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:

Association of resting energy expenditure with phase angle in hospitalized older patients: a cross-sectional analysis

European Journal of Clinical Nutrition volume 78, pages 187–192 (2024)Cite this article

Subjects

Abstract

Background/Objectives

Resting energy expenditure (REE) constitutes the largest component of total energy expenditure and undergoes an age-related decline that is unexplained by decreased fat-free mass. Phase angle (PhA) is a cellular health indicator that is possibly associated with REE. We investigated the association of REE and PhA in hospitalized older adults.

Subjects/Methods

This single-center, cross-sectional analysis utilized the baseline data from a prospective longitudinal study and included 131 eligible patients aged ≥70 years. The REE was measured using indirect calorimetry, and PhA and body composition were assessed using bioelectrical impedance. The association between REE, PhA, and body composition was examined, and REE was compared using previously reported PhA cutoff values.

Results

In this cohort with a mean (±standard deviation) age of 87.4 (±7.0) years, 34.4% of the participants were men. REE and PhA correlated strongly (r: 0.562, p < 0.001) and significantly after adjusting for age and sex (r: 0.433, p < 0.001). Multivariate analysis showed a significant independent association between REE and PhA and skeletal muscle mass (standardized β [95% CI]; 28.072 [2.188–53.956], p = 0.035) without any significant interaction between PhA and age on REE. The low PhA group had a significantly lower REE (kcal/day; 890 [856–925] vs. 1077 [1033–1122], p < 0.001), and this remained significant after adjusting for age, sex, and skeletal muscle mass index.

Conclusions

PhA is associated with REE in older adults. Adjusting REE calculation algorithms based on PhA values and correcting predicted REE according to PhA may aid in determining more accurate energy requirements.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Associations between resting energy expenditure (REE) and phase angle (PhA).

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are not publicly available due to privacy reasons and ethical restrictions related to patient data but are available from the corresponding author upon reasonable request.

References

  1. Kyu HH, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, et al. Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392:1859–922.

    Article  Google Scholar 

  2. Maeda K, Ishida Y, Nonogaki T, Mori N. Reference body mass index values and the prevalence of malnutrition according to the Global Leadership Initiative on Malnutrition criteria. Clin Nutr. 2020;39:180–4.

    Article  PubMed  Google Scholar 

  3. Leij-Halfwerk S, Verwijs MH, van Houdt S, Borkent JW, Guaitoli PR, Pelgrim T, et al. Prevalence of protein-energy malnutrition risk in European older adults in community, residential and hospital settings, according to 22 malnutrition screening tools validated for use in adults ≥65 years: a systematic review and meta-analysis. Maturitas. 2019;126:80–9.

    Article  PubMed  Google Scholar 

  4. Schuetz P, Fehr R, Baechli V, Geiser M, Deiss M, Gomes F, et al. Individualised nutritional support in medical inpatients at nutritional risk: a randomised clinical trial. Lancet. 2019;393:2312–21.

    Article  PubMed  Google Scholar 

  5. Bendavid I, Lobo DN, Barazzoni R, Cederholm T, Coëffier M, De Van Der Schueren M, et al. The centenary of the Harris–Benedict equations: How to assess energy requirements best? Recommendations from the ESPEN expert group. Clinical Nutrition. 2021;40:690–701.

    Article  PubMed  Google Scholar 

  6. Zusman O, Theilla M, Cohen J, Kagan I, Bendavid I, Singer P. Resting energy expenditure, calorie and protein consumption in critically ill patients: a retrospective cohort study. Crit Care. 2016;20:367.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Omura T, Tamura Y, Yamaoka T, Yoshimura Y, Sakurai T, Umegaki H, et al. Assessing the association between optimal energy intake and all‐cause mortality in older patients with diabetes mellitus using the Japanese Elderly Diabetes Intervention Trial. Geriatr Gerontol Int. 2020;20:59–65.

    Article  PubMed  Google Scholar 

  8. Soares MJ, Müller MJ. Resting energy expenditure and body composition: critical aspects for clinical nutrition. Eur J Clin Nutr. 2018;72:1208–14.

    Article  CAS  PubMed  Google Scholar 

  9. Achamrah N, Delsoglio M, De Waele E, Berger MM, Pichard C. Indirect calorimetry: the 6 main issues. Clin Nutr. 2021;40:4–14.

    Article  PubMed  Google Scholar 

  10. Pontzer H, Yamada Y, Sagayama H, Ainslie PN, Andersen LF, Anderson LJ, et al. Daily energy expenditure through the human life course. Science. 2021;373:808–12.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  11. Manini TM. Energy expenditure and aging. Ageing Res Rev. 2010;9:1–11.

    Article  CAS  PubMed  Google Scholar 

  12. Ward LC, Brantlov S. Bioimpedance basics and phase angle fundamentals. Rev Endocr Metab Disord. 2023;24:381–91.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Norman K, Stobäus N, Pirlich M, Bosy-Westphal A. Bioelectrical phase angle and impedance vector analysis–clinical relevance and applicability of impedance parameters. Clin Nutr. 2012;31:854–61.

