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Energetic efficiency, menstrual irregularity, and bone mineral density in elite professional female ballet dancers.

Sports that emphasize low body weight for optimal performance are often associated with an increased prevalence of the female athlete triad (FT).1 Low energy availability with or without disordered eating, menstrual irregularity or amenorrhea, and bone loss or osteoporosis are the three interrelated conditions that comprise the FT. (1) Because the focus of ballet is on beauty of the body in its movement and shape, many dancers exhibit a preoccupation with body image that increases risk for disordered eating. (2) Ballet dancers consistently weigh 10% to 12% below ideal body weight, and these low weight ranges are commonly achieved by low energy intakes (EI). (3) It has been observed that student and professional ballet dancers, specifically females, consume 70% to 80% or below of the recommended daily allowance (RDA) of calories for total energy. (4-7) An energy availability (dietary intake minus exercise energy expenditure) of 30 kcal/kg fat-free mass (FFM)/day or less is associated with the development of the FT. (1) Moreover, low energy intake relative to predicted energy expenditure may increase energetic efficiency in endurance trained female athletes, (8) and possibly also in ballet dancers.

Low energy availability (EA) appears to be the key component responsible for the development of the remaining two components of the FT. (1) Research suggests that during chronically decreased EA, processes that require energy, such as reproduction, thermoregulation, growth, and cellular maintenance are suppressed. (1) Amenorrhea, the absence of menstrual periods, often results. (9) Potential contributors to disruption in the normal endocrine process include exercise volume, low body weight, inadequate EA, low EI, and psychological stress. (10,11) The most recent data, however, implicate EA as the leading cause of exercise-induced amenorrhea. (12,13)

Relative to women participating in various other sports, ballet dancers have poor bone density status, possibly due to the low EI and body weights reported in this population. (14) This may occur through reduced resting metabolic rate (RMR) and concentrations of leptin, a hormone that plays an important role in appetite regulation and metabolism. (3) Ballet dancers would be expected to have slightly higher metabolic rates than weight-matched non-dancers, due to a higher fat-free mass (FFM). (15) FFM is a primary determinant of RMR, which is composed primarily of muscle but also of bone, organ, and body water weight. (16,17) However, previous research suggests that the metabolic rates of dancers may be less than would be predicted, based on weight and lean body mass (LBM). (3)

There has been limited research investigating the health of professional ballet dancers; prior work has focused primarily on adolescent dancers training in classical ballet. Because body composition, bone density, hormonal balance, and other aspects of metabolism fluctuate greatly during this period of maturation, the observed "traits" of adolescent dancers may not be indicative of those found in elite adult dancers. The effects of continuous, rigorous demands throughout childhood into adulthood may produce severe consequences found only in the professional population that has been under-studied.

The usefulness of previous work in this area has also been limited by the fact that control groups are often not included. Kaufman and colleagues observed 21 female dancers and found a correlation between low RMR and decreased bone density. (3) Without the inclusion of control subjects pair-matched by age and body composition, however, it is difficult to determine if the low RMR of dancers resulted from restrictive eating patterns, amenorrhea, or differences in LBM. By comparing physically active dancers to sedentary, recreationally active non-dancers of similar body composition the primary characteristics that lead to increased energetic efficiency may be determined.

With regard to assessment of the FT, a challenge for both researchers and medical practitioners is that there are currently no objective indicators of EA, thus making it difficult to assess (researchers have had to rely on self-reported measures). Energetic efficiency as determined by comparison of measured to predicted RMR may, however, be a useful surrogate indicator of low EA. Determinations made in this way could be relevant for other populations as well, including female endurance athletes and those who chronically diet to control their body weight.

