International Journal of Sport Nutrition and Exercise Metabolism, 2010, 20, 418-426
© 2010 Human Kinetics, Inc.
Bone-Mineral Density and Other Features of the Female
Athlete Triad in Elite Endurance Runners: A Longitudinal
and Cross-Sectional Observational Study
Noel Pollock, Claire Grogan, Mark Perry, Charles Pedlar, Karl Cooke,
Dylan Morrissey, and Lygeri Dimitriou
Low bone-mineral density (BMD) is associated with menstrual dysfunction and negative energy balance in
the female athlete triad. This study determines BMD in elite female endurance runners and the associations
between BMD, menstrual status, disordered eating, and training volume. Forty-four elite endurance runners
participated in the cross-sectional study, and 7 provided longitudinal data. Low BMD was noted in 34.2% of
the athletes at the lumbar spine, and osteoporosis in 33% at the radius. In cross-sectional analysis, there were
no significant relationships between BMD and the possible associations. Menstrual dysfunction, disordered
eating, and low BMD were coexistent in 15.9% of athletes. Longitudinal analysis identified a positive association between the BMD reduction at the lumbar spine and training volume (p = .026). This study confirms
the presence of aspects of the female athlete triad in elite female endurance athletes and notes a substantial
prevalence of low BMD and osteoporosis. Normal menstrual status was not significantly associated with normal
BMD, and it is the authors’ practice that all elite female endurance athletes undergo dual-X-ray absorptiometry
screening. The association between increased training volume, trend for menstrual dysfunction, and increased
loss of lumbar BMD may support the concept that negative energy balance contributes to bone loss in athletes.
Keywords: menstrual disturbances, eating attitudes, endurance athletes, bone health
Physical activity and weight-bearing sports can
increase bone-mineral density (BMD; Wolff, Van
Croonenborg, Kemper, Kostense, & Twisk, 1999). However, low BMD has been recognized in female athletes
participating in sports such as endurance running and
gymnastics, which traditionally emphasize leanness
(Torstveit & Sundgot-Borgen, 2005a, 2005c). In athletes,
low BMD has been associated with menstrual dysfunction
and negative energy balance, with or without disordered
eating, as part of the coexisting spectrum disorder noted in
the female athlete triad (Braam et al., 2003; Drinkwater et
al., 1984; Hetland et al., 1993; Marcus et al. 1985; Nattiv
et al., 2007; Rutherford, 1993; Wolman, Clark, McNally,
Harries, & Reeve, 1990, 1992). It is recognized that low
energy availability is often the underpinning pathology
in the triad (Nattiv et al., 2007).
Pollock is with UK Athletics, Hospital of St. John and St.
Elizabeth, London, UK. Grogan, Perry, and Morrissey are with
the Centre for Sports and Exercise Medicine, Barts, London,
UK. Pedlar is with the English Institute of Sport, London, UK.
Cooke is with the National Tennis Centre, Lawn Tennis Association, London, UK. Dimitriou is with Middlesex University,
London, UK.
418
Energy availability is defined as the energy remaining for physiological functions after the energy expended
in exercise is subtracted from dietary energy intake. This
is an essential therapeutic concept; it has been elegantly
shown that it is not the intensity or stress of training that
results in menstrual or bone dysfunction but the reduction
in energy availability that may be present in these athletes
if nutrition is not adequate (Loucks, Verdun, & Heath,
1998). Endurance running in a negative energy balance
suppresses synthesis of collagen and insulin-like growth
factor-1 (Zanker & Swaine, 2000), the latter of which is
known to promote cell proliferation and matrix formation
in bone (Rosen, 1999).
