Comparative analysis of basal metabolic rate measurement methods in overweight and obese individuals: A retrospective study

Author:

Karagun Baris1ORCID,Baklaci Nuh1

Affiliation:

1. Department of Endocrinology and Metabolism Diseases, Faculty of Medicine, Baskent University, Adana, Turkey.

Abstract

The global prevalence of overweight and obesity is on the rise, presenting significant health challenges worldwide. Obesity is associated with various chronic diseases and imposes substantial economic burdens on society. Accurate assessment of basal metabolic rate (BMR) is crucial for effective weight management strategies. This retrospective study, conducted at Baskent University Hospital between October 2019 and October 2023, analyzed data from 133 overweight and obese individuals. Various methods including indirect calorimetry (IC), predictive equations (Harris–Benedict and Mifflin–St Jeor), and bioelectrical impedance analysis (BIA) were used to estimate BMR. Additionally, demographic, clinical, and body composition data were recorded. The mean BMR measured using IC was 1581 ± 322 kcal/day, which was significantly lower than the BMR estimated by other methods such as BIA (1765.8 ± 344.09 kcal/day), Harris–Benedict (1787.64 ± 341.4 kcal/day), and Mifflin–St Jeor equations (1690.08 ± 296.36 kcal/day) (P < .001). Among the predictive equations, the Mifflin–St Jeor method provided BMR estimates closest to the gold standard IC. When BMR measurement methods were compared to IC, 36.8% of measurements with the Harris–Benedict equation method, 50.4% with the Mifflin–St Jeor equation method, and 36.1% with the BIA method were within ± 10% agreement with IC measurements. Significant correlations were found between BMR and body composition parameters such as fat-free mass, muscle mass, and fat mass (R = 0.681, P < .001; R = 0.699, P < .001; R = 0.595, P < .001, respectively). Regression analysis identified that variables such as weight, height, body mass index, and muscle mass significantly predicted BMR measured by IC, accounting for 69.1% of the variance. This study underscores the challenges in assessing BMR in overweight and obese individuals. While IC remains the gold standard, predictive equations and BIA offer alternative methods. The Mifflin–St Jeor equation emerged as a practical option, closely aligning with IC results. However, discrepancies between methods and the influence of body composition highlight the importance of individualized approaches to BMR assessment in weight management strategies.

Publisher

Ovid Technologies (Wolters Kluwer Health)

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