The purpose of this investigation was to compare the RMR of professional female ballet dancers to non-dancing, healthy control women who were pair-matched by age, body mass index, and FFM. The objectives of the study were to: 1. describe the physical and behavioral characteristics of female professional ballet dancers, including symptoms of the FT; 2. determine if there are differences in RMR between this population and controls; and 3. identify associations between FT characteristics and energetic efficiency (observed versus predicted RMR based on body composition). We hypothesized that the dancers would report a low habitual energy intake and, therefore, be subject to conditions comprising the FT, including low EA, suboptimal bone density, and menstrual irregularities. We also hypothesized that elite female dancers would have lower than expected RMR based on their FFM, and thus greater energetic efficiency as a result of chronic energy (caloric) deficiency.

Materials and Methods Participants

Women aged 18 to 35 years (N = 39) were recruited for participation. Group 1 ("dancers"; N = 15) consisted of elite (> 27 hours/week of dancing) female ballet dancers recruited from two national professional ballet companies. Group 2 ("controls"; N = 24) consisted of non-dancing females recruited from the local university community who were sedentary to recreationally active (less than 150 minute moderate to high-intensity physical activity/week). The control participants were over-sampled in order to insure adequate matching with dancers on pair-matched variables. They were representative of the typical "thin" woman; therefore, possible menstrual irregularities were not exclusion criteria.

All 15 dancers were successfully pair-matched to 15 non-dancers via age, body mass index (BMI), LBM, and FFM, as these factors are the largest predictors of RMR. (16,17) Age was matched plus or minus five years; this range is within that utilized by the Institute of Medicine in their dietary intake recommendation ranges. (18) BMI was matched plus or minus one unit, indicating similar weight status between matches. LBM and FFM were matched plus or minus two kilograms to account for dual energy x-ray absorptiometry (DEXA) measurement error. (19) Three of the dancers were younger than 20 years of age; therefore, Z-scores (measurement system for bone density of individuals > 20 years) for these participants were determined as if they were 20 years of age.

All laboratory testing was conducted when participants were in the early-mid follicular phase of their menstrual cycle (days 1 to 10); oligomenorrheic and amenorrheic subjects were studied when convenient, as hormonal fluctuations were less predictable in these participants. Women using hormonal contraception were included. Three of the dancers and seven of the controls reported using oral contraceptive pills (OCPs) at the time of the study. Participants were not pregnant, smokers, or suffering from major chronic disease (e.g., diabetes, chronic obstructive pulmonary disease, renal disease, thyroid disease, or cancer). Written informed consent was obtained from each participant following explanation of the purpose, risks, and potential benefits of the study. The study protocol was approved by the Institutional Review Board of Virginia Polytechnic and State University (Virginia Tech).

Protocol

A health history questionnaire (HHQ), menstrual history questionnaire, instructions for completing a four-day habitual dietary intake record, three-factor eating questionnaire (TFEQ), (20) and eating attitudes test (EAT-26) (21) were distributed to participants immediately following the return of their signed informed consent document. The HHQ included questions related to current and previous medical conditions, medication usage, changes in body weight and diet, and physical activity history. Current physical activity levels of intensity were self-reported by use of a 1-10 range scale (1-3, low intensity; 4-6, moderate intensity; 7-10, high intensity); the minutes per week that subjects engaged in low, moderate, and high intensity physical activity were recorded. The menstrual history questionnaire assessed irregularities, patterns, and family history. The TFEQ elicits information regarding three dimensions of eating behavior: 1. dietary restraint, or the conscious, cognitive control of food intake; 2. lack of inhibition or control in eating; and 3. perceived hunger (i.e., the degree to which individuals feel that their eating is driven by feelings of hunger).20 Each of the three factors of the TFEQ has been shown to be reliable and valid. (20) A score of 11 or more for each individual factor indicates abnormal eating behaviors. Lastly, participants completed the EAT-26, which is a 26-item, objective, self-report measure of symptoms of disordered eating. Scoring of this tool is based on a six-point scale, with answers ranging from "never" to "always," A score of 20 or more on the EAT-26 distinguishes individuals with disordered eating. (21)

To determine habitual dietary intake, four-day dietary intake records were obtained from all participants, who were instructed in methods for accurately recording their food and beverage intake. Measuring spoons, cups, and food models were used to determine portion sizes. All records were reviewed for accuracy and completeness prior to analysis using the NDS-R nutritional analysis software program (Version 2006, University of Minnesota, Minneapolis, MN). The completed food record and questionnaires were returned on the day of scheduled laboratory testing.