Optimal bone health is achieved by accruing high
peak bone mass in the first 3 decades of life, in combination with prevention of bone loss up to and beyond
menopause (Nattiv et al., 2007). Should this not occur,
athletes risk developing stress fractures and premature
osteoporosis (Torstveit & Sundgot-Borgen, 2005a; Nattiv
et al., 2007; Okano et al., 1995). The American College
of Sports Medicine (ACSM) recommends investigating bone health in at-risk athletes by using dual-X-ray
absorptiometry (DXA; Nattiv et al., 2007). The ACSM
recommends using Z scores for adolescents and premenopausal females. It is not clear whether the application
of the reference range based on healthy nonathletes is
Bone-Mineral Density in Endurance Runners 419
appropriate in the elite athlete population (Sangenis et
al., 2005).
There have been a limited number of cross-sectional
studies assessing BMD in elite athletes (Marcus et al.,
1985; Torstveit & Sundgot-Borgen, 2005b). These have
demonstrated lower Z scores in elite endurance athletes
with menstrual dysfunction than in elite enduranceathlete controls and lower BMD in leanness sports than
in elite athletes in sports defined as not emphasizing leanness (Marcus et al., 1985; Torstveit & Sundgot-Borgen,
2005b). There have been several longitudinal studies in
predominantly nonelite athlete groups that noted small
gains in BMD over less than a year (Bennell et al., 1997;
Nichols et al., 1994). However, to our knowledge there
have been no longitudinal studies over several years in
elite athletes.
This study aimed to compare the distribution of
BMD in UK elite endurance athletes with the parameters
established in healthy nonathletic women. We assessed
associations between the BMD data and training hours,
menstrual status, and disordered eating. In addition, in a
subgroup of athletes we evaluated longitudinal changes in
BMD in elite female athletes and the relationship between
the rate of change and training hours, menstrual status,
and disordered eating.
Methods
Participants
A cohort of 44 elite female endurance athletes age 16–42
years (22.9 ± 6.0) supported by UK Athletics (UKA)
who had attended bone-density screening at the Human
Performance Laboratory, Middlesex University, between
November 2003 and April 2008 were studied. The screening service was offered annually between November
2003 and 2008 to all elite endurance (>800-m) athletes
representing Great Britain, regardless of medical history.
All athletes who attended were included in the study.
Access to this population was granted through the UKA
Endurance Performance Centre at St. Mary’s University
College, Twickenham. This study was approved by the
Middlesex University Research Ethics Committee.
Physical and Bone Measurements
BMD of the total body, anteroposterior L2–4 vertebrae,
anteroposterior femoral neck, and dominant-arm distal
radius was measured by DXA (fan beam, Lunar DPX-L
series, GE Medical Systems, Lunar, Madison, WI, USA).
The machine was calibrated using a spine phantom before
each participant was scanned. BMD (g/cm2) and Z scores
were collected for analysis using manufacturer-supplied
normative data. All scans were performed and analyzed
by the same technician.
Questionnaire Data
Athletes attending for DXA scan completed two questionnaires. The first measured training and menstrual history.
This was a simple internally designed questionnaire in
which athletes recorded the number of hours spent training per day in a normal training week. Current menstrual
status was obtained from the reported number of menses
in the preceding 12 months according to three categories:
eumenorrheic (>10 cycles/year), oligomenorrheic (4–9
cycles/year), or amenorrheic (0–3 cycles/year; 19). Current use of oral contraceptive pills was recorded.
The Three-Factor Eating Questionnaire (revised
18-item; TFEQ-R18) was used to describe eating behavior (de Lauzon et al., 2004). It assesses cognitive restraint
(conscious restriction of food for weight control), uncontrolled eating (tendency to eat more than normal because
of loss of control or hunger), and emotional eating (inability to resist emotional cues). The answers were given on
a 4-point scale from definitely true to definitely false. The
sum of the raw answers was translated into a 0–100 scale:
{[(raw score – lowest possible raw score)/possible raw
score range] × 100} (Nichols et al., 1994). High levels
of cognitive restraint or uncontrolled or emotional eating
are denoted by a high score on the scale.
