Keywords
menstrual cycle - reproductive health - exercise - sports
Introduction
Physical activity helps to prevent and treat the most common diseases like heart
disease, stroke, diabetes, and breast and colon cancers. Additionally, it can help
to prevent hypertension and obesity, improve mental health and quality of life, and
reduce mortality [1]. In young women, studies
revealed that exercise also reduces the likelihood of substance abuse and the risk
of depression, and it improves self-esteem and academic performance [2]. Due to its positive outcomes, the World Health
Organization (WHO) [3] and the American College of
Sports Medicine (ACSM) [4] agree to encourage regular
physical activity for all ages and genders.
However, while physical exercise is commonly associated with positive effects, if
abused it can also have negative consequences. In 1992, the American College of
Sports Medicine (ACSM) coined the term “female athlete triad” to describe the
combination of disordered eating (DE), amenorrhea, and osteoporosis observed in
female athletes [5]. In 2007, the ACSM updated this
concept as follows: energy availability (ranging from optimal to low), menstrual
function (from eumenorrhea to oligo-amenorrhea), and bone mineral density (BMD) from
normal to low [6], excluding the presence of DE.
Subsequent studies highlighted the role of energy availability in affecting many
body system and performance aspects [7].
Consequently, the definition “Relative energy deficiency in Sports” (RED-S) was
coined by the International Olympic Committee (IOC) in 2014 [8].The reproductive function depends on energy
availability, and the insufficient energy supply in women whose expenditure is
higher frequently leads to hormonal impairments, caused by the suppression of the
hypothalamic-hypo pituitary axis. This leads to reduced gonadotropin-releasing
hormone (GnRH) pulsatility, which usually pulses every 30–60 minutes; in these
women, pulsatility is suppressed, driving to reduced production of luteinizing
hormone (LH) and, to a lesser extent, follicle-stimulating hormone (FSH) [9]. When energy availability decreases, there is a
disruption in luteinizing hormone (LH) pulsatility, its amplitude increases, and its
frequency decreases [10]. Williams et al. [11] demonstrated that there is a dose-response
relationship between the frequency of menstrual irregularities and the magnitude of
energy deficit.
This suppression leads to chronic anovulation with low estrogen levels which
compromise BMD and health in general. The reduction in BMD increases the risk of
fractures, in particular stress fractures [12], which
are of deep concern since they can affect athletes’ performance. Depending on
severity and duration of low energy availability (LEA), adolescent athletes may
develop secondary amenorrhea (no menses for more or equal to 90 days),
oligomenorrhea (menses>45 days apart), anovulation, luteal phase defects (<10
days) [6]
[13]. If the
athletes start practicing before menarche, there can be delayed menarche or primary
amenorrhea. In athletes who run, menstrual dysfunction has been reported in
26–43%[14]
[15]
[16]. All female athletes are at high
risk of menstrual dysfunction, but running athletes have an exceptionally high
risk.
Menstrual dysfunctions are frequent in athletes due to LEA, and they lead to health
and performance consequences that are likely inevitable. Not all endurance athletes
develop LEA, and it is crucial to identify women at higher risk to better prevent
systemic dysfunctions. This study aims to investigate running female athletes and
look for clinical predictors of menstrual dysfunctions to prevent them and to
promote a healthy experience of sports.
Materials and Methods
This is a prospective observational study of female athletes of fertile age from
local, regional, and national sports clubs. Women were recruited between January
2022 and May 2022. All recruited patients practiced running at different distances
(100/200/400 meters, track middle distance and long-distance, the half marathon, the
marathon, and the mountain run). Patients enrolled completed an anonymous
questionnaire about baseline clinical features, menstrual cycle characteristics,
physical activity characteristics, and food habits.
Clinical characteristics considered included age and BMI. Regarding the menstrual
cycle, the study collected data about age at menarche (less than 9 years old,
between or equal to 9 and 14 years old, between or equal to 14 and 16 years old, and
more or equal to than 16 years old), lengths of menstrual cycles (regular menstrual
cycles [length between 25 and 35 days], irregular menstrual cycles [length of less
than 25 days or more than 35 days], and current or previous history of amenorrhea),
and the use of hormonal therapy.
