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Identifying Sex-Specific Risk Factors for Low Bone Mineral Density in Adolescent Runners Adam Sebastian Tenforde, Michael Fredericson, Lauren Carter Sayres, Phil Cutti and Kristin Lynn Sainani Am J Sports Med published online March 6, 2015 DOI: 10.1177/0363546515572142 The online version of this article can be found at: http://ajs.sagepub.com/content/early/2015/03/06/0363546515572142

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Identifying Sex-Specific Risk Factors for Low Bone Mineral Density in Adolescent Runners Adam Sebastian Tenforde,*y MD, Michael Fredericson,yz MD, Lauren Carter Sayres,§ BA, Phil Cutti,z MS, and Kristin Lynn Sainani,|| PhD Investigation performed at Stanford University, Stanford, California, USA Background: Adolescent runners may be at risk for low bone mineral density (BMD) associated with sports participation. Few prior investigations have evaluated bone health in young runners, particularly males. Purpose: To characterize sex-specific risk factors for low BMD in adolescent runners. Study Design: Cross-sectional study; Level of evidence, 3. Methods: Training characteristics, fracture history, eating behaviors and attitudes, and menstrual history were measured using online questionnaires. A food frequency questionnaire was used to identify dietary patterns and measure calcium intake. Runners (female: n = 94, male: n = 42) completed dual-energy x-ray absorptiometry (DXA) to measure lumbar spine (LS) and total body less head (TBLH) BMD and body composition values, including android-to-gynoid (A:G) fat mass ratio. The BMD was standardized to Z-scores using age, sex, and race/ethnicity reference values. Questionnaire values were combined with DXA values to determine risk factors associated with differences in BMD Z-scores in LS and TBLH and low bone mass (defined as BMD Z-score –1). Results: In multivariable analyses, risk factors for lower LS BMD Z-scores in girls included lower A:G ratio, being shorter, and the combination of (interaction between) current menstrual irregularity and a history of fracture (all P \ .01). Later age of menarche, lower A:G ratio, lower lean mass, and drinking less milk were associated with lower TBLH BMD Z-scores (P \ .01). In boys, lower body mass index (BMI) Z-scores and the belief that being thinner improves performance were associated with lower LS and TBLH BMD Z-scores (all P \ .05); lower A:G ratio was additionally associated with lower TBLH Z-scores (P \ .01). Thirteen girls (14%) and 9 boys (21%) had low bone mass. Girls with a BMI 17.5 kg/m2 or both menstrual irregularity and a history of fracture were significantly more likely to have low bone mass. Boys with a BMI 17.5 kg/m2 and belief that thinness improves performance were significantly more likely to have low bone mass. Conclusion: This study identified sex-specific risk factors for impaired bone mass in adolescent runners. These risk factors can be helpful to guide sports medicine professionals in evaluation and management of young runners at risk for impaired bone health. Keywords: stress fractures; female athlete; track/field; running; bone mineral density

Adolescence is a critical time of bone mineral accrual.22 Peak bone mass is attained by early in the third decade of life for both sexes.11 Although children and adolescent athletes typically have higher bone mass compared with their nonathletic peers, a subset of athletes may have impaired bone health.30 The female athlete triad (triad) is defined as the interrelationship of energy availability with or without disordered eating to menstrual function and bone health.30 Most high school–aged females, both athletes and nonathletes alike, were reported to have 1 or more components of the triad.24 Emerging research suggests an analogous process of the triad may exist in males.4

Adolescent athletes who participate in sports that emphasize leanness such as long-distance running may have triad risk factors, and this is concerning as inadequate nutrition and hormonal disruptions may interfere with obtaining peak bone mass.4 Energy availability is defined as the difference of energy intake to energy expenditure per kilogram of fat-free mass per day.27 Low energy availability negatively influences reproductive and skeletal health and is more common in female endurance runners than in males.27 Female adolescent athletes with components of the triad are at increased risk for musculoskeletal injuries.34 However, limited studies have characterized risk factors for impaired bone health in adolescent runners, particularly in males.5,8,9,23 In a cross-sectional study evaluating 93 adolescent female runners, Barrack and colleagues8 identified longer duration of running participation, menstrual irregularities, lower body mass index (BMI), and less lean

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tissue mass as risk factors for impaired bone health in adolescent female runners. At a 3-year prospective study follow-up in 40 female runners 18.9 years of age, ‘‘catch-up’’ or delayed gain in bone mineral accrual was seen in a minority of runners studied.9 We identified 2 published investigations conducted in male adolescent runners evaluating risk factors for impaired bone health. Greene and colleagues23 evaluated a population of 20 elite adolescent male runners and reported higher lean mass to be associated with improved bone mineral density (BMD) but did not identify additional risk factors in this population. In a population of 27 male adolescent athletes, Barrack et al5 reported that a greater proportion of runners (2/9 vs 0/18) had prior stress fractures, trended toward having lower BMD (Z-scores \–1), and consumed fewer snacks than nonendurance athletes. Both studies had a small sample size of runners (n  20 in each investigation), so evaluating a larger population of male adolescent runners may help detect additional risk factors for impaired bone health. The objectives of our study were to characterize sexspecific risk factors that may contribute to lower BMD in adolescent runners and to identify potential clinical predictors of low bone mass (BMD Z-scores –1) in this population. We examined risk factors that have previously been associated with low BMD or stress fractures in adolescent or nonadolescent runners, including lower lean mass,8,23 low BMI,6,39 prior fracture,25,39 menstrual variables (menstrual irregularity, history of amenorrhea, number of menstrual periods),6,8,17,25,39 sports participation,6,17,19,39 disordered eating,6,18,30 and nutritional variables including calcium and vitamin D.25,32,36 We hypothesized that adolescent runners would have sex-specific risk factors for impaired bone health, including the triad in female runners and impaired nutrition in male runners.

