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Abstract
Objectives Athlete health, training continuity and performance can be impeded as a result of Relative Energy Deficiency in Sport (RED-S). Here we report the point prevalence of symptoms described by the RED-S model in a mixed-sport cohort of Australian female athletes.
Methods Elite and pre-elite female athletes (n=112) from eight sports completed validated questionnaires and underwent clinical assessment to assess the point prevalence of RED-S symptoms. Questionnaires included the Depression, Anxiety and Stress Questionnaire (DASS-21), Generalized Anxiety Disorder (GAD-7), Center for Epidemiological Studies Depression Scale (CES-D), SCOFF questionnaire for disordered eating, Low Energy Availability in Females Questionnaire (LEAF-Q), and a custom questionnaire on injury and illness. Clinical assessment comprised resting metabolic rate (RMR) assessment, dual-energy X-ray absorptiometry-derived body composition and bone mineral density, venous and capillary blood samples, and the Mini International Neuropsychiatric Interview (MINI 7.0.2). Descriptive prevalence statistics are presented.
Results Almost all (80%) participants (age 19 (range 15–32) years; mass 69.5±10.3 kg; body fat 23.1%±5.0%) demonstrated at least one symptom consistent with RED-S, with 37% exhibiting between two and three symptoms. One participant demonstrated five symptoms. Impaired function of the immunological (28%, n=27), haematological (31%, n=33) and gastrointestinal (47%, n=51) systems were most prevalent. A moderate to high (11%–55%) prevalence of risk of low energy availability was identified via RMR and LEAF-Q, and identified mental illnesses were prevalent in one-third of the assessed cohort.
Conclusion Symptoms described by the RED-S model were prevalent in this cohort, supporting the need for improved awareness, monitoring and management of these symptoms in this population.
- relative energy deficiency
- athlete
- female
- health
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Introduction
In 2018, the International Olympic Committee (IOC) updated their definition of Relative Energy Deficiency in Sport (RED-S). It is a syndrome of impaired physiological function of the reproductive, gastrointestinal, cardiovascular, endocrine, metabolic, psychological, skeletal, haematological and immunological systems underpinned by low energy availability (LEA).1 2 LEA describes a state where an individual’s energy intake is inadequate to support optimal function of all physiological systems once the energy committed to exercise has been removed. Prolonged exposure to LEA leads to RED-S.2 Female athletes have impaired function in various body systems when energy availability (EA) is <30 kcal/kg fat-free mass (FFM)/day.2
The mechanisms that underpin the development of LEA—and subsequent RED-S—are multifactorial.3–6 LEA may be driven by intentional or inadvertent restriction of dietary energy in circumstances including, but not limited to, disordered eating behaviours or diagnosed eating disorders,6–8 lack of financial resources for dietary needs,2 misguided approaches to body composition manipulation6 or inadequate matching of energy intake to increased training/event demands.2 6 Subsequent consequences include menstrual disturbances such as amenorrhoea or oligomenorrhea;9 low bone mineral density (BMD)10; dysregulation of hormones related to sex, thyroid, appetite and metabolism11; suppressed resting metabolic rate12; iron deficiency2; growth retardation2; psychological disturbances1; dyslipidaemia and hypotension2; bloating and constipation13; and immunosuppression.14 Although it is unclear whether these consequences appear in isolation or across multiple systems, they have a substantial negative impact on training, performance and health.2 4 15 The varied aetiology of LEA and other contributing factors has impeded the definition of a natural history of RED-S and the development of diagnosis, treatment and prevention protocols.
In response to the IOC call for further research on this area,1 we investigated the prevalence of symptoms, clinical signs and abnormal laboratory test as outlined in the RED-S model in a mixed-sport cohort. The aim of this study was to extend investigations typically undertaken in endurance athletes to a wider population to capture the prevalence of symptoms that may be associated with RED-S.
