Ballet dancers are reported to have an increased risk for low energy availability (EA) with or without disordered eating (DE) behavior or eating disorders (Doyle-Lucas et al., 2010; Lagowska et al., 2014; Nattiv et al., 2007). Energy deficiency is related to impaired performance and a wide range of health consequences in both female and male athletes and dancers (Mountjoy et al., 2014). Premature osteoporosis, cardiovascular risk factors, and eating disorders with clinical signs, such as underweight, extremely low body fat, menstrual dysfunction (MD), and hypotension, are regarded as the most serious clinical outcomes due to persistent energy deficiency, with increased risk for mortality and impaired long-term health (Keen & Drinkwater, 1997; Nattiv et al., 2007). Therefore, early detection of energy deficiency is utmost important.
Energy deficiency and related conditions have been reported to be frequent in female professional ballet dancers (Doyle-Lucas et al., 2010; Hoch et al., 2011; Kaufman et al., 2002; Warren et al., 2003), although the existing literature concerning bone health is controversial (Amorim et al., 2015). Studies investigating the prevalence of energy deficiency or surrogate markers in male professional ballet dancers are scarce.
The assessment of both energy intake and exercise energy expenditure outside a laboratory setting to estimate EA and energy balance has several methodological challenges (Doyle-Lucas et al., 2010; Mountjoy et al., 2014). Resting metabolic rate (RMR) represents the energy cost of basic physiological functions, including reproductive function and thermoregulation. The ratio (RMRratio) between standardized measured RMR (mRMR) and predicted RMR (pRMR) has been reported to be lower in amenorrheic compared with eumenorrheic ballet dancers, as well as in dancers with low versus optimal EA (Doyle-Lucas et al., 2010; Myburgh et al., 1999). Furthermore, an increased drive for thinness (DT) score has been associated with lower RMRratio in recreational active women (De Souza et al., 2007). A prediction error of 10% is expected when calculating RMR for an individual (Cunningham, 1980), and therefore, an expected normal range of RMRratio is 0.9–1.1 (Sterling et al., 2009). A RMRratio < 0.90 has therefore been widely used in the literature and accepted as a useful surrogate marker for energy deficiency in active females, although different equations for pRMR has been used (De Souza et al., 2007, 2008; Gibbs et al., 2011; Melin et al., 2015; Scheid et al., 2009; Vescovi et al., 2008). How the use of different prediction equations influences the prevalence of suppressed RMR have not previously been investigated. In addition, knowledge concerning the prevalence of risk factors related to energy deficiency in professional ballet dancers is needed, especially among males.
Hence, we aimed to study the impact of using different equations to predict RMR on the prevalence of suppressed RMRratio and to explore associations with additional physiological conditions related to energy deficiency in professional male and female ballet dancers.
Methods
Permission to undertake the study was provided by the regional ethical committee, the Capital Region of Denmark (H-15012549). All subjects had been informed orally and in writing, and signed an informed consent form. Inclusion criteria were female and male professional dancers at the Royal Danish Ballet between 18 and 40 years. One female subject was excluded due to fever on the day of examination. A total of 40 subjects (n = 20 males and n = 20 females) were included in the study and were examined at the Department of Nutrition, Exercise and Sports at the University of Copenhagen. Subjects were instructed to arrive in a fasting (no food, caffeine, or alcohol intake for minimum 12 hr and no fluid intake for minimum 8 hr) and rested state (no training the day before and transportation to the university by car or public transportation). Female subjects were to contact the research team on their first day of menstruation, and they were examined on the second to the seventh day of bleeding. Amenorrheic females were examined at any given time.
Anthropometry and Bone Health Assessment
Bone health and body composition were assessed using dual-energy X-ray absorptiometry (DXA; Lunar Prodigy Pro; GE Medical Systems, Madison, WI). Bone mineral density (BMD) was determined for whole body, lumbar spine (L1–L4), total left hip, and neck. Normal BMD was defined as Z-score > −1SD in all sites, and low BMD was defined as Z-score < −1SD in at least one of the measured sites (Nattiv et al., 2007). Low body fat percentage was defined as <5% and <12% for male and female subjects, respectively (Meyer et al., 2013). Height was measured to the nearest 0.1 cm, and body weight was measured in underwear after voiding with an accuracy of 0.05 kg on a decimal scale. (Lindeltronic 8000, Copenhagen, Denmark). Body mass index (BMI) was calculated as body weight (in kg)/height (in m2), and underweight was defined as BMI < 18.5 kg/m2.