METHODS Study Design and Recruitment Subjects were recruited from a larger population of adolescent runners enrolled in a study to identify risk factors for stress fracture injuries.39 Study investigators recruited participants from 28 high schools in the San Francisco Bay Area in Northern California. We invited teams to participate by contacting coaches by email and at local crosscountry and track and field events. We obtained written permission from the high school administrator before

visiting the team. Study investigators introduced the study and provided participation information to athletes at each school. Athletes who participated on their high school cross-country and/or track and field team were eligible to enroll in the study. Each subject was provided a unique study identifier and web link to an online survey by email. Athletes who completed the online survey were provided a US$15 coupon to a local running store. After completion of the online survey, each subject was invited to complete an online food frequency questionnaire and dual-energy x-ray absorptiometry (DXA) scan. All runners who completed the online survey were eligible to complete a DXA scan. The inclusion criterion for the DXA portion of the study was completion of the online survey. In our DXA analysis, we excluded any female runner who was younger than 15 years and had not reached menarche, as we could not accurately assess the menstrual variables in analysis of these subjects. Subjects who completed a DXA scan were compensated with a US$20 gift card. We obtained online assent from each subject before completing the surveys. We obtained written informed consent before completing bone density and body composition scans from each participant. For subjects younger than 18 years, written assent was obtained from participants, and a parent or guardian provided consent. The institutional review board at Stanford University approved our research protocol.

Online Surveys We used the program Surveyor to generate and collect online survey data that were encrypted and password protected.38 The online questionnaire contained a series of questions designed to identify risk factors for prospective stress fracture injury.39 As there are no validated surveys to evaluate for risk factors of impaired bone health and stress fractures in adolescent runners, questionnaires were developed by study investigators to define both health and running-specific characteristics in this age group. This included both categorical (such as prior participation in ball sports) and continuous variables (for example, age of menarche; see Figure A1 of the Appendix, available online at http://ajsm.sagepub.com/supplemental).

Training History and Performance Each athlete recorded the age of onset for competing in running races and best current or estimated performance

*Address correspondence to Adam Sebastian Tenforde, MD, Division of Physical Medicine and Rehabilitation, Department of Orthopaedic Surgery, Stanford University, 450 Broadway Street, Pavilion A, 2nd Floor MC 6120, Redwood City, CA 94063, USA (email: [email protected]). y Division of Physical Medicine and Rehabilitation, Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA. z Sports Medicine Center and Boswell Human Performance Laboratory, Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA. § Duke School of Medicine, Duke University, Durham, North Carolina, USA. || Division of Epidemiology, Department of Health Research and Policy, Stanford University, Stanford, California, USA. One or more of the authors has declared the following potential conflict of interest or source of funding: A.S.T. was awarded the 2010 Richard S. Materson Education Research Fund New Investigator Research Grant and the 2008 Medical Student Research Grant awarded by the Education Research Fund for Physical Medicine and Rehabilitation and Stanford Medical Scholars Research Program to support conducting the study. The Sports Medicine Center and Boswell Human Performance Laboratory at Stanford provided access to the dual-energy x-ray absorptiometry scanner for collection of bone density and body composition scans, without which this study would not have been possible. M.F. has an ongoing consultancy with Cool Systems Inc, and his institution receives a grant awarded by Ipsen Inc.

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in the mile and 5000-m cross-country events. Training variables assessed included average weekly mileage over the past year, percentage of mileage on pavement or hills, number of workouts per week, and average workout speed. For 11 subjects who did not list their mile performances, the best mile time was imputed from 5000-m performance, BMI, and weekly mileage using linear regression.

Sports Participation We developed a series of questions designed to measure participation in sports and cross-training activities, representing a more extensive version of the Ball Sports Questionnaire.19 As prior participation in ball sports has been associated with reduced risk of stress fracture,19,28,38 we asked detailed questions about prior participation in basketball and soccer, ages of participation, hours per week, and level of participation (club team, interscholastic, or recreational). Similar questions assessed weight lifting (upper and/or lower body), core strengthening, and plyometric exercises. Subjects were asked to record prior sports participation in the following: baseball, dance, football, gymnastics, hockey, swimming, tennis, volleyball, and water polo.