    Article  PubMed  Google Scholar 

  14. Slee A, Birc D, Stokoe D. Bioelectrical impedance vector analysis, phase-angle assessment and relationship with malnutrition risk in a cohort of frail older hospital patients in the United Kingdom. Nutrition. 2015;31:132–7.

    Article  PubMed  Google Scholar 

  15. Yamada Y, Yoshida T, Murakami H, Kawakami R, Gando Y, Ohno H. et al. Phase angle obtained via bioelectrical impedance analysis and objectively measured physical activity or exercise habits. Sci Rep. 2022;12:17274.

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  16. Norman K, Herpich C, Müller-Werdan U. Role of phase angle in older adults with focus on the geriatric syndromes sarcopenia and frailty. Rev Endocr Metab Disord. 2023;24:429–37.

    Article  PubMed  Google Scholar 

  17. Martins PC, Alves Junior CAS, Silva AM, Silva DAS. Phase angle and body composition: a scoping review. Clin Nutr ESPEN. 2023;56:237–50.

    Article  PubMed  Google Scholar 

  18. Garlini LM, Alves FD, Ceretta LB, Perry IS, Souza GC, Clausell NO. Phase angle and mortality: a systematic review. Eur J Clin Nutr. 2019;73:495–508.

    Article  PubMed  Google Scholar 

  19. Marra M, Di Vincenzo O, Cioffi I, Sammarco R, Morlino D, Scalfi L. Resting energy expenditure in elite athletes: development of new predictive equations based on anthropometric variables and bioelectrical impedance analysis derived phase angle. J Int Soc Sports Nutr. 2021;18:68.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Marra M, Cioffi I, Sammarco R, Santarpia L, Contaldo F, Scalfi L, et al. Are raw BIA variables useful for predicting resting energy expenditure in adults with obesity? Nutrients. 2019;11:216.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Marra M, Sammarco R, Cioffi I, Morlino D, Di Vincenzo O, Speranza E, et al. New predictive equations for estimating resting energy expenditure in subjects with normal weight and overweight. Nutrition. 2021;84:111105.

    Article  PubMed  Google Scholar 

  22. Kojima G, Iliffe S, Taniguchi Y, Shimada H, Rakugi H, Walters K. Prevalence of frailty in Japan: a systematic review and meta-analysis. J Epidemiol. 2017;27:347–53.

    Article  PubMed  Google Scholar 

  23. Weir JB. New methods for calculating metabolic rate with special reference to protein metabolism. J Physiol. 1949;109:1–9.

    Article  PubMed  PubMed Central  Google Scholar 

  24. 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 

  25. Rubenstein LZ, Harker JO, Salvà A, Guigoz Y, Vellas B. Screening for undernutrition in geriatric practice: developing the short-form mini-nutritional assessment (MNA-SF). J Gerontol A Biol Sci Med Sci. 2001;56:M366–72.

    Article  CAS  PubMed  Google Scholar 

  26. Prados-Torres A, Calderón-Larrañaga A, Hancco-Saavedra J, Poblador-Plou B, van den Akker M. Multimorbidity patterns: a systematic review. J Clin Epidemiol. 2014;67:254–66.

    Article  PubMed  Google Scholar 

  27. Yamada M, Kimura Y, Ishiyama D, Nishio N, Otobe Y, Tanaka T, et al. Phase angle is a useful indicator for muscle function in older adults. J Nutr Health Aging. 2019;23:251–5.

    Article  CAS  PubMed  Google Scholar 

  28. 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. Clin Nutr. 2019;38:1–9.

    Article  CAS  PubMed  Google Scholar 

  29. 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  PubMed  Google Scholar 

  30. Mukaka MM. Statistics corner: a guide to appropriate use of correlation coefficient in medical research. Malawi Med J. 2012;24:69–71.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Iacobucci D, Schneider MJ, Popovich DL, Bakamitsos GA. Mean centering, multicollinearity, and moderators in multiple regression: the reconciliation redux. Behav Res Methods. 2017;49:403–4.

    Article  PubMed  Google Scholar 

  32. R Core Team. R: A Language and Environment for Etatistical Computing. Vienna, Austria: R Foundation for Statistical Computing. 2023. https://R-project.org/.

  33. Austin PC, Steyerberg EW. The number of subjects per variable required in linear regression analyses. J Clin Epidemiol. 2015;68:627–36.

    Article  PubMed  Google Scholar 

  34. Harrell FE Jr., Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15:361–87.

    Article  PubMed  Google Scholar 

  35. Marra M, Cioffi I, Morlino D, Vincenzo OD, Pagano MC, Imperatore N, et al. New predictive equations for estimating resting energy expenditure in adults with Crohn’s disease. J Parenter Enteral Nutr. 2020;44:1021–8.

