Characterizing the resting metabolic rate ratio in ovulatory exercising women over 12 months

Accuracy of Resting Metabolic Rate Prediction Equations in Athletes: A Systematic Review with Meta-analysis

BACKGROUND

Resting metabolic rate (RMR) prediction equations are often used to calculate RMR in athletes; however, their accuracy and precision can vary greatly.

OBJECTIVE

The aim of this systematic review and meta-analysis was to determine which RMR prediction equations are (i) most accurate (average predicted values closest to measured values) and (ii) most precise (number of individuals within 10% of measured value).

DATA SOURCES

A systematic search of PubMed, CINAHL, SPORTDiscus, Embase, and Web of Science up to November 2021 was conducted.

ELIGIBILITY CRITERIA

Randomised controlled trials, cross-sectional observational studies, case studies or any other study wherein RMR, measured by indirect calorimetry, was compared with RMR predicted via prediction equations in adult athletes were included.

ANALYSIS

A narrative synthesis and random-effects meta-analysis (where possible) was conducted. To explore heterogeneity and factors influencing accuracy, subgroup analysis was conducted based on sex, body composition measurement method, athlete characteristics (athlete status, energy availability, body weight), and RMR measurement characteristics (adherence to best practice guidelines, test preparation and prior physical activity).

RESULTS

Twenty-nine studies (mixed sports/disciplines n = 8, endurance n = 5, recreational exercisers n = 5, rugby n = 3, other n = 8), with a total of 1430 participants (822 F, 608 M) and 100 different RMR prediction equations were included. Eleven equations satisfied criteria for meta-analysis for accuracy. Effect sizes for accuracy ranged from 0.04 to - 1.49. Predicted RMR values did not differ significantly from measured values for five equations (Cunningham (1980), Harris-Benedict (1918), Cunningham (1991), De Lorenzo, Ten-Haaf), whereas all others significantly underestimated or overestimated RMR (p < 0.05) (Mifflin-St. Jeor, Owen, FAO/WHO/UNU, Nelson, Koehler). Of the five equations, large heterogeneity was observed for all (p < 0.05, I2 range: 80-93%) except the Ten-Haaf (p = 0.48, I2 = 0%). Significant differences between subgroups were observed for some but not all equations for sex, athlete status, fasting status prior to RMR testing, and RMR measurement methodology. Nine equations satisfied criteria for meta-analysis for precision. Of the nine equations, the Ten-Haaf was found to be the most precise, predicting 80.2% of participants to be within ± 10% of measured values with all others ranging from 40.7 to 63.7%.

CONCLUSION

Many RMR prediction equations have been used in athletes, which can differ widely in accuracy and precision. While no single equation is guaranteed to be superior, the Ten-Haaf (age, weight, height) equation appears to be the most accurate and precise in most situations. Some equations are documented as consistently underperforming and should be avoided. Choosing a prediction equation based on a population of similar characteristics (physical characteristics, sex, sport, athlete status) is preferable. Caution is warranted when interpreting RMR ratio of measured to predicted values as a proxy of energy availability from a single measurement.

PROSPERO REGISTRATION

CRD42020218212.

The Influence of Energy Balance and Availability on Resting Metabolic Rate: Implications for Assessment and Future Research Directions

Resting metabolic rate (RMR) is a significant contributor to an individual's total energy expenditure. As such, RMR plays an important role in body weight regulation across populations ranging from inactive individuals to athletes. In addition, RMR may also be used to screen for low energy availability and energy deficiency in athletes, and thus may be useful in identifying individuals at risk for the deleterious consequences of chronic energy deficiency. Given its importance in both clinical and research settings within the fields of exercise physiology, dietetics, and sports medicine, the valid assessment of RMR is critical. However, factors including varying states of energy balance (both short- and long-term energy deficit or surplus), energy availability, and prior food intake or exercise may influence resulting RMR measures, potentially introducing error into observed values. The purpose of this review is to summarize the relationships between short- and long-term changes in energetic status and resulting RMR measures, consider these findings in the context of relevant recommendations for RMR assessment, and provide suggestions for future research.