Methods
Study design, setting and participants
This was a cross-sectional, observational study in elite and pre-elite female athletes across multiple sports, using screening questionnaires and clinical assessments to establish the point prevalence of symptoms described in association with RED-S. Questionnaires were administered, as previously described,14 15 via an electronic management system. Clinical assessments were undertaken at the Australian Institute of Sport (AIS) Physiology Laboratory in Canberra, Australia. Recruitment and data collection were completed over 18 months (13 April 2017–29 November 2018), determined by researchers and staff from each National Sporting Organisation.
Eligibility criteria
Eight of 11 National Sporting Organisations approached by the research team chose to participate. The Foundations Talent Elite Mastery (FTEM) framework was used to identify eligible participants categorised as Elite 1 or 2 (elite), or Talent 3 or 4 (pre-elite).16 Three National Sporting Organisations (n=66) declined due to logistical and scheduling issues. Of 116 athletes who the National Sporting Organisations deemed as eligible to participate, 114 (98%) chose to participate; however, n=2 athletes were sick on the scheduled day of clinical assessments and were unable to participate (total n=112 athletes). Online supplemental 1 provides further details. Participants were training/competing at eligible FTEM levels at the time of recruitment. Informed consent was obtained from participants ≥18 years old and from parents/guardians of participants <18 years old. Informed assent was obtained from athletes <18 years old. This manuscript follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (online supplemental 2).17
Supplemental material
Supplemental material
Data sources
Questionnaires
Six questionnaires were administered (table 1). Most participants completed these prior to their clinical assessment (n=83), and a few provided data up to 7 days after their clinical assessment. Additionally, a subgroup of participants (n=5) provided questionnaire data 2 months after their clinical assessments to accommodate a scheduled break immediately after assessment.
Summary of administered questionnaires
Supplemental material
Clinical assessment
Dual-energy X-ray absorptiometry
Total body composition and BMD of the femoral neck and anteroposterior lumbar spine were assessed via dual-energy X-ray absorptiometry (DXA) (Lunar iDXA, GE Healthcare Asia-Pacific), with analysis performed using enCORE software (V.16, GE Healthcare Asia-Pacific). Daily calibration checks were conducted prior to the first scan.
BMD scans were undertaken using standard positioning recommendations from The International Society for Clinical Densitometry. Automatic analysis software, including the ‘Paediatric’ option selected for participants <20 years old, was used to calculate Z-scores.18 Body composition scans were undertaken using the Nana positioning protocol.19 Positioning of the regions of interest for the analysis of these scans was conducted by the same researcher for each scan. These scans provided a value for fat mass (FM), lean mass (LM) and bone mineral content (BMC). FFM was calculated as LM plus BMC.
Resting metabolic rate
Resting metabolic rate (RMR) assessed via the criterion outpatient Douglas Bag method was used as an indicator of EA.20 When undertaken at the AIS, this method has a typical error of 455.3 kJ or 6.6% (90% confidence limits 4.8% to 11.1%) between days.21
RMRratio was calculated using measured resting energy expenditure (REE) derived from RMR and predicted REE using Cunningham’s equation.22 LEA was indicated by low RMR (<30 kcal/kg FFM/day) and/or RMRratio (<0.9).2 23
Cardiovascular health
Supine blood pressure and resting heart rate (HR) were measured using an automatic monitor (Omron HEM-7320, AMA Medical Products, Nedlands, WA, Australia) 10 minutes into the RMR protocol. Total cholesterol and low-density lipoproteins (LDL) were assessed from capillary blood samples (cobas b 101, Roche, Castle Hill, NSW, Australia). Given the variation in reference ranges resulting from varying laboratories, cholesterol thresholds consistent with those previously described were used.7 12
Biochemical markers
Fasted blood samples were taken via venepuncture from an antecubital forearm vein by qualified phlebotomists. External analysis (Laverty Pathology, Macquarie Park, NSW, Australia) was conducted for full blood count (FBC), serum ferritin, follicle-stimulating hormone (FSH), luteinising hormone (LH), oestradiol, progesterone, thyroid-stimulating hormone (TSH), free triiodothyronine (fT3) and leptin. When the external laboratory was unavailable, analysis of FBC and ferritin was undertaken at the AIS Biochemistry Laboratory (n=4). Samples from participants who reported current or recent (≤90 days prior) use of hormonal contraceptives were excluded from analysis of FSH, LH, oestradiol and progesterone (n=41). Fasting blood glucose was assessed from capillary blood samples (AccuChek Performa II, Roche, Castle Hill, NSW, Australia). Participants with a needle phobia declined the blood test (n=3).
