New predictive equations for estimating resting energy expenditure in subjects with normal weight and overweight

• Published in: Nutrition (Burbank, Los Angeles County, Calif.) Vol. 84; p. 111105
• Main Authors: Marra, Maurizio, Sammarco, Rosa, Cioffi, Iolanda, Morlino, Delia, Di Vincenzo, Olivia, Speranza, Enza, Pasanisi, Fabrizio
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.04.2021; Elsevier Limited
• Subjects: Accuracy; Age; Bias; bioelectrical impedance; Bioimpedance analysis; Body composition; Body mass index; Body size; Body weight; Calibration; Calorimetry; Carbon dioxide; Clinical medicine; Energy; Energy expenditure; equations; Estimation; Formulas (mathematics); Indirect calorimetry; Mathematical models; Men; Normal weight; nutrition; Overweight; Phase angle; Predictive equations; Resting energy expenditure; Sexes; Variables; Womens health; United States > US; Phase angle; Bioimpedance analysis; Normal weight; Indirect calorimetry; Resting energy expenditure; Predictive equations
• ISSN: 0899-9007; 1873-1244; 1873-1244
• DOI: 10.1016/j.nut.2020.111105

•Indirect calorimetry is considered the reference method for the measurement of resting energy expenditure (REE).•Predictive equations are commonly used as an alternative method for estimating REE.•New REE equations with bioimpedance analysis (BIA) variables for subjects with normal weight and overweight.•The equation with raw BIA variables showed the highest accuracy at individual level. The aim of this study was to develop and validate new predictive equations for estimating resting energy expenditure (REE) in subjects with normal weight and overweight, considering anthropometric parameters as well as raw variables from bioimpedance analysis (BIA). Adult participants with normal weight and overweight were recruited and randomly split into calibration and validation groups. Indirect calorimetry (IC) and BIA were performed in all subjects. New predictive equations were developed using the following models: model 1 with age, weight, stature, and body mass index (BMI) as predictors; and model 2: model 1 + raw BIA variables (bioimpedance index and phase angle). The accuracy of the new equations at both the group (bias) and individual (within ±10%) levels was tested in the validation group. Three published predictive equations were also compared, with the REE values measured by IC. A total of 2483 adults were included for developing and validating the new equations. All selected formulas, including the new ones, showed a bias of <5% in estimating REE at the group level. Accuracy at the individual level was slightly higher for the new equations, especially for the equation based on raw BIA variables (men = 70.3%; women = 72.3%). Compared to the equations in the literature, the new equations showed good accuracy at both the group and individual levels, with a slight improvement in individual accuracy for the formula including raw BIA variables. However, future research is required to verify the role of the raw BIA variables in predicting REE in subjects with normal weight and overweight.