Prevalence of Low Energy Availability in Competitively Trained Male Endurance Athletes

Background and Objectives:Relative energy de ciency in sport (RED-S) has been introduced as a broad-spectrum syndrome leading to possible dysfunction in numerous physiological systems, driven primarily by low energy availability (EA). Research in females has identi ed speci c EA cut-points indicative of risk level for developing physiological and performance disturbances. Cut-points in males have yet to be evaluated. This study examined the prevalence of low EA in competitive (non-elite), recreationally trained (CRT) male endurance athletes.Materials and Methods: Subjects were 108 CRT (38.6 13.8 y; 12.2 5.4 h/wk training) male endurance athletes (runners, cyclists, triathletes) who completed a descriptive survey online via Qualtrics ® and returned 3 day diet and exercise training records. EA was calculated from returned surveys and training records. Resting metabolic rate (RMR) and lean body mass (LBM) were estimated from self-reported survey data. Prevalence of risk group was categorized based on the female cut-points: At risk (AR) 30 kcal/kg LBM, moderate risk (MR)=30–45 kcal/kg LBM, or no risk (NR) 45 kcal/kg LBM.Results: In this sample, 47.2% (n=51) were classi ed as AR, 33.3% (n=36) as MR, and 19.4% (n=21) as NR for low EA. Cyclists had lower EA (26.9 17.4 kcal/kg LBM,n=45) than runners (34.6 13.3 kcal/kg LBM,n=55,p=0.016) and all other sport categories (39.5 19.1 kcal/kg LBM,n=8,p=0.037). Conclusions: The ndings indicate this sample had a high prevalence of risk for low EA, at 47.2%. Only 19.4% of participants were at

as AR, 33.3% (n=36) as MR, and 19.4% (n=21) as NR for low EA. Cyclists had lower EA (26.9 17.4 kcal/kg LBM,n=45) than runners (34.6 13.3 kcal/kg LBM,n=55,p=0.016) and all other sport categories (39.5 19.1 kcal/kg LBM,n=8,p=0.037). Conclusions: The ndings indicate this sample had a high prevalence of risk for low EA, at 47.2%. Only 19.4% of participants were at no risk, meaning ~80% of participants were at some degree of risk of experiencing low EA. Cyclists were at greater risk in this cohort of low EA, although why this occurred was unclear and is in need of further investigation. Future research should address whether the current female cut-points for low EA are appropriate for use in male populations. Keywords: relative energy de ciency in sport (RED-S); exercise; eating habits; reproductive dysfunction; sex 1. Introduction Relative energy de ciency in sport (RED-S) was identi ed in 2014 as a broad-spectrum syndrome leading to possible dysfunction in numerous physiological systems, driven primarily by the development of low energy availability (EA) [1]. Traditionally, low EA has been associated with the female athlete triad (triad) [2,3], but the introduction of the RED-S (2014) terminology has expanded the potential breadth of impact and recognizes that males too can be a ected negatively by low EA [1]. Furthermore, and importantly within sport, athletes at risk of or experiencing RED-S are more likely to Medicina2019,55, 665; doi:10.3390 /medicina55100665 /journal/medicina

Medicina2019,55, 665 2 of 11 have an increased risk of injury and/or decreased athletic performance when training/competing [1–3]. In addition to injury and physiological performance disturbances, the e ects of RED-S from low EA may increase the risk for future detrimental health e ects later in life, e.g., earlier onset of osteoporosis. Low EA risk is thought to be greater in aesthetic (e.g., gymnastics), weight-sensitive (e.g., jockeys, wrestling) and endurance-based (e.g., running, cycling) sports than in team or ball-based sports (e.g., soccer, basketball) [1]. The rationale for this line of thought is due in part to: (a) eating disorders or disordered eating, which can in uence energy intake (EI), can be more prevalent in aesthetic and weight-sensitive sport athletes, thereby increasing their risk for low EA; and, (b) endurance sports involve high volumes of training and as such, these athletes have tremendous daily exercise energy expenditure (EEE; high caloric cost) rates, which increases their risk for low EA [4]. Extensive research in females has identi ed low EA cut-points indicative of risk level for the development of physiological and performance disturbances. These cut-points are: at risk (AR) 30 kcal/kg lean body mass (LBM), moderate risk (MR) 30–45 kcal/kg LBM, and no risk (NR) 45 kcal/kg LBM [5,6]. Whether male athletes share the same risk factor cut-points is currently unknown. That is, insu cient research has been conducted to determine whether there are male-speci c thresholds; nonetheless, the female cut-points have been applied in male-based studies [7–10]. Furthermore, even with an understanding of the components involved in and contributing to EA, the occurrence of low EA in male athletes is not well known, as to date there has been limited research investigating the prevalence of it in male athletes [7–11]. Most individuals participating in sport are not at an elite level, but tend to be more health-focused and recreationally inclined with their exercise [12,13]. To that end, a majority of the existing male-based prevalence research has investigated athletes at more professional, elite levels, leaving little known about recreationally trained men's risk for low EA [1,12,13]. Furthermore, more attention has historically been

