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Identification of Preoperative Fat-Free Mass Index for the Prognosis of Curatively Resected Esophageal Cancer

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Abstract

Background

The progressive, systemic depletion of muscle mass is a poor prognostic factor for various types of cancers. However, the assessment of body composition for patients with esophagectomy remains unclear. Therefore, we evaluated the significance of the fat-free mass index (FFMI) and estimated the appropriate cutoff value.

Methods

We compiled clinicopathological characteristics of patients who underwent curative operation for esophageal cancer between October 2013 and March 2018 at Toranomon Hospital and reviewed them until December 2020. We analyzed the short- and long-term outcomes, compared to conventional nutritional factors, and calculated the area under the receiver operating characteristic (ROC) curve.

Results

A total of 200 patients were eligible for inclusion. FFMI was ineffective in predicting postoperative complications, with no correlation with other nutritional biomarkers. Preoperative low FFMI led to poor overall survival (OS), and the lower cutoff values based on the time-dependent ROC analysis were 14.4 and 16.8 kg/m2 in women and men, respectively. Multivariate analysis for OS revealed that low FFMI (p = 0.010, HR 2.437, 95% CI 1.234–4.815) and clinical stage (p = 0.010, HR 4.781, 95% CI 1.447–15.796) were independent prognostic factors. The 3-year survival rates were 68.9% in low FFMI and 88.6% in normal FFMI.

Conclusions

The low FFMI was not predictive of postoperative complications but an independent prognostic factor in esophageal cancer with curative resection, having no correlation with other biomarkers. Our cutoff FFMI values could be useful in selecting the target for muscle improvement programs.

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References

  1. Dodson S, Baracos VE, Jatoi A et al (2011) Muscle wasting in cancer cachexia: clinical implications, diagnosis, and emerging treatment strategies. Annu Rev Med 62:265–279

    Article  CAS  Google Scholar 

  2. Tamandl D, Paireder M, Asari R, Baltzer PA, Schoppmann SF, Ba-Ssalamah A (2016) Markers of sarcopenia quantified by computed tomography predict adverse long-term outcome in patients with resected oesophageal or gastro-oesophageal junction cancer. Eur Radiol 26:1359–1367

    Article  Google Scholar 

  3. Miyamoto Y, Baba Y, Sakamoto Y et al (2015) Sarcopenia is a negative prognostic factor after curative resection of colorectal cancer. Ann Surg Oncol 22:2663–2668

    Article  Google Scholar 

  4. Cederholm T, Jensen GL, Correia MITD et al (2019) GLIM criteria for the diagnosis of malnutrition – a consensus report from the global clinical nutrition community. Clin Nutr 38:1–9

    Article  CAS  Google Scholar 

  5. Cederholm T, Bosaeus I, Barazzoni R et al (2015) Diagnostic criteria for malnutrition - an ESPEN consensus statement. Clin Nutr 34:335–340

    Article  CAS  Google Scholar 

  6. Roubenoff R, Baumgartner RN, Harris TB et al (1997) Application of bioelectrical impedance analysis to elderly populations. J Gerontol - Ser A Biol Sci Med Sci 52(3):M129–M136

    Article  CAS  Google Scholar 

  7. Sergi G, De Rui M, Stubbs B, Veronese N, Manzato E (2017) Measurement of lean body mass using bioelectrical impedance analysis: a consideration of the pros and cons. Aging Clin Exp Res 29:591–597

    Article  Google Scholar 

  8. Matsubara H, President F, Ando N et al (2017) Japanese classification of esophageal cancer, 11th edition: part I. Esophagus 14:1–36

    Article  CAS  Google Scholar 

  9. Kitagawa Y, Uno T, Oyama T et al (2019) Esophageal cancer practice guidelines 2017 edited by the Japan esophageal society: part 1. Esophagus 16:1–24

    Article  Google Scholar 

  10. Akiyama H, Miyazono H, Tsurumaru M et al (1978) Use of the stomach as an esophageal substitute. Ann Surg 188:606–610

    Article  CAS  Google Scholar 

  11. Udagawa H, Akiyama H (2001) Surgical treatment of esophageal cancer: Tokyo experience of the three-field technique. Dis Esophagus 14:110–114