Accuracy of Resting Metabolic Rate Prediction Equations in Sport Climbers

Resting metabolic rate (RMR) represents the energy required to maintain vital body functions. In dietary practice, RMR is determined by predictive equations on the basis of using body weight or fat-free mass. Our study aimed to assess whether predictive equations used to estimate RMR are reliable tools for estimating the energy requirements of sport climbers. The study included 114 sport climbers whose RMR was measured with a Fitmate WM. Anthropometric measurements were performed with X-CONTACT 356. The resting metabolic rate was measured by indirect calorimetry and was compared with the RMR estimated by 14 predictive equations on the basis of using body weight/fat-free mass. All equations underestimated RMR in male and female climbers, except for De Lorenzo's equation in the group of women. The De Lorenzo equation demonstrated the highest correlation with RMR in both groups. The results of the Bland-Altman tests revealed an increasing measurement error with increasing metabolism for most of the predictive equations in male and female climbers. All equations had low measurement reliability according to the intraclass correlation coefficient. Compared with the indirect calorimetry measurement results, none of the studied predictive equations demonstrated high reliability. There is a need to develop a highly reliable predictive equation to estimate RMR in sport climbers.

RMR Ratio as a Surrogate Marker for Low Energy Availability

PURPOSE OF REVIEW

Low energy availability (EA) poses severe consequences to athlete performance and overall health. Suppressed resting metabolic rate (RMR) has been observed during periods of low EA. Thus, it has been suggested that the ratio of RMR measured via indirect calorimetry to predictive RMR using a standard predictive equation (RMR ratio) may be a useful assessment of EA in athletes. This review evaluated the use of RMR ratio as a surrogate marker for low EA in athletes and compared methodologies for measuring RMR ratio.

RECENT FINDINGS

Decreased RMR ratio in recent studies often correlates with signs of low EA; however, athletes with less severe cases of energy deficiency may not present with a low RMR ratio. Additionally, the methodology for RMR ratio measurements lacks standardization and varies in recent studies. Use of RMR ratio has promise as a complementary EA measurement when used in combination with other assessment tools.

The Path Towards Progress: A Critical Review to Advance the Science of the Female and Male Athlete Triad and Relative Energy Deficiency in Sport

Energy status plays a key role in the health of athletes and exercising individuals. Energy deficiency/low energy availability (EA), referring to a state in which insufficient energy intake and/or excessive exercise energy expenditure has resulted in compensatory metabolic adaptations to conserve fuel, can affect numerous physiological systems in women and men. The Female Athlete Triad, Male Athlete Triad, and Relative Energy Deficiency in Sport (RED-S) models conceptualize the effects of energy deficiency in athletes, and each model has strengths and limitations. For instance, the Female Athlete Triad model depicts relationships between low EA, reproductive, and bone health, underpinning decades of experimental evidence, but may be perceived as limited in scope, while the more recent RED-S model proposes a wider range of potential health effects of low EA, though many model components require more robust scientific justification. This critical review summarizes current evidence regarding the effects of energy deficiency on athlete health by addressing the quality of the underlying science, the strengths and limitations of each model, and highlighting areas where future research is needed to advance the field. With the health and wellness of athletes and exercising individuals as the overarching priority, we conclude with specific steps that will help focus future research on the Female and Male Athlete Triad and RED-S, and encourage all researchers, clinicians, and practitioners to collaborate to support the common goal of promoting the highest quality science and evidence-based medicine in pursuit of the advancement of athletes' health, well-being, and performance.

Prevalence of Surrogate Markers of Relative Energy Deficiency in Male Norwegian Olympic-Level Athletes

The syndrome of Relative Energy Deficiency in Sport (RED-S) includes wide-ranging effects on physiological and psychological functioning, performance, and general health. However, RED-S is understudied among male athletes at the highest performance levels. This cross-sectional study aimed to investigate surrogate RED-S markers prevalence in Norwegian male Olympic-level athletes. Athletes (n = 44) aged 24.7 ± 3.8 years, body mass 81.3 ± 15.9 kg, body fat 13.7% ± 5.8%, and training volume 76.1 ± 22.9 hr/month were included. Assessed parameters included resting metabolic rate (RMR), body composition, and bone mineral density by dual-energy X-ray absorptiometry and venous blood variables (testosterone, free triiodothyronine, cortisol, and lipids). Seven athletes (16%) grouped by the presence of low RMR (RMRratio < 0.90) (0.81 ± 0.07 vs. 1.04 ± 0.09, p < .001, effect size 2.6), also showed lower testosterone (12.9 ± 5.3 vs. 19.0 ± 5.3 nmol/L, p = .020) than in normal RMR group. In low RMRratio individuals, prevalence of other RED-S markers (-subclinical-low testosterone, low free triiodothyronine, high cortisol, and elevated low-density lipoprotein) was (N/number of markers): 2/0, 2/1, 2/2, 1/3. Low bone mineral density (z-score < -1) was found in 16% of the athletes, all with normal RMR. Subclinical low testosterone and free triiodothyronine levels were found in nine (25%) and two (5%) athletes, respectively. Subclinical high cortisol was found in 23% of athletes while 34% had elevated low-density lipoprotein cholesterol levels. Seven of 12 athletes with two or more RED-S markers had normal RMR. In conclusion, this study found that multiple RED-S markers also exist in male Olympic-level athletes. This highlights the importance of regular screening of male elite athletes, to ensure early detection and treatment of RED-S.