Controls visited the laboratory twice; they signed the written informed consent and completed the same questionnaires as the dancers during their first laboratory visit. Their completed food records were returned at their second visit (for assessment of RMR).

All participants visited the laboratory for assessment of RMR using indirect calorimetry and body composition and bone density using DEXA (GE Lunar Prodigy; GE Healthcare, Madison, WI). During the laboratory sessions a technician performed the indirect calorimetry, weight and height measurements, and DEXA on a maximum of two subjects per morning. All subjects arrived at the laboratory upon waking in hourly intervals beginning at 6:30 a.m., in a fasted state (10 to 12 hours). Participants were instructed not to engage in strenuous exercise within 24 hours of testing. For three of the dancers this was not feasible due to their performance schedule and menstrual cycle phase; however, strenuous exercise was avoided for 12 hours prior to indirect calorimetry for these subjects. The participants were instructed to refrain from ingestion of medication, nicotine, and caffeine on the morning of the test.

Laboratory Assessments

Height was measured in meters without shoes using a wall-mounted stadiometer. Body weight was measured to the nearest 0.1 kg with a digital scale (Scale Tronix; Wheaton, IL). Body mass index was calculated as weight/ height (2) (kg/[m.sup.2]). Waist circumference was measured across the umbilicus, without clothing. A Gulick tape measure (Gulick, Country Technology, Inc; Gays Mill, WI) was used to obtain two measurements to the nearest 1.0 cm. The total fat, fat-free (bone, muscle, and organs), and lean (muscle) body mass of all participants was measured using DEXA. Bone density at the posterior-anterior spine and left femur was also measured using DEXA according to guidelines established by the International Society for Clinical Densitometry (ISCD). (22) An ISCD Certified Densitometry Technologist performed all scans. Z-scores, comparing individuals' bone mineral density (BMD) to age and sex-matched controls, were determined for each respective regional bone density area assessed. Resting metabolic rate was determined by indirect calorimetry using a ventilated hood system (ParvoMedics; Sandy, UT). As noted above, measurements were made in the early morning hours after a 10- to 12-hour fast. Participants rested quietly for 10 minutes prior to the 45-minute test with a clear plastic ventilated hood over their head, during which time inspired and expired gases were analyzed. The predicted RMR of each individual was estimated via three calculation methods: the Cunningham equation (500 + 22FFM) which considers FFM (kg), the Mifflin equation (5 + 10W + 6.25H - 5A), and the Harris-Benedict (HB) equation (655.09 + 9.56W + 1.84H - 4.67A). Both of these latter methods consider weight (kg), height (cm), and age (years). (23-25) Because the Cunningham equation includes FFM, it is considered a better prediction equation for athletes. (26,27) The Mifflin equation has been identified as producing a more accurate representation of RMR than the commonly used HB equation (26,28); inclusion of the HB equation in this analysis is important, however, as it has been found to accurately predict RMR in eumenorrheic professional ballet dancers. (29) Energy availability was calculated as dietary energy intake minus exercise energy expenditure normalized to FFM in units of kcal/ kg FFM/day. (1) Exercise energy expenditure was calculated from the equation METS x kcal/hr (from RMR) x hrs exercising. (30) The estimated rate of energy expenditure for ballet used in calculations was 4.8 METS. (31)

Statistical Analysis

Data analyses included descriptive characteristics, paired t-tests for group comparisons, and simple correlation analyses to test for relationships between variables of interest, using SPSS V.12.0. One-way ANOVA and post hoc (Tukey's) tests were used when significant differences were detected, and to determine group differences according to menstrual status. Power calculations on two of the key outcome variables, BMD and RMR, using preliminary data (expected group differences, standard deviations, a = 0.05) indicated that statistical power was 99.9% for BMD and 89.4% for RMR with our proposed sample size of 15 per group.