Measurement of RMR and Calculation of RMRratio
After 15 min of rest, subjects’ RMR was assessed by measuring their oxygen consumption and carbon dioxide production for 35 min using a ventilated open hood system (Oxycon Pro 4; Jeager, Hoechberg, Germany). From the measured 35 min, the last 20 min were used to calculate mRMR using the equation derived by Elia and Livesey (1992). Subjects were instructed to relax but stay awake during the measurement. pRMR was calculated by using the Cunningham equation (CRMR; Cunningham, 1980), the Harris–Benedict (HB) equation (HBRMR; Harris & Benedict, 1919), and the method described by Koehler et al. (2016) based on different tissue compartments derived from whole-body DXA assessment (DXARMR). RMRratio was calculated as mRMR/PRMR for CRMR (
Assessment of DE Behavior
DE was assessed using the Eating Disorder Inventory-3 (EDI-3), and subjects were defined as having DE when the EDI subscale DT score was ≥14 and/or body dissatisfaction score ≥19, according to the classification by Garner (2004). The subjects were tested for a false low EDI-response profile as defined by O’Connor et al. (1995; all EDI-2 subscales scores ≤2 and a perfectionist score ≥9), but no subjects were excluded due to a fake EDI profile.
Assessment of Blood Pressure
After 5 min of rest, blood pressure was measured twice in a lying position with an electronic sphygmomanometer (BP A100; Microlife AG, Widnau, Switzerland), and the mean value was calculated. Hypotension was defined as systolic blood pressure <90 mmHg and/or diastolic blood pressure <60 mmHg (Casiero & Frishman, 2006).
The Low Energy Availability in Females Questionnaire
The Low Energy Availability in Females Questionnaire (LEAF-Q; Melin et al., 2014), validated for detecting female athletes at risk for energy deficiency by assessing injury history, gastrointestinal, and menstrual function, was used. MD was defined as oligomenorrhea (<9 menstrual cycles the past year) or amenorrhea (an absence of menstrual cycles for >3 months). A total LEAF-Q score ≥8 was used to identify female subjects at risk for energy deficiency (Melin et al., 2014).
Statistics
Statistical calculations were performed using RStudio™ (version 0.99.879; Boston, MA), with a significance level of <0.05. The dataset was checked for missing data, and nonnormality tests were performed. Normally distributed data were summarized as mean ± SD, and nonnormally distributed data were reported by median and interquartile range (25th percentile and 75th percentile). Differences in variables between subjects with suppressed versus normal RMR and female versus male subjects were investigated by using Student’s unpaired t test for normally distributed data and Wilcoxon rank-sum test for nonnormally distributed data. Fisher’s exact test was used to determine whether there were differences between prevalence of low BMD, DE, underweight, and low fat mass in male and female subjects and subjects with suppressed and normal RMR. To measure the degree of positive or negative association between continuous outcomes, Pearson’s correlation coefficient was calculated for normally distributed data, and Spearman’s rank correlation coefficient was calculated for nonnormally distributed data. Sensitivity and specificity were calculated for each RMR equation to identify which equation was most likely to correctly identify subjects at risk for low EA.
Results
Subject characteristics are presented in Table 1. As expected, male subjects were taller, had higher absolute and relative fat-free mass (FFM), and lower absolute and relative fat mass compared with female subjects. All males had a body weight and body composition within the normal range, whereas 50% of the females were underweight and 10% had low body fat percentage. Ten percent of both female and male subjects were diagnosed with DE, whereas 25% of female and male dancers had hypotension. The mean DT score was 1.5 (0.0–6.0) for females and 1.0 (0.8–6.3) for males, whereas the body dissatisfaction score was 4.5 (2.8–10.3) in females and 6.0 (1.0–9.0) in males, and no differences in DT or body dissatisfaction scores between genders were found. Males had higher systolic blood pressure compared with females (115.3 ± 10.2 vs. 103.1 ± 10.2 mmHg, p < .001), but there was no difference in diastolic blood pressure between genders (65.3 ± 9.1 mmHg in males vs. 65.7 ± 8.6 mmHg in females, p = .88). Forty percent of the females had a LEAF-Q score ≥8, 45% reported late menarche (first menstruation after the age of 15; primary amenorrhea), and in total, 65% reported to previously having had MD (primary and/or secondary amenorrhea). Four females reported currently using hormonal contraceptives (three used oral pills and one used hormonal coil), while 11 had previously used hormonal contraceptives. Three of the 16 female subjects (19%) not using hormonal contraceptives reported current MD. All females using hormonal contraceptives reported >9 menstrual cycles per year.
