Nutritional Status and Other Clinical Variables Are Associated to the Resting Energy Expenditure in Patients With Chronic Kidney Disease: A Validity Study

Samuel Ramos-Acevedo; Luis Rodríguez-Gómez; Sonia López-Cisneros; Ailema González-Ortiz; Ángeles Espinosa-Cuevas

doi:10.3389/fnut.2022.881719

Nutritional Status and Other Clinical Variables Are Associated to the Resting Energy Expenditure in Patients With Chronic Kidney Disease: A Validity Study

Front Nutr. 2022 May 18:9:881719. doi: 10.3389/fnut.2022.881719. eCollection 2022.

Authors

Samuel Ramos-Acevedo^{1

2}, Luis Rodríguez-Gómez¹, Sonia López-Cisneros¹, Ailema González-Ortiz³, Ángeles Espinosa-Cuevas^{1

4}

Affiliations

¹ Department of Nephrology and Mineral Metabolism, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico.
² Programa de Maestría y Doctorado en Ciencias Médicas y Odontológicas y de la Salud, Universidad Nacional Autónoma de México, Mexico City, Mexico.
³ Translational Research Center, Instituto Nacional de Pediatría, Mexico City, Mexico.
⁴ Department of Health Care, Universidad Autónoma Metropolitana, Mexico City, Mexico.

Abstract

Background: Estimating energy requirements (ER) is crucial for nutritional attention to chronic kidney disease (CKD) patients. Current guidelines recommend measuring ER with indirect calorimetry (IC) when possible. Due to clinical settings, the use of simple formulas is preferred. Few studies have modeled equations for estimating ER for CKD. Nevertheless, variables of interest such as nutritional status and strength have not been explored in these models. This study aimed to develop and validate a model for estimating REE in patients with CKD stages 3-5, who were not receiving renal replacement therapy (RTT), using clinical variables and comparing it with indirect calorimetry as the gold standard.

Methods: In this study 80 patients with CKD participated. Indirect calorimetry (IC) was performed in all patients. The calorimeter analyzed metabolic measurements every minute for 15 min after autocalibration with barometric pressure, temperature, and humidity. Bioelectrical Impedance Analysis (BIA) was performed. Fat-free mass (FFM) was registered among other bioelectrical components. Handgrip strength (HGS) was evaluated and an average of 3 repetitions was recorded. Nutritional status was assessed with the subjective global assessment (SGA). Patients categorized as B or C were then considered as having malnutrition.

Results: We analyzed 71 patients and 3 models were generated. Model 1a included FFM; Model 2a included weight; Model 3c included handgrip strength (HGS). All other variables were stepwise, computer-selected with a p < 0.01 significance level; Malnutrition was consistently associated with ER among other clinical variables in all models (p < 0.05). The model that included BIA-FFM had R ² _adjusted = 0.46, while the model that included weight (Kg) had an adjusted R ² _adjusted = 0.44. The models had moderate concordance, LC = 0.60-0.65 with the gold standard, whereas other energy expenditure estimation equations had LC = 0.36 and 0.55 with indirect calorimetry. Using these previously validated equations as a reference, our models had concordance values ranging from 0.66 to 0.80 with them.

Conclusion: Models incorporating nutritional status and other clinical variables such as weight, FFM, comorbidities, gender, and age have a moderate agreement with REE. The agreement between our models and others previously validated for the CKD patient is good; however, the agreement between the latter and IC measurements is moderate. The KDOQI lowest recommendation (25 Kcals/kg body weight) considering the 22% difference with respect to the IC for total energy expenditure rather than for REE.

Keywords: chronic kidney disease; energy; energy requirements; equation; indirect calorimetry; nutritional attention; resting energy expenditure; validity.