Body Composition’s Association with Resting Energy Expenditure Prediction in a Large Population Sample from Different Age Groups, Sex, and Physical Activity Levels

TL;DR

This study shows that fat-free mass strongly predicts resting energy expenditure in people of all ages and activity levels, while fat mass only shows a similar link in older adults.

Contribution

The study demonstrates the consistent relationship between fat-free mass and resting energy expenditure across diverse populations and highlights fat mass's limited role in older adults.

Findings

Fat-free mass strongly correlates with resting energy expenditure across all age groups and activity levels.

Fat mass shows a significant correlation with resting energy expenditure only in participants aged 60 and older.

FFM-based equations predict lower resting energy expenditure compared to weight- and height-based equations.

Abstract

Background: Resting energy expenditure (REE) represents 60–75% of total daily energy expenditure and is mainly determined by fat-free mass (FFM). Indeed, the predictive equations vary according to FFM techniques and population characteristics. Therefore, this study aimed to explore the influence of dual-energy X-ray absorptiometry (DXA)-derived FFM on REE prediction by different predictive equations in a large and diverse cohort. Methods: A total of 1987 active and sedentary participants of both sexes (43.8 ± 19.4 years) underwent body composition assessment by DXA. REE was predicted using the Harris–Benedict, Schofield, Mifflin–St Jeor (weight- and height-based), and Mifflin (FFM-based) equations. Statistical analyses included Kruskal–Wallis, Spearman correlations, and linear regression. Results: Men presented higher absolute FFM, whereas women exhibited higher relative fat mass (FM) (p < 0.01). Across age groups, FFM declined progressively, while FM increased (p < 0.01). The REE differed significantly (p < 0.001) between equations, with the lowest values predicted from the FFM-based model, while the Harris–Benedict and Schofield equations showed the highest REE, especially in women. Strong correlations were observed between FFM and REE (r = 0.77–0.98; p < 0.01) for all age groups and equations, whereas FM showed strong correlations (r = 0.77–0.85; p < 0.01) only for the ≥60 years group. REE tended to be higher in active than sedentary participants, with the correlations to FFM and FM exhibiting a similar profile to that observed for the whole group. Conclusions: FFM showed a strong association with the estimate of REE in active and sedentary participants from both sexes and different age groups, but FM showed a similar trend in older participants only. Therefore, the increase or the maintenance of FFM with an active lifestyle is important to keep REE at high and efficient levels regardless of sex and age.

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