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:

Phenotyping in clinical nutrition

Several anthropometric measurements and cancer mortality: predictor screening, threshold determination, and joint analysis in a multicenter cohort of 12138 adults

European Journal of Clinical Nutrition volume 76, pages 756–764 (2022)Cite this article

Subjects

Abstract

Background

Anthropometric measurements (AMs) are cost-effective surrogates for evaluating body size. This study aimed to identify the optimal prognostic AMs, their thresholds, and their joint associations with cancer mortality.

Methods

We performed an observational cohort study including 12138 patients with cancer at five institutions in China. Information on demographics, disease, nutritional status, and AMs, including the body mass index, mid-arm muscle circumference, mid-arm circumference, handgrip strength, calf circumference (CC), and triceps-skinfold thickness (TSF), was collected and screened as mortality predictors. The optimal stratification was used to determine the thresholds to categorize those prognostic AMs, and their associations with mortality were estimated independently and jointly by calculating multivariable-adjusted hazard ratios (HRs).

Results

The study included 5744 females and 6394 males with a mean age of 56.9 years. The CC and TSF were identified as better mortality predictors than other AMs. The optimal thresholds were women 30 cm and men 32.8 cm for the CC, and women 21.8 mm and men 13.6 mm for the TSF. Patients in the low CC or low TSF group had a 13% (HR = 1.13, 95% CI = 1.03–1.23) and 22% (HR = 1.22, 95% CI = 1.12–1.32) greater mortality risk compared with their normal CC/TSF counterparties, respectively. Concurrent low CC and low TSF showed potential joint effect on mortality risk (HR = 1.39, 95% CI = 1.25–1.55).

Conclusions

These findings support the importance of assessing the CC and TSF simultaneously in hospitalized cancer patients to guide interventions to optimize their long-term outcomes.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

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

Fig. 1: Prognostic factors were screened using the least absolute shrinkage and selection operator (LASSO).
Fig. 2: Analysis of the associations of the calf circumference (CC) and triceps-skinfold thickness (TSF) with survival.

Similar content being viewed by others

Data availability

The datasets generated and/or analyzed during the current study are not publicly available to protect patient confidentiality, but are available from the corresponding author on reasonable request.

References

  1. Caan BJ, Cespedes Feliciano EM, Prado CM, Alexeeff S, Kroenke CH, Bradshaw P, et al. Association of muscle and adiposity measured by computed tomography with survival in patients with nonmetastatic breast cancer. JAMA Oncol. 2018;4:798–804.

    Article  Google Scholar 

  2. Martinez-Tapia C, Diot T, Oubaya N, Paillaud E, Poisson J, Gisselbrecht M, et al. The obesity paradox for mid- and long-term mortality in older cancer patients: a prospective multicenter cohort study. Am J Clin Nutr. 2020. https://doi.org/10.1093/ajcn/nqaa238 (Online ahead of print).

  3. Paixao EMS, Gonzalez MC, Nakano EY, Ito MK, Pizato N. Weight loss, phase angle, and survival in cancer patients undergoing radiotherapy: a prospective study with 10-year follow-up. Eur J Clin Nutr. 2020. https://doi.org/10.1038/s41430-020-00799-w (Online ahead of print).

  4. Yin L, Lin X, Zhao Z, Li N, He X, Zhang M, et al. Is hand grip strength a necessary supportive index in the phenotypic criteria of the GLIM-based diagnosis of malnutrition in patients with cancer? Support Care Cancer 2021. https://doi.org/10.1007/s00520-020-05975-z (Online ahead of print).

  5. Yin L, Lin X, Liu J, Li N, He X, Zhang M, et al. Classification tree-based machine learning to visualize and validate a decision tool for identifying malnutrition in cancer patients. JPEN J Parenter Enteral Nutr. 2021. https://doi.org/10.1002/jpen.2070 (Online ahead of print).

  6. Calle EE, Thun MJ, Petrelli JM, Rodriguez C, Heath CW Jr. Body-mass index and mortality in a prospective cohort of US adults. N Engl J Med. 1999;341:1097–105.

