Weight and body composition changes affect resting energy expenditure predictive equations during a 12‐month weight‐loss intervention

Objective Mathematical equations that predict resting energy expenditure (REE) are widely used to derive calorie prescriptions during weight‐loss interventions. Although such equations are known to introduce group‐ and individual‐level error into REE prediction, their validity has largely been assessed in weight‐stable populations. Therefore, this study sought to characterize how weight change affects the validity of commonly used REE predictive models throughout a 12‐month weight‐loss intervention. Methods Changes in predictive error of four models (Mifflin‐St‐Jeor, Harris‐Benedict, Owen, and World Health Organization/Food and Agriculture) were assessed at 1‐, 6‐, and 12‐month time points in adults (n = 66, 76% female, aged 18‐55 years, BMI = 27‐45 kg/m2) enrolled in a randomized clinical weight‐loss trial. Results All equations experienced significant negative shifts in bias (measured − predicted REE) toward overprediction from baseline to 1 month (p < 0.05). Three equations showed reversal of bias in the positive direction (toward underprediction) from baseline to 12 months (p < 0.05). Early changes in bias were correlated with decreased fat‐free mass (p ≤ 0.01). Conclusions Changes in body composition and mass during a 12‐month weight‐loss intervention significantly affected REE predictive error in adults with overweight and obesity. Weight history should be considered when using mathematical models to predict REE during periods of weight fluctuation.