2025/04/22 by Ida A. Heikura, Ming‐Chang Tsai, Erik Sesbreno +4 · 1 voice · 1 citation
Biochemistry, Genetics and Molecular Biology · Medicine · Nursing · #Adipose Tissue and Metabolism #Clinical Nutrition and Gastroenterology #Muscle metabolism and nutrition
paper · doi:10.1123/ijsnem.2024-0254
openalex publication_date 2025/04/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/08
OBJECTIVES: Measured resting metabolic rate (RMR) was compared to predicted RMR equations (RMRratio) to see whether a low RMRratio relates to the Relative Energy Deficiency in Sport (REDs) Clinical Assessment Tool 2 (CAT2) severity/risk score. METHODS: Female (n = 127) and male (n = 53) athletes (performance Tiers 3-5) were assigned green/yellow/orange/red light according to CAT2. RMR and submaximal exercise energy expenditure (via cycle ergometer) were assessed fasted on the same morning via indirect calorimetry. Low RMR was defined as RMRratio < 0.90, with 11 RMR prediction equations tested for sensitivity, specificity, and predictive validity against the CAT2. RESULTS: RMRratio (Cunningham) was only lower in red versus green light (0.90 ± 0.07 vs. 0.99 ± 0.10; p = .023; but RMRratio was only low in 44% of red light athletes). The prevalence of low RMRratio ranged from 1% (Owen equation) to 68% (van Hooren equation) despite the overall prevalence of REDs being 46%. As a diagnostic (no REDs [green] vs. REDs [yellow + orange + red]), Cunningham equation reported sensitivity (true positives) of 0.77 at RMRratio of 1.00 and specificity (true negatives) of 1.00 at RMRratio of 0.70. Exercise energy expenditure was significantly lower in green versus orange (0.131 ± 0.013 vs. 0.142 ± 0.008 kcal·kg fat-free mass-1·min-1; p < .001) but was greater in red (0.127 ± 0.011) versus orange. CONCLUSION: Interpretation of RMRratio is radically impacted by choice of prediction equation. Although there may be some utility in cross-sectionally detecting extreme REDs cases (red light) via RMRratio, more research with a focus on sport/phenotype-specific prediction equations and varying risk thresholds is required to strengthen the validity and reliability of RMR as a part of REDs diagnostics.