All questionnaires were completed in person at the
time of scanning. Response rates were 84.1% and 70.4%
for the menstrual-history and eating questionnaires,
respectively. Questionnaire completion was voluntary,
and consent was obtained before completion.
Data Analysis
Retrospective cross-sectional and longitudinal analysis of
the data was carried out using SPSS version 16.0 (SPSS,
Inc., Chicago, IL, USA). Background characteristics of
the three menstrual groups were compared with a oneway ANOVA. Normality plots were observed to assess
distribution of the BMD, training, and diet data, and
all were deemed nonparametric. Menstrual status was
banded into three groups—eumenorrheic, oligomenorrheic, and amenorrheic—for the cross-sectional analysis,
but for the longitudinal analysis banding was into two
groups of eumenorrheic and oligo-/amenorrheic, because
of the small sample size.
Cross-Sectional Analysis. As recommended in the
ACSM guidelines, Z scores were used for analysis.
Differences in each of the regional BMD Z-scores
between the three menstrual-status groups were analyzed
using a Kruskall–Wallis test. To assess for differences
between the Z values in each group at each body region
and established diagnostic Z values (Z = 0, Z = –1, and
Z = –2), a Wilcoxon signed-ranks test was used. Within
each menstrual-status group, each patient’s Z score for
each region was paired, in turn, with each of the three
diagnostic Z values (0, –1, or –2). Spearman’s correlation
analyses were used to assess associations between
regional BMD Z scores and each of training hours and
eating behaviors.
Longitudinal Analysis. Inclusion in the longitudinal
group required availability of data from two sequential
DXA scans and baseline questionnaire data from the
420 Pollock et al.
time of the first scan (n = 7). These participants were also
included in the cross-sectional analysis. Percent change
in BMD from baseline to follow-up was calculated for
each skeletal region and normalized to a standard unit of
follow-up time (1 month).
BMD was used to observe change within individuals;
a number of Z scores were not available in the youngest
athletes. Results without Z scores were not included in
the cross-sectional analysis. Because a change was being
measured, differences between athletes in BMD because
of age and ethnicity were considered unimportant. There
were no longitudinal data for the femoral neck, so only
longitudinal analyses for change in BMD for the total
body, lumbar region, and radius were performed.
Differences in scores for each of the regional BMD
rates of loss between the two menstrual-status groups
were analyzed using a Mann–Whitney test. Spearman’s
rank correlation analyses were used to assess associations
between regional BMD rate of loss and each of training
hours and eating behaviors.
Results
Descriptive Statistics
The eumenorrheic, oligomenorrheic, and amenorrheic
groups did not differ in any of the characteristics listed
in Table 1. The athletes in the longitudinal analysis were
18–32 years old, with an average age of 22 years (± 4.8).
There were 6–14 months between the two DXA scans,
with an average of 8 months (± 3.5).
lumbar spine and 33.3% (n = 9) at the radius but was 0%
for total body and femoral neck.
Menstrual Status. Of those stating current menstrual
status (n = 36), 38% were oligomenorrheic (n = 14) and
25% amenorrheic (n = 9). There were no significant
differences in Z score at each of the four sites between
the three menstrual-status groups (Table 2). However
there was a trend (p = .059) for a difference between the
groups for radius Z score, with the eumenorrheic group
having a lower radius Z score than the oligomenorrheic
group. There was no difference between the eumenorrheic
group and the amenorrheic group. When comparing with
diagnostically relevant Z values, the L2–4 Z values were
significantly lower than Z = 0 for all three menstrualstatus groups (eumenorrheic p = .017, oligomenorrheic
p = .03, amenorrheic p = .017). The radius Z values were
significantly lower than Z = 0 for the oligomenorrheic (p =
.036) and amenorrheic (p = .018) groups and significantly
lower than Z = –1 for the eumenorrheic group (p = .025).
