The Female Athlete Triad (Triad) is defined as the interrelationship of 1) low-energy availability (EA) with or without disordered eating/eating disorder (DE/ED), 2) menstrual dysfunction, and 3) low bone mineral density (BMD) (1). Low EA, defined as <30 kcal·kg−1 fat-free mass per day (2), is the key aspect of the Triad, with menstrual dysfunction and low BMD as consequences of low EA. The Triad may be highly prevalent early in young women, with one report stating 78% of athletes and 65% of nonathletes display at least one of the Triad components (3). Left untreated, the Triad may result in failure to reach optimal BMD and reduced lifelong bone strength, impaired reproductive function, and detrimental effects on the adrenal, thyroid, and gonadal axes, as well as negative cardiovascular effects (4–6). Low EA is also recognized as the cause of Relative Energy Deficiency in Sport (RED-S), a concept that expands the Triad to reflect potential health and performance consequences of low EA for both sexes (7,8).
Early identification of athletes with low EA may contribute to improved health and performance outcomes including higher BMD, fewer stress fractures, and interruption of ED progression (9,10). To better screen and manage athletes, the Female Athlete Triad Cumulative Risk Assessment tool was developed to serve as an objective method of risk stratification built on evidence-based risk factors (1). It rates the magnitude of risk of six different risk factors each defined as low (0 point), moderate (1 point), or high risk (2 points) to produce a cumulative risk score. Risk factors measured with this tool include the following:
- Low EA with or without DE/ED
- Low body mass index (BMI)
- Delayed menarche
- Oligomenorrhea and/or amenorrhea
- Low BMD
- Stress reaction/fracture
These criteria can be used to identify female athletes at increased risk for injury and impaired BMD. Athletes classified as moderate (2–5 points) and high risk (≥6 points) were more likely to sustain a bone stress injury (BSI) than those in the low-risk (0–1 points) category (11). Although this tool is particularly useful for identifying those at increased risk for BSI in populations suspected to have low EA (e.g., endurance and lean sports), it can also be used to screen athletes at elevated risk for low BMD in jumping and multidirectional sports (11,12).
Low EA can be difficult to accurately measure; it relies on the athlete’s accurate self-report of eating behaviors and dietary intake to inform the clinician’s subjective diagnosis (in the Triad risk assessment tool, low risk is “no dietary restriction,” moderate-risk is “some dietary restriction; current/past history or disordered eating,” and high-risk is “meets DSM-5 criteria for eating disorder” ). Because EDs are underreported in female athletes (13), screening for low EA can be challenging. Specifically, low EA might be hard to identify for reasons including omission of appropriate measures of DE/ED (such as detailed dietary history or a validated DE/ED tool) on standard preparticipation evaluations (PPE) for collegiate sport (14). Even if athletes are asked about DE/ED, it might be underreported because of concerns of stigma associated with EDs or because of lack of nutrition education by the athlete resulting in unintentional undereating. Although a number of methods to more accurately quantify risk for low EA exist (e.g., Low EA in Females Questionnaire, reproductive biomarkers (e.g., estrogen and progesterone levels during one or more menstrual cycles), and the ratio of measured to predicted resting metabolic rate ), they are expensive and/or time-consuming and, consequently, impractical and inappropriate for widespread use as screening tools. Thus, there is a need to increase the ability to accurately, sensitively, and specifically screen for low EA, the key aspect of the Triad and RED-S, and to identify athletes who would benefit from further evaluation. A more ideal Female Athlete Triad Cumulative Risk Assessment tool might include objective criteria to more sensitively identify those athletes who should receive further workup for low EA.
Recent studies and reviews have suggested low-iron status might serve as a novel and objective marker for low EA. For example, total caloric and iron intake have been shown to be both low and correlated within female athletes (16,17), and low caloric intake might preclude intake of recommended dietary iron (18). In addition, iron is critical in multiple enzymatic pathways and deficiency might reduce metabolism, thereby contributing to energy deficiency and impaired thyroid function (19). The model of RED-S recognizes that low EA may have hematological consequences including anemia (8), positing a bidirectional relationship between low iron and low EA. Collectively these studies suggest that iron status may be a novel marker to explore in association with low EA. Furthermore, because of hesitancy among athletes to address factors (such as caloric intake and BMI) currently used to assess low EA risk due to the false notion perpetuated in endurance sports that leaner is faster or better, recognition among athletes regarding impaired performance with low-iron status may help motivate athletes to meet with a sports dietician to optimize nutrition strategies that both improve iron absorption and address the low EA state.