Subject Characteristics
| Females (n = 20) | Males (n = 20) | p value | |
|---|---|---|---|
| Age (years) | 25.1 ± 4.8 | 24.5 (21.0–28.5) | .79 |
| Height (cm) | 170.4 ± 4.9 | 183.0 ± 4.4 | <.001 |
| Weight (kg) | 55.1 ± 5.4 | 72.8 ± 4.6 | <.001 |
| BMI (kg/m2) | 18.7 (18.1–19.4) | 21.7 (20.7–22.1) | <.001 |
| FM (%) | 17.3 ± 4.7 | 8.0 (6.4–11.2) | <.001 |
| FM (kg) | 9.8 ± 3.0 | 5.6 (5.0–8.6) | .001 |
| FFM (%) | 82.4 ± 4.7 | 92.0 (88.8–93.6) | <.001 |
| FFM (kg) | 45.6 ± 4.9 | 66.7 ± 4.7 | <.001 |
| Training (hr/week) | 33.3 ± 11.9 | 35.0 ± 11.3 | .75 |
| Years of dancing | 16.5 (14.8–24.8) | 14.0 (12.8–18.8) | .07 |
| 1,504 ± 108 | 1,967 ± 104 | <.001 | |
| 1,355 ± 127 | 1,896 ± 135 | <.001 | |
| 1,378 ± 69 | 1,813 ± 73 | <.001 | |
| mRMR (kcal/day) | 1,215 ± 106 | 1,692 ± 103 | <.001 |
| mRMR (kcal/kg FFM/day) | 26.9 ± 2 | 25.3 ± 1 | .02 |
| Total EDI-3 score | 32.9 (20.8–54.0) | 54.5 ± 28.9 | .19 |
| DT score | 1.5 (0.0–6.0) | 1.0 (0.8–6.3) | .73 |
| BD score | 4.5 (2.8–10.3) | 6.0 (1.0–9.0) | .89 |
| Systolic BP | 103.1 ± 10.2 | 115.3 ± 10.2 | <.001 |
| Diastolic BP | 65.7 ± 8.6 | 65.3 ± 9.1 | .88 |
Note. Normally distributed data were summarized as mean ± SD, and nonnormally distributed data were reported by median and interquartile range. BMI = body mass index; FM = fat mass; FFM = fat-free mass; RMR = resting metabolic rate;
mRMR and pRMR were higher in males compared with females (Table 1 and Figure 1a), but lower mRMR when adjusted for FFM (Table 1).
—(a) Measured and predicted RMR in male and female subjects and (b) RMRratios in male and female subjects. RMR = resting metabolic rate; mRMR = measured RMR; CRMR = RMR by Cunningham equation; DXARMR = RMR by dual-energy X-ray absorptiometry; HBRMR = RMR by Harris–Benedict equation. Different letters denote significant differences. *Statistical difference between female and male subjects; in (a), males’ RMR was higher compared with females’ RMR in all cases.
Citation: International Journal of Sport Nutrition and Exercise Metabolism 28, 4; 10.1123/ijsnem.2017-0327
As expected, men had higher BMD when assessed as g/cm2 compared with females, but there were no differences in Z-scores (Table 2). There was no difference in BMD assessed by g/cm2 or Z-score between subjects with normal versus suppressed RMR independent of method used to calculate pRMR in either males or females. Underweight females had lower total hip BMD (1.10 vs. 1.21 g/cm2, p = .035) and Z-score (1.10 vs. 1.86SD, p = .046), and lower femur neck BMD (1.136 vs. 1.253 g/cm2, p = .024) and Z-score (1.57 vs. 2.38SD, p = .045) compared with normal weight females. One male and one female subjects had low BMD, and both had a Z-score < −1.0 in the lumbar spine. The male with low BMD had low
Bone Mineral Density in Male and Female Subjects
| Males (n = 20) | Females (n = 20) | p valuea | |
|---|---|---|---|
| Whole body | |||
| BMD (g/cm2) | 1.31 ± 0.1 | 1.16 ± 0.1 | <.001 |
| Z-score (SD) | 1.30 ± 0.7 | 0.87 ± 0.8 | .07 |
| Lumbar spine (L1–L4) | |||
| BMD (g/cm2) | 1.25 ± 0.1 | 1.16 ± 0.1 | <.01 |
| Z-score (SD) | 0.37 ± 0.7 | 0.13 ± 0.9 | .35 |
| Neck and left | |||
| BMD (g/cm2) | 1.29 ± 0.1 | 1.20 ± 0.1 | <.01 |
| Z-score (SD) | 1.70 ± 0.8 | 1.96 ± 0.9 | .35 |
| Total hip and left | |||
| BMD (g/cm2) | 1.24 ± 0.1 | 1.15 ± 0.1 | .01 |
| Z-score (SD) | 1.30 ± 0.7 | 1.47 ± 0.9 | .48 |
Note. BMD = bone mineral density.
aDifference between male and female subjects.