    Article  CAS  Google Scholar 

  7. Sun YQ, Burgess S, Staley JR, Wood AM, Bell S, Kaptoge SK, et al. Body mass index and all cause mortality in HUNT and UK Biobank studies: linear and non-linear mendelian randomisation analyses. BMJ. 2019;364:l1042.

    Article  Google Scholar 

  8. Kroenke CH, Neugebauer R, Meyerhardt J, Prado CM, Weltzien E, Kwan ML, et al. Analysis of body mass index and mortality in patients with colorectal cancer using causal diagrams. JAMA Oncol. 2016;2:1137–45.

    Article  Google Scholar 

  9. Lauby-Secretan B, Scoccianti C, Loomis D, Grosse Y, Bianchini F, Straif K, et al. Body fatness and cancer-viewpoint of the IARC Working Group. N Engl J Med. 2016;375:794–8.

    Article  Google Scholar 

  10. Chan DSM, Vieira AR, Aune D, Bandera EV, Greenwood DC, McTiernan A, et al. Body mass index and survival in women with breast cancer-systematic literature review and meta-analysis of 82 follow-up studies. Ann Oncol. 2014;25:1901–14.

    Article  CAS  Google Scholar 

  11. Weiss L, Melchardt T, Habringer S, Boekstegers A, Hufnagl C, Neureiter D, et al. Increased body mass index is associated with improved overall survival in diffuse large B-cell lymphoma. Ann Oncol. 2014;25:171–6.

    Article  CAS  Google Scholar 

  12. Nagai M, Kuriyama S, Kakizaki M, Ohmori-Matsuda K, Sugawara Y, Sone T, et al. Effect of age on the association between body mass index and all-cause mortality: the Ohsaki cohort study. J Epidemiol. 2010;20:398–407.

    Article  Google Scholar 

  13. Sheean P, Gonzalez MC, Prado CM, McKeever L, Hall AM, Braunschweig CA. American Society for Parenteral and Enteral Nutrition Clinical Guidelines: the validity of body composition assessment in clinical populations. JPEN J Parenter Enter Nutr. 2020;44:12–43.

    Article  Google Scholar 

  14. Oh H, Kwak SY, Jo G, Lee J, Park D, Lee DH, et al. Adiposity and mortality in Korean adults: a population-based prospective cohort study. Am J Clin Nutr. 2020. https://doi.org/10.1093/ajcn/nqaa258 (Online ahead of print).

  15. van der Werf A, Langius JAE, de van der Schueren MAE, Nurmohamed SA, van der Pant K, Blauwhoff-Buskermolen S, et al. Percentiles for skeletal muscle index, area and radiation attenuation based on computed tomography imaging in a healthy Caucasian population. Eur J Clin Nutr. 2018;72:288–96.

    Article  Google Scholar 

  16. Ueno A, Yamaguchi K, Sudo M, Imai S. Sarcopenia as a risk factor of severe laboratory adverse events in breast cancer patients receiving perioperative epirubicin plus cyclophosphamide therapy. Support Care Cancer. 2020;28:4249–54.

    Article  Google Scholar 

  17. Ulmann G, Kai J, Durand JP, Neveux N, Jouinot A, De Bandt JP, et al. Creatinine-to-cystatin C ratio and bioelectrical impedance analysis for the assessement of low lean body mass in cancer patients: Comparison to L3-computed tomography scan. Nutrition. 2020;81:110895.

    Article  Google Scholar 

  18. Hurt RT, Ebbert JO, Croghan I, Nanda S, Schroeder DR, Teigen LM, et al. The comparison of segmental multifrequency bioelectrical impedance analysis and dual-energy X-ray absorptiometry for estimating fat free mass and percentage body fat in an ambulatory population. JPEN J Parenter Enteral Nutr. 2020;45:1231–8.

  19. Chen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K, et al. Asian Working Group for Sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment. J Am Med Dir Assoc. 2020;21:300–7 e2.

    Article  Google Scholar 

  20. Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyere O, Cederholm T, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48:601.

    Article  Google Scholar 

  21. Jensen GL, Cederholm T, 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. JPEN J Parenter Enter Nutr. 2019;43:32–40.