To our knowledge, the associations between markers of low-iron status (history of anemia or use of iron supplementation) and the Triad risk assessment score, including low EA, have not been characterized in a population of collegiate female athletes. Identifying these associations could help advance clinical care if self-reported low-iron status could serve as a screening measure for low EA, as clinicians could then more specifically apply the Triad risk tool to their athlete-patients. Therefore, the purpose of this study is to examine the relationships between self-reported low-iron status with the Triad risk assessment measures in large collegiate population across sports. We hypothesize that self-reported markers of low-iron status will be associated with low EA, each associated component of the Triad risk assessment, and higher cumulative risk assessment score.
Study participants and methodology have been previously described (11,12). Athletes were from a population of Division I collegiate athletes from 16 different sports participating during the years 2008 to 2014. The only exclusion criterion used in this study was hormonal therapy, given accurate menstrual status, and therefore, Triad cumulative risk score could not be calculated retrospectively. (Of note, imputed analyses performed for the original cohort study noted no difference in scores between the athletes on contraceptives vs not on contraceptives. However, for clarity, we chose to present only those athletes for whom a complete score could be calculated. ) In brief, data were obtained reviewing electronic PPE (ePPE) forms required by the institution annually for clearance to participate in sports. All female athletes were invited to participate in a study that aimed to establish BMD of participants using dual-energy x-ray absorptiometry (DXA). This study was approved by the Stanford University institutional review board, and written informed consent was collected by each athlete before performing a DXA scan.
DXA scans were performed by the same technician using the GE Lunar iDXA device and enCORE analysis software (version 14.1; GE Medical Systems Lunar). BMD measurements were obtained at total body, lumbar spine, and dual femur regions, and standardized to BMD z-scores using normative values for age, sex, and ethnicity provided by GE software. Height and weight were also measured at the time of the DXA scan in order to calculate BMI.
Health status measures
All Stanford University athletes completed an ePPE before clearance for sports participation, as required by the National Collegiate Athletic Association and institution rules. The ePPE questionnaire was completed by each athlete in conjunction with mandatory preseason physicals. This study captured the period of time in which the version PrivITePPE (PrivIT patent 8.275.632) was in use. Two research assistants reviewed ePPE forms and manually entered information relevant to this study into the Research Electronic Database. Study data were collected and managed using Research Electronic Data Capture tools hosted at Stanford University (20,21). Research Electronic Data Capture is a secure, web-based software platform designed to support data capture for research studies, providing 1) an intuitive interface for validated data capture, 2) audit trails for tracking data manipulation and export procedures, 3) automated export procedures for seamless data downloads to common statistical packages, and 4) procedures for data integration and interoperability with external sources. Questions pertinent to this study included menstrual history (age of menarche and number of menstrual cycles in the last 12 months), BSI history (as well as anatomical location and radiographic evidence), medication usage (including hormonal therapy and iron supplementation), and medical history (including ED/DE and anemia). Sport type, age, and underlying metabolic/hormonal conditions were asked in an open-ended format and were later categorized as defined by the Female Athlete Triad Cumulative Risk Assessment tool into 1) history of low EA with or without DE/ED (moderate-risk: current or history of DE for 6 months or greater; high-risk: history of a DSM-5 diagnosed ED), 2) delayed menarche (moderate-risk: menarche age ≥15 yr but <16 yr; high-risk: menarche age ≥16), 3) oligomenorrhea and/or amenorrhea in the last 12 months (moderate-risk: current or history of 6–8 menses over 12 months; high-risk: current or history of <6 menses over 12 months), and 4) and history of stress reaction/fracture related to sports participation (moderate-risk: one prior stress reaction/fracture; high-risk: two prior stress reactions/fractures, one high-risk stress reaction/fracture, or a low-energy nontraumatic complete fracture). DXA scans and information gathered at the time of the scan were used to generate the final two scoring elements: 5) low BMI (moderate-risk: >17.5 and ≤18.5 kg·m−2; high-risk: ≤17.5 kg·m−2, <85% estimated weight, or recent weight loss of ≥10% in 1 month) and 6) low BMD (high-risk: z-score <−2, moderate-risk: z-scores >−2 and ≤−1) (1). Notably, modification of oligomenorrhea/amenorrhea score was used to define this variable for the past 12 months as to historical time since attaining menarche due to the limitations in PPE query; this modified approach was used previously (11,12). The values of each risk factor generated a cumulative risk assessment score and assignment to low risk (0–1 point), moderate risk (2–5 points), or high risk (≥6) (1).