    Article  CAS  Google Scholar 

  12. Udagawa H, Ueno M, Kinoshita Y (2009) Rationale for video-assisted radical esophagectomy. Gen Thorac Cardiovasc Surg 57:127–131

    Article  Google Scholar 

  13. Udagawa H, Ueno M, Shinohara H et al (2012) The importance of grouping of lymph node stations and rationale of three-field lymphoadenectomy for thoracic esophageal cancer. J Surg Oncol 106(6):742–747

    Article  Google Scholar 

  14. Udagawa H, Ueno M, Shinohara H et al (2014) Should lymph nodes along the thoracic duct be dissected routinely in radical esophagectomy? Esophagus 11:204–210

    Article  Google Scholar 

  15. Xue Y, Zhou X, Xue L, Zhou R, Luo J (2019) The role of pretreatment prognostic nutritional index in esophageal cancer: a meta-analysis. J Cell Physiol 234:19655–19662

    Article  CAS  Google Scholar 

  16. Kinoshita A, Onoda H, Imai N et al (2012) Comparison of the prognostic value of inflammation-based prognostic scores in patients with hepatocellular carcinoma. Br J Cancer 107:988–993

    Article  CAS  Google Scholar 

  17. Ignacio De Ulíbarri J, González-Madroño A, De Villar NGP et al (2005) CONUT A tool for controlling nutritional status first validation in a hospital population. Nutr Hosp 20:38–45

    PubMed  Google Scholar 

  18. Kuroda D, Sawayama H, Kurashige J et al (2018) Controlling nutritional status (CONUT) score is a prognostic marker for gastric cancer patients after curative resection. Gastric Cancer 21:204–212

    Article  Google Scholar 

  19. Lee DH, Keum N, Hu FB et al (2017) Development and validation of anthropometric prediction equations for lean body mass, fat mass and percent fat in adults using the National Health and Nutrition Examination Survey (NHANES) 1999–2006. Br J Nutr 118:858–866

    Article  CAS  Google Scholar 

  20. Heagerty PJ, Lumley T, Pepe MS (2000) Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 56:337–344

    Article  CAS  Google Scholar 

  21. Kudou K, Saeki H, Nakashima Y et al (2019) Postoperative development of sarcopenia is a strong predictor of a poor prognosis in patients with adenocarcinoma of the esophagogastric junction and upper gastric cancer. Am J Surg 217:757–763

    Article  Google Scholar 

  22. Harada K, Ida S, Baba Y et al (2016) Prognostic and clinical impact of sarcopenia in esophageal squamous cell carcinoma. Dis Esophagus 29:627–633

    Article  CAS  Google Scholar 

  23. Ida S, Watanabe M, Yoshida N et al (2015) Sarcopenia is a predictor of postoperative respiratory complications in patients with esophageal cancer. Ann Surg Oncol 22:4432–4437

    Article  Google Scholar 

  24. Prado CMM, Lieff JR, Mccargar LJ et al (2008) Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study. Lancet Oncol 9:629–635

    Article  Google Scholar 

  25. Demling RH (2009) Nutrition, anabolism, and the wound healing process: an overview. Eplasty 9:e9

    PubMed  PubMed Central  Google Scholar 

  26. Aoyama T, Kawabe T, Fujikawa H et al (2015) Loss of lean body mass as an independent risk factor for continuation of S-1 adjuvant chemotherapy for gastric cancer. Ann Surg Oncol 22:2560–2566

    Article  Google Scholar 

  27. Nakashima Y, Saeki H, Nakanishi R et al (2018) Assessment of sarcopenia as a predictor of poor outcomes after esophagectomy in elderly patients with esophageal cancer. Ann Surg 267:1100–1104

    Article  Google Scholar 

  28. Muscaritoli M, Anker SD, Argiles J et al (2010) Consensus definition of sarcopenia, cachexia and pre-cachexia: joint document elaborated by special interest groups (SIG) “cachexia-anorexia in chronic wasting diseases” and “nutrition in geriatrics.” Clin Nutr 29:154–159

    Article  CAS  Google Scholar 

  29. Tan LJ, Liu SL, Lei SF et al (2012) Molecular genetic studies of gene identification for sarcopenia. Hum Genet 131:1–31