None of the total-body or femoral-neck Z values were
significantly below Z = 0 in any of the three groups. There
were no Z values significantly below Z = –2 for any of
the groups at any body region (Table 2).
Contraceptive Pill Use. There were 12 athletes
using oral contraceptives. There were 4 athletes in the
amenorrheic group, 3 in the oligomenorrheic group,
and 5 in the eumenorrheic group. The 5 athletes in the
eumenorrheic group using the contraceptive pill were
eumenorrheic before pill use.
Training. There was no significant correlation between
Cross-Sectional Analysis
number of hours training per week and regional BMD
Z scores.
BMD. BMD values expressed as Z scores are shown
in Figure 1. There were clear reductions in Z scores for
the lumbar spine and radius but not for the femoral neck
or the total body. The prevalence of low BMD (Z = –1
to –2 SD) in the athletes was 4.9% (n = 2) for the total
body, 34.2% (n = 14) at the lumbar spine, 13.8% (n =
4) at the femoral neck, and 29.6% (n = 13) at the radius.
The prevalence of osteoporosis (Z < –2 SD; Laughlin &
Yen, 1996; Nattiv et al., 2007) was 7.3% (n = 3) at the
Eating Behavior. Response rate to the TFEQ-R18 was
70.4% (n = 31).There were no significant correlations
demonstrated between the cognitive restraint, uncontrolled
eating, or emotional eating scale scores and regional
BMD Z scores.
The three aspects of menstrual dysfunction, disordered eating, and low BMD were present in 15.9% (n = 7)
of participants. These participants were oligomenorrheic
or amenorrheic at the time of scanning, with a Z score
Table 1 Characteristics of the Participants Included in the CrossSectional Analysis, M (SD)
All,
N = 44
Eumenorrheic, Oligomenorrheic, Amenorrheic,
n = 14
n = 14
n=9
Age (years)
22.9 (6.0)
22.3 (7.1)
23.6 (6.2)
21.6 (3.3)
Height (m)
1.66 (0.1)
1.66 (0.4)
1.65 (0.1)
1.66 (0.1)
Weight (kg)
52.8 (4.8)
51.2 (3.5)
52.4 (4.1)
53.6 (7.4)
Body-mass index
19.1 (1.5)
18.4 (0.9)
19.5 (1.4)
19.4 (2.7)
Menarche onset age 13.9 (1.5)
13.9 (1.7)
13.8 (1.1)
13.8 (1.8)
Training hr/week
13.4 (5.2)
12.9 (3.5)
16.0 (7.2)
13.8 (5.2)
Note. Data on menstrual status were not available for 7 participants. Groups did not differ significantly.
Bone-Mineral Density in Endurance Runners 421
Figure 1 — Bone-mineral-density Z scores (median, interquartile ranges) in the four measured regions in the elite athletes. The
continuous line refers to the Z score of the normal population (from which Z = 0 is defined), and the dashed lines refer to the 95%
CIs of the normal population.
Table 2 Participants’ Z Scores at the Four Anatomical Sites
Median (Interquartile Range) Z Score
Eumenorrheic,
n = 14
Oligomenorrheic,
n = 14
Amenorrheic,
n=9
Total body
0.20
(–0.35 to 0.82)
0.15
(–0.32 to 1.20)
–0.50
(–0.70 to 0.30)
.308
L2–4
–0.70*
(–1.32 to –0.13)
–1.10*
(–1.40 to 0.00)
–0.80*
(–1.60 to –0.70)
.609
Femoral neck
0.90
(–0.70 to 1.50)
–0.10
(–0.55 to 1.55)
–0.60
(–0.70 to –0.60)
.287
Radius
–2.25**
(–2.85 to –1.28)
–1.20*
(–1.75 to –0.20)
–1.90*
(–2.40 to –0.60)
.059
pa
Note. The significance of the differences between each group’s Z value and diagnostic Z values (Z =
0, Z = –1, and Z = –2) is also given.
a
Kruskall–Wallis test.