    Article  Google Scholar 

  22. Lee DH, Keum N, Hu FB, Orav EJ, Rimm EB, Willett WC, et al. Predicted lean body mass, fat mass, and all cause and cause specific mortality in men: prospective US cohort study. BMJ. 2018;362:k2575.

    Article  Google Scholar 

  23. Braganza MZ, Kitahara CM, Berrington de Gonzalez A, Inskip PD, Johnson KJ, Rajaraman P. Ionizing radiation and the risk of brain and central nervous system tumors: a systematic review. Neuro Oncol. 2012;14:1316–24.

    Article  Google Scholar 

  24. Meulepas JM, Ronckers CM, Smets A, Nievelstein RAJ, Gradowska P, Lee C, et al. Radiation exposure from pediatric CT scans and subsequent cancer risk in the Netherlands. J Natl Cancer Inst. 2019;111:256–63.

    Article  Google Scholar 

  25. Ferrao B, Neves PM, Santos T, Capelas ML, Makitie A, Ravasco P. Body composition changes in patients with head and neck cancer under active treatment: a scoping review. Support Care Cancer. 2020;28:4613–25.

    Article  Google Scholar 

  26. Cao J, Xu H, Li W, Guo Z, Lin Y, Shi Y, et al. Nutritional assessment and risk factors associated to malnutrition in patients with esophageal cancer. Curr Probl Cancer. 2020;45:100638.

  27. Yin L, Lin X, Li N, Zhang M, He X, Liu J, et al. Evaluation of the global leadership initiative on malnutrition criteria using different muscle mass indices for diagnosing malnutrition and predicting survival in lung cancer patients. JPEN J Parenter Enteral Nutr. 2020. https://doi.org/10.1002/jpen.1873 (Online ahead of print).

  28. Wang N, Khankari NK, Cai H, Li HL, Yang G, Gao YT, et al. Prediagnosis body mass index and waist-hip circumference ratio in association with colorectal cancer survival. Int J Cancer. 2017;140:292–301.

    Article  CAS  Google Scholar 

  29. Ho SY, Guo HR, Chen HH, Peng CJ. Nutritional predictors of survival in terminally ill cancer patients. J Formos Med Assoc. 2003;102:544–50.

    PubMed  Google Scholar 

  30. Sousa IM, Bielemann RM, Gonzalez MC, da Rocha IMG, Barbalho ER, de Carvalho ALM, et al. Low calf circumference is an independent predictor of mortality in cancer patients: a prospective cohort study. Nutrition. 2020;79-80:110816.

    Article  Google Scholar 

  31. Yin L, Liu J, Lin X, Li N, Guo J, Fan Y, et al. Nutritional features-based clustering analysis as a feasible approach for early identification of malnutrition in patients with cancer. Eur J Clin Nutr 2021. https://doi.org/10.1038/s41430-020-00844-8 (Online ahead of print).

  32. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424.

    Article  Google Scholar 

  33. Xu H, Song C, Wang C, Fu Z, Guo Z, Lin Y, et al. Investigation on nutrition status and clinical outcome of patients with common cancers in Chinese patients: a multicenter prospective study protocol. Int J Clin Trials. 2020;7:94–102.

    Article  Google Scholar 

  34. Kondrup J, Allison SP, Elia M, Vellas B, Plauth M, Educational and Clinical Practice Committee, European Society of Parenteral and Enteral Nutrition (ESPEN). ESPEN guidelines for nutrition screening 2002. Clin Nutr. 2003;22:415–21.

    Article  CAS  Google Scholar 

  35. Murri R, Scoppettuolo G, Damiano F, Ammassari A, Fantoni M, Antinori A. Karnofsky performance status and assessment of global health status. J Acquir Immune Defic Syndr Hum Retrovirol. 1996;13:294–5.

    Article  CAS  Google Scholar 

  36. Wan C, Meng Q, Yang Z, Tu X, Feng C, Tang X, et al. Validation of the simplified Chinese version of EORTC QLQ-C30 from the measurements of five types of inpatients with cancer. Ann Oncol. 2008;19:2053–60.

