Theodoros Stampoulis, Alexandra Avloniti, Dimitrios Draganidis, Dimitrios Balampanos, Polyxeni Efthimia Chalastra, Anastasia Gkachtsou, Dimitrios Pantazis, Nikolaos-Orestis Retzepis, Maria Protopapa, Athanasios Poulios, Nikolaos Zaras, Maria Michalopoulou, Ioannis G Fatouros, Athanasios Chatzinikolaou
{"title":"New Bioelectrical Impedance-Based Equations to Estimate Resting Metabolic Rate in Young Athletes.","authors":"Theodoros Stampoulis, Alexandra Avloniti, Dimitrios Draganidis, Dimitrios Balampanos, Polyxeni Efthimia Chalastra, Anastasia Gkachtsou, Dimitrios Pantazis, Nikolaos-Orestis Retzepis, Maria Protopapa, Athanasios Poulios, Nikolaos Zaras, Maria Michalopoulou, Ioannis G Fatouros, Athanasios Chatzinikolaou","doi":"10.3390/mps8030053","DOIUrl":null,"url":null,"abstract":"<p><p>Resting metabolic rate (RMR) significantly impacts total daily energy expenditure, particularly on training days, and varies among trained individuals. Studies estimating RMR in this population show notable discrepancies. This study aimed to develop and validate new bioelectrical impedance analysis-based (BIA) RMR equations for young athletes, using a calibration and a validation group of 219 and 51 participants, respectively. RMR was measured via indirect calorimetry, while body composition was assessed through DXA and BIA. Correlation and agreement were evaluated by using Pearson's correlation coefficients and Bland-Altman analysis. Multiple linear regression was applied for the estimation of RMR and a one-way ANOVA was used to compare the new BIA-based equations with other specific formulas. A significant correlation was noted between the BIA and DXA measurements. The final equation, applicable to both genders, was significantly correlated with intracellular water (ICW) and trunk fat, predicting 71.1% of RMR variance. When analyzed separately, body weight and protein displayed a moderate correlation with RMR in men (r = 0.616, <i>p</i> < 0.001), while ICW was correlated with the percentage of body fat in women (r = 0.579, <i>p</i> < 0.001). In the validation group, the values obtained through the three BIA-based equations were similar to the measured RMR, but differed significantly from those obtained through the four existing equations for trained individuals. In conclusion, the developed equations based on BIA-mediated body composition analysis provide a reliable method for estimating RMR in trained populations daily.</p>","PeriodicalId":18715,"journal":{"name":"Methods and Protocols","volume":"8 3","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12101231/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Methods and Protocols","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/mps8030053","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Resting metabolic rate (RMR) significantly impacts total daily energy expenditure, particularly on training days, and varies among trained individuals. Studies estimating RMR in this population show notable discrepancies. This study aimed to develop and validate new bioelectrical impedance analysis-based (BIA) RMR equations for young athletes, using a calibration and a validation group of 219 and 51 participants, respectively. RMR was measured via indirect calorimetry, while body composition was assessed through DXA and BIA. Correlation and agreement were evaluated by using Pearson's correlation coefficients and Bland-Altman analysis. Multiple linear regression was applied for the estimation of RMR and a one-way ANOVA was used to compare the new BIA-based equations with other specific formulas. A significant correlation was noted between the BIA and DXA measurements. The final equation, applicable to both genders, was significantly correlated with intracellular water (ICW) and trunk fat, predicting 71.1% of RMR variance. When analyzed separately, body weight and protein displayed a moderate correlation with RMR in men (r = 0.616, p < 0.001), while ICW was correlated with the percentage of body fat in women (r = 0.579, p < 0.001). In the validation group, the values obtained through the three BIA-based equations were similar to the measured RMR, but differed significantly from those obtained through the four existing equations for trained individuals. In conclusion, the developed equations based on BIA-mediated body composition analysis provide a reliable method for estimating RMR in trained populations daily.