Data collected about physical activity and diet included number of kilometers run by
week, level of sports (professional or amateur), number of days per week of training
(more or less than 5 days), the practice of other sports than athletics (yes or no),
having had injuries in the last year (yes or no), strength workout (yes or no), and
the caloric intake (more or equal to, or less than 2,000 kcal/day, which is the mean
daily caloric intake suggested in most guidelines [17]). The analysis reported the number of years of training regularly
(meaning at least two times a week) adjusted by post-pubertal age (years
training)/(actual age minus age at menarche).
This study was approved by the local IRB (number 4328) and performed in accordance
with the ethical standards in the Declaration of Helsinki. Informed consent was
obtained from all individual participants included in the study.
To evaluate the clinical features and the relation with the outcomes, women were
divided in two groups according to the menstrual cycle: athletes with a regular
menstrual cycle and those with an irregular menstrual cycle (defined as length less
than 25 days or more than 35 days).
Continuous, ordinal, and dichotomous variables were presented as mean (standard
deviation [SD]), median (interquartile range [IQR]), and frequency (%),
respectively. They were compared between the groups (regular menstrual
cycles/irregular menstrual cycles) by Mann-Whitney test, and chi-squared tests as
appropriate. Since menstrual dysfunctions are one of the main consequences of LEA,
the study used menstrual cycle irregularities as outcome for patients. To test the
association between variables and the presence of regular menstrual cycles, we
performed logistic multivariate analysis after adjusting for age and BMI and other
variables which may be a confounder of the outcome. The two variables involved where
the level of sport and the caloric intake. Confounders were selected based on a
priori evidence.
Furthermore, using receiver operating characteristics (ROC) analysis, we performed a
secondary analysis to determine the sensitivity and specificity of km run per week
as a diagnostic marker for irregular menstrual cycle. For all the analyses, we
evaluated a P-value of less than 0.05 as statistically significant.
Analyses were performed using STATA 18 (Stata Corp, College Station, TX, USA) and
SPSS software version 28 (SPSS Inc., Chicago, 202 Illinois, USA).
Results
Fifty-three women were contacted and completed the anonymous questionnaire.
Demographic, physical activity and hormonal characteristics of patients included in
the study are detailed in [Table 1]. The majority of
patients (60%) reported regular menstrual cycles; however, approximately half of
women in our population have a history (past or current) of amenorrhea. 80% of
patients with menstrual irregularities reported moderate to severe oligo-amenorrhea
(menstrual cycle length of more than 45 days). Hormonal therapy (past or current)
was reported by 54% of respondent. The two principal reasons were contraception and
irregular menstrual cycle (45% and 28%) (
[Fig.
1]
). The median number of km run per week was 46 (IQR 10–160 km; 26
miles; IQR 6–99), and approximately half of women reported training for 6 or 7 days
per week. 18 women reported a personalized diet (33%) and, of them, 10 for more than
one year. Caloric intake was below the recommended value (2000 kcal/day) in 51% of
our population.
Fig. 1 Reasons for taking hormonal therapy (n=29, 54.7%):
contraception (13, 45%), amenorrhea (1, 3.4%), ovarian cyst (1, 3.4%),
stress microfractures (1, 3.4%), polycystic ovary syndrome (1, 3.4%),
hypermenorrhea (1, 3.4%), irregular menstrual cycle (8, 28%), unknown (3,
10%).
Table 1 Demographic, physical activity, and hormonal
characteristics of patients included in the study.
