Skip to main content
SpringerLink
Log in

Indirect calorimetry: best results with close monitoring of the carbon dioxide fraction in exhaled air

  • Research
  • Published:
Nutrire Aims and scope Submit manuscript

Abstract

Purpose

This study describes methods to improve resting energy expenditure (REE) estimated by the indirect calorimetry by evaluating predictors for the achievement of steady-state conditions (SSC).

Methods

Cross-sectional study carried out with 20 women (30.2 ± 4.1 years; 27.8 ± 6.7 kg/m2). Indirect calorimetry was performed with the Quark RMR® calorimeter, during a 15-min period. VO2 consumption and VCO2 production were measured and data was collected every 5 s. Specific spreadsheet was used to compute the values of Steady REE and Software REE (deltaREE). Bias and concordance between values were assessed by the Bland-Altman plot and Lin’s correlation coefficient.

Results

There is an almost perfect concordance between Software REE and Steady REE (p = 0.001). Steady% was important for predicting deltaREE while variation of carbon dioxide exhaled (CVFECO2) was significant to predict Steady%. Also, CVFECO2 was strongly and significantly correlated with the CV of VO2 (r = 0.83, p = 0001) and with the CV of VCO2 (r = 0.9, p = 0001).

Conclusion

FECO2 should be sustained without huge variations to ensure SSC when using a calorimeter based on the canopy dilution technique with spontaneous breathing patients of similar characteristics. Also, these results suggest that since SSC occurs at least one time in the test, the Software REE may be considered reliable.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Data availability

Raw data are available upon request from the corresponding author.

Abbreviations

BLUPs:

best linear unbiased predictions

BMI:

body mass index

CVs:

coefficients of variation

CVFECO2 :

variation of carbon dioxide exhaled

FIO2 :

concentrations of oxygen inhaled

FICO2 :

concentrations of carbon dioxide inhaled

FEO2 :

concentrations of oxygen exhaled

FECO2 :

concentrations of carbon dioxide exhaled

IC:

indirect calorimetry

REE:

resting energy expenditure

SSC:

Steady-state conditions

VCO2 :

amount (volume) of carbon dioxide released

VO2 :

amount (volume) of oxygen used

References

  1. Gupta RD, Ramachandran R, Venkatesan P, Anoop S, Joseph M, Thomas N. Indirect calorimetry: from bench to bedside. Indian J Endocrinol Metab. 2017;21(4):594–9.

    Article  Google Scholar 

  2. Ferrannini E. The theoretical bases of indirect calorimetry: a review. Metabolism. 1988;37:287–01.

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  4. Oshima T, Graf S, Heidegger C, Genton L, Pugin J, Pichard C. Can calculation of energy expenditure based on CO2 measurements replace indirect calorimetry? Crit Care. 2017;21:13.

    Article  Google Scholar 

  5. McClave SA, Spain DA, Skolnick JL, Lowen CC, Kieber MJ, Wickerham PS, et al. Achievement of steady state optimizes results when performing indirect calorimetry. JPEN J Parenter Enteral Nutr. 2003;27(1):16–20.

    Article  Google Scholar 

  6. Borges JH, Langer RD, Cirolini VX, Pascoa MA, Guerra-Júnior G, Gonçalves EM. Minimum time to achieve the steady state and optimum abbreviated period to estimate the resting energy expenditure by indirect calorimetry in healthy young adults. Nutr Clin Pract. 2016;31(3):349e54.

    Article  Google Scholar 

  7. Sanchez-Delgado G, Alcantara JMA, Ortiz-Alvarez L, Xu H, Martinez-Tellez B, Labayen I, et al. Reliability of resting metabolic rate measurements in young adults: impact of methods for data analysis. Clin Nutr. 2018;37(5):1618e24.

    Article  Google Scholar 

  8. Cadena-Méndez M, Escalante-Ramírez B, Azpiroz-Leehan J, Infante-Vázquez O. VO2 and VCO2 variabilities through indirect calorimetry instrumentation. Springerplus. 2013;2:688.

    Article  Google Scholar 

  9. Schutz Y. On problems of calculating energy expenditure and substrate utilization from respiratory exchange data. Z Ernahrungswiss. 1997;36(4):255–62.

    Article  CAS  Google Scholar 

  10. Schutz Y Terminology, factors and constants in studies on energy metabolism of humans. In: Human energy metabolism: physical activity and energy expenditure measurements in epidemiological research based upon direct and indirect calorimetry. Van Es AJH (eds). EURO-NUT, report 5. Wageningen. 1985;153-I68.

  11. Hanneman SK. Design, analysis, and interpretation of method-comparison studies. AACN Adv Crit Care. 2008;19:223–34.

    PubMed  PubMed Central  Google Scholar 

  12. Bland JM, Altman DG. Correlation, regression, and repeated data. BMJ. 1994;308:896.

    Article  CAS  Google Scholar 

  13. Lin L. A concordance correlation coefficient to evaluate reproducibility. Biometrics. 1989;45:255–68.

    Article  CAS  Google Scholar 

  14. West BT, Welch KB, Galecki AT, Gillespie BW. Linear mixed models: a practical guide using statistical software. Boca Raton: Chapman and Hall / CRC Press; 2006.