Psychiatric interview
A proportion of the cohort (n=59) attended a semistructured interview with one of six Registered Psychologists using an abridged version of the Mini International Neuropsychiatric Interview (MINI V.7.0.2). The MINI consists of modules for identifying current and prior history of 17 psychiatric disorders according to the American Psychiatric Association’s (2013) Diagnostic and Statistical Manual of Mental Disorders, fifth edition.24 Two modules were excluded: Substance Dependence/Use (due to mandatory reporting requirements of Australian Sports Anti-Doping Authority and Sport Australia) and Psychotic and Anti-social Disorders (due to low rates in the general population and time constraints). The suicide module was shortened if current and history of suicidal ideation was denied. Psychologists were familiarised with the abridged protocol and scoring by coauthor (RNA) who is experienced in the use of the MINI protocol. Where athletes’ symptom presentations were unclear or complex, the interviewing psychologist and the coauthor (RNA) consulted. This interview was offered to all participants. One National Sporting Organisation was unable to participate due to time constraints. Athletes not participating (n=34) did so at their own discretion, or that of their National Sporting Organisation. Psychology questionnaires were administered prior to the interview to flag in advance any elevated distress.
Symptoms, clinical findings and laboratory investigations relevant to RED-S
Cut-offs were identified from the literature for limited aspects of the symptoms that may be associated with RED-S.2 4 12 Menstrual dysfunction was indicated by self-reported amenorrhea and/or oligomenorrhea, obtained from the LEAF-Q and/or the AIS Pre-DXA Questionnaire (online supplemental 4).25 Compromised bone health was indicated by BMD Z-score <−1.0.26 Endocrine dysfunction was indicated by abnormal TSH levels or low fT3 levels.11 Compromised metabolic function was indicated by RMR <30 kcal/kg FFM/day and/or RMRratio<0.9, in the absence of primary hypothyroidism.2 Serum ferritin <30 μg/L indicated compromised haematological health.4 27 A psychiatric condition was diagnosed when required criteria were met for one or more of the MINI 7.0.2 modules.28 LDL ≥3.0 mmol/L indicated cardiovascular dysfunction.7 12 Time loss was defined as any period of missed training reported in the General Health questionnaire, due to self-reported injury or illness, for a period of ≥24 consecutive hours.29 A score ≥2 on the LEAF-Q gastrointestinal calculator, or self-reported time loss due to gastrointestinal symptoms in the month prior to clinical assessment indicated gastrointestinal dysfunction.13 15 Immunological dysfunction was indicated by self-reported time loss in the month prior, as a result of illness symptoms.15 Growth and development were not assessed. To compare prevalence across sports, the presence/absence of symptoms was summed. Symptoms are denoted by (*) in the results. The presence of one or more symptoms does not necessarily mean that RED-S is present, but rather it indicates compromised functioning of that system and the need for further assessment of that athlete’s health in relation to LEA and RED-S.
Supplemental material
Statistics and data analysis
Results are presented as mean and SD for parametric data, and median and IQR for non-parametric data. Analyses were undertaken using Stata (Stata Corp, V.15 IC, USA). Whole of cohort data were analysed as were those of distinct groups (eg, sport/level), where the number of participants in each group was >1. Point prevalence was calculated as the number of participants above/below cut-off thresholds for each assessment, divided by the total number of participants who completed that assessment. Missing data were excluded.
