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

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

JOURNAL=Frontiers in Nutrition

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2022.881719

DOI=10.3389/fnut.2022.881719

ISSN=2296-861X

ABSTRACT=Background: Estimating energy requirements (ER) is crucial for giving nutritional attention to chronic kidney disease (CKD) patients. Current guidelines recommend measuring ER with indirect calorimetry (IC) when possible. Due to complex clinical settings, the use of simple formula and clinical criteria based are suggested. 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). 
Methods: Eighty patients with CKD participated. Indirect calorimetry (IC) was performed in all patients. The calorimeter analyzed metabolic measurements every minute for 15 minutes after autocalibration with barometric pressure, temperature, and humidity. Bioelectrical Impedance Analysis was performed. Fat-free mass (FFM) was registered among other bioelectrical components. Hand grip strength was evaluated and an average of 3 repetitions was recorded. Nutritional status was assessed with the subjective global assessment.  Patients categorized as B or C were then considered as having PEW. 
Results. Seventy-one patients were analyzed. Model 2a incorporated BIA-FFM, model 2b substituted weight (kg) for BIA-FFM, and model 2c substituted weight for dominant handgrip strength. All other variables were computed-selected stepwise with a p<0.01 significance level; malnutrition was consistently associated with ER among other clinical variables. The model that included BIA-FFM had an adjusted R2 of 0.46, while the model that included weight (Kg) had an adjusted R2 of 0.44. Our models had concordance values between 0.60 and 0.65 with the gold standard, whereas other energy expenditure estimation equations had concordance values between 0.36 and 0.55 with it. 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 measurements obtained via IC. The agreement between our models and others previously validated for the CKD patient is high; however, the agreement between the latter and IC measurements is lower.