Fracture History On a baseline questionnaire, athletes were instructed to report prior fracture and details to confirm diagnosis, including anatomic location, physician diagnosis, and radiographic confirmation. We also counted prospective stress fractures during the study that occurred during participation in cross-country, track and field, or while training for a running event. In our analysis, fracture refers to history of fracture or development of stress fracture during study participation. All fractures included in our analysis were localized to the lower extremity, diagnosed by a physician, and confirmed with radiography. We focused primarily on fractures that occurred during running, and only 2 female athletes reported prior history of a traumatic fracture. Excluding these subjects did not change our statistical analysis, so these fractures were included in the final analysis to be consistent with a prior report in this population.39

Menstrual History Each female subject completed a detailed menstrual history. This included age of menarche, history no period for 3 months over 1 year, and number of menstrual periods in the past year. We defined current menstrual irregularity as no period for 3 months or 9 periods in the past year, a threshold of menstrual periods used by other investigators.21,25 In addition, subjects were evaluated for amenorrhea. History of amenorrhea was defined as secondary amenorrhea (no period for 3 months in 1 year) and/or history of primary amenorrhea (defined as age of menarche 15 years).33 Girls were asked to record use of oral contraceptives as well as reason for and duration of use. Three girls were younger than 15 years and did not

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reach menarche by the time they completed their DXA scan, so these subjects were excluded from analysis.

Dietary Intake and Eating Behaviors Each subject completed a series of questions regarding dietary intake, including cups of milk, dairy products, and calcium-containing foods. Milk, dairy, and calciumcontaining foods were converted into servings per day. We also inquired about intake of coffee (black coffee and milk-containing beverages), soda, and caffeine-containing energy drinks. Girls completed a 23-item questionnaire based on the Eating Disorder Inventory (EDI), version 1.20 The total EDI score is derived from the sum of the 3 subscales (drive for thinness, body dissatisfaction, and bulimic tendencies). Boys were queried about healthy and unhealthy eating behaviors from questions adapted from Project EAT.31 All subjects were asked to respond to the following question: ‘‘Do you feel that being thinner helps you run faster?’’

Food Frequency Questionnaire A separate online questionnaire was administered to assess dietary intake based on a modified version of the 97-item National Cancer Institute Health Habits and History food frequency questionnaire (see Appendix Figure A2, available online).12 Subjects recorded frequency and serving size of each food or beverage. We modified the questionnaire to include food items rich in calcium, similar to a prior report,32 and that were likely to be consumed by adolescent runners. From these items, we created an automated analysis program to calculate the nutrient intake of the following: calcium, vitamin C, vitamin D, phosphorus, potassium, iron, fiber, and fat. Total dietary calcium intake was adjusted for energy intake (kcal/d) using the standardized residual method.40 We assessed daily servings of vegetables and fruits, intake of animal and vegetable protein, and total fat intake (g/d). We asked each subject to record calcium intake from supplement or multivitamins containing calcium.

BMD and Body Composition Measurements We measured the height and weight of each subject without shoes using a standard stadiometer and balance beam scale. From these values, we generated BMI values. BMI Z-scores were calculated based on age and sex references from Centers for Disease Control (CDC) growth charts.14 The DXA scans were acquired using the GE Lunar i-DXA (GE Medical Systems Lunar) machine to measure BMD for the lumbar spine (LS: L1-L4) and total body less head (TBLH), the 2 anatomic sites recommended for screening athletes younger than 20 years.16 Quality control measures were used to calibrate the machine on each study date before acquiring data, and the same trained and licensed technician performed all scans. From total body scans, we used enCORE version 14.1 software (GE Medical Systems Lunar), which automated measurement

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of fat mass (including android to gynoid [A:G] fat mass ratio, defined as 2 types of fat mass), lean mass, and bone mass. From the total body protocol, we generated TBLH BMD, as the skull represents a large amount of total body BMD and does not change significantly with activity.3 We generated Z-scores for LS and TBLH BMD using available age-, sex-, and race/ethnicity-matched reference values analyzed using enCORE software. Two girls were missing TBLH BMD values because the total body scans did not acquire correctly; these girls were excluded from all analyses involving TBLH BMD. For these 2 girls, we imputed body composition values (lean mass, fat mass, A:G ratio, etc) from height, weight, and the number of periods in the past year using linear regression; thus, their data could be included in analyses of LS BMD Z-scores. Two physicians (A.S.T. and M.F.) reviewed all scans for artifact or computer errors in assignment of anatomy. On review, 3 LS films (3 girls) did not capture the L1-L4 vertebrae and were adjusted to ensure the accurate region of interest. The adjustments resulted in accurately capturing only the L2-L4 vertebrae, so these 3 studies were standardized to reference values L2-L4.

Statistical Analysis Responses from the baseline survey and food frequency questionnaire were examined with BMD and body composition values for analysis. Statistical analyses were completed using SAS version 9.3 (SAS Institute). We evaluated risk factors for low BMD. These included risk factors for stress fracture and low BMD identified previously, including lower lean mass,8,23 low BMI,6,39 prior fracture,25,39 menstrual variables (menstrual irregularity, history of amenorrhea, number of menstrual periods),6,8,17,25,39 sports participation,6,17,19,39 disordered eating,6,18,30 and nutritional variables including calcium and vitamin D.25,32,36 To evaluate for sex-specific contributors to impaired bone health (for example, menstrual variables are measured only in female runners), male and female runners were analyzed separately. Because the food frequency questionnaire provided numerous nutrient and food variables, we performed a principal components analysis (PCA) to reduce the number of variables and identify primary dietary patterns.35 None of the identified components were significantly related to BMD in boys or girls except the component that had high loadings in calcium and dairy foods. Thus, we decided to focus only on calcium and milk and dairy consumption for these analyses. We used linear regression models to assess the association of risk factors to BMD Z-scores for LS and TBLH. All continuous risk factors were converted to Z-scores before the regression analysis. Thus, b coefficients for continuous risk factors represent the change in BMD Z-score for every 1 standard deviation increase in the risk factor. Variables that were significant or marginally significant in univariable linear regression (P \ .10) were further evaluated in multivariable analyses. We identified the most robust predictors of BMD using an iterative model-building process. For girls, we tested for interactions with menstrual