    Article  Google Scholar 

  36. Müller MJ, Geisler C, Hübers M, Pourhassan M, Braun W, Bosy-Westphal A. Normalizing resting energy expenditure across the life course in humans: challenges and hopes. Eur J Clin Nutr. 2018;72:628–37.

    Article  PubMed  Google Scholar 

  37. Neelemaat F, van Bokhorst-de van der Schueren MA, Thijs A, Seidell JC, Weijs PJ. Resting energy expenditure in malnourished older patients at hospital admission and three months after discharge: predictive equations versus measurements. Clin Nutr. 2012;31:958–66.

    Article  PubMed  Google Scholar 

  38. Kawase F, Masaki Y, Ozawa H, Imanaka M, Sugiyama A, Wada H, et al. Resting energy expenditure in older inpatients: a comparison of prediction equations and measurements. Nutrients. 2022;14:5210.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Itoi A, Yamada Y, Yokoyama K, Adachi T, Kimura M. Validity of predictive equations for resting metabolic rate in healthy older adults. Clin Nutr ESPEN. 2017;22:64–70.

    Article  PubMed  Google Scholar 

  40. Visser M, van Venrooij LM, Wanders DC, de Vos R, Wisselink W, van Leeuwen PA, et al. The bioelectrical impedance phase angle as an indicator of undernutrition and adverse clinical outcome in cardiac surgical patients. Clin Nutr. 2012;31:981–6.

    Article  PubMed  Google Scholar 

  41. Lahaye C, Derumeaux-Burel H, Guillet C, Pereira B, Boirie Y. Determinants of resting energy expenditure in very old nursing home residents. J Nutr Health Aging. 2022;26:872–8.

    Article  CAS  PubMed  Google Scholar 

  42. Pourhassan M, Daubert D, Wirth R. Measured and predicted resting energy expenditure in malnourished older hospitalized patients: a cross-sectional and longitudinal comparison. Nutrients. 2020;12:2240.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Del Giorno R, Quarenghi M, Stefanelli K, Rigamonti A, Stanglini C, De Vecchi V, et al. Phase angle is associated with length of hospital stay, readmissions, mortality, and falls in patients hospitalized in internal-medicine wards: a retrospective cohort study. Nutrition. 2021;85:111068.

    Article  PubMed  Google Scholar 

  44. Genton L, Herrmann FR, Spörri A, Graf CE. Association of mortality and phase angle measured by different bioelectrical impedance analysis (BIA) devices. Clin Nutr. 2018;37:1066–9.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to thank the medical staff at Asuke Hospital for their support and assistance with data collection. We would also like to thank Editage (http://www.editage.jp) for English language editing.

Author information

Authors and Affiliations

  1. Department of Nutrition, Asuke Hospital Aichi Prefectural Welfare Federation of Agricultural Cooperatives, Toyota, Aichi, Japan

    Fumiya Kawase

  2. Graduate School of Nutritional Science, Nagoya University of Arts and Sciences, Nisshin, Aichi, Japan

    Fumiya Kawase & Takayoshi Tsukahara

  3. Department of Internal Medicine, Asuke Hospital Aichi Prefectural Welfare Federation of Agricultural Cooperatives, Toyota, Aichi, Japan

    Yoshiyuki Masaki & Shinya Kobayashi

  4. Department of Community-Based Medical Education, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan

    Yoshiyuki Masaki

  5. Department of Nursing, Asuke Hospital Aichi Prefectural Welfare Federation of Agricultural Cooperatives, Toyota, Aichi, Japan

    Hiroko Ozawa, Manami Imanaka & Aoi Sugiyama

  6. Department of Rehabilitation Therapy, Asuke Hospital Aichi Prefectural Welfare Federation of Agricultural Cooperatives, Toyota, Aichi, Japan

    Hironari Wada

Authors
  1. Fumiya Kawase
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yoshiyuki Masaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hiroko Ozawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Manami Imanaka
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Aoi Sugiyama
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hironari Wada
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shinya Kobayashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Takayoshi Tsukahara
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: FK, YM, HO, MI, AS, HW, SK, and TT; Data curation: FK, HO, HW, MI, and AS; Formal analysis: FK; Investigation: FK, HO, HW, MI, AS, and HW; Methodology: FK, YM, and TT; Project administration: FK, YM, SK, and TT; Roles/Writing—original draft: FK; Writing—review and editing: FK, YM, HO, MI, AS, HW, SK, and TT. All the authors have read and approved the final version of the manuscript.

Corresponding author

Correspondence to Fumiya Kawase.

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.

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

Kawase, F., Masaki, Y., Ozawa, H. et al. Association of resting energy expenditure with phase angle in hospitalized older patients: a cross-sectional analysis. Eur J Clin Nutr 78, 187–192 (2024). https://doi.org/10.1038/s41430-023-01370-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41430-023-01370-z

Search

Advanced search

Quick links