Results
One hundred and twelve female athletes (age 19 (range 15–32) years; mass 69.5±10.3 kg; body fat 23.1%±5.0%) participated. The distribution by sport was as follows: athletics, n=1; U21 rowing, n=9 (open weight n=4, lightweight n=3, coxswain n=2); boxing, n=11; weightlifting, n=11; basketball, n=12; triathlon, n=13; water polo, n=19; netball, n=36 (junior (state level) n=22, senior (international level) n=14).
Prevalence of abnormal results
The prevalence of responses above/below cut-off thresholds for each questionnaire is presented in table 2. Table 3 presents illness and injury prevalence, derived from the General Health questionnaire. Table 4 shows the prevalence of RED-S-related system symptomology.
Prevalence of respondents above/below questionnaire cut-off thresholds
Prevalence of illness and injury in the month prior to clinical assessment (n=95)
Prevalence of impaired function consistent with RED-S
Menstrual function could not be determined in participants who reported use of hormonal contraceptives (37%, n=41), or in those who did not provide sufficient information (19%, n=21). Non-eumenorrheic athletes with diagnosed polycystic ovarian syndrome (PCOS, n=1, 50%) were not assessed for menstrual function as dysfunction may have resulted from the syndrome in addition to or rather than LEA.11 Female sex hormone concentrations were assessed in eumenorrheic participants only, where cycle phase could be determined (n=22). Mean leptin relative to fat mass was 0.87±0.32 ng/mL/kg FM. Participants reporting illness on the day of clinical assessment were excluded from RMR data (n=3). Participants with diagnosed PCOS (n=2) were included in pooled data for all measures.
Figure 1 illustrates the prevalence of physiological systems with impaired function consistent with RED-S by sport.
The prevalence of physiological systems with impaired function consistent with Relative Energy Deficiency in Sport.
Twenty-two participants (20%) did not show any symptoms associated with RED-S. Most athletes had one symptom present (41%, n=46); the maximum was five symptoms in one participant. Figure 2 shows the distribution of various physiological systems where function was compromised by our definitions.
The distribution of impaired physiological function across each body system. Note: Netball (senior) and water polo did not participate in the psychiatric interview, n= total number of athletes assessed.
Discussion
The clinical consequences of LEA can extend much more broadly than the three elements first identified as the Triad ‘syndrome’ (bone health, menstrual function and eating behaviour) when their cause (energy deficiency) was not well understood. Indeed, according to the expanded RED-S model, LEA can affect at least 10 body systems.1 2 In the current study, 80% of participants demonstrated at least one symptom that is described within the RED-S model.1 The most prevalent body systems affected were the haematological, psychological, gastrointestinal and immunological systems. The prevalence of multiple symptoms consistent with the RED-S model falls within previously reported rates of 50%–90% among varying athletic groups and ethnicities.4 7 12
Features of the Triad are less prevalent than broader RED-S symptoms
In terms of the classical Triad features, BMD of participants in the current study was typically above normal ranges. MINI-diagnosed eating disorders were uncommon (<1%), while the SCOFF identified risk of disordered eating in fewer than 25% of participants. Finally, according to the instruments we used, there was a low prevalence of menstrual dysfunction. However, we note that this evaluation was limited to the subsample not using hormonal contraceptives (63%). Furthermore, the self-reported information was deemed to be poor quality; 56% of self-reports of recent menstrual activity were absent, deemed unreliable (athletes were unable to recall details, did not monitor their activity or were on hormonal contraception) or not specific enough to determine cycle phase.
Such challenges with protocols to assess menstrual function are not unique to this study. Currently, there is no accepted gold standard diagnostic tool to determine menstrual phase, which limits the ability to report epidemiological data consistently.30
Cycle length is not a marker of ovarian function so the presence of anovulation could not be determined.31 As between 14% and 50% of female athletic populations report using oral contraceptives,32 their ability to report menstrual function is limited. Given these limitations, focussing on Triad risk factors alone may misidentify RED-S cases and introduce a misclassification bias that lowers prevalence.