Prevalence of Suppressed RMR and Relations to Energy Deficiency Risk Factors
All females and 80% of males had low
Distribution of Conditions Associated With Energy Deficiency Among Male Subjects Expressed in % (n)
| Cunningham | DXA | Harris–Benedict | ||||
|---|---|---|---|---|---|---|
| Hypotension | 31 (5) | – | 36 (4) | 11 (1) | 40 (2) | 20 (3) |
| DE | 13 (2) | – | 9 (1) | 11 (1) | – | 13 (2) |
| Low BMD | 6 (1) | – | 9 (1) | – | – | 7 (1) |
| Number of conditions associated with energy deficiency (excluding suppressed RMR) | ||||||
| 0 | 56 (9) | 100 (4) | 45 (5) | 89 (8) | 40 (2) | 47 (7) |
| 1 | 38 (6) | – | 55 (6) | – | 40 (2) | 33 (5) |
| 2 | 6 (1) | – | – | 11 (1) | 20 (1) | 20 (3) |
| Sensitivity | 100 | 86 | 27 | |||
| Specificity | 31 | 62 | 78 | |||
Note. DXA = dual-energy X-ray absorptiometry; RMR = resting metabolic rate;
Distribution of Conditions Associated With Energy Deficiency Among Female Subjects Expressed in % (n)
| Cunningham | DXA | Harris–Benedict | |||
|---|---|---|---|---|---|
| Underweight | 50 (10) | 57 (4) | 46 (6) | 67 (6) | 36 (4) |
| Hypotension | 25 (5) | 29 (2) | 23 (3) | 33 (3) | 18 (2) |
| DE | 10 (2) | 14 (1) | 8 (1) | 22 (2) | – |
| Low fat percentage | 10 (2) | 14 (1) | 8 (1) | – | 18 (2) |
| Low BMD | 5 (1) | – | 8 (1) | 11 (1) | – |
| LEAF-Q ≥8 | 40 (8) | 71 (5) | 23 (3) | 67 (6) | 18 (2) |
| Oligomenorrhea | 10 (2) | 14 (1) | 8 (1) | 22 (2) | − |
| Amenorrhea | 5 (1) | 14 (1) | − | 11 (1) | − |
| Number of conditions associated with energy deficiency (excluding suppressed RMRa) | |||||
| 0 | 20 (4) | 14 (1) | 23 (3) | 11 (1) | 27 (3) |
| 1 | 40 (8) | 14 (1) | 54 (7) | 22 (2) | 55 (6) |
| 2 | 20 (4) | 43 (3) | 8 (1) | 22 (2) | 18 (2) |
| 3 | 20 (4) | 29 (2) | 15 (2) | 44 (4) | – |
| Sensitivity | 100 | 38 | 50 | ||
| Specificity | – | 75 | 75 | ||
Note. DXA = dual-energy X-ray absorptiometry; RMR = resting metabolic rate;
aOnly females with LEAF-Q ≥ 8 and females with MD. Since LEAF-Q assesses MD, it is not included as an independent condition.
Discussion
The prevalence of suppressed RMR found in this group of professional ballet dancers was generally high but also clearly dependent on the method used to calculate pRMR, ranging from 25% to 80% in males and from 35% to 100% in females. Hence, the choice of equation seems vital for correctly identifying dancers at risk for energy deficiency when using the RMRratio as a surrogate marker. These results are in contrast to the findings by De Souza et al. (2008), who reported that using the HB over the Cunningham equation did not affect groupings of energy status in exercising women, suggesting that the anthropometry of professional ballet dancers is central in the explanation for the great variety of prevalence rates. Previous studies have found a prevalence of suppressed RMR of 53% in female elite endurance athletes using the Cunningham equation (Melin et al., 2015) and between 27% and 66% in active females using the HB’s equation (De Souza et al., 2007, 2008).