irregularity, given previous research that has found that the effects of body composition and nutrition on BMD may be modified by menstrual status.1,32 Because of concerns about multiple comparisons, we also reduced the number of independent variables using PCA with a Varimax rotation.35 When we fit multivariable linear regression models to the components, we found similar risk factors for lower BMD for each sex (see Appendix Tables A1 and A2, available online). Because components are harder to measure in a clinical setting, we report the original risk factors in the main text rather than the PCA results. We then tested whether the variables identified in the linear regression could be used to screen for low bone mass (BMD Z-scores –1) in this population. The American College of Sports Medicine (ACSM) defines low BMD in athletes as Z-score below –1.30 As athletes are expected to have higher BMD values than the average population, the threshold for low BMD defined by the ACSM is different than the defined threshold of Z-score –2.0 for children and adolescents by the International Society of Clinical Densiometry.41 We focused on easily measured clinical predictors (for example, low BMI rather than low A:G ratio). We compared the percentage of low bone mass in those with and without the clinical predictors using x2 tests.

RESULTS Subject Characteristics A total of 94 girls and 42 boys completed bone density and body composition scans. This population represents a sample of 20% of girls and 13% of boys who enrolled in the larger prospective study evaluating risk factors for stress fractures.39 Three girls were younger than 15 years at the time of DXA scans and did not reach menarche, so they were excluded from the final analysis. Table 1 shows the characteristics of each population.

Female Runners BMD and Body Composition Values. Female runners’ BMI Z-scores values were below average for their ages based on CDC reference values. The mean BMD Z-scores values were above average TBLH and average for LS. In the population studied, 13 girls (14%) met the criteria for low bone mass in LS (BMD Z-scores –1), including 4 (4%) with TBLH BMD Z-score –1. Four girls (4%) had BMD LS BMD Z-scores –2. In univariable analysis, increased weight, height, BMI, lean mass, fat mass, and A:G ratio were all associated with higher BMD Z-scores for LS and TBLH (see Appendix Table A3, available online). Older chronological age was associated with lower BMD Z-scores (despite the fact that Z-scores were already age referenced) for both LS and TBLH. In girls, older age correlated significantly with faster mile time (r = 0.34, P = .0009) and more mileage (r = 0.47, P \ .0001), higher chance of a fracture during the prospective study (hazard ratio = 2.04, P = .041), older age

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TABLE 1 Baseline Characteristics of the Study Populationa

Age, y Bone mineral density Z-scoreb Lumbar spine Total body less head Periods in the past year History of amenorrheac Current menstrual irregularityd Menarche age, y Weight, kg Height, cm Body mass index, kg/m2 Body mass index Z-scoree Lean mass, kg Fat mass, kg Android:gynoid fat ratiof Best mile time, min:s Running volume, km/wk Years of competitive running Ever had a fracture Milk consumption, cups/d Other dairy foods, servings/d Other foods high in calcium, servings/d Calcium, (mg/d)g EDI-DT Belief that ‘‘thinner is faster’’ Prior basketball participation Prior soccer participation Weightlifting participation Plyometric participation

Girls (n = 91)

Boys (n = 42)

16.9 6 1.3

16.3 6 1.3

20.03 0.37 8.9 39 32 12.9 55.0 163.9 20.4 20.27 38.4 14.2 0.70 6:19 27.4 3.4 22 1.6 1.5 0.9 1500 3.5 49 56 66 54 43

6 1.0 6 0.8 6 3.6 (43%) (35%) 6 1.5 6 8.5 6 7.2 6 2.4 6 0.82 6 4.9 6 5.2 6 0.16 6 0:53 6 19.2 6 2.1 (24%) 6 1.3 6 1.2 6 0.8 6 809 6 5.2 (54%) (62%) (73%) (59%) (47%)

20.15 6 1.0 0.04 6 0.8 — — — — 61.0 6 9.3 173.6 6 7.9 20.1 6 2.0 20.33 6 0.72 49.8 6 7.5 8.8 6 3.4 0.66 6 0.16 5:23 6 0:36 31.7 6 22.8 3.2 6 2.1 10 (24%) 2.1 6 1.3 1.3 6 1.1 1.1 6 1.0 1752 6 793 — 28 (67%) 23 (55%) 34 (81%) 17 (40%) 23 (55%)

a

Values are expressed as mean 6 SD or n (%). EDI-DT, Eating Disorder Inventory drive for thinness subscale.20 Z-scores standardized to available age, sex, and race/ethnicity normal values. c Defined as primary amenorrhea (menarche age 15 years) or history of no period for 3 months in 1 year. d Defined as missing 3 consecutive periods or having 9 periods over the past 12 months. e Based on Centers for Disease Control reference values for age and sex.14 f Ratio of android to gynoid fat mass. g Calcium intake calculated using residual method.40 Nondietary calcium intake did not influence bone mineral density values and is not included in total calcium intake. b