Suboptimal RMR and risk of LEA is prevalent
Direct measurement of EA is challenging.2 5 33 An EA assessment was intended within our test battery; however, its implementation was challenged by the lack of a recognised and standardised protocol, difficulties in obtaining reliable dietary information or estimates of resting and exercise energy expenditure, and the significant time burden for participants and researchers. Therefore, we focused on risks and symptoms of LEA using the LEAF-Q and RMR. The LEAF-Q identified 55% of participants at risk of LEA, and measurement of RMR identified 11% of participants as meeting the criteria for low RMR at the time of testing. We note that the LEAF-Q is a resource to identify the risk of LEA, first based on the Triad, rather than provide a diagnosis of RED-S. Furthermore, since it was validated for use in an adult endurance athlete population, differences in sensitivity or specificity in other groups highlight the need for broader validation. RMR and RMRratio were assessed as surrogates for EA. This is not without substantial limitations including difficulty in determining the magnitude of influence of individual laboratory methodologies, age, body mass, FFM, recent exercise, training volume, exposure to altitude and other metabolically stimulating/depressing variables.21 22 34 The apparent discrepancy in prevalence of risk of LEA (as determined by the LEAF-Q) and the prevalence of low RMR can also be explained in terms of differences in time course, since the RMR assessment may reflect recent diet and exercise practices while the LEAF-Q responses reflect prior or chronic practices.
Some symptoms associated with RED-S may occur independently or differently according to athlete population
A slower than usual HR is associated with LEA in athletes, at least in non-aerobic sports.35 However, interpretation of bradycardia in relation to LEA without consideration of the degree of aerobic fitness may result in a collider bias (unmeasured confounding), since endurance training has been shown to reduce HR.36 The distribution of resting heart rate by sport is provided in online supplemental file 5. Furthermore, abnormal TSH levels indicate primary thyroid dysfunction, hence low RMR should be considered independently of LEA in these individuals. Other issues such as low iron status (measured) and gastrointestinal disturbances (self-reported within the LEAF-Q and General Health questionnaire) appeared frequently among the current study participants. However, we note that these presentations can also occur independently of LEA and warrant separate investigation.37 38
Supplemental material
Prevalent high leptin concentrations conflict with current RED-S literature
Reductions in the hormone leptin have been associated with direct and surrogate measures of LEA.11 39 Despite the low to moderate prevalence of risk of LEA identified in this cohort, leptin levels did not appear to follow this trend. This may have been confounded by the presence of higher fat mass in the mixed-sport cohort than is typically reported in endurance sports,11 or the inclusion of adolescent athletes given the rise in leptin during puberty.40 This novel finding warrants further interrogation to determine the suitability of leptin as a marker for RED-S in adolescents and populations that are less focused on weight for performance.
Illness/injury symptoms and resultant time loss are prevalent
Athletes need to complete planned training sessions with consistency.41 Despite the fact that all participants in this study were currently training and competing, most participants (78%) reported at least one illness symptom from any body system in the previous month (table 3). Time loss due to illness symptoms occurred in 38% of participants who reported symptoms. Over one-quarter (27%) of participants reported an injury in the month prior. Time loss due to injury occurred in 62% of participants who reported an injury. These data suggest that individual illness symptoms are more relevant when monitoring athlete health across a group or population than only investigating time loss from sport. Regardless of its contributing factors, time loss prohibits consistent high-quality training and likely impaired the subsequent performance of 38% of this cohort.2
High prevalence of psychopathology in elite female athletes
Elite athletes may have an increased risk of mental illness due to unique stressors including injuries, media scrutiny and financial pressures.42 To our knowledge, this is the first study to use the MINI with elite athletes. The MINI identified 15 participants (34%) who met the criteria for having one or more current psychiatric conditions. This is higher than the most recent Australian population prevalence of 20% in 2007,43 and the 2014–2015 prevalence in Australian females of 19.2%.44 A potential Berkson’s bias was introduced by the low participation rate.45 This most likely reduced the prevalence due to stigma attached to this component, suggesting that concerns were known within the sport/individual. Furthermore, almost 10% of participants met ≥75% of criteria for diagnosis, suggesting a moderate prevalence of subclinical symptoms (eg, some athletes participating in weight-restricted sports reported bingeing, purging, or a general preoccupation with food and weight, but little distress as this was perceived as normal within their sport). An athlete-specific, diagnostic interview may be warranted to effectively identify unhealthy behaviours normalised within a sport culture.46 Treatment and prevention of mental illness in Australian athletes should be prioritised.47 48