Persistent energy deficiency can be indicated by a low BMI (Mountjoy et al., 2014). We found 50% of female dancers to be underweight, which is higher than the 33% reported by Ribeiro and Da Veiga (2010), whereas the prevalence of 0% underweight male dancers in their study is similar to our findings. The difference between genders could be explained by a gender-specific focus on body shape in classical ballet, as male ballet dancers have more lifts routines, requiring a certain body strength, whereas female dancers are the ones being lifted, creating a focus on low body weight (Twitchett et al., 2009).
The majority of both female and male dancers with suppressed RMR were identified without DE, indicating that energy deficiency also exists inadvertently in this group of dancers. This may partly be supported by the negative association found between
Forty percent of the female dancers in the present study had a LEAF-Q-score ≥8, which is comparable with what have been reported in ultraendurance female runners (44%) and recreational active women (45%; Folscher et al., 2015; Slater et al., 2016). The lower RMRratio (assessed by all methods) seen in female dancers with LEAF-Q score ≥8 supports the likelihood of energy deficiency in these subjects.
The prevalence of low BMD has earlier been reported to be 23% in professional female ballet dancers (Hoch et al., 2011), whereas only 5% in the present study had low BMD. Bone health reflects genetics and a lifelong history of an individual’s mechanical load, EA, and reproductive function (Mountjoy et al., 2014). The results from the present study suggest that despite high prevalence of factors negatively effecting BMD, such as underweight and MD, ballet training can be protective, by the odd-impact movements and high-impact jumps (Tenforde & Fredericson, 2011; Twitchett et al., 2009). Even in a hypogonadal state, mechanical loading of the skeleton plays a significant role in bone accrual, and in contrast to, for example, amenorrheic runners, amenorrheic athletes in sports involving high-impact jumps may to a greater extend maintain their bone mass (Ackerman & Misra, 2011).
When expressed in kcal/kg FFM, the present study found male dancers to have lower RMR compared with females. This is not surprising, since vital compartments, such as the brain or inner organs, require considerably more energy compared with nonvital compartments of lean mass, such as skeletal muscle. Hence, RMR (in kcal/kg FFM) is naturally higher in individuals with lower FFM and a higher proportion of tissues with a high metabolic activity compared with individuals with high FFM (Koehler et al., 2016).
The two prediction equations most often used to estimate RMR in active individuals are the Cunningham and HB equations (Thomas et al., 2016). Since FFM is the main determinant of RMR, this may explain our finding that the HBRMR was lower compared with CRMR and DXARMR, and HBRMR may therefore underestimate pRMR in well-trained populations with high relative FFM. The FFM-based Cunningham equation has been reported to be the best predictor for RMR in male and female endurance athletes (Thompson & Manore, 1996) and in male adolescent soccer players (Kim et al., 2015), supporting the findings in the present study. The Cunningham equation has, however, been reported to underestimate RMR in heavyweight male rowers and canoeists but not in females (Carlsohn et al., 2011), suggesting that different equations may be used in different populations and genders. There may also be other equations equally or more suitable, such as the Maffeis’ equation in adolescent females (Kim et al., 2015) or the DXA-derived method (Koehler et al., 2016), which in this study yielded an acceptable sensitivity in males but not in females.
The choice of equation for calculating pRMR is important to avoid over- or underestimation of RMRratio and misclassification of subjects being at risk for energy deficiency. We found considerable differences in the prevalence of suppressed RMR between the three different methods used to calculate RMRratio, which in turn yield considerable different values for sensitivity and specificity. The long-term negative impact on both health and performance emphasizes the importance of early identification and treatment of athletes with energy deficiency (Mountjoy et al., 2014). Therefore, an RMRratio using an equation yielding a high sensitivity although a lower specificity is to be prioritized in order to identify athletes at risk and enabling further clinical evaluation. Hence, in order to minimize the risk of missing athletes and dancers at risk for energy deficiency, the Cunningham equation seems most suitable when assessing RMRratio in a highly trained athletic population, although the DXA-derived method also may be useful in male populations.
RMRratio is an objective and useful marker for energy deficiency, but more studies are needed to determine the most suitable predictive equation for specific populations and genders. The chosen 0.90 cut point for suppressed RMRratio has been widely used in the literature (De Souza et al., 2007, 2008; Gibbs et al., 2011; Melin et al., 2015; Scheid et al., 2009; Vescovi et al., 2008). Although the expected normal range of RMRratio is reported to be 0.9−1.1 (Cunningham, 1980; Sterling et al., 2009), the appropriateness of a 0.9 cut point when using different equations or the potential differences between study populations or genders has not been investigated and need to be explored.