Results

A total of 24 control women and 15 dancers completed the study protocol. All dancers were then pair-matched with 15 controls by age (dancers: 24.3 [+ or -] 1.3; controls: 23.7 [+ or -] 0.9 years), FFM (44.3 [+ or -] 0.8 vs. 44.1 [+ or -] 0.9 kg), LBM (41.8 [+ or -] 0.8 vs. 41.5 [+ or -] 0.8 kg), and BMI (18.9 [+ or -] 0.2 vs. 19.3 [+ or -] 0.2 kg/m2). Body weight and body fat percentages were significantly lower in dancers compared to controls (Table 1). As expected, physical activity (PA) levels were also significantly lower in controls than in dancers (Table 1), with dancers reporting habitual moderate to high intensity PA of approximately 36 hours per week (more than 5 hours/day). Controls reported participating in low to high intensity PAs, including activities such as walking, running, weight lifting, and biking.

As hypothesized, reported energy intake of dancers was significantly lower than that of controls, as was EA (Tables 2 and 3). However, there were no group differences in self-reported macro- and micronutrient intakes (Table 2). Dancers reported higher levels of dietary restraint and disinhibition than controls, and a higher EAT-26 score. However, these group differences were not significant (Table 2).

Despite having a similar FFM, dancers had a significantly lower ab solute RMR than matched controls (Fig. 1), and lower RMR relative to FFM (Fig. 2). The association between RMR relative to FFM and EA, however, did not quite reach statistical significance (r = 0.34; p = 0.067). No significant differences were found in total (whole body) BMD or total Z-scores, although mean values were slightly lower in dancers. Regional Zscores of the posterior-anterior spine and left femur were higher in dancers, although this difference did not reach statistical significance.

Amenorrhea and irregular menses were more prevalent in dancers than matched controls. Dancers experienced relatively delayed menarche, on average beginning menses at the age of 15 years as compared to controls beginning at the age of 13 years (Table 3). While only one control reported irregular menses, six dancers reported either irregular or no menstrual cycles (Table 4). As shown in Table 4, body fat, absolute RMR, RMR relative to FFM, total Z-score, and EA decreased progressively from regularly menstruating controls to regularly menstruating dancers to irregularly menstruating and amenorrheic ("dysfunctional") dancers. Dysfunctional dancers were observed to have the lowest EA, energy intake, body fat percentage, and total Z-score, and more abnormal eating behaviors (as demonstrated by TFEQ dietary restraint and EAT-26 scores). This group of dancers (N = 6) exhibited symptoms of the three conditions comprising the female athlete triad.

[FIGURE 1 OMITTED]

Observed versus predicted RMR among dancers varied significantly (Fig. 3). Average observed RMR (measured via indirect calorimetry) among all dancers was 1,367 kcal/ day, while predicted RMR by the Cunningham equation was significantly higher at 1475 kcal/day (p [less than or equal to] 0.01). RMR predicted by the Mifflin equation was significantly lower than both the Cunningham equation and observed values (Fig. 3). The HB equation most accurately predicted RMR in dancers. When comparing RMR in eumenorrheic versus dysfunctional dancers, the HB equation was most accurate for predicting RMR in the dysfunctional group, with differences (predicted minus observed RMR) at +4 kcals in dysfunctional dancers (p = 0.94) and -43 kcals in eumenorrheic dancers (p = 0.07). Conversely, observed versus predicted RMR among controls was most similar with the Cunningham equation (Fig. 4).