of menarche (r = 0.25, P = .019), and lower milk (r = 20.32, P = .002) and calcium consumption (r = 20.27, P = .010). Training Variables, Injury, and Sports Participation. Female runners participated on average for greater than 3 years in running races at the time of study participation. Faster mile times and higher mileage were each associated with lower BMD Z-scores for LS and TBLH (see Appendix Table A3, available online). Girls with a history of fracture had lower BMD Z-scores for LS but not TBLH. Of the 4 girls with BMD LS Z-scores –2, there was a history of fracture in 3. Most girls participated in basketball and soccer (Table 1), and each sport was associated with increased BMD Zscores for LS and TBLH, although these values did not reach statistical significance (Appendix Table A3, available online). Subjects who participated in dance and/or gymnastics did not have measurable changes in BMD compared with those who did not participate (differences between participants and nonparticipants were 0.07 standard

deviations for LS, P = .72, and 0.02 standard deviations for TBLH, P = .92). Menstrual History. On average, girls reported fewer than 9 periods over the past 12 months. More than onethird reported current menstrual irregularity, and 43% had a history of amenorrhea. Multiple menstrual characteristics were associated with low BMD Z-scores, including history of amenorrhea, current menstrual irregularity, and older age of menarche. Four girls reported history of oral contraceptive pill use for 1 year; this was too few to analyze as a separate group. Each subject had a different reason to take this medication (birth control, being underweight, to start menstrual periods, and as an estrogen supplement for bone health). Dietary Pattern and Behavior. Two female runners did not complete the food frequency questionnaire, so 89 girls were included in evaluation of the questionnaire to BMD values. High dairy and milk consumption predicted greater BMD Z-scores, particularly for TBLH. Calorie-adjusted

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TABLE 2 Multivariable Linear Regression Analysis of Risk Factors for Girls With BMD Z-Scoresa Risk Factor

LS BMD Z-Score (n = 91)

A:G fat ratio Fat mass (per SD) Height (per SD) Lean mass (per SD) Current menstrual irregularities (yes/no) History of fracture (yes/no) Interaction of menstrual irregularities with history of fracturee (yes/no) Later age of menarche (yes/no) Cups of milk per day (per SD)

0.49b 20.30c 0.33b — 20.03 0.43 21.18b — —

TBLH BMD Z-Score (n = 89) 0.17b — — 0.33d

— 20.26d 0.19b

a For continuous predictors, beta coefficients represent the change in BMD Z-score for every 1–standard deviation increase in the predictor. A:G, android to gynoid; BMD, bone mineral density. BMI, body mass index; LS, lumbar spine, TBLH, total body less head. b P \ .01. c P \ .05. d P \ .001. e Having both current menstrual irregularity (defined as absence of menstrual periods for 3 months or having 9 periods over the past 12 months) and history of fracture.

calcium and vitamin D values were highly correlated together (r = 0.90, P \ .0001). Higher vitamin D approached a positive trend to greater TBLH BMD Z-scores (P = .13); however, this association disappeared after adjusting for calcium intake. Nondietary calcium intake from sources including supplemental calcium and multivitamins did not influence BMD values. No athletes reported use of diet pills. Four athletes (4%) reported prior diagnosis of an eating disorder (anorexia or bulimia nervosa), although the small sample size limited the evaluation to BMD. Elevated EDI total scores were not predictive of lower BMD Z-scores. Higher scores on the EDI drive for thinness subscale and the belief that thinness improves performance were paradoxically associated with higher BMI (r = 0.51, P \ .0001), increased fat mass (r = 0.40, P \ .0001), and slower performances in the mile (r = 0.20, P = .05) in girls. A belief that thinness improves performance was also associated with greater BMI (r = 0.22, P \ .05), fat mass (r = 0.21, P = .05), and higher EDI drive for thinness scores (r = 0.34, P \ .0008). Multivariable Analysis and Low Bone Mass Risk Factors. In multivariable regression analyses, a lower A:G ratio was associated with lower BMD for both LS and TBLH Z-scores (Table 2). For LS, a higher fat mass was associated with lower BMD Z-scores after accounting for the A:G ratio. For LS, being shorter also predicted lower BMD Z-scores (although a model that included weight rather than height gave a similar R2 value, suggesting that body size is the key factor rather than height per se). Finally, the interaction between current menstrual irregularity and a history of fracture was significant, while the main effects for current menstrual irregularity and history of fracture to BMD Z-scores were not. For TBLH, in addition to a lower A:G ratio, later age of menarche, decreased lean mass, and fewer cups of daily milk consumption were independently associated with lower BMD Z-scores. Notably, mile performance and weekly mileage were no longer related to lower BMD

Z-scores at either LS or TBLH after accounting for body size/composition. Thirteen athletes met the criterion for low bone mass (BMD Z-score –1). All subjects with low bone mass met this criterion for LS, while an additional 4 subjects had low TBLH BMD. We found that either being underweight (BMI  17.5 kg/m2) or having both a history of fracture and current menstrual irregularity was predictive of low bone mass in this population. Girls with either of these easily measured risk factors were nearly 6 times more likely to have low bone mass based on differences using the x2 test (40% vs 7%, P = .002). Notably, girls with current menstrual irregularity but no fractures or with fracture but no current menstrual irregularity were not more likely to have low bone mass than girls with neither of these risk factors (10% of women with current menstrual irregularity but no fractures had low bone mass vs 10.2% of those with neither risk factor, P . .999 [Fisher exact test]; 0% of women with fracture but no current menstrual irregularity had low bone mass vs 10.2% of those with neither risk factor, P = .58 [Fisher exact test]).