The use of RMR as a diagnostic tool alone is debatable as there may be a risk of both “false positive” and “false negative” results and should therefore be used in conjunction with other clinical signs, such as hypotension and underweight as well as subclinical low testosterone levels in males and amenorrhea in females.
Strengths and Limitations
Thirty-five percent of the eligible dancers at the Royal Danish Ballet chose not to participate in the present study; thus, there is a risk of selection bias. The lack of a clinical diagnostic interview for assessing eating disorders is another limitation, considering the risk of underreporting DE symptoms in leanness sports (Sundgot-Borgen, 1993; Torstveit et al., 2008). Self-reported MD may also result in underreporting and do not identify the underlying cause. It is, however, a strength that bone health and body composition were assessed by DXA and that time of menstrual cycle was accounted for when measuring RMR. The inclusion of female dancers using hormonal contraceptives increases the risk for masked MD (Mountjoy et al., 2014). In addition, the frequent use of hormonal contraceptives in this group of female dancers might have overestimated the
Acknowledgments
The authors highly appreciate the extraordinary cooperation of the dancers participating in this study and the support of the Royal Danish Ballet. The authors declared that they have no conflicts of interest.
References
Ackerman, K.E., & Misra, M. (2011). Bone health and the female athlete triad in adolescent athletes. The Physician and Sportsmedicine, 39, 131–141. PubMed ID: 21378496 doi:10.3810/psm.2011.02.1871
Amorim, T., Wyon, M., Maia, J., Machado, J., Marques, F., Metsios, G., … Koutedakis, Y. (2015). Prevalence of low bone mineral density in female dancers. Sports Medicine, 45, 257–268. PubMed ID: 25281333 doi:10.1007/s10862-010-9207-4
Carlsohn, A., Scharhag-Rosenberger, F., Cassel, M., & Mayer, F. (2011). Resting metabolic rate in elite rowers and canoeists: Difference between indirect calorimetry and prediction. Annals of Nutrition & Metabolism, 58, 239–244. PubMed ID: 21811063 doi:10.1159/000330119
Casiero, D., & Frishman, W.H. (2006). Cardiovascular complications of eating disorders. Cardiology in Review, 14, 227–231. PubMed ID: 16924163 doi:10.1097/01.crd.0000216745.96062.7c
Clausen, L., Rosenvinge, J.H., & Friborg, O. (2011). Validating the Eating Disorder Inventory-3 (EDI-3): A comparison between 561 female eating disorders patients and 878 females from the general population. Journal of Psychopathology and Behavioral Assessment, 33, 101–110. PubMed ID: 21472023 doi:10.1007/s10862-010-9207-4
Cunningham, J.J. (1980). A reanalysis of the factors influencing basal metabolic rate in normal adults. The American Journal of Clinical Nutrition, 33, 2372–2374. PubMed ID: 7435418 doi:10.1093/ajcn/33.11.2372
De Souza, M.J., Hontscharuk, R., Olmsted, M., Kerr, G., & Williams, N.I. (2007). Drive for thinness score is a proxy indicator of energy deficiency in exercising women. Appetite, 48, 359–367. PubMed ID: 17184880 doi:10.1016/j.appet.2006.10.009
De Souza, M.J., West, S.L., Jamal, S.A., Hawker, G.A., Gundberg, C.M., & Williams, N.I. (2008). The presence of both an energy deficiency and estrogen deficiency exacerbate alterations of bone metabolism in exercising women. Bone, 43, 140–148. PubMed ID: 18486582 doi:10.1016/j.bone.2008.03.013
Diffey, B., Piers, L.S., Soares, M.J., & O’dea, K. (2007). The effect of oral contraceptive agents on the basal metabolic rate of young women. British Journal of Nutrition, 77, 853. PubMed ID: 9227183 doi:10.1079/BJN19970084
Doyle-Lucas, A.F., Akers, J.D., & Davy, B.M. (2010). Energetic efficiency, menstrual irregularity, and bone mineral density in elite professional female ballet dancers. Journal of Dance Medicine & Science, 14, 146–155. PubMed ID: 21703085
Elia, M., & Livesey, G. (1992). Energy expenditure and fuel selection in biological systems: The theory and practice of calculations based on indirect calorimetry and tracer methods. World Review of Nutrition and Dietetics, 70, 68–131. PubMed ID: 1292242 doi:10.1159/000421672
Folscher, L., Grant, C.C., Fletcher, L., & Janse van Rensberg, D.C. (2015). Ultra-marathon athletes at risk for the female athlete triad. Sports Medicine—Open, 1, 29. PubMed ID: 26380807 doi:10.1186/s40798-015-0027-7
Garner, D. (2004). Eating Disorder Inventory-3: Professional manual. Professional Manual. Lutz, FL: Psychological Assessment Resources, Inc.
