Resting metabolic rate (RMR) of recreational female CrossFit® practitioners: Agreement between indirect calorimetry and predictive equations and correlation between RMR ratio and metabolic hormone levels

Resting metabolic rate ratio (RMR ratio ) is calculated dividing the RMR from indirect calorimetry (RMR IC ) by the RMR estimate by equations (RMR e ). RMR ratio is a tool for energy suppression assessment and it can be useful in situations of low energy availability. To date, predictive equations have not been evaluated regarding their agreement with RMR of female CrossFit® practitioners. As such, we aimed to verify the agreement between RMR IC and RMR e and its correlation with metabolic hormones. This was a cross-sectional study with nonprobability sampling. Participants (healthy women aged 18–39 years, with at least 1 year of regular CrossFit® training experience) visited the laboratory once for blood collection and nutritional, body composition, and RMR assessment. RMR IC was estimated using K5; COSMED®. We evaluated its agreement with the RMR estimated by several equations. Then, for each predictive equation, the RMR ratio was calculated. We assessed triiodothyronine (TT 3 ), leptin, and insulin-like growth factor (IGF-1) levels. The Bland–Altman method was used to identify the agreement between the RMR IC and the RMR e . Thirty female recreational CrossFit® practitioners (33.11 ± 5.23 y) were assessed. The Cunningham (1980) equation was the one that most agreed with the RMR IC . Still, RMR IC did not differ when compared to TT 3 (p = 0.470), leptin (p = 0.848), and IGF-1 (p = 0.634) levels. Likewise, we found no differences when RMR ratio was compared by the median and tertiles of hormones. In summary, our findings revealed that the RMR e showed low agreement with the RMR IC . Besides, the RMR ratio did not differ according to the hormones TT 3 , leptin, and IGF-1 of female recreational CrossFit® practitioners. • Available prediction equations showed poor agreement with RMR IC of recreational female CrossFit® practitioners. • The equations that closely aligned with the RMR IC were those of Cunningham 1980 and Watson et al. (2019). • The RMR ratio was not correlated with TT 3 , leptin, and IGF-1 levels.

Authors

Publication Details

Journal
Human Nutrition & Metabolism
Published
2025-04-01
DOI
https://doi.org/10.1016/j.hnm.2025.200311
Citations
1
Primary Topic
Muscle metabolism and nutrition
Type
article
Field-Weighted Citation Impact
1.15
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Resting metabolic rate (RMR) of recreational female CrossFit® practitioners: Agreement between indirect calorimetry and predictive equations and correlation between RMR ratio and metabolic hormone levels

Fernanda Patti Nakamoto, Marcus Vinicius Lúcio dos Santos Quaresma, Cristiano Santos, Leonardo Azevedo Mobília Alvares et al.
1 citations
Human Nutrition & Metabolism
Muscle metabolism and nutrition
1.15
article

Resting metabolic rate (RMR) of recreational female CrossFit® practitioners: Agreement between indirect calorimetry and predictive equations and correlation between RMR ratio and metabolic hormone levels

Fernanda Patti Nakamoto, Marcus Vinicius Lúcio dos Santos Quaresma, Cristiano Santos, Leonardo Azevedo Mobília Alvares, Raphael Einsfeld Simões Ferreira, Ronaldo Vagner Thomatieli dos Santos, Bruno Brecaro
article en
1 citations

Abstract

Resting metabolic rate ratio (RMR ratio ) is calculated dividing the RMR from indirect calorimetry (RMR IC ) by the RMR estimate by equations (RMR e ). RMR ratio is a tool for energy suppression assessment and it can be useful in situations of low energy availability. To date, predictive equations have not been evaluated regarding their agreement with RMR of female CrossFit® practitioners. As such, we aimed to verify the agreement between RMR IC and RMR e and its correlation with metabolic hormones. This was a cross-sectional study with nonprobability sampling. Participants (healthy women aged 18–39 years, with at least 1 year of regular CrossFit® training experience) visited the laboratory once for blood collection and nutritional, body composition, and RMR assessment. RMR IC was estimated using K5; COSMED®. We evaluated its agreement with the RMR estimated by several equations. Then, for each predictive equation, the RMR ratio was calculated. We assessed triiodothyronine (TT 3 ), leptin, and insulin-like growth factor (IGF-1) levels. The Bland–Altman method was used to identify the agreement between the RMR IC and the RMR e . Thirty female recreational CrossFit® practitioners (33.11 ± 5.23 y) were assessed. The Cunningham (1980) equation was the one that most agreed with the RMR IC . Still, RMR IC did not differ when compared to TT 3 (p = 0.470), leptin (p = 0.848), and IGF-1 (p = 0.634) levels. Likewise, we found no differences when RMR ratio was compared by the median and tertiles of hormones. In summary, our findings revealed that the RMR e showed low agreement with the RMR IC . Besides, the RMR ratio did not differ according to the hormones TT 3 , leptin, and IGF-1 of female recreational CrossFit® practitioners. • Available prediction equations showed poor agreement with RMR IC of recreational female CrossFit® practitioners. • The equations that closely aligned with the RMR IC were those of Cunningham 1980 and Watson et al. (2019). • The RMR ratio was not correlated with TT 3 , leptin, and IGF-1 levels.

Human Nutrition & MetabolismVol. 40
Gender equality
Openalex Percentile: Top 26%
Muscle metabolism and nutrition
1.15
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.