Male Runners BMD and Body Composition Values. Similar to girls, BMI Z-score average values for boys were below 0 as a population. BMD Z-scores were slightly below average for LS although not for TBLH. Nine subjects (21%) had low bone mass in LS (Z-scores –1), including 4 boys (10%) who also had TBLH Z-scores –1. Two male runners (5%) had BMD LS Z-scores –2. Multiple body composition values were associated with increased BMD Z-scores, including higher weight, height, BMI, lean mass, and A:G ratio (Appendix Table A3, available online). Training Variables, Injury, and Prior Sports Participation. On average, boys had .3 years of participation in running at the time of study participation. Both fracture history and greater mileage were associated with lower

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TABLE 3 Multivariable Linear Regression Analysis of Risk Factors for Boys With BMD Z-Scoresa Risk Factor

LS BMD Z-Score (n = 42)

TBLH BMD Z-Score (n = 42)

0.57c 20.90c —

0.60d 20.46f 0.25c

BMI Z-scoreb Belief that ‘‘thinner is faster’’e A:G fat ratio

a For continuous predictors, b coefficients represent the change in BMD Z-score for every 1–standard deviation increase in the predictor. A:G, android to gynoid; BMD, bone mineral density. BMI, body mass index; LS, lumbar spine, TBLH, total body less head. b BMI Z-scores based on Centers for Disease Control reference values.14 c P \ .01. d P \ .001. e Answered yes to the question ‘‘Do you feel that being thinner helps you run faster?’’ f P \ .05.

TABLE 4 Summary of Sex-Specific Risk Factors for Lower BMD and for Low Bone Mass (BMD Z-score –1)a Multivariable Linear Regression Analysis of Risk Factors to Lower BMD Z-Scores Girls LS

Lower A:G ratio, higher fat mass, lower height, current menstrual irregularity with history of fracture Lower A:G ratio, lower lean mass, later age of menarche, fewer cups of milk per day

TBLH Boys LS TBLH

Lower BMI Z-score,b belief that ‘‘thinner is faster’’c Lower BMI Z-score,b belief that ‘‘thinner is faster,’’c lower A:G ratio

Sex-specific risk factors for BMD Z-scores –1 Girls Boys

BMI 17.5 kg/m2, menstrual irregularity plus history of fracture BMI 17.5 kg/m2, belief that ‘‘thinner is faster’’c

a

A:G, android to gynoid; BMD, bone mineral density. BMI, body mass index; LS, lumbar spine, TBLH, total body less head. BMI Z-scores based on Centers for Disease Control reference values.14 c Answered yes to the question ‘‘Do you feel that being thinner helps you run faster?’’

b

BMD Z-scores, but these differences did not reach statistical significance. Both boys with BMD LS Z-scores –2 had a history of fracture. Most boys participated in basketball and soccer (Table 1), and participation in ball sports was consistently associated with positive but nonsignificant trends of higher BMD Z-scores. Dietary Pattern and Behavior. Two-thirds of boys agreed that being thinner makes you run faster, and this belief was strongly associated with lower BMD Z-scores. Similar to girls, in boys the belief that thinness improves performance was associated with higher BMI (r = 0.22, P . .05) and higher fat mass (r = 0.22, P . .05), but these correlations did not reach statistical significance likely because of the smaller sample size for boys. But in boys, this belief was also associated with a lower A:G ratio (r = 20.20, P . .05) and lower caloric intake (r = 20.21, P . .05), although these values did not reach statistical significance. Calcium intake, dairy servings, milk consumption, and consuming more calcium-containing foods were all positively associated with increased BMD, but these did not reach statistical significance. No male runners reported a history of a diagnosed eating disorder. Multivariable Analysis and Low Bone Mass Risk Factors. Multivariable regression models identified lower

BMI Z-scores and the belief that thinness makes you faster each as independent risk factors associated with lower BMD Z-scores for both LS and TBLH (Table 3). In addition, a higher A:G ratio was associated with improved BMD Z-score for TBLH. Nine boys met criteria for low bone mass (BMD Z-scores –1). We found that being underweight (BMI  17.5 kg/m2) and believing that thinness leads to faster performances were predictive of low bone mass in this population. For boys with neither risk factor (n = 14), 7.1% had low bone mass, increasing to 24% (6 of 25) with 1 risk factor and 67% (2 of 3) with both risk factors (P = .08). A summary of the most significant findings for each sex are displayed in Table 4.

DISCUSSION Our investigation identified sex-specific risk factors associated with low BMD Z-scores in adolescent runners, adding to the limited studies in this population.5,8,9,23 For both sexes, a lower android to gynoid fat mass ratio (A:G) was associated with lower BMD Z-score values. To our knowledge, the association of A:G ratio to BMD Z-scores has