    Book  Google Scholar 

  15. Blond E, Maitrepierre C, Normand S, Sothier M, Roth H, Goudable J, et al. A new indirect calorimeter is accurate and reliable for measuring basal energy expenditure, thermic effect of food and substrate oxidation in obese and healthy subjects. ESPEN J. 2011;6:e7–15.

    Google Scholar 

  16. Branson RD, Johannigman JA. The measurement of energy expenditure. Nutr Clin Pract. 2004;19(6):622–36.

    Article  Google Scholar 

  17. Hamilton LC. Statistics with Stata, version 12. 8th ed. Belmont: Cengage; 2013.

    Google Scholar 

Download references

Acknowledgments

Special thanks go to Neusa Aparecida Brasão Santos Carlos and Maria do Rosário Del Lama de Unamuno for all technical support and to Claire Helen Wardle Munhoz for checking the spelling of the text.

Funding

This study was supported by São Paulo Research Foundation (FAPESP) grants #2010/19821-6, #2012/03485-2, #2012/21579-4, and #2012/21626-2 and National Council for Scientific and Technological Development (CNPq) grant #306824/2006-0 and grant #425775/2016-0 Universal 01/2016.

Author information

Authors and Affiliations

  1. Division of Medical Nutrition, Department of Internal Medicine, Ribeirão Preto School of Medicine of the University of São Paulo, Ribeirão Preto, Brazil

    José Henrique Silvah, Cristiane Maria Mártires de Lima, Camila Fernanda Brandão, Carolina Ferreira Nicoletti & Júlio Sérgio Marchini

  2. Department of Health Science, University of São Paulo, Ribeirão Preto, Brazil

    Carolina Ferreira Nicoletti

  3. Division of General Internal and Geriatric Medicine, Department of Internal Medicine, Ribeirão Preto School of Medicine of the University of São Paulo, Ribeirão Preto, Brazil

    Karina Pfrimer

  4. Section of Nuclear Medicine, Image Science and Medical Physics Center, Department of Internal Medicine, Ribeirão Preto School of Medicine of the University of São Paulo, Ribeirão Preto, Brazil

    Lauro Wichert-Ana

  5. Center for Interdisciplinary Research on Applied Neurosciences – NAPNA, University of Sao Paulo, Sao Paulo, Brazil

    Lauro Wichert-Ana

Authors
  1. José Henrique Silvah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cristiane Maria Mártires de Lima
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Camila Fernanda Brandão
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Carolina Ferreira Nicoletti
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Karina Pfrimer
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lauro Wichert-Ana
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Júlio Sérgio Marchini
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JHS, CMML, LWA, and JSM designed the research; JHS and CCML conducted the research; JHS, CFN, CFB, and KP analyzed data and wrote the initial draft of the manuscript; JHS had primary responsibility for final content. All authors contributed towards, and read and approved the final manuscript.

Corresponding author

Correspondence to Carolina Ferreira Nicoletti.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethics approval

The research was conducted according to the Helsinki declaration, and the University’s ethical review board approved the study.

Consent to participate

Informed consent was obtained from all participants.

Consent for publication

Informed consent was obtained from all authors.

Code availability

Not applicable.

Supplementary information

Supplementary file 1

In this spreadsheet, each row starting at row 14, in columns A to F (in yellow), represents one of the five second measurements of the values of VO2, VCO2, VE, FEO2 and FECO2 recorded. Still starting at row 14, columns G to O (in gray) computed the means of each 5-min interval (i.e., between 5 min and 1 s to 10 min and 1 s; 5 min and 6 s to 10 min and 6 s and so forth). In row 9, from columns A to G (in blue), mean values of VO2, VCO2, Steady REE, RQ, FEO2, FECO2 and VE, obtained from the 60 possible intervals that are compatible with the SSC (SSC intervals), were computed. The columns H up to L, at row 9, showed the percentage of SSC intervals (Steady%) and if all Rest Phase were compatible with SSC’s definition. Row 6, columns N and O (in green) computed the CVs of VO2 and VCO2 from all the Rest Phase. Similarly, row 9, columns M to P, showed the mean values of VO2, VCO2, Software REE and coefficient of variation of the FECO2 (CVFECO2) from all the Rest Phase. Finally, in each test with at least one SSC interval, a deltaREE was calculated as the difference between Steady REE and Software REE. (PNG 979 kb)

High Resolution Image (TIFF 851 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Silvah, J.H., de Lima, C.M.M., Brandão, C.F. et al. Indirect calorimetry: best results with close monitoring of the carbon dioxide fraction in exhaled air. Nutrire 45, 26 (2020). https://doi.org/10.1186/s41110-020-00129-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s41110-020-00129-x

Keywords

Search

Navigation