*Z value significantly below 0. **Z value significantly below –1 but not significantly below –2.
<–1 SD, and they also scored in the upper quartile on any
of the three scales of the TFEQ.
Longitudinal Analysis
Menstrual Status. Menstrual status was shown to have
no significant effect on the rate of loss of BMD (Table 3).
However, a trend was demonstrated between higher rates
of BMD loss at both the total body (p = .064) and radius
(p = .063) in the oligo-/amenorrheic group.
Oral Contraceptive Pill Use. There were no athletes
in the longitudinal analysis using oral contraceptives.
Training. There was a significant negative correlation
between increased hours of training per week and a
change in BMD at L2–4 (p = .026, r = .87, n = 6; Figure
2). There was no significance at other regions. One athlete
failed to produce a training diary.
Eating Behavior. A high emotional eating score was
positively correlated with changes in lumbar-spine
422 Pollock et al.
Table 3 Change in Bone-Mineral Density (BMD; g/cm2) per Month
Median (IQR) % Change in BMD per Month
Region
Eumenorrheic, n = 3
Oligo-/Amenorrheic, n = 4
pa
0.23 (0.17–0.28)
–0.16 (–0.061 to –0.26)
.064
–0.074 (0.00 to –0.15)
–0.12 (–0.25 to 0.031)
.814
0.24 (0.0–0.48)
–0.71 (–0.34 to –1.07)
.063
Total body
L2–4
Radius
Note. IQR = interquartile range.
a
Mann–Whitney test.
Figure 2 — Relationship between change in bone-mineral density (BMD) per month in the lumbar region and training hours per
week. As training load increased, BMD loss in the lumbar region was evident.
BMD (rho = 0.71, p = .038, n = 7; Figure 3). There were
no further significant correlations between cognitive
restraint, uncontrolled eating, and emotional eating scale
scores and regional BMD.
Discussion
Cross-Sectional Analysis
Some elite endurance runners in our study had lower
BMD than the expected range for age in the normal
nonathletic population at non-weight-bearing or relatively
non-weight-bearing sites. The Z-score distributions noted
in our study demonstrate the effect of mechanical loading on bone, which depends on site and type of exercise
(Bennell et al., 1997). Endurance running involves high
mechanical loads through the lower limb, which may be
protective against bone loss. Our range of Z scores for
total body and femoral neck reflect this and were within
the normal range (Z = –1 to +1; Nattiv et al., 2007; Sangenis et al., 2005). However, the radius and L2–4 are
relatively less loaded and therefore less protected. The
assessment of these sites may identify possible underlying
bone metabolism pathology.
The exact prevalence of low BMD and osteoporosis
in elite athletes has been elusive in the literature. One
group reported prevalence rates of osteoporosis in 6%,
and osteopenia in 48%, at the lumbar spine of endurance
runners (Cobb et al., 2003). However, this was probably
an underestimation because World Health Organization
criteria (Sangenis et al., 2005) were applied, contrary
to the recommendations of the International Society of
Clinical Densitometry. The ACSM defines osteoporosis
as secondary clinical risk factors with a BMD Z score
Bone-Mineral Density in Endurance Runners 423
Figure 3 — Relationship between change in bone-mineral density (BMD) per month in the lumbar region and emotional eating scale.
of less than –2 (Nattiv et al., 2007). Using this Z score,
similar levels of osteoporosis (7.3%) and low BMD (–1
> Z score > 2; 34.1%) were noted at L2–4 in our study,
as in Cobb et al.’s study. We have used the terms low
BMD and osteoporosis herein to enable comparison of
our Z-score data with previous investigations.