Gibbs, J.C., Williams, N.I., Scheid, J.L., Toombs, R.J., & De Souza, M.J. (2011). The association of a high drive for thinness with energy deficiency and severe menstrual disturbances: Confirmation in a large population of exercising women. International Journal of Sport Nutrition and Exercise Metabolism, 21, 280–290. PubMed ID: 21813911 doi:10.1123/ijsnem.21.4.280
Harris, J., & Benedict, F. (1919). A biometric study of basal metabolism in man. Washington, DC: Carnegie Institution of Washington.
Hoch, A.Z., Papanek, P., Szabo, A., Widlansky, M.E., Schimke, J.E., & Gutterman, D.D. (2011). Association between the female athlete triad and endothelial dysfunction in dancers. Clinical Journal of Sport Medicine, 21, 119–125. PubMed ID: 21358502 doi:10.1097/JSM.0b013e3182042a9a
Kaufman, B.A., Warren, M.P., Dominguez, J.E., Wang, J., Heymsfield, S.B., & Pierson, R.N. (2002). Bone density and amenorrhea in ballet dancers are related to a decreased resting metabolic rate and lower leptin levels. The Journal of Clinical Endocrinology and Metabolism, 87, 2777–2783. PubMed ID: 12050250 doi:10.1210/jcem.87.6.8565
Keen, A., & Drinkwater, B. (1997). Irreversible bone loss in former amenorrheic athletes. Osteoporosis International, 7, 311–315. PubMed ID: 9373563 doi:10.1007/BF01623770
Kim, J.H., Kim, M.H., Kim, G.S., Park, J.S., & Kim, E.K. (2015). Accuracy of predictive equations for resting metabolic rate in Korean athletic and non-athletic adolescents. Nutrition Research and Practice, 9(4), 370–378. PubMed ID: 26244075 doi:10.4162/nrp.2015.9.4.370
Koehler, K., Williams, N.I., Mallinson, R.J., Southmayd, E.A., Allaway, H.C.M., & De Souza, M.J. (2016). Low resting metabolic rate in exercise-associated amenorrhea is not due to a reduced proportion of highly metabolically active tissue compartments. American Journal of Applied Physiology, Endocrinology and Metabolism, 311, 480–487. PubMed ID: 27382033 doi:10.1152/ajpendo.00110.2016
Lagowska, K., Kapczuk, K., & Jeszka, J. (2014). Nine-month nutritional intervention improves restoration of menses in young female athletes and ballet dancers. Journal of the International Society of Sports Nutrition, 11, 1–9. PubMed ID: 25389380 doi:10.1186/1550-2783-11-1
Melin, A., Tornberg, A.B., Skouby, S., Faber, J., Ritz, C., Sjödin, A., & Sundgot-Borgen, J. (2014). The LEAF questionnaire: A screening tool for the identification of female athletes at risk for the female athlete triad. British Journal of Sports Medicine, 48, 540–545. PubMed ID: 24563388 doi:10.1136/bjsports-2013-093240
Melin, A., Tornberg, A.B., Skouby, S., Møller, S.S., Sundgot-Borgen, J., Faber, J., … Sjödin, A. (2015). Energy availability and the female athlete triad in elite endurance athletes. Scandinavian Journal of Medicine and Science in Sports, 25, 610–622. PubMed ID: 24888644 doi:10.1111/sms.12261
Meyer, N.L., Sundgot-Borgen, J., & Lohman, T.G. (2013). Body composition for health and performance: A survey of body composition assessment practice carried out by the Ad Hoc Research Working Group on Body Composition, Health and Performance under the auspices of the IOC Medical Commission. British Journal of Sport Medicine, 47, 1044–1053. PubMed ID: 24065075 doi:10.1136/bjsports-2013-092561
Mountjoy, M., Sundgot-Borgen, J., Burke, L., Carter, S., Constantini, N., Lebrun, C., … Ljungqvist, A. (2014). The IOC consensus statement: Beyond the Female Athlete Triad—Relative Energy Deficiency in Sport (RED-S). British Journal of Sport Medicine, 48, 491–497. PubMed ID: 24620037 doi:10.1136/bjsports-2014-093502
