Prediction Equations to Estimate Resting Metabolic Rate in Healthy, Community-Dwelling Chinese Older Adults

TL;DR

This study created new equations to estimate resting metabolic rate in older Chinese adults, which are more accurate than existing ones.

Contribution

Developed and validated population-specific RMR prediction equations for Chinese older adults.

Findings

Two new equations (Cai1 and Cai2) showed 82.5% accuracy in predicting RMR.

Existing equations overestimated RMR by 8.39% to 38.03% in this population.

The new equations had minimal bias and strong correlations with measured RMR.

Abstract

Background: China’s rapidly aging population demonstrates the importance of conducting an accurate resting metabolic rate (RMR, kcal/day) assessment to mitigate geriatric nutritional imbalances—amid concurrent undernutrition (e.g., ~1/3 with protein insufficiency) and overnutrition (e.g., high obesity and type 2 diabetes rates). While RMR prediction equations exist for other populations, none are specific to Chinese older adults. This study aimed to develop and validate population-specific RMR prediction equations for community-dwelling Chinese older adults. Methods: A total of 189 healthy participants (Aged 69.5 ± 6.3, range: 60–94 years; BMI: 24.0 ± 3.1 kg/m2) were recruited from the Shanghai, China, community. RMR was measured via indirect calorimetry, and body composition via dual-energy X-ray absorptiometry. Results: Two novel prediction equations were derived: Cai1 (fat-free mass [FFM] + age): RMR = 1393.019 − (11.112 × age) + (11.963 × FFM); R2 = 0.572, and Cai2 (sex + age + weight [WT]): RMR = 1537.513 + (91.038 × sex) − (11.515 × age) + (5.436 × WT); R2 = 0.528. Both novel prediction equations achieved 82.5% adequacy (predicted RMR within 90–110% of measured values), minimal systematic bias (%) (−0.72% and −1.08%) and strong positive correlations with measured RMR (r = 0.792 and 0.773, both p < 0.001). Bland–Altman analysis confirmed no systematic bias. In contrast, 11 widely used published prediction equations (e.g., Harris–Benedict, Mifflin–St. Jeor) exhibited significant overestimation (systematic bias +8.39% to +38.03%). Conclusions: The novel population-specific RMR equations outperform published ones, providing a clinically reliable tool for individualized energy prescription in nutritional interventions to support healthy aging in Chinese older adults.

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