Are Raw BIA Variables Useful for Predicting Resting Energy Expenditure in Adults with Obesity?

Abstract

This study aimed to develop and validate new predictive equations for resting energy expenditure (REE) in a large sample of subjects with obesity also considering raw variables from bioimpedance-analysis (BIA). A total of 2225 consecutive obese outpatients were recruited and randomly assigned to calibration (n = 1680) and validation (n = 545) groups. Subjects were also split into three subgroups according to their body mass index (BMI). The new predictive equations were generated using two models: Model 1 with age, weight, height, and BMI as predictors, and Model 2 in which raw BIA variables (bioimpedance-index and phase angle) were added. Our results showed that REE was directly correlated with all anthropometric and raw-BIA variables, while the correlation with age was inverse. All the new predictive equations were effective in estimating REE in both sexes and in the different BMI subgroups. Accuracy at the individual level was high for specific group-equation especially in subjects with BMI > 50 kg/m2. Therefore, new equations based on raw-BIA variables were as accurate as those based on anthropometry. Equations developed for BMI categories did not substantially improve REE prediction, except for subjects with a BMI > 50 kg/m2. Further studies are required to verify the application of those formulas and the role of raw-BIA variables for predicting REE.

External Validation of Equations to Estimate Resting Energy Expenditure in 2037 Children and Adolescents with and 389 without Obesity: A Cross-Sectional Study

“…Using purposely developed BIA equations to estimate FFM, we found that anthropometry-and FFM-based equations were similarly accurate at estimating REE in children with severe obesity [9], confirming the findings of Müller et al [15] in children and adolescents with and without obesity. Our findings are also in agreement with those of Marra et al [27], who found that raw BIA predictors (whole body impedance and phase angle) were not superior to anthropometric indicators at estimating REE. The second limitation is that we studied Caucasian children only, the reason being that non-Caucasian individuals with obesity account for less than 2% of the persons currently followed at the Istituto Auxologico Italiano, that is, the study center that enrolled most study subjects [3].…”

Accuracy of Predictive Resting-Metabolic-Rate Equations in Chinese Mainland Adults

“…The limits of agreement were defined as the mean difference ± 1.98 SD. The accuracy of the predictive equations at the individual level was defined as the percentage of predicted RMR that was within ±10% of the measured RMR; overpredictions were considered to be ≥10%, and underpredictions were ≤−10% [25]. The new prediction equations (Chinese mainland equations) were developed by using multiple stepwise regression analysis to estimate RMR based on demographic and anthropometric data.…”

Resting Energy Expenditure in Elite Athletes: Development of New Predictive Equations Based on Anthropometric Variables and Bioelectrical Impedance Analysis Derived Phase Angle

“…Not surprisingly, the results of these studies showed that the raw BIA variables may improve the prediction power under physiological conditions [18], but only by a limited extent in subjects possibly with altered body water distribution [19,20].…”

“…Alternatively, raw BIA variables such as bioimpedance index (BI-Index) and phase angle (PhA) might be taken into account. Given that BI-index is a direct proxy marker of FFM, while PhA is related to body cell mass [17], a relationship between energy expenditure and BI-index and PhA has already been observed in normal-weight or overweight subjects [18] as well as in patients with obesity [19] and Crohn's disease [20].Not surprisingly, the results of these studies showed that the raw BIA variables may improve the prediction power under physiological conditions [18], but only by a limited extent in subjects possibly with altered body water distribution [19,20].Based on this background, the primary aim of this study was to develop and validate new predictive equations of REE speci c for elite athletes, considering not only the anthropometric measures, but also the raw BIA variables as predictors. Additionally, we evaluated in elite athletes of different sports the accuracy of selected predictive equations of REE (for general population or athletes) at both population and individual level.…”