Lean/endurance sport participation was defined as participation in crew, cross-country, field hockey, gymnastics, lacrosse, rowing, swimming/diving, synchronized swimming, track and field, and water polo similar to prior report (11). High-impact sport participation was defined as participation in basketball, gymnastics, volleyball, sand volleyball, softball, and soccer similar to prior report (12). Lean/endurance sports and high-impact sports were not mutually exclusive categorizations; for example, gymnastics was classified as both a lean/endurance sport because of the emphasis on body shape/size in the sport, but also as high impact because of the high forces generated in performing skills. Our analysis compared lean/endurance sports against non–lean/endurance sports and, independently, high-impact sports against non–high-impact sports.
The study design evaluated ePPE self-report and did not include laboratory data (ferritin or presence of microcytic anemia). With the goal to use self-report questions, low-iron status was defined by two types of questions contained within the ePPE: 1) history of anemia and/or 2) use of supplemental iron. Self-reported anemia was assessed during the ePPE (“Do you have any ongoing medical conditions? (choice = Anemia),” “Other ongoing medical conditions,” “Do you have, or have you ever had any symptoms of medical problems such as: Blood disorders such as anemia,” and “Explanation” (for other history of medical problems)). Women who reported iron supplementation were also included in markers of low-iron status as iron supplementation is typically used when serum iron or ferritin levels are low (22). Of the 30 athletes assigned to the low-iron status, 13 were assigned based on reported history of anemia, 14 on history of iron supplementation, and 3 on both history of anemia and use of iron supplementation.
SAS Version 9.4 (SAS Institute) was used for data analysis. Univariate and multivariate regression analyses were performed with P < 0.05 as the threshold of significance for all associations.
The primary outcomes of interest included the six independent components included in the Triad cumulative risk score.
Cross-sectional data were stratified by self-reported low-iron status and each of the components of the Triad cumulative risk score, including overall risk category; associations between self-reported low-iron status and each of the components of the Triad cumulative risk score were assessed using Fisher exact tests. Next, we performed an exploratory measure of using self-reported low-iron status to adjust risk for low EA by increasing the cumulative risk score by 1 point for those not already reporting low EA, deemed to be a conservative estimate if iron status can be used as a proxy for low EA, as we suggest. Triad cumulative risk score and associated risk category were adjusted accordingly, and then the association between self-reported low-iron status and adjusted risk category was reassessed. Finally, study participants were stratified by Triad risk category and a variety of health characteristics including age, participation in a lean/endurance sport, participation in a high-impact sport, history of medical conditions, history of gastrointestinal conditions, and ongoing medical conditions. Associations with low-iron status within these categories were assessed.
A total of 323 women had complete DXA and ePPE information. Within this cohort, 84 women were taking oral contraceptive pills and excluded from analysis, as oligomenorrhea/amenorrhea could not be accurately assessed. Thus, the analytic sample consisted of 239 athletes from 16 sports. Most women (70.7%; n = 169) were classified in the low-risk category for the Triad cumulative risk score, 25.5% (n = 61) were classified in the moderate-risk category, and 3.8% (n = 9) were classified in the high-risk category. Baseline demographic characteristics and body compositions measures of this population are described in Table 1. Of these 239, 12.6% (n = 30) self-reported low-iron status (Table 1).
TABLE 1 -
Demographics and anthropomorphic characteristics of 239 athletes.
||19.9 ± 1.2
||169.7 ± 8.6
||66.9 ± 8.6
||22.9 ± 2.8
|Body fat, %
||24.9 ± 5.8
|Fat mass, kg
||16.2 ± 5.9
|Lean mass, kg
||47.6 ± 6.9
|Total body BMD, z-score
||1.01 ± 1.06
|Total body BMC, z-score
||1.35 ± 1.17
|Lumbar spine BMD, z-score
||0.73 ± 1.34
|Triad risk category
| Low risk
| Moderate risk
| High risk
|Self-reported iron status
Basic demographic and anthropomorphic characteristics of the 239 athletes. Data are reported as averages ± SD or % (n).
BMC, bone mineral content.
The proportion of low-iron status was greater in the high-risk group compared with the moderate- and low-risk groups, with 55.6% (n = 5) reporting low iron in the high-risk Triad category, 14.8% (n = 9) reporting low iron in the moderate-risk category, and only 9.5% (n = 16) reporting low iron in the low-risk category (P = 0.002). Each component of the Triad cumulative risk score, excluding delayed menarche, was associated with low-iron status in the cross-sectional analysis (Table 2). Furthermore, when low-iron status was used as a proxy for low EA, more athletes scored in a higher-risk category than when it was not included as a predictor (n = 6 more athletes scored in the moderate-risk category).