[FIGURE 2 OMITTED]

Discussion

This is the first investigation to pair-match elite professional female dancers with sedentary, recreationally active control women in order to compare energetic efficiency, menstrual irregularities, BMD and RMR, while accounting for age, FFM, and BMI. As hypothesized, characteristics of the FT were more prevalent among dancers, specifically with regard to menstrual irregularities and low EA. In general, dancers began menses at least one year later than controls and were more likely to experience irregular or lack of menses. Bone density and Z-scores among dancers and controls were not significantly different; however, when comparing menstrual status to bone density, total Z-score and BMD progressively decreased from eumenorrheic controls to eumenorrheic dancers to "dysfunctional" (both irregularly menstruating and amenorrheic) dancers. The mean posterior-anterior spine Z-score for dysfunctional dancers was indicative of low bone density. Athletes participating in weightbearing sports often have 5% to 15% higher total BMD than non-athletes. (1,32,33) Therefore, our findings suggest that although not significantly lower than control women, the dancers' BMD was not at a level of optimal bone health.

Interestingly, energetic efficiency increased (and EA decreased) with dysfunctional menstrual status. The ratio of kilocalories of food intake (i.e., energy intake) per day to kilogram FFM decreased steadily from eumenorrheic controls to dysfunctionally menstruating dancers. These results are similar to those reported by Drinkwater and associates and Marcus and coworkers who also observed increased energetic efficiency in amenorrheic female runners. (8,34,35) Our findings indicate that the irregularly menstruating and amenorrheic dancers as a group have the lowest energy requirement to sustain a kilogram of body weight and the overall lowest calorie requirement despite having the highest FFM in the study.

Abnormal eating behaviors, indicated by a score of 11 or more on the TFEQ, were prevalent among the dysfunctional menstrual status dancers. This group scored a 12.7, indicating a high level of dietary restraint and conscious cognitive control of food intake. The eumenorrheic dancers and controls scored 6.1 and 5.8, respectively. The dysfunctional menstrual status dancers also scored significantly higher on the EAT-26 than the other two groups. This group of dancers meets the criteria of the FT (including low BMD, menstrual irregularities, and eating pathology accompanied by low EA) and is, therefore, a representative population of individuals at high risk for this disorder.

An other main finding of this investigation is the significantly lower RMR as measured by indirect calorimetry (both absolute and RMR relative to FFM) in dancers as compared to controls, which suggests an increased energetic efficiency and decreased energy requirement in these athletes. Energy intake was also observed to be lower in dancers, providing evidence for low energy intake relative to predicted energy expenditure. Because the dancers were matched by FFM and age, our findings indicate that the low EA of the dancers may be a primary contributor to their lower than expected RMR.

These results are similar to those of Kaufman and colleagues who also observed a depressed absolute and resting metabolic rate relative to FFM in dancers as compared to controls, and related this to energy (caloric) deficiency. (3)

Unlike our findings and those of many other studies supporting depressed RMR in female athletes, Horton and associates observed no significant differences in RMR or energy efficiency among endurance-trained female cyclists versus untrained controls. (36) This study, however, did not account for a population of under-eating female athletes. The subjects of this study were fed a diet of fixed composition (30% fat, 15% protein, and 55% carbohydrates as a proportion of the total energy they habitually consumed). Although no subjects gained weight during this five-day diet period, the composition of their pre-test diet was likely altered, and the average total energy consumed per day was 2,300 kcal. (36) Therefore, it is uncertain if chronic undereating was characteristic of this study population. The dancers in our study exhibited energy restriction, and that may explain the differences between our results and those of Horton and associates. (36)

The Horton study also suggested that previous reports of increased energetic efficiency in female athletes may be due to under-reporting on dietary intake records, under-eating by subjects during the measurement of energy intake, and over-reporting of exercise. The calculation of EA is subject to inaccuracies due to a similar reliance on self-reported measures and estimations. Energy availability is now thought to be the central marker for development of the two remaining conditions of the FT, so the ability to measure it accurately in female athletes is important. With dependence on self-reported measures of energy intake and estimations of exercise energy expenditure this can prove difficult, especially for dancers whose exercise energy expenditure varies greatly depending on their rehearsal schedule, ranking in the ballet company, and roles they perform.