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not been previously reported in a young running population. As expected, body size and composition were also strongly related to BMD Z-scores in both sexes. In girls, the combination of fracture and menstrual irregularities was associated with lower spine bone mass; having only one but not both of these risk factors was not associated with lower BMD Z-scores in this young population. For both sexes, BMI 17.5 kg/m2 appears to be threshold for increased risk of low BMD. The 2014 Female Athlete Triad Coalition consensus statement on treatment and return to play for the female athlete triad identifies BMI 17.5 kg/m2 as a high risk factor, and prior stress reaction/fracture and 6 to 8 menses over a 12-month period are each moderate risk factors for the triad.16 Our athletes had similar risk factors to these screening criteria defined by expert panel recommendations, and notably, this population of athletes does have a higher rate of low BMD values (Z-score –1). The belief that thinness leads to improved performance was associated with low bone mass in boys and may suggest a male version of the triad. From these findings, we identified easy-to-measure clinical risk factors that may predict low bone mass (BMD Z-score –1) in adolescent runners. We previously identified risk factors for prospective stress fractures in girls, which included late menarche and a history of fracture.39 Our findings that older age of menarche is associated with lower TBLH BMD Z-score values may help explain the observed association to increased stress fracture risk reported in other studies,17,39 as lower whole-body bone mineral content has been linked to increased risk for stress fractures25 and longer time for return to sport after sustaining a bone stress injury.29 The importance of milk and calcium intake in fracture prevention and predicting additional bone mass accrual in older female runners is also well established.25,32 Menstrual irregularity has been shown to be an independent predictor of low BMD Z-scores in adolescent female runners.8 In our study, menstrual irregularity in the absence of a history of fracture was not associated with lower LS BMD Z-scores, and fracture in the absence of menstrual irregularity was not associated with lower LS BMD Z-scores. Irregular periods are common in young women in general, and thus menstrual irregularity alone may be a less specific predictor of low bone mass in this age group. Lower weight, height, BMI, lean mass, fat mass, and A:G ratio were associated with lower LS and TBLH BMD Zscores in univariable models. In multivariable models, smaller body size (either height, weight, or lean mass) and lower A:G ratio independently predicted lower LS and TBLH BMD Z-scores. For LS BMD, fat mass was negatively associated with BMD Z-scores after accounting for the A:G ratio. This suggests that it is specifically android, rather than gynoid, fat mass that is associated with higher BMD Z-scores in our adolescent running population studied. The A:G represents the ratio of 2 subtypes of regional fat and does not represent total fat content. It is plausible that exceedingly low A:G would represent a state of low energy availability, as an elevated A:G ratio has been observed to correlate with insulin resistance.2 To our knowledge, the

association of lower A:G fat mass ratio to lower bone mass has not been reported in this population. Further research is required to understand this relationship. We did not find an association between EDI scores and thinness idealization and BMD Z-scores in our study. However, girls who agreed that thinness improves performance or had elevated EDI scores had paradoxically higher BMI and increased fat mass, suggesting that these questions may not have detected restrictive or disordered eating behaviors in our population. We observed that BMD age-referenced Z-scores declined with increased chronological age for girls. This association was observed in a prior investigation and postulated to reflect behaviors associated with participation in endurance running.7 In our population, older age correlates significantly with faster mile time and more mileage, a higher chance of a fracture during the prospective study, later menarche, and less milk and calcium consumption. Thus, all of these factors could explain the negative correlation of BMD Z-scores with chronological age. We identified potentially useful clinical predictors of low bone mass (BMD Z-score –1) in girls. Those with a BMI 17.5 kg/m2 or with current menstrual irregularity plus a history of fracture were 6 times more likely to have low bone mass than girls without one of these risk factors. In our study population, low BMI reflected a low A:G ratio but is easier to measure. Our investigation calculated BMI, and we completed the analysis using both BMI and BMI Z-scores. The revised 2000 CDC growth charts provide a method to generate BMI Z-scores based on age and sex of a child or adolescent aged 2 to 20 years, and Z-scores at or below the 5th percentile have historically been defined as a threshold for individuals at increased risk of nutritional deficiencies.26 As BMI Z-scores are used more frequently in research and evaluated for clinical application in athletes, our results suggest a different threshold may be important to consider for nutritional deficiencies in adolescent runners. A BMI 17.5 kg/m2 corresponded on average to a BMI Z-score –1 based on CDC reference values, a suggested threshold for thinness.15 A BMI less than 17.5 kg/m2 has been suggested as evidence of low energy availability.16 Barrack et al10 identified that BMI values below the 10th percentile were associated with increased bone marker turnover and energy deficiency in adolescent female runners. In addition, in a larger multicenter study, Barrack and colleagues6 identified a greater number of triad risk factors associated with higher risk for prospectively sustaining a bone stress injury. Our current investigation supports their findings and identifies a fracture with menstrual irregularities to be associated with a higher risk for an athlete with a low BMD. Menstrual irregularity and stress fracture have been previously identified as risk factors for low bone mass,16 but this is the first published study that we are aware of that suggests an interaction between these 2 variables. In a young population, the combination of menstrual irregularity plus fracture may have a higher specificity for detecting low bone mass without a cost to sensitivity. Given the small number of girls with low bone mass (n = 13), these predictors need to be confirmed in a larger sample. Our identified risk factors may be early