TABLE 2 -
Associations between self-reported iron status and individual Triad risk factors.
|Low EA (n = 5; 2.1%)
|Low BMI (n = 7; 2.9%)
|Delayed menarche (n = 56’ 23.4%)
|Oligomenorrhea (n = 64; 26.8%)
|Low BMD (n = 15; 6.3%)
|History of stress reaction or fracture (n = 37; 15.5%)
Distribution of self-reported low-iron status across Triad risk category and the components of Triad cumulative risk score at baseline.
an is the number of women with moderate or high risk for risk factor; percent is the number of women with moderate or high risk for risk factor divided by total number of women in study.
bPercentage is the number of women with or without low-iron status divided by the total number of women within the given low-risk, moderate-risk, or high-risk category.
cFisher exact testing for an association between low-iron status and Triad risk category or risk factor level.
In addition, use of an iron supplement alone, regardless of self-reported history of anemia, was significantly associated with Triad risk category (P = 0.0001), with only 4.7% of those in the low-risk category reporting supplementation, yet 55.6% of those in the high-risk category reporting supplementation.
We compared athletes from lean/endurance sports (n = 181) and self-reported low-iron status, which aligns with previous findings showing a relationship between lean/endurance sport participation and Triad risk category (11). Of those reporting participating in a lean/endurance sport, 15.5% (n = 28) reported low-iron status, whereas self-report of low-iron status was only 3.4% (n = 2) among those not participating in a lean/endurance sport (P = 0.02).
The purpose of this study was to explore the relationship between markers for low-iron status and indicators of low EA, in addition to the other Triad risk factors, in a population of collegiate female athletes. Low EA has been previously postulated to coexist with low-iron status but has not been thoroughly studied, particularly in female collegiate athletes (19). As hypothesized, we observed significant associations between markers of low-iron status and five of six measures within the Triad risk assessment score: low EA, low BMI, oligomenorrhea, low BMD, and history of stress reaction or fracture. Although exploratory, we also observed that low iron did indeed increase the frequency of being assigned to moderate-risk Triad risk category when it was used as a proxy for low EA among athletes not already reporting low EA, showing that low iron might have practical utility in assessing Triad risk. This is in agreement with proposed bidirectional mechanisms of interaction between low-iron status and components of the Triad described by Petkus and colleagues (19). Supportive evidence also included an association between low-iron status with athletes assigned to higher Triad risk categories and in athletes who participated in sports emphasizing leanness, both populations that are expected to be at elevated risk for low EA (1,23). Taken together, these data suggest that our criteria of defining self-reported low-iron status should be considered as an addition to the evidence-based Triad consensus panel screening questions that have been previously proposed in the 2014 Female Athlete Triad Coalition Consensus Statement on Treatment and Return to Play of the Female Athlete Triad (1). We observed an overall increased risk for each Triad factor excluding delayed menarche when low iron was self-reported. The findings suggest markers of low-iron status may serve as an additional screening question that may be useful to identify athletes who would benefit from the full Triad risk assessment and/or other testing for low EA.
Low-iron status and low EA
Low-iron status may be associated with low EA through a number of mechanisms. Perhaps the most straightforward explanation is that low dietary intake contributes to reduced micronutrient intake, including iron. Supportive evidence from other studies suggests elite female athletes often have low energy intake with concurrent low iron intake (16,17,24–28). Without adequate iron supplementation, suboptimal iron intake may serve as proxy of poor dietary energy intake or low EA (18). Furthermore, low-iron status might potentiate low EA by inducing a hypometabolic state. For example, low-iron status results in impaired thyroid function and lower circulating levels of triiodothyronine, the most active form of thyroid hormone, resulting in a hypometabolic state (19). In addition, low-iron status reduces cortisol synthesis in exercising women, thus decreasing metabolic fuel availability and potentiating low EA (19). Furthermore, this state of decreased metabolic fuel availability is also associated with growth hormone (GH) dysfunction in exercising women. When iron is low, GH production is suppressed and target sites show increased resistance to GH, leading to reduced serum free fatty acids and glucose, again potentiating low EA (19). Iron also serves as a cofactor in the synthesis of the neurotransmitters serotonin, dopamine, and norepinephrine. Low-iron status may increase anxiety by disrupting neuroendocrine signaling, potentially initiating DE/ED in exercising women and resulting in low EA (19). In addition, a common symptom of iron deficiency anemia is loss of appetite; inadvertent undereating, then, can exacerbate the undereating that initially caused the iron deficiency (19). Iron also plays a vital role as a cofactor in the production of energy as adenosine triphosphate and may reduce metabolic efficiency, therefore depleting energy stores more quickly and resulting in low EA, especially in exercising women (19). Low-iron status might also be related to low EA more indirectly. Iron might be lost insidiously via increased erythrocyte hemolysis due to poor nutrient availability or overexercising, among other mechanisms (29). Collectively, these explanations create biological plausibility for how low-iron status may contribute to complex and interactive pathways of the low EA state.