A possible surrogate marker for EA is energetic efficiency (observed RMR versus predicted RMR based on FFM). If energetic efficiency is increased, or if the observed RMR is less than the predicted RMR, this may indicate a low EA. Energetic efficiency relative to FFM in dancers was 30.9 (+ 0.6) kcal/kg FFM, which appears to be a reasonably accurate value for EA. Calculated EA for the dancers (reported energy intake minus exercise energy expenditure) was 3.8 kcal/kg FFM, a potentially inaccurate value. By comparing observed RMR versus predicted RMR based upon FFM, it may be possible to derive a value representative of EA that is not dependent on self-reported measures. Although not statistically significant, our findings suggest a trend for an association between RMR relative to FFM and EA. A depressed relative RMR may, therefore, be indicative of low EA and increased energetic efficiency.

In comparing observed to predicted RMR using the Cunningham, Mifflin, and HB equations, we found, similar to Glace and coworkers, that RMR in dancers is most accurately predicted by the HB equation. (29) However, in contrast to findings by Glace and coworkers, who observed the HB equation to better predict RMR in eumenorrheic than amenorrheic dancers, (29) we found that the HB only slightly overestimated amenorrheic dancers' RMR (+4 kcal/day) yet notably underestimated eumenorrheic dancers' RMR (-43 kcal/day). Despite the variance in outcomes, the HB equation appears to be the most accurate prediction equation in this athletic population.

We acknowledge several limitations of this investigation. First, the use of self-reported physical activity habits may serve as a source of inaccuracy due to possible over-estimation. In future studies objective measures such as accelerometry or doubly labeled water might be used to assess physical activity energy expenditure. Second, self-reported dietary intake is also subject to inaccuracies due to over- or under-estimation. Specific to populations of lean female athletes, including dancers, a strong bias toward under-reporting energy intake has been observed. (29,37,38) To avoid this, prior to recording dietary intake our participants were individually instructed on how to complete an accurate food record, provided with food portion measurement aids, and specifically told not to alter their eating habits on recorded days. Once completed, records were reviewed by the investigators for completeness. These procedures should help to improve the accuracy of self-reported dietary intake measures; nevertheless, we acknowledge these limitations. The extremely low reported intakes of the dancers may be indicative of under-reporting, resulting in lowered corresponding EA values. Third, our sample size was limited, which may have precluded the detection of group differences in some outcome variables. However, we had adequate statistical power to detect significant differences in several important physiological outcomes. Lastly, we did not restrict eligibility only to women not using OCP or to dancers with menstrual dysfunction. Our intent was to recruit from the general population of both elite ballet dancers and recreationally active, thin women. A more homogenous sample would have decreased variability in some outcomes, yet would not be a true representation of the two general populations. We recognize that not controlling for OCP may have affected outcome variables such as menstrual status, bone density, and possibly RMR. We also recognize that self-reported menstrual histories may be a source of inaccuracy that could affect findings related to menstrual status.

To overcome these limitations future studies, using larger sample sizes, should: 1. assess actual energy expended through physical activity; 2. control for dietary intake of dancers to avoid reliance on self-reported measures; and 3. include the direct measurement of relevant hormones such as estrogen or leptin to determine their influence on conditions associated with the FT. Such studies are also warranted to better address the possibility that low RMR may be a surrogate marker for low EA in female athletes.

Conclusion

There are no pharmacological agents that reverse the negative consequences of the female athlete triad; no treatment fully restores bone loss or corrects metabolic abnormalities that impair the health and performance of amenorrheic, energy deficient athletes. (1) By analyzing the RMR, body composition, bone density, and energy intake of professional dancers as compared to non-dancing controls, our findings provide insight to the potential metabolic impact of chronic energy restriction and also suggest possible alternative methods for measuring energy availability in athletes. Proper eating behaviors of dancers and all athletes begin during adolescence. Young dancers are rarely taught nutrition along with their classical training, yet the ideal thin body type is continuously reinforced. Proper nutrition and eating habits should be encouraged and integrated into the daily training of adolescent ballet dancers to prevent the future development of amenorrhea, osteo porosis, and low energy availability.