    Article  CAS  Google Scholar 

  37. Giesinger JM, Kieffer JM, Fayers PM, Groenvold M, Petersen MA, Scott NW, et al. Replication and validation of higher order models demonstrated that a summary score for the EORTC QLQ-C30 is robust. J Clin Epidemiol. 2016;69:79–88.

    Article  Google Scholar 

  38. Chen C, Lu FC, Department of Disease Control Ministry of Health PRC. The guidelines for prevention and control of overweight and obesity in Chinese adults. Biomed Environ Sci. 2004;17:1–36. Suppl

    PubMed  Google Scholar 

  39. Sauerbrei W, Royston P, Binder H. Selection of important variables and determination of functional form for continuous predictors in multivariable model building. Stat Med. 2007;26:5512–28.

    Article  Google Scholar 

  40. Durrleman S, Simon R. Flexible regression models with cubic splines. Stat Med. 1989;8:551–61.

    Article  CAS  Google Scholar 

  41. 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  Google Scholar 

  42. Prado CM, 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  Google Scholar 

  43. Xu MC, Huelster HL, Hatcher JB, Avulova S, Stocks BT, Glaser ZA, et al. Obesity is associated with longer survival independent of sarcopenia and myosteatosis in metastatic and/or castrate-resistant prostate cancer. J Urol. 2020;101097JU0000000000001428.

  44. Pan YP, Kuo HC, Hsu TY, Lin JY, Chou WC, Lai CH, et al. Body mass index-adjusted body weight loss grading predicts overall survival in esophageal squamous cell carcinoma patients. Nutr Cancer. 2020;73:1130–7.

  45. Gonzalez MC, Pastore CA, Orlandi SP, Heymsfield SB. Obesity paradox in cancer: new insights provided by body composition. Am J Clin Nutr. 2014;99:999–1005.

    Article  CAS  Google Scholar 

  46. Souza NC, Gonzalez MC, Martucci RB, Rodrigues VD, de Pinho NB, Qureshi AR, et al. Comparative analysis between computed tomography and surrogate methods to detect low muscle mass among colorectal cancer patients. JPEN J Parenter Enteral Nutr. 2020;44:1328–37.

  47. Xu JY, Zhu MW, Zhang H, Li L, Tang PX, Chen W, et al. A cross-sectional study of GLIM-defined malnutrition based on new validated calf circumference cut-off values and different screening tools in hospitalised patients over 70 years old. J Nutr Health Aging. 2020;24:832–8.

    Article  CAS  Google Scholar 

  48. Ottery FD. Rethinking nutritional support of the cancer patient: the new field of nutritional oncology. Semin Oncol. 1994;21:770–8.

    CAS  PubMed  Google Scholar 

  49. Fu X, Tian Z, Wen S, Sun H, Thapa S, Xiong H, et al. A new index based on serum creatinine and cystatin C is useful for assessing sarcopenia in patients with advanced cancer. Nutrition. 2020;82:111032.

  50. 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  Google Scholar 

Download references

Funding

This work was funded by the National Key Research and Development Program (2017YFC1309200) and the National Natural Science Foundation of China (81673167).

Author information

Authors and Affiliations

  1. Department of Clinical Nutrition, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, 400042, China

    Liangyu Yin, Ling Zhang, Na Li, Jing Guo, Lijuan Liu, Xin Lin, Yang Fan, Jie Liu, Mengyuan Zhang, Feifei Chong & Hongxia Xu

  2. Institute of Hepatopancreatobiliary Surgery, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, 400038, China

    Liangyu Yin

  3. Cancer Center of the First Hospital of Jilin University, Changchun, Jilin, 130021, China

    Xiao Chen, Chang Wang, Xu Wang, Tingting Liang, Xiangliang Liu, Li Deng, Wei Li & Jiuwei Cui

  4. Department of Medical Oncology, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, Fujian, 350014, China

    Mei Yang, Jiami Yu, Xiaojie Wang & Zengqing Guo

  5. Department of Nutrition and Metabolism of Oncology, the First Affiliated Hospital of University of Science and Technology of China (Anhui Provincial Cancer Hospital), Hefei, Anhui, 230031, China

