Centre, Parkville, VIC, Australia

    Nicholas Hardcastle

  8. Institute for Health Transformation, Deakin University, Geelong, VIC, Australia

    Anna Ugalde

  9. Olivia Newton John Cancer Centre, Austin Hospital, Heidelberg, VIC, Australia

    Adeline Lim

  10. Andrew Love Cancer Centre, Barwon Health, Geelong, VIC, Australia

    Andrew Hui

Authors
  1. Nicole Kiss
    View author publications

    Search author on:PubMed Google Scholar

  2. Carla M. Prado
    View author publications

    Search author on:PubMed Google Scholar

  3. Gavin Abbott
    View author publications

    Search author on:PubMed Google Scholar

  4. Lara Edbrooke
    View author publications

    Search author on:PubMed Google Scholar

  5. Linda Denehy
    View author publications

    Search author on:PubMed Google Scholar

  6. Annie R. Curtis
    View author publications

    Search author on:PubMed Google Scholar

  7. Shankar Siva
    View author publications

    Search author on:PubMed Google Scholar

  8. David Ball
    View author publications

    Search author on:PubMed Google Scholar

  9. Nicholas Hardcastle
    View author publications

    Search author on:PubMed Google Scholar

  10. Anna Ugalde
    View author publications

    Search author on:PubMed Google Scholar

  11. Steve F. Fraser
    View author publications

    Search author on:PubMed Google Scholar

  12. Andrew Wirth
    View author publications

    Search author on:PubMed Google Scholar

  13. Adeline Lim
    View author publications

    Search author on:PubMed Google Scholar

  14. Andrew Hui
    View author publications

    Search author on:PubMed Google Scholar

  15. Greg Wheeler
    View author publications

    Search author on:PubMed Google Scholar

  16. Robin M. Daly
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Conceptualisation: NK, GA, CMP, RMD, LD, DB, LE, SS, SFF, AU, NH, AW, GW. Data curation: ARC, NK, NH, AW, GW, AL, AC. Formal analysis: NK, GA, ARC. Writing—original draft preparation: NK. Writing—reviewing and editing: all authors.

Corresponding author

Correspondence to Nicole Kiss.

Ethics declarations

Competing interests

NK reports honoraria from Abbott Nutrition Australasia. CP reports honoraria or consulting fees from Abbott Nutrition, Nestle Health Sciences, Nutricia and Pfizer. RD reports honoraria from Abbott Nutrition Australasia and Fresenius Kabi. GA, ARC, LD, LE, DB, SS, SFF, AU, NH, AW, GW, AL and AC have no conflict of interest to declare.

Ethical approval and consent to participate

This study received ethics approval from the Human Research Ethics Committee at Peter MacCallum Cancer Centre (HREC/53147/PMCC-2019) and Deakin University (2019-320). All methods were performed in accordance with the Declaration of Helsinki and relevant ethical guidelines at Peter MacCallum Cancer Centre and Deakin University and written informed consent was obtained from all participants.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kiss, N., Prado, C.M., Abbott, G. et al. Sociodemographic and clinical factors associated with low muscle mass and composition in people treated with (chemo)radiotherapy for lung cancer. Eur J Clin Nutr 79, 369–378 (2025). https://doi.org/10.1038/s41430-024-01552-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41430-024-01552-3

Search

Advanced search

Quick links