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Ashley F. Doyle-Lucas, Ph.D., Jeremy D. Akers, Ph.D., R.D., and Brenda M. Davy, Ph.D., R.D., F.A.C.S.M.

Ashley F. Doyle-Lucas, Ph.D., Jeremy D. Akers, Ph.D., R.D., and Brenda M. Davy, Ph.D., R.D., F.A.C.S.M., are in the Department of Human Nutrition, Foods, and Exercise, Virginia Polytechnic Institute and State University, Blacksburg, Virginia.

Correspondence: Brenda M. Davy, Ph.D., R.D., F.A.C.S.M., Department of Human Nutrition, Foods, and Exercise, Virginia Polytechnic Institute and State University, 221 Wallace Hall (0430), Blacksburg, Virginia 24061; bdavy@vt.edu.
Table 1
Demographic Characteristics of Pair-Matched Female Elite
Dancers and Lean Control Women

                       Dancers (N = 15)     Controls (N = 15)
                       Mean [+ or -] SEM    Mean [+ or -] SEM

Age, yrs               24.3 [+ or -] 1.3   23.7 [+ or -] 0.9
Weight, kg             51.9 [+ or -] 0.7   56.5 [+ or -] 0.9 *
Height, cm              166 [+ or -] 1.0    170 [+ or -] 1.0 *
BMI, kg/[m.sup.2]      18.9 [+ or -] 0.2   19.4 [+ or -] 0.2
Fat-free mass, kg      44.3 [+ or -] 0.8   44.1 [+ or -] 0.9
Lean body mass, kg     41.8 [+ or -] 0.8   41.5 [+ or -] 0.8
Body fat, %            15.5 [+ or -] 1.3   22.6 [+ or -] 1.0 *
Physical activity,        1 [+ or -] 1.0   33.0 [+ or -] 17
  low, min/wk
Physical activity,     1118 [+ or -] 32     109 [+ or -] 35 *
  mod, min/wk
Physical activity,     1060 [+ or -] 42     165 [+ or -] 88 *
  high, min/wk

* Significantly different from dancers (p < 0.01).

Table 2
Eating Behaviors in Pair-Matched Female Elite Dancers and Lean
Control Women

                           Dancers             Controls
                          (N = 15)             (N = 15)
                      Mean [+ or -] SEM    Mean [+ or -] SEM

Energy Intake,        1557 [+ or -] 89     2075 [+ or -] 163 *
kcal/d

Macronutrients, %
of total energy:
  Carbohydrates         56 [+ or -] 3        54 [+ or -] 3
  Protein               17 [+ or -] 1        16 [+ or -] 1
  Fat                   26 [+ or -] 2        30 [+ or -] 2
  Alcohol                4 [+ or -] 1         3 [+ or -] 1

Micronutrients:
  Calcium (mg)         851 [+ or -] 80      997 [+ or -] 101
  Vitamin D (mcg)        5 [+ or -] 1         4 [+ or -] 1
  Iron (mg)             13 [+ or -] 1        16 [+ or -] 1

Three Factor Eating
Questionnaire
(TFEQ) score:
  Dietary Restraint    8.7 [+ or -] 1.6     5.7 [+ or -] 1.1
  Disinhibition        5.8 [+ or -] 1.1     3.5 [+ or -] 0.6
  Perceived Hunger     5.4 [+ or -] 0.7     6.5 [+ or -] 0.8

Eating Attitudes       7.9 [+ or -] 2.0     4.5 [+ or -] 1.7
Test-26 score

* Significantly different from dancers (p [less than or
equal to] 0.01).