These Z scores in these elite athletes are presented
in this article as suggestive of pathology associated with
the female athlete triad. However, it should be noted that
this may not be true for all of our elite athlete population,
some of whom may be genetically predisposed to low
BMD. There is some recent evidence that women with
very low BMI or constitutional thinness not associated
with pathology can have low bone mass and impaired
bone quality despite normal hormonal and nutritional
status (Galusca et al., 2008). Our study does not have
sufficient further clinical investigations or follow-up
to address this issue. However, given the association
between low BMD, fracture risk, and energy availability,
it is essential to assess for causative factors and maximize
bone health through education, nutrition, exercise, and
clinical practices.
The incidence of menstrual dysfunction in our study,
with 63.9% (n = 23) of participants being oligomenorrheic or amenorrheic, is higher than previously reported
in nonelite athletes (Hetland et al., 1993). As discussed
previously, in the female athlete triad menstrual disturbance is directly mediated by reduced energy availability
(Nattiv et al., 2007), with the resultant hypothalamic,
insulin-like growth factor-1, and leptin level disturbance implicated in the abnormal luteinizing hormone
pulsatility (Laughlin & Yen, 1996; Loucks, Mortola,
Girton, & Yen, 1989). There are a number of previous
articles on nonelite athletes noting significantly lower
BMD in amenorrheic participants (Braam et al., 2003;
Drinkwater et al., 1984; Marcus et al., 1985; Rutherford,
1993; Wolman et al., 1990, 1992). Our study found no
difference between athletes with different menstrual
statuses. However, we noted low BMD, particularly in
relatively unloaded sites, in each of the eumenorrheic,
oligomenorrheic, and amenorrheic groups. Z values were
significantly lower than normal (i.e., Z = 0) for the L2–4
and radius regions, and it is interesting that this was seen
across all three menstrual-status groups. Indeed, the only
group to show a Z value significantly less than –1, the
threshold of osteopenia, was the normal-menstrual-status
group, in the radius region. These results suggest that
the relationship between menstrual status and BMD is
not as clear-cut as previously thought. It is recognized
that menstrual dysfunction and BMD changes may have
different temporal relationships (Nattiv et al., 2007), and
we did not evaluate our eumenorrheic group for periods
of amenorrhea or undernutrition in the past. In addition,
our study would not have excluded athletes with menstrual disorders unrelated to the female athlete triad or
detected subclinical menstrual disorders such as lutealphase defects in eumenorrheic athletes, because we did
not analyze reproductive hormones.
This study does confirm coexistent features of the
female athlete triad in elite female endurance runners.
Features of menstrual dysfunction, disordered eating,
and low BMD were exhibited in 15.9% (n = 7) of
424 Pollock et al.
participants. These participants were oligomenorrheic
or amenorrheic at the time of scanning, with a Z score
less than –1 SD, and also scored in the upper quartile on
any of the three scales of the TFEQ. It should be noted
that this figure is probably an underestimation of the
number of athletes with the female athlete triad because
disordered eating and clinical menstrual disorders are
not necessary components of the triad. It is essential to
note that an athlete can have low energy availability in
the absence of disordered eating.
Longitudinal Analysis
Of particular concern are the rates of bone loss in these
young athletes at a time when their bone density should be
increasing. There were trends between oligo-/amenorrhea
and rate of loss at total body (p = .06) and the radius
(p = .06) in this small group of athletes, and these may
have been detected as statistically significant in a larger
group. There has been no previous longitudinal work in
elite athletes, that we are aware of, that has noted this
relationship. Despite the small participant number, this is
important novel evidence that suggests that the pathology
noted in these athletes can be progressive. This should
be considered pilot work that suggests that further longitudinal work in this field is warranted.
A higher rate of loss in the lumbar spine was linked
to increased time spent training (p = .026, r = .866).
Unfortunately, we do not have data on the athletes’
energy intake or expenditure and are therefore unable to
confirm the work of Loucks et al. (1998), who noted that
the underpinning pathology is not the stress of training
but the association with low energy availability. However,
the increase in loss of bone mass with increased training
time does suggest a plausible link to low energy availability in this group.