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markers of the triad, including low energy availability (BMI threshold of 17.5 kg/m2), functional hypothalamic amenorrhea (menstrual irregularities), and impaired bone health (fracture). In boys, a low A:G ratio, low BMI, and the belief in thinness improving performance were all associated with lower BMD Z-scores. The belief in thinness improving performance was held by most boys in our study, and other investigators have seen concern for body shape in a significant portion of the adolescent male population.18 We postulate that risk factors for low bone mass may reflect low energy availability in boys and may suggest an analogous process of the triad occurring in males.4 Fracture was negatively associated with LS BMD Z-scores in boys, but this did not reach statistical significance. This may be due to a smaller sample size limiting our statistical power. Similarly, prior participation in basketball and soccer was positively associated with BMD Z-scores but did not reach statistical significance. In a previous report, we found that prior basketball participation was associated with a significantly reduced risk of prospective stress fractures in young male runners.39 The lack of a strong association between ball sports participation and BMD Z-scores may indicate that ball sports affect bone quality more than bone density. The DXA detects differences in bone density, so the protective effects of ball sports on fracture prevention may be the result of improved geometric properties that would not be detected using DXA. In contrast to our prior investigation identifying risk factors for stress fracture, dance or gymnastic participation did not predict lower BMD Z-scores in girls.39 These findings are expected as high- and odd-impact-loading sports have been consistently shown to be associated with improved bone health in both sexes.37 We identified potentially useful clinical predictors of low bone mass (BMD Z-score –1) in boys. Those with a BMI 17.5 kg/m2 and/or the belief that thinness improves performance were significantly more likely to have low bone mass than boys with neither risk factor. Thus, screening for low BMI or for thinness idealization may help identify boys at risk for low bone mass (BMD Z-score –1), but the small number of boys with low bone mass (n = 9) limits our ability to draw strong conclusions.

Limitations Limitations to our investigation include risk factor data being self-reported, retrospective design, and using a cross-sectional study design. We are unable to detect changes in risk factors to prospective changes in BMD in this current investigation. We used nonvalidated surveys to characterize risk factors in this population, as no prior survey tool has been developed and universally adopted in this population. Questions including whether being thinner leads to faster running performances are exploratory and have not been validated to predict clinical diagnosis of an eating disorder or other potential behaviors that negatively influence bone health in this population. The food frequency questionnaire does not allow for an accurate measure of energy availability, although it

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strongly detects differences in calcium consumption.13 The relatively smaller sample size of male runners may have limited statistical power to detect less robust risk factors for impaired bone health. A portion of all runners eligible from a larger study participated in having a DXA performed, making selection bias possible. The sample may have been enriched in participants with risk factors for low bone mass, but this would likely not affect the magnitude of the associations between low bone mass and risk factors within the sample. Our sample size may not be sufficiently large to detect all risk factors and was limited to runners, and this may limit generalizability to the high school population. Given the importance of identifying risk factors for impaired bone health during a critical time of bone mineral accrual, more studies are needed in children and adolescent athletes. We were limited in the number of each fracture type and degree of injury to determine whether risk factors were different based on anatomic location. Interestingly, a recent multicenter investigation in a slightly older athletic population (average age, 18.1 years) reported potential risk factors for prospective bone stress injuries that are similar to risk factors for low bone density in our current investigation, including athletes who participate in sport/exercise activity that requires leaness, BMI \21 kg/m2, elevated dietary restraint, and those with oligoor amenorrhea.6 In addition, we acknowledge that menstrual irregularities were common in our investigation and may be observed in young females who do not participate in sports. Despite these limitations, our study has a number of strengths, including a priori investigation of previously identified risk factors for stress fracture to bone density in other running populations evaluated in our population of adolescent runners. To our knowledge, our study includes the largest sample size of adolescent runners published to date and is the first to evaluate risk factors for low BMD in a population composed of both male and female adolescent runners, allowing us to better characterize sex-specific risk factors.

CONCLUSION In summary, both female and male adolescent runners are at risk for impaired BMD. Our findings substantiate the limited prior research in adolescent runners that identifies a subset of female runners with impaired bone health. Importantly, we find that adolescent male runners are also at risk for suboptimal bone mass. We found a subset of male athletes with low bone mass associated with both low BMI and belief in thinness improving performance, suggestive of low energy availability in males. We identified easy-to-measure variables associated with low bone mass (BMD Z-score –1) including menstrual irregularities with history of fracture in girls, BMI 17.5 kg/m2 for both sexes, and belief that thinness improves performance in boys. The sensitivity and specificity of these risk factors for identifying low BMD in this population are unknown and require further study. However, clinicians may consider these risk factors as potential tools to screen for male and female runners in this age group who are at

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increased risk for low bone mass. The 2014 Female Athlete Triad Consensus statement on the triad recommends screening female athletes with DXA for a BMI threshold of 17.5 kg/m2 is consistent with our findings.16 Screening for risk factors in both sexes is critical to identify these individuals and to address modifiable risk factors including nutrition to ensure athletes optimize accrual of peak bone mass. We also identified a novel predictor of low bone mass in both sexes, the A:G fat mass ratio, which requires further study. Given that both sexes are at risk for low bone mass, future studies should include both females and males to develop sex-specific strategies to improve the health of the adolescent running population. Directions for future research may include bone-loading protocols to stimulate higher bone mass and nutritional interventions that ensure adequate energy availability and calcium and vitamin D intake to promote bone health.

ACKNOWLEDGMENT The authors acknowledge the assistance of Herve Collado, Julia Arroyo Martin, Mary Liz McCurdy, and Shelli McDonald in subject recruitment and data acquisition. Thanks to Dr Neumark-Sztainer for permission to use her questionnaire and to Dr Laura Bachrach for her contribution to bone mineral density interpretation. The authors thank the study participants and their families and coaches for making this study successful.

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