The association we identified between low-iron status and low EA may help identify athletes at elevated risk for low EA, particularly in populations of lean sport/endurance athletes. Our findings suggest the standard PPEs for collegiate women athletes may benefit from explicitly screening for iron deficiency anemia and use of iron supplementation. Those at risk could be prioritized for more specific testing including the Female Athlete Screening Tool, which has the nuance to detect subclinical EDs (14), and serum metabolic markers, such as leptin, insulin, insulin-like growth-factor 1 (IGF-1), triiodothyronine, estradiol, and progesterone (all lower in females with low EA compared with controls) (30). In addition, the widely accepted negative effects of low iron on athletic performance (especially endurance performance) (31) might serve as a motivating factor for women who are otherwise resistant to treatment to see a sports dietitian. Women with low EA are often resistant to treatment, so any factor encouraging this intervention would be helpful (5).
Furthermore, the association between self-reported low-iron status and low EA may help explain the relationships between self-reported low-iron status and both low BMI and oligomenorrhea, as both are downstream consequences of low EA (8). It is noteworthy, however, that these relationships are likely bidirectional, as menstrual function is known to affect iron status (19).
Low-iron status and low BMD
As discussed previously, we found a significant relationship between low-iron status and both low BMD and history of stress reaction/fracture. Petkus and colleagues (19) propose a number of mechanisms by which these relationships might be potentiated. First, as mentioned previously, low iron can result in direct and indirect suppression of GH and IGF-1, essential mediators of bone growth, development, and maintenance (19). Because exercising women with amenorrhea already have markedly reduced IGF-1, further reduction of IGF-1 due to low iron can lead to severely suppressed bone formation and decreased BMD (19). Also mentioned previously, low iron can lead to hypothyroidism, which can compromise bone health (19); hypothyroidism is associated with lower BMD and poor bone architecture, and thyroid hormones play a role in stimulating bone-forming osteoblast activity, among other growth factors (19). In addition, low iron can induce hypoxia in bones due to reduced oxygen delivery, impairing bone health (19). The effects of hypoxia might be exacerbated in exercising women with low estrogen because the inhibiting effects of estrogen on hypoxia-inducible factor-1, which stimulates bone-resorbing osteoclasts and inhibits bone-forming osteoblasts, are removed (19). All in all, the literature shows that low iron can contribute to poor bone health, especially in exercising women, in agreement with our findings. Identifying athletes with self-reported low iron, then, might prove useful in identifying athletes who would benefit from further testing and interventions during a period in which accruing bone density is paramount. Once again, identifying low iron as a screening question for the Triad risk assessment tool might have clinical utility in identifying those athletes in need of further workup.
The major limitation of our study was self-report and lack of direct measures for low-iron status. We did not measure ferritin or perform a complete blood count to assess for microcytic anemia, nor can we verify that all cases of reported anemia were due to iron deficiency. However, in this age group and particularly among athletes, the vast majority of anemia is the result of iron deficiency (32,33). The cross-sectional design limits our ability to explore causality and mechanisms for how low-iron status may contribute to low EA or predict changes in acquiring Triad risk factors over time. Prospective studies are needed to understand the possible causal mechanism of this relationship, including true measures of EA (e.g., 3- to 7-d dietary and exercise recall, and wearable technology (34,35)) and serum iron and ferritin levels. Exploration of ferritin levels, an early marker for low-iron status (36), and low EA in future research may help to confirm these findings.
Our findings suggest that markers for low-iron status such as history of anemia or iron supplementation may have clinical utility as indicators to increase the sensitivity of detecting low EA and the Triad in female collegiate athletes, especially among lean sport athletes. Further research is required to evaluate whether low-iron status may serve as a proxy for low EA, but our findings do show that at present self-reported low iron would provide additional sensitivity as one of multiple screening questions in identifying athletes who would benefit from further screening for the Triad. These screening questions are low in cost and easy to implement yet might help identify women who could benefit greatly from intervention. Importantly, if replicated with diagnosis of true iron deficiency anemia (i.e., measuring serum ferritin levels and complete blood count with differential outside the time of illness or acute stress (36)), identifying low-iron status might motivate dietitian-averse athletes to seek treatment. This study adds to the limited number of studies in populations at high risk for low EA and may lead to advances in their care.
There was no funding for this study.
No authors have any conflicts of interest to report. The study results are being presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. The results of this study do not constitute endorsement by the American College of Sports Medicine.
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