Table 3
Female Athlete Triad Characteristics in Pair-Matched Elite
Female Dancers and Lean Control Women1

                         Dancers (N = 15)     Controls (N = 15)
                        Mean [+ or -] SEM     Mean [+ or -] SEM

RMR                     1367 [+ or -] 27     1454 [+ or -] 34 *
RQ                      0.87 [+ or -] 0.02   0.85 [+ or -] 0.01
EA, kcal/kg FFM/d       3.75 [+ or -] 2.2    41.1 [+ or -] 4.6
                                                  ([dagger])
Onset of Menses, yr     14.9 [+ or -] 0.4    13.4 [+ or -] 0.3 *
Menstrual Status, n
  Normal                        9                    14
  Irregular                     3                     1
  Amenorrheic                   3                     0
Total Z-score           0.99 [+ or -] 0.2    1.20 [+ or -] 0.3
Total BMD, g/           1.16 [+ or -] 0.01   1.19 [+ or -] 0.02
  [cm.sup.2]
Left Femur Z-score      1.00 [+ or -] 0.2    0.64 [+ or -] 0.3
Posterior-Anterior      0.23 [+ or -] 0.2    0.07 [+ or -] 0.2
  Spine Z-score

* Significantly different from dancers (p [less than or equal to]
0.05); ([dagger]) Significantly different from dancers (p < 0.01);
RMR, Resting Metabolic Rate; RQ, Respiratory Quotient; EA, Energy
Availability; FFM, Fat Free Mass; BMD, Bone Mineral Density.

Table 4
Differences in FT Characteristics and Energetic Efficiency
According to Menstrual Status Among Pair-Matched Elite Female
Dancers and Lean Control Women (1)

                          Control        Dancer          Dancer
                        Eumenorrheic  Eumenorrheic  "Dysfunctional" *
                          (N = 14)      (N = 9)          (N = 6)

Weight, kg                56.1 (a)      51.9 (b)      52.0 (b)
Body fat, %               22.4 (a)      17.4 (ab)     12.5 (b)
FFM, kg                   44.0          43.4          45.6
RMR, kcal/d             1468          1376          1353
Energetic Efficiency,     33.5 (a)      31.7 (ab)     29.7 (b)
  RMR kcal/kgFFM/d
EA, kcal/kgFFM/d          40.9 (a)       5.8 (b)       0.6 (b)
Energy Intake, kcal/d   2073 (a)      1685 (ab)     1415 (b)
BMD, g/[cm.sup.2]          1.19          1.17          1.14
Total Z-score              1.18          1.17          0.73
Posterior-Anterior         0.05          0.56         -0.25
  Spine Z-score
TFEQ, score:
  Dietary Restraint        5.8 (a)       6.1 (a)      12.7 (b)
  Disinhibition            3.6           4.9           7.2
  Perceived Hunger         6.7           4.6           6.7
Eating Attitudes           4.8 (a)       4.1 (a)      13.5 (b)
  Test-26, score

FT, Female Athlete Triad; FFM, Fat Free Mass; RMR, Resting
Metabolic Rate; EA, Energy Availability; BMD, Bone Mineral
Density; TFEQ, Three Factor Eating Questionnaire; *
"Dysfunctional" group includes irregular menses and amenorrheic
dancers; (a,b,c) Different letters indicate significant mean
differences after Tukey post hoc test.

Figure 3 Observed versus predicted RMR in elite female ballet
dancers; * Significantly different from observed RMR
(p [less than or equal to] 0.01).

Resting Metabolic Rate Prediction Equation

Observed           1367
Cunningham         1475 *
Mifflin            1274 *
Harris-Benedict    1343

Note: table made from bar graph

Figure 4 Observed versus predicted RMR in lean control women;
* Significantly different from observed RMR
(p [less than or equal to] 0.01).

Resting Metabolic Rate Prediction Equation

Observed           1454
Cunningham         1471
Mifflin            1351 *
Harris-Benedict    1398

Note: table made from bar graph
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Article Details
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Title Annotation:Original Article
Author:Doyle-Lucas, Ashley F.; Akers, Jeremy D.; Davy, Brenda M.
Publication:Journal of Dance Medicine & Science
Article Type:Report
Geographic Code:1USA
Date:Oct 1, 2010
Words:6205
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