A lower rate of loss at L2–4 was also associated with
a high level of emotional eating (p = .038, r = .782). This
may be explained by higher emotional eating’s resulting
in increased energy intake. High cognitive restraint has
been linked with negative impacts on bone (Barrack,
Rauh, Barkai, & Nichols, 2008; McLean, Barr, & Prior,
2001); however, this association was not confirmed in this
study. Given the small numbers analyzed longitudinally
in this study, this warrants further work.
There have been no studies with substantial followup of elite-level athletes with impaired bone health, and
as such this study provides a reference for future investigations. Future research should focus on the longitudinal
ramifications for athletes with low BMD. Mineral content
is lost at a rate of 1% per year from the early 30s leading
up to the menopause, and 5% per year thereafter in normal
participants (Warren & Goodman, 2003). The rate of loss
in amenorrheic athletes is thought to mimic this (Warren
& Goodman, 2003). This study supports this, with rates
of loss per year of 5.6%, 1.2%, and 4.9% for total body,
L2–4, and radius, respectively. When these athletes reach
menopause, BMD is likely to be severely decreased,
and the prospect of further bone loss carries a high risk
of morbidity. Recognition of menstrual disturbance is
essential, particularly because duration of amenorrhea is
negatively proportional to BMD (Drinkwater, Breumner,
& Chesnut, 1990; Louis et al., 1991). Low BMD in the
premenopausal stage has been linked with a doubling in
fracture risk (Khan et al., 2002), and a 1% increase in
BMD equates to a 7% decline in fracture risk (Wasnich
& Miller, 2000). Prompt recognition and treatment should
be initiated to prevent long-term irreversible bone loss
(Keen & Drinkwater, 1997).
The results presented are novel and important, but
we recognize some limitations. First, although inclusion in the study was offered to all funded endurance
athletes, uptake was voluntary. As such, true prevalence
rates cannot be calculated. Our study may overestimate
the level of these pathologies because athletes may have
only agreed to participate if they had concerns regarding
their menstrual or bone health. Athletes who were noted
to have pathology were referred back to their sportsmedicine physician and may have undergone further
investigation for menstrual dysfunction or bone health,
but this information is not included in our study. In addition, training questionnaires were completed by participant recall, which may call their accuracy into question.
However, high levels of self-awareness seen in athletes
(Gould, Dieffenbach, & Moffett, 2002) and the fact that
many kept a training diary are likely to support accuracy.
It should also be noted that the identification and
assessment of oligomenorrhea and amenorrhea by
questionnaire excludes subclinical abnormalities such
as luteal-phase defects that form part of the continuum
of menstrual dysfunction in the triad (De Souza & Williams, 2004), and this is likely to underestimate the
prevalence of this pathology. Future studies should aim to
collect information regarding the total number of missed
menses and, if possible, information on luteal function
and estrogen levels. It is a limitation of the current study
that energy-availability data are not available because we
commenced data collection before the ACSM position
stand of 2007. Future studies should include an estimation
of this through detailed nutritional and training records
including mileage and intensity.
Nonetheless, the implications of the findings should
not be underestimated. There are no longitudinal observational studies of this kind involving elite, international
female runners, and as such novel evidence is put forward.
In addition, this study adds to the limited work on BMD
in elite athletes.
We recommend targeted clinical and radiological
screening assessment, follow-up, and management in this
at-risk patient group. It is important to note from this work
that normal menstruation at the time of scanning was not
protective for normal bone density, so our current practice
and recommendation is that all female endurance athletes
should undergo DXA screening. Further effective screening for menstrual dysfunction and energy availability is
also required. Treatment strategies should emphasize
the importance of optimum energy availability in this
population, which is central to preventing the develop-
Bone-Mineral Density in Endurance Runners 425
ment of the female athlete triad (Nattiv et al., 2007). It
is our opinion that involvement of athletes, parents, and
coaches in education programs is critical to preventing
and treating this pathology.
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