    Article  CAS  Google Scholar 

  30. Prado CMM, Baracos VE, McCargar LJ et al (2007) Body composition as an independent determinant of 5-fluorouracil based chemotherapy toxicity. Clin Cancer Res 13:3264–3268

    Article  CAS  Google Scholar 

  31. Schutz Y, Kyle UUG, Pichard C (2002) Fat-free mass index and fat mass index percentiles in Caucasians aged 18–98 y. Int J Obes Relat Metab Disord 26:953–960

    Article  CAS  Google Scholar 

  32. Kyle UG, Schutz Y, Dupertuis YM, Pichard C (2003) Body composition interpretation: contributions of the fat-free mass index and the body fat mass index. Nutrition 19:597–604

    Article  Google Scholar 

  33. Beck FK, Rosenthal TC, York N (2002) Prealbumin: a marker for nutritional evaluation. Am Fam Phys 65:1575–1578

    Google Scholar 

  34. Bharadwaj S, Ginoya S, Tandon P et al (2016) Malnutrition: laboratory markers vs nutritional assessment. Gastroenterol Rep 4:272–280

    Google Scholar 

  35. Rossi AP, D’Introno A, Rubele S et al (2017) The Potential of β-Hydroxy-β-methylbutyrate as a new strategy for the management of sarcopenia and sarcopenic obesity. Drugs Aging 34:833–840

    Article  CAS  Google Scholar 

  36. Koyama Y, Moro K, Nakano M et al (2017) Intravenous carnitine administration in addition to parenteral nutrition with lipid emulsion may decrease the inflammatory reaction in postoperative surgical patients. J Clin Med Res 9:831–837

    Article  CAS  Google Scholar 

  37. Ohara M, Ogawa K, Suda G et al (2018) L-carnitine suppresses loss of skeletal muscle mass in patients with liver cirrhosis. Hepatol Commun 2:910–922

    Article  Google Scholar 

  38. Tamaki M, Miyashita K, Hagiwara A et al (2017) Ghrelin treatment improves physical decline in sarcopenia model mice through muscular enhancement and mitochondrial activation. Endocr J 64:S47–S51

    Article  Google Scholar 

  39. Yanagita I, Fujihara Y, Kitajima Y et al (2019) A high serum cortisol/DHEA-S ratio is a risk factor for sarcopenia in elderly diabetic patients. J Endocr Soc 3:801–813

    Article  CAS  Google Scholar 

  40. Yamamoto K, Nagatsuma Y, Fukuda Y et al (2017) Effectiveness of a preoperative exercise and nutritional support program for elderly sarcopenic patients with gastric cancer. Gastric Cancer 20:913–918

    Article  Google Scholar 

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Acknowledgements

We thank Prof. Hideo Yasunaga, Department of Clinical Epidemiology and Health Economics, The University of Tokyo, for his helpful advice on statistical analyses.

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Authors and Affiliations

  1. Department of Gastroenterological Surgery, Toranomon Hospital, 2-2-2 Toranomon, Minato-ku, Tokyo, 105-8470, Japan

    Akikazu Yago, Yu Ohkura, Masaki Ueno, Kentoku Fujisawa, Yusuke Ogawa, Hayato Shimoyama, Masayuki Urabe, Shusuke Haruta & Harushi Udagawa

  2. Okinaka Memorial Institute for Medical Research, Tokyo, Japan

    Yu Ohkura, Masaki Ueno & Harushi Udagawa

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  1. Akikazu Yago
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  2. Yu Ohkura
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  3. Masaki Ueno
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  9. Harushi Udagawa
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Correspondence to Akikazu Yago.

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The authors have no conflicts of interest, and this work is not supported by any financial assistance funds.

Ethical approval

This study was approved by the research ethics committee of Toranomon Hospital (no. 2087).

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Yago, A., Ohkura, Y., Ueno, M. et al. Identification of Preoperative Fat-Free Mass Index for the Prognosis of Curatively Resected Esophageal Cancer. World J Surg 46, 845–854 (2022). https://doi.org/10.1007/s00268-021-06435-3

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  • DOI: https://doi.org/10.1007/s00268-021-06435-3

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