The Male Athlete Triad-A Consensus Statement From the Female and Male Athlete Triad Coalition Part II: Diagnosis, Treatment, and Return-To-Play

ABSTRACT

The Male Athlete Triad is a medical syndrome most common in adolescent and young adult male athletes in sports that emphasize a lean physique, especially endurance and weight-class athletes. The 3 interrelated conditions of the Male Athlete Triad occur on spectrums of energy deficiency/low energy availability (EA), suppression of the hypothalamic-pituitary-gonadal axis, and impaired bone health, ranging from optimal health to clinically relevant outcomes of energy deficiency/low EA with or without disordered eating or eating disorder, functional hypogonadotropic hypogonadism, and osteoporosis or low bone mineral density with or without bone stress injury (BSI). Because of the importance of bone mass acquisition and health concerns in adolescence, screening is recommended during this time period in the at-risk male athlete. Diagnosis of the Male Athlete Triad is best accomplished by a multidisciplinary medical team. Clearance and return-to-play guidelines are recommended to optimize prevention and treatment. Evidence-based risk assessment protocols for the male athlete at risk for the Male Athlete Triad have been shown to be predictive for BSI and impaired bone health and should be encouraged. Improving energetic status through optimal fueling is the mainstay of treatment. A Roundtable on the Male Athlete Triad was convened by the Female and Male Athlete Triad Coalition in conjunction with the 64th Annual Meeting of the American College of Sports Medicine in Denver, Colorado, in May of 2017. In this second article, the latest clinical research to support current models of screening, diagnosis, and management for at-risk male athlete is reviewed with evidence-based recommendations.

Female Athlete Triad Coalition cumulative risk assessment tool: proposed alternative scoring strategies

We (i) identified alternative scoring strategies for the Female Athlete Triad Coalition cumulative risk assessment (CRA) tool to be utilized when particular risk factors (bone mineral density (BMD), oligomenorrhea/amenorrhea) cannot be determined; (ii) objectively defined dietary restriction for use in the CRA tool; and (iii) explored proxy measures of energy deficiency. This cross-sectional investigation of exercising women (n = 166) utilized an existing database derived from multiple studies designed to assess health, exercise, and menstrual function. Data from the screening/baseline period of each study included: anthropometrics, dual-energy X-ray absorptiometry, disordered eating questionnaires, descriptive data, and proxy measures of energy deficiency (total triiodothyronine (TT₃) and ratio of measured-to-predicted resting metabolic rate (mRMR/pRMR)). Substituting delayed menarche for BMD was the best-fit replacement resulting in 15 (9%) participants being categorized in different clearance categories. When menstrual status cannot be assessed, such as during hormonal contraceptive use, low energy availability (EA) determined using self-report and disordered eating questionnaires was the best substitution, resulting in 34 (20%) participants being categorized in different clearance categories. Based on original clearance categorizations, the provisional group had lower TT₃ (78.3 ± 2.2 ng/dL; 92.7 ± 2.7 ng/dL) and Harris-Benedict mRMR/pRMR (0.85 ± 0.01; 0.90 ± 0.01) than the full group. Until an updated risk assessment tool is developed, delayed menarche can substitute for low BMD and low EA for oligomenorrhea/amenorrhea. Novelty This investigation addresses previous limitations of the Triad CRA tool. Disordered eating questionnaires can be used to objectively identify dietary restriction for the low EA risk factor. When a risk factor cannot be assessed, delayed menarche can substitute for low BMD and low EA for oligomenorrhea/amenorrhea.