    Xing Liu, Shoumei Yang, Zheng Zuo & Suyi Li

  6. Center of Gastrointestinal Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, 510080, China

    Kaitao Yuan & Miao Yu

  7. Department of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, Henan, 450001, China

    Chunhua Song

  8. Department of Gastrointestinal Surgery and Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China

    Hanping Shi

Authors
  1. Liangyu Yin
    View author publications

    Search author on:PubMed Google Scholar

  2. Ling Zhang
    View author publications

    Search author on:PubMed Google Scholar

  3. Na Li
    View author publications

    Search author on:PubMed Google Scholar

  4. Jing Guo
    View author publications

    Search author on:PubMed Google Scholar

  5. Lijuan Liu
    View author publications

    Search author on:PubMed Google Scholar

  6. Xin Lin
    View author publications

    Search author on:PubMed Google Scholar

  7. Yang Fan
    View author publications

    Search author on:PubMed Google Scholar

  8. Jie Liu
    View author publications

    Search author on:PubMed Google Scholar

  9. Mengyuan Zhang
    View author publications

    Search author on:PubMed Google Scholar

  10. Feifei Chong
    View author publications

    Search author on:PubMed Google Scholar

  11. Xiao Chen
    View author publications

    Search author on:PubMed Google Scholar

  12. Chang Wang
    View author publications

    Search author on:PubMed Google Scholar

  13. Xu Wang
    View author publications

    Search author on:PubMed Google Scholar

  14. Tingting Liang
    View author publications

    Search author on:PubMed Google Scholar

  15. Xiangliang Liu
    View author publications

    Search author on:PubMed Google Scholar

  16. Li Deng
    View author publications

    Search author on:PubMed Google Scholar

  17. Wei Li
    View author publications

    Search author on:PubMed Google Scholar

  18. Mei Yang
    View author publications

    Search author on:PubMed Google Scholar

  19. Jiami Yu
    View author publications

    Search author on:PubMed Google Scholar

  20. Xiaojie Wang
    View author publications

    Search author on:PubMed Google Scholar

  21. Xing Liu
    View author publications

    Search author on:PubMed Google Scholar

  22. Shoumei Yang
    View author publications

    Search author on:PubMed Google Scholar

  23. Zheng Zuo
    View author publications

    Search author on:PubMed Google Scholar

  24. Kaitao Yuan
    View author publications

    Search author on:PubMed Google Scholar

  25. Miao Yu
    View author publications

    Search author on:PubMed Google Scholar

  26. Chunhua Song
    View author publications

    Search author on:PubMed Google Scholar

  27. Jiuwei Cui
    View author publications

    Search author on:PubMed Google Scholar

  28. Suyi Li
    View author publications

    Search author on:PubMed Google Scholar

  29. Zengqing Guo
    View author publications

    Search author on:PubMed Google Scholar

  30. Hanping Shi
    View author publications

    Search author on:PubMed Google Scholar

  31. Hongxia Xu
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Conceptualization and study design: LYY, SYL, ZQG, HPS and HXX; investigation: LYY and HXX; data interpretation: LYY, LZ, NL, JG, LJL, XL, YF, JL, MYZ, FFC, XC, CW, XW, TTL, XLL, LD, WL, MY, JMY, XJW, XL, SMY, ZZ, KTY, MY, CHS, JWC, SYL, ZQG, HPS and HXX; statistical analysis: LYY; paper preparation: LYY. All authors have read and approved the final paper.

Corresponding authors

Correspondence to Suyi Li, Zengqing Guo, Hanping Shi or Hongxia Xu.

Ethics declarations

Competing interests

The authors declare no competing interests.

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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yin, L., Zhang, L., Li, N. et al. Several anthropometric measurements and cancer mortality: predictor screening, threshold determination, and joint analysis in a multicenter cohort of 12138 adults. Eur J Clin Nutr 76, 756–764 (2022). https://doi.org/10.1038/s41430-021-01009-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41430-021-01009-x

This article is cited by

Search

Advanced search

Quick links