Myburgh, K., Berman, C., Novick, I., Noakes, T.D., & Lambert, E.V. (1999). Decreased resting metabolic rate in ballet dancers with menstrual irregularities. International Journal of Sport Nutrition, 9(3), 285–294. PubMed ID: 10477364 doi:10.1123/ijsn.9.3.285
Nattiv, A., Loucks, A.B., Manore, M.M., Sanborn, C.F., Sundgot-Borgen, J., & Warren, M.P. (2007). The female athlete triad. Medicine & Science in Sports & Exercise, 39, 1867–1882. PubMed ID: 17909417 doi:10.1249/mss.0b013e318149f111
O’Connor, P.J., Lewis, R.D., & Kirchner, E.M. (1995). Eating disorder symptoms in female college gymnasts. Medicine & Science in Sports & Exercise, 27(4), 550–555. PubMed ID: 7791586 doi:10.1249/00005768-199504000-00013
Ribeiro, L.G., & Da Veiga, G.V. (2010). Risk behaviors for eating disorders in Brazilian dancers. International Journal of Sports Medicine, 31, 283–288. PubMed ID: 20148375 doi:10.1055/s-0030-1248241
Scheid, J.L., Williams, N.I., West, S.L., Van Heest, J.L., & De Souza, M.J. (2009). Elevated PYY is associated with energy deficiency and indices of subclinical disordered eating in exercising women with hypothalamic amenorrhea. Appetite, 52, 184–192. PubMed ID: 18929607 doi:10.1016/j.appet.2008.09.016
Slater, J., McLay-Cooke, R., Brown, R., & Black, K. (2016). Female recreational exercisers at risk of low energy availability. International Journal of Sports Nutrition and Exercise Metabolism, 26, 421–427. PubMed ID: 26841435 doi:10.1123/ijsnem.2015-0245
Sterling, W.M., Golden, N.H., Jacobsen, M.S., Ornstein, R.M., & Hertz, S.M. (2009). Metabolic assessment of menstruating and nonmenstruating normal weight adolescents. International Journal of Eating Disorders, 42, 658–663. PubMed ID: 19247996 doi:10.1002/eat.20604
Sundgot-Borgen, J. (1993). Prevalence of eating disorders in elite female athletes. International Journal of Sports Nutrition, 3, 29–40. PubMed ID: 8499936 doi:10.1123/ijsn.3.1.29
Tenforde, A.S., & Fredericson, M. (2011). Influence of sports participation on bone health in the young athlete: A review of the literature. PM & R: The Journal of Injury, Function, and Rehabilitation, 3, 861–867. PubMed ID: 21944303 doi:10.1016/j.pmrj.2011.05.019
Thomas, D.T., Erdman, K.A., & Burke, L.M. (2016). Position of the academy of nutrition and dietetics, dietitians of Canada, and the American college of sports medicine: Nutrition and athletic performance. Journal of the Academy of Nutrition and Dietetics, 116, 501–528. PubMed ID: 26920240 doi:10.1016/j.jand.2015.12.006
Thompson, J., & Manore, M.M. (1996). Predicted and measured resting metabolic rate of male and female endurance athletes. Journal of the American Dietetic Association, 96, 30–34. PubMed ID: 8537566 doi:10.1016/S0002-8223(96)00010-7
Torstveit, M.K., Rosenvinge, J.H., & Sundgot-Borgen, J. (2008). Prevalence of eating disorders and the predictive power of risk models in female athletes: A controlled study. Scandinavian Journal of Medicine & Science in Sports, 18, 108–118. PubMed ID: 17490455 doi:10.1111/j.1600-0838.2007.00657.x
Twitchett, E., Koutedakis, Y., & Wyon, M. (2009). Video analysis of classical ballet performance. Journal of Dance Medicine & Science, 13, 124–128. PubMed ID: 19930814
Vescovi, J.D., Scheid, J.L., Hontscharuk, R., & De Souza, M.J. (2008). Cognitive dietary restraint: Impact on bone, menstrual and metabolic status in young women. Physiology & Behavior, 95, 48–55. PubMed ID: 18508099 doi:10.1016/j.physbeh.2008.04.003
Warren, M.P., Brooks-Gunn, J., Fox, R.P., Holderness, C.C., Hyle, E.P., Hamilton, W.G., & Hamilton, L. (2003). Persistent osteopenia in ballet dancers with amenorrhea and delayed menarche despite hormone therapy: A longitudinal study. Fertility and Sterility, 80, 398–404. PubMed ID: 12909505 doi:10.1016/S0015-0282(03)00660-5