|
Characteristics
|
All patients (n=53)
|
|
Weight (kg), mean (SD)
|
54.2 (5.9)
|
|
range
|
44–68
|
|
BMI (kg/m2), mean (SD)
|
19.7 (2.1)
|
|
range
|
14.5–25.7
|
|
Age (years), mean (SD)
|
24.7 (6.9)
|
|
range
|
16–43
|
|
Age at menarche (years), n (%)
|
|
|
1 (1.9)
37 (69.8)
14 (26.4)
1 (1.9)
|
|
Regular menstrual cycles, n (%)
|
32 (60.4)
|
|
Irregular menstrual cycles (days), n (%)
|
21 (39.6)
|
-
>46 days and<89 days
-
>36 days and<45 days
-
<25 days
-
>90 days
|
8 (38.1)
6 (28.5)
4 (19.1)
3 (14.3)
|
|
History of amenorrhea (previous or currently), n (%)
|
25 (47.2)
|
|
Hormonal therapy, n (%)
|
29 (54.7)
|
|
|
14 (48.4)
7 (24.1)
5 (17.2)
3 (10.3)
|
|
Type of physical activity, n (%)
|
|
|
27 (50.9)
26 (49.1)
|
|
Years of training, mean (SD)
|
10.1 (5.8)
|
|
range
|
Feb-30
|
|
Years of training standardized by post-puberal age, mean (SD)
|
1.1 (0.6)
|
|
range
|
0.1–3.0
|
|
Number of km run per week (km)*, mean (SD)
|
52 (35)
|
|
Range
|
4–160
|
|
Number of miles run per week*, mean (SD)
|
32 (22)
|
|
Range
|
Feb-99
|
|
Number of days per week training, n (%)
|
-
2–3 days per week
-
4–5 days per week
-
6–7 days per week
|
5 (9.5)
20 (37.7)
28 (52.8)
|
|
Number of races per year, mean (SD)
|
15.4 (8.9)
|
|
range
|
Feb-35
|
|
Additional workouts, n (%)
|
25 (47.2)
|
|
Strength workouts, n (%)
|
40 (75.5)
|
|
Hours per week spent on strength workouts (hours), mean (SD)
|
2.5 (1.4)
|
|
(1–9)
|
|
On a personalized diet, n (%)
|
18 (33.9)
|
-
For less than 1 year
-
Between 1 and 4 years
-
For more than 4 years
|
8 (44.4)
6 (33.4)
4 (22.2)
|
|
Daily caloric intake, n (%)
|
|
|
26 (49.1)
27 (50.9)
|
*missing data for 6 patients.
As described in the inclusion criteria, all the athletes performed athletics but at
different athletic disciplines. [Fig. 2] summarizes
the number of athletes performing each discipline.
Fig. 2 Races performed (more than one answer possible). Middle
distance running (n=18), sprint running (n=17), track running (n=15),
mountains race (n=10), half marathon (n=6), marathon (n=1), ultra-marathon
(n=1).
The relationship between menstrual cycle disorders and clinical variables are shown
in [Table 2]. Women with irregular menstrual cycles
reported a significantly higher number of kilometers run per week (67 vs. 35,
p:0.02; 42 vs. 22 miles). At the univariate analysis, no other variable, including
BMI, number of days of training or daily caloric intake, showed a relation with
menses regularity. Only additional workouts were more common among women with
menstrual irregularities (62% vs. 37%), even if this result did not reach
statistical significance (p: 0.07).
Table 2 The relationship between menstrual cycle disorders and
clinical variables.
|
Menstrual cycle
|
|
Regular (n=32)
|
Irregular (n=21)
|
p-value
|
|
Km run per week mean (SD)*
|
41.1 (26.6)
|
67.0 (40.1)
|
0.02
|
|
median (IQR)
|
40 (16.0–65.0)
|
70 (36.3–88.8)
|
|
range
|
4–100
|
10–160
|
|
Miles run per week mean (SD)*
|
25.5 (16.5)
|
41.6 (24.9)
|
|
median (IQR)
|
24.9 (9.9–40.4)
|
43.5 (22.5–55.1)
|
|
range
|
Feb-62
|
Jun-99
|
|
BMI mean (SD)
|
20.0 (1.9)
|
19.1 (2.2)
|
0.12
|
|
median (IQR)
|
19.6 (17.9–20.2)
|
19.1 (17.9–20.2)
|
|
range
|
17.2–25.2
|
14.5–25.7
|
|
Age mean (SD)
|
25.0 (7.2)
|
24.4 (6.1)
|
0.84
|
|
median (IQR)
|
24 (19–28)
|
23 (21–26)
|
|
range
|
17–43
|
16–39
|
|
Age at menarche
|
0.6
|
|
<14 years old (%)
|
23 (71.9)
|
15 (71.4)
|
|
>14 years old (%)
|
9 (28.1)
|
6 (28.6)
|
|
Level of sport:
|
0.15
|
|
amateur (%)
|
18 (56.3)
|
8 (38.1)
|
|
professional (%)
|
14 (43.8)
|
13 (61.9)
|
|
Training per week
|
0.30
|
|
<5 days (%)
|
18 (56.3)
|
7 (33.3)
|
|
>5 days (%)
|
14 (43.7)
|
14 (66.7)
|
|
Additional workouts (%)
|
12 (37.5)
|
13 (61.9)
|
0.07
|
|
Strength workouts (%)
|
24 (75.0)
|
16 (76.19)
|
0.59
|
|
Injuries in the last year (%)
|
19 (59.4)
|
12 (57.1)
|
0.54
|
|
Years of training standardized by postpuberal age mean (SD)
|
1.1 (0.7)
|
1.1 (0.5)
|
0.51
|
|
median (IQR)
|
1.0 (0.7–1.3)
|
1.0 (0.8–1.5)
|
|
range
|
(0.1–3.0)
|
(0.2–2.3)
|
|
Daily caloric intake
|
0.15
|
|
>2000 kcal (%)
|
14 (43.7)
|
13 (61.9)
|
|
<2000 kcal (%)
|
18 (56.3)
|
8 (38.1)
|
*missing data for 6 patients.
Multivariate logistic regression adjusted for age, BMI, level of sport and caloric
intake confirmed that the number of kilometers run per week was associated with
menstrual irregularities (for 10 km/6 miles, OR 1.35; IC95% 1.05–1.73; p: 0.02)
([Table 3]).
Table 3 Multivariate logistic regression adjusted for age,
BMI, level of sport, and caloric intake.
|
Regular menstrual cycle
|
Univariate analysis
|
Multivariate analysis
|
|
OR
|
IC95%
|
p-value
|
OR
|
IC95%
|
p-value
|
|
Age
|
0.98
|
0.90–1.07
|
0.72
|
0.93
|
0.82–1.05
|
0.24
|
|
BMI
|
0.78
|
0.57–1.06
|
0.12
|
1.03
|
0.69–1.52
|
0.89
|
|
Km run per week (10 km/6 miles)
|
1.28
|
1.04–1.57
|
0.02
|
1.35
|
1.05–1.73
|
0.02
|
|
Caloric intake
|
2.08
|
0.67–6.42
|
0.19
|
2.60
|
0.64–10.5
|
0.18
|
|
Level of sport
|
2.08
|
0.67–6.42
|
0.19
|
0.53
|
0.09–3.05
|
0.48
|
In terms of longitudinal assessment, ROC analysis was conducted to identify the
optimal cut-offs of km run per week to predict irregular menstrual cycle. The
variable of “km run per week” appeared as a diagnostic indicator of irregular
menstrual cycle in female athletes with statistical significance (AUC ROC curve
0.71, IC95% 0.54–0.86, p-value=0.01). The cut-off of 65 km (40 miles) run per week
is a good indicator of the presence of irregular menstrual cycle, with a sensitivity
(SE) and specificity (SP) of 55% and 81.48%; it correctly classifies (CC) 70.21% of
patients (
[Table 4]
,
[Fig. 3]
).
Fig. 3 Receiver operating characteristics (ROC) curve for km run per
week as an indicator for irregular menstrual cycle in female running
athletes.
Table 4 Receiver operating characteristics (ROC) curve for km
run per week as an indicator for irregular menstrual cycle in female
running athletes and its characteristics (SE=sensitivity ,
SP=specificity, CC=correctly classified, PPV=positive predicted value,
NPV=negative predicted value, AUC=area under the ROC
curve).
|
Regular menstrual cycle
|
SE n, %
|
SP n, %
|
CC n, %
|
PPV n, %
|
NPV n, %
|
IC95% n, %
|
p-value
|
|
Cut off 65 km/40 miles AUC 0.71
|
11/20, 55%
|
22/27, 81.5%
|
33/47, 70.2%
|
6/7, 85.7%
|
26/40, 65%
|
0.54–0.86
|
0.01
|
Discussion
The most important result emerging from our study is that, among female running
athletes, a significant proportion of women suffer from menstrual irregularities,
and half of them experienced amenorrhea at least once in her life. These results are
particularly relevant, given that the occurrence of oligomenorrhea in adolescent
girls in the general population commonly reported is around 20% [18]. The doubled prevalence of menstrual
irregularities indicates that female athletes are a population at risk of these
disorders, as suggested by other studies published in the literature [14]
[15]
[16].