individuals participating in sport are not at an elite level, but tend to be more health-focused and recreationally inclined with their exercise [12,13]. To that end, a majority of the existing male-based prevalence research has investigated athletes at more professional, elite levels, leaving little known about recreationally trained men's risk for low EA [1,12,13]. Furthermore, more attention has historically been paid to female athletes regarding low EA research, due to the serious, major negative health consequences (e.g., athletic amenorrhea or osteoporosis) associated with RED-S and the triad [4]. Therefore, the purpose of this study was to investigate the prevalence of low EA in competitive (non-elite), recreationally trained male endurance athletes using an epidemiological survey approach. The survey was distributed to competitive, recreational exercise training groups in North America who were registered members of USA Track and Field, USA Cycling, and USA Triathlon, as well as comparable sport collegiate clubs. 2. Materials and Methods To investigate the prevalence of low EA by risk category (AR, MR, and NR) a cross-sectional design including an online survey with diet and exercise training logs was implemented. This study was reviewed for all methods, procedures, and recruitment techniques by the O ce of Human Research Ethics at the University of North Carolina, which granted approval for its implementation (Institutional Review Board study #16-3137, approved on 19th January 2017). The purpose was explained to and informed consent was obtained from the participants as they completed the initial two pages of the survey, which also noti ed them of the inclusion/exclusion criteria and the total study requirements and expectations. 2.1. Participants The population for this study consisted of competitive, recreationally trained male endurance athletes. Participants were recruited from active running, cycling, and triathlon clubs across the United States. Inclusion criteria included exercise training for at least 10 h per week and currently training for a speci c endurance event [14]. Individuals were excluded if they were under 18 years of age, currently injured, or in a ramping phase of training (i.e., acute periods of increasing/decreasing training volume–intensity).

States. Inclusion criteria included exercise training for at least 10 h per week and currently training for a speci c endurance event [14]. Individuals were excluded if they were under 18 years of age, currently injured, or in a ramping phase of training (i.e., acute periods of increasing/decreasing training volume–intensity).

Medicina2019,55, 665 3 of 11 2.2. Survey 2.2.1. Development The current survey was adapted from the previously validated Community Health Activities Model Program for Seniors (CHAMPS) physical activity questionnaire with guidance from a content expert [15]. Consultation and review with the University of North Carolina Odum Institute for Research in Social Sciences was also conducted to maximize survey quality and minimize respondent burden. The survey was designed to collect descriptive information regarding physical characteristics, exercise training behaviors and history, nutritional practices, injury-induced training disruptions, and upcoming competitive events. The survey was piloted by ve local male endurance athletes prior to distribution and reviewed for inconsistencies, clarity, duration, and electronic issues. Upon release, this was an open survey requiring no password to complete, and it was distributed online through Qualtrics ® with an anonymous weblink. At the end of the survey, participants were able to request that an individualized report be emailed to them. 2.2.2. Recruitment and Response Rate Emails were sent to 22 cycling, 116 triathlon, 158 running, and 118 club teams registered with USA Track & Field, USA Cycling, and USA Triathlon, as well as similar sports collegiate clubs across the United States (414 total). Team/club contacts were asked to send the information and link to their club membership, speci cally the males. A Facebook ® page was also created to share the link, along with dissemination through Twitter ® . Con rmation of distribution to club team members was rare, and subsequent emails from the researchers requesting the number of team members provided few responses. While some response was received regarding roster numbers from organizations, it is impossible to accurately extrapolate the few numbers received to the potential number of overall participates contacted. Con rmed team numbers ranged from 6 to over 400 members. The recruitment email detailed the purpose of the study and included expectations of participants and link to the survey. Participants were made aware they would need to provide a personal email to receive the diet and exercise training logs. They could either return the logs through the Qualtrics ® survey platform or email the

from 6 to over 400 members. The recruitment email detailed the purpose of the study and included expectations of participants and link to the survey. Participants were made aware they would need to provide a personal email to receive the diet and exercise training logs. They could either return the logs through the Qualtrics ® survey platform or email the principal investigator (PI) directly. A total of 396 individuals responded, with consent provided by 285 (72.0%) participants. Of these, 76.8% (n=219) of consented participants completed the survey and subsequently received the diet and training record information. Of these, 49.3% (n=108) of participants who completed the survey also completed the additional study activities by returning the requested diet and training records (see the following section). Only participants completing the entire survey (all relevant questions) and returning the diet and training records were included in the analysis for this study. 2.2.3. Administration and Details The descriptive survey was open from 1 February 2017, to 1 February 2018, and could be accessed through the Qualtrics ® link. All participants answered a minimum of 24 questions, and adaptive questioning was implemented to minimize unrelated questions (e.g., cycling questions for runners) and decrease the respondent burden. The most questions a respondent would have encountered was 38 of the 49 total questions. The survey included 15 pages with no more than four questions per page. The average time to complete the survey was approximately 16 min. Upon completion of the survey, participants were sent the diet and exercise training record forms that had been introduced before consent. 2.3. Diet and Exercise Training Records Upon completion of the survey, participants received an automatic email providing further details on how to record their three days (one weekend day and two weekdays) of dietary intake and exercise training, used by investigators to assess EI and EEE, respectively. Directions for measurement and