Biases
This study was offered to 11 National Sporting Organisations; three declined due to logistical and scheduling issues. This may represent some recruitment bias, as participating National Sporting Organisations may have prioritised their involvement to address a suspected high risk of compromised health among their athletes. The inclusion criteria of current training/competition may have introduced a survivor bias resulting in an under-representation of athletes with symptoms, as athletes with severe problems may have been excluded if they were not currently training/competing. The phase during the menstrual cycle when blood samples and RMR were measured was not standardised due to the use of convenience sampling. Two laboratories were used for blood sample analysis due to laboratory availability, potentially leading to differing results. Four samples were analysed in the separate laboratory, minimising this bias. Hypercholesterolaemia has been associated with restricted energy intake in earlier literature on other populations12 49; however, our results suggest that total cholesterol may not be an indicative stand-alone metric. Finally, recall bias may have affected the self-reported questionnaire data. The use of valid and reliable questionnaires minimised this. We note that the General Health questionnaire has not been validated. Causation was not attempted nor can it be inferred from our data.
Conclusion
Symptoms that can be associated with RED-S were prevalent in up to 80% of this cohort, although the data collected by commonly used tools and diagnostic protocols are not specific for determining LEA. We note that some of the tools we used in this research are impractical for use with elite athletes. We recommend clinicians consider monitoring symptoms associated with RED-S in athletes regularly. Although we draw attention to the high rate of diagnosable mental illnesses in our study sample, we recognise that we cannot link LEA as the cause of mental illness or vice versa in this cohort.
Key messages
What are the new findings?
Most participants demonstrated symptoms described by the RED-S model.
There was a high prevalence of at least a degree of impaired function of the immunological, gastrointestinal, and haematological systems, despite all participants undertaking training and competition at the time of data collection.
Athletes can train (and compete) with impaired physiological function in one or more body systems—hence, our call for careful clinician attention to features that may point to RED-S.
How might it impact on clinical practice in the near future?
Practitioners should be aware that symptoms described by the RED-S model are prevalent.
Given the high prevalence of mental illness in this cohort of elite athletes, clinicians should assess and, where appropriate, treat mental illness.
Acknowledgments
We acknowledge the contribution of the broader ‘Stay Healthy’ project team; Greg Lovell, David Pyne, Shona Halson, Nic West, Bronwen Lundy and Marijke Welvaert. We thank the contributions of the National Sporting Organisations of the participating athletes, the staff who facilitated the data collection and the athletes for donating their time to participate. We also acknowledge Trent Garrett for his assistance with electronic data management.
References
Supplementary materials
Supplementary Data
This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.
Footnotes
Twitter @MargotRogers_, @PrepareNPerform, @_mickdrew
Contributors This study was designed by MAR, MD and LMB; data were collected and analysed by MAR, MD, LMB and RNA; data interpretation and manuscript preparation were undertaken by MAR, MD, LMB, GW, NV, RNA and DH. All authors approved the final version of the paper. All data presented are part of the ‘Stay Healthy’ project, an initiative that supports Australia’s elite athletes.
Funding This work was supported by the Australian Institute of Sport High Performance Research Fund (Immune Health Multiple Sports, 2017) and the University of Canberra Research Institute for Sport and Exercise (internal grant).
Competing interests None declared.
Patient consent for publication Not required.
Ethics approval Approval for this study, which adheres to the Declaration of Helsinki, was obtained through the AIS and University of Canberra Human Research Ethics Committees (approval number: AIS 20170402).
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement No data are available. Due to the personal nature of the health data collected from participants, no data are available to be shared.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.