Clinical variables such as BMI, age (and age at menarche) showed no statistical
difference between the two groups and no correlations with the outcome.
In our cohort of patients, BMI was at the inferior limit of normal range for both
groups of patients, and it was not linked to cycle irregularities. Even if BMI has
been related to reproductive health [19], its role in
menstrual cycle dysfunctions is under debate [20] and
this relationship about athletes is conflicting [21]
[22]
[23].
Also, daily caloric intake was not correlated with the outcome of menstrual cycle
irregularities, probably because most of our patients with menstrual cycle
irregularities have a caloric intake of more than 2000 kal (61.90%). However, in the
literature a correlation between the caloric intake and menstrual irregularities was
not demonstrated [24].
The variable of kilometers run per week was associated with menstrual irregularities
with statistical significance. Also, a higher proportion of women with menstrual
irregularities reported more than 5 days of training and additional workout. Even if
these two variables did not reach statistical significance, probably due to the
small number of women in our population, these observations, taken together, might
suggest that the greater the effort put into training, the greater the chance of
hormonal disorders. This is perfectly reasonable, and we were also able to identify
a cut-off (65 km per week; 40 miles), which may help physicians and coaches to
screen patients at higher risk and to work with them to prevent dysfunctions.
However, the increase of 10 km run per week (6 miles) was associated with nearly 40%
higher odds of menstrual irregular cycles (OR 1.35, p-value 0.02) after adjusting
for all the confounders. This result may help identify patients who have a higher
risk of menstrual irregularities and to act to prevent dysfunctions.
Patients who perform endurance professionally are more stressed than amateur
athletes, and this may increase the prevalence of menstrual cycle disorders, as a
previous study demonstrated [24]. Interestingly, in
our study the level of sport did not influence the outcome, even if 61.9% of
professional athletes reported irregular menstrual cycles.
In our study the rate of injuries was similar between the two groups (59.37% and
57.14%). Thein et al. described a correlation between musculoskeletal injuries and
menstrual cycle irregularities in female athletes [25]. In our study athletes are on average younger than patients from the
study of Thein et al., and this might be the reason for this difference.
We evaluated the years of training, standardized by post-pubertal age, and the
average value obtained was 1.4, which indicates that the years spent in constant
training were more than post-pubertal years by almost one-and-a-half times. Even if
this variable was not found to be related to menstrual regularities, it shows the
great impact of training in the daily life of female athletes.
Possible limitations of this study are the limited sample size, the voluntary
adhesion to this study, which may create a selection bias (patients with more
awareness of menstrual cycle dysfunction), and the self-reported caloric intake and
km run per week.
To our knowledge, this is the first study evaluating clinical predictors of menstrual
dysfunction in female running athletes. Menstrual irregularities in female athletes
are an essential concern since they can affect performance and, above all, the
positive sports experience in young girls. Many times, menstrual irregularities are
a red flag for LEA. It is crucial to find easy ways to screen for patients at high
risk for LEA in order not to underestimate the clinical impact of it, and the data
of km run per week may have a role in evaluating this risk. Further studies are
needed to confirm this data and to determine whether there is a better cut-off for
this variable. Systemic consequences of LEA are widely known. However, there are
performance consequences as well. Female athletes with LEA are at higher risk of
decreased training response, decreased coordination and concentration, irritability,
depression, and decreased endurance performance [7].
Female athletes have a higher risk of developing DE; in many cases, low energy
availability is associated with these conditions [26]. The rates of DE vary by sports, and are generally more frequent in lean
sports or aesthetic sports [27]. Karlsson et al.
[28] found that 18% of recreational female
runners have symptoms of DE. This result is similar to the 18–21% reported for
endurance athletes [29]
[30].
