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New predictive equations to estimate resting energy expenditure of non-dialysis dependent chronic kidney disease patients

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Journal of Nephrology Aims and scope Submit manuscript

Abstract

Background

Determination of resting energy expenditure (REE) is an important step for the nutritional and medical care of patients with chronic kidney disease (CKD). Methods such as indirect calorimetry or traditional predictive equations are costly or inaccurate to estimate REE of CKD patients. We aimed to develop and validate predictive equations to estimate the REE of non-dialysis dependent-CKD patients.

Methods

A database comprising REE measured by indirect calorimetry (mREE) of 170 non-dialysis dependent-CKD patients was used to develop (n = 119) and validate (n = 51) a new REE-predictive equation. Fat free mass (FFM) was assessed by anthropometry and by bioelectrical impedance (BIA).

Results

The multiple regression analysis generated three equations: (1) REE (kcal/day) = 854 + 7.4*Weight + 179*Sex – 3.3*Age + 2.1 *eGFR + 26 (if DM) (R2 = 0.424); (2) REE (kcal/day) = 678.3 + 14.07*FFM.ant + 54.8*Sex – 2*Age + 2.5*eGFR + 140.7* (if DM) (R2 = 0.449); (3) REE (kcal/day) = 668 + 17.1*FFM.BIA – 2.7*Age − 92.7*Sex + 1.3*eGFR − 152.3 (if DM) (R2 = 0.45). The estimated REE (eREE) was not different from the mREE (P = 0.181), a high ICC was found and the mean difference between mREE and eREE was not different from zero for the three equations in the validation group. eREE accuracy between 90 and 110% was observed in 55.3%, 62.5% and 61% of the patients for Eqs. (1), (2) and (3), respectively.

Conclusion

The equations showed acceptable accuracy for REE prediction making them a valuable tool to support practitioners to provide more reliable energy recommendations for this group of patients.

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Funding

This study was supported by Fundação Oswaldo Ramos. Lilian Cuppari receives a scholarship from the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) 302765/2017-4.

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Authors and Affiliations

  1. Nutrition Program, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil

    Thais de Oliveira Fernandes & Lilian Cuppari

  2. Hospital do Rim-Fundação Oswaldo Ramos, Rua Pedro de Toledo, 282, São Paulo, 04039-000, Brazil

    Thais de Oliveira Fernandes & Lilian Cuppari

  3. Division of Nephrology, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil

    Danilo Takashi Aoike & Lilian Cuppari

  4. Department of Applied Nutrition, Nutrition Institute, Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro, Brazil

    Carla Maria Avesani

  5. Division of Renal Medicine-Baxter Novum, Department of Clinical Science, Intervention and Technology, Karolinska Institute (KI), Solna, Sweden

    Carla Maria Avesani

Authors
  1. Thais de Oliveira Fernandes
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  2. Carla Maria Avesani
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  3. Danilo Takashi Aoike
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  4. Lilian Cuppari
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Contributions

TOF and LC conceptualized the study design and wrote the manuscript; CMA conceptualized the study and performed data collection; TOF and DAT performed statistical analysis and data interpretation. All authors revised the manuscript and provided important intellectual considerations.

Corresponding author

Correspondence to Lilian Cuppari.

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de Oliveira Fernandes, T., Avesani, C.M., Aoike, D.T. et al. New predictive equations to estimate resting energy expenditure of non-dialysis dependent chronic kidney disease patients. J Nephrol 34, 1235–1242 (2021). https://doi.org/10.1007/s40620-020-00899-7

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  • DOI: https://doi.org/10.1007/s40620-020-00899-7

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