Medicina2019,55, 665 4 of 11 recording food along with guidance on portion size was provided (i.e., handouts and web-links to reference sites) along with the diet record forms to complete. Food items on records were analyzed using a nutritional analysis system (Food Processor, ESHA, Salem, OR, USA) to determine total macronutrient (not reported herein) and subsequent EI per day (kcal/day). Instructions for quantifying exercise training sessions were included along with the forms to record on. Relative to exercise sessions, assessment variables captured included: Exercise mode (i.e., running, cycling, etc.), duration (minutes per day), and intensity of daily exercise (ratings of perceived exertion (RPE); guidance was provided on how to use the RPE scale), heart rate (applicable to those participants that used heart rate monitors) and type of training session (e.g., long run, intervals, etc.). Exercise energy expenditure (EEE) was calculated using the Compendium of Physical Activity [16], using the procedures as described by Heikura and associates [9]. Participants were encouraged to contact the PI by email or in person if they had questions about how to provide proper diet/training information. Furthermore, once submitted, all diet/training records were reviewed by the PI and if any anomalies were noted, the participant was contacted to clarify issues. 2.4. Energy Availability (EA) Information collected from the diet and exercise training records was utilized to estimate measures of EA. Energy availability was calculated as [4,9]: EA=(energy intake (EI; kcals) (exercise energy expenditure (EEE; kcals) resting metabolic rate (RMR]/min of exercise))/kilograms of estimated lean body mass (eLBM). (1) Physical characteristics from the survey (age, height, mass) provided the details used to calculate eLBM [17] and estimate resting metabolic rate (eRMR) [18]. Energy availability status was categorized by risk level for low EA: AR: 30 kcal/kg eLBM; MR: 30–45 kcal/kg eLBM; and NR: 45 kcal/kg eLBM [6]. Risk levels were based on the research-based cut-points identi ed in females, as risk thresholds are currently unidenti ed in males. The Boer formula [17,19] incorporating height and weight (mass) was implemented to estimate LBM (eLBM). Estimated RMR was calculated using the Cunningham equation, recognized as the most appropriate for

MR: 30–45 kcal/kg eLBM; and NR: 45 kcal/kg eLBM [6]. Risk levels were based on the research-based cut-points identi ed in females, as risk thresholds are currently unidenti ed in males. The Boer formula [17,19] incorporating height and weight (mass) was implemented to estimate LBM (eLBM). Estimated RMR was calculated using the Cunningham equation, recognized as the most appropriate for endurance athletes [20,21]. Additionally, eRMR per minute of exercise was subtracted from the EEE in the equation, as the resting caloric cost would have occurred regardless of exercise [9]. 2.5. Statistical Analysis Only fully completed, questionnaires were included in this analysis. Prevalence, the primary outcome of the study, was determined by the percentage of participants in each EA risk category. Additionally, ANOVA and, where appropriate, t-tests were conducted as secondary analysis to investigate mean ( SD) di erences in physical and training characteristics, modes of exercise training, and e ect of injury-induced exercise training breaks between EA risk groups (i.e., AR, MR, NR) (SPSS version 21, Chicago, IL). If signi cant F-ratios were detected in the ANOVAs, Tukey's post hoc procedures were utilized to determine speci c mean di erences. Alpha level for statistical signi cance was set a priori at 0.05. 3. Results 3.1. Physical Characteristics, Energy Availability, and Prevalence In total, 219 individuals completed the survey; however, only 108 participants completed all aspects of the study (i.e., survey, diet, and training records). Chi-square analyses were conducted to determine whether any variable might explain why some individuals did not return all study

Medicina2019,55, 665 5 of 11 components. There were no meaningful di erences to explain the lack of completion, and these data have not been reported. A summary of the physical and training characteristics for participants is presented in Table (all data are presented as mean SD unless otherwise indicated). The physical characteristics of the groups (see following below for risk category breakdown) were remarkably similar in age, mass, and height (no measures were signi cantly di erent from one another). The lone exception was BMI, which was signi cantly lower in the NR group (22.4 2.3) compared to the AR group (23.7 2.3, F2.105=3.181,p=0.023). Table 1.Summary of characteristics by energy availability risk classi cation status. Characteristic Total (n=108) At Risk (n=51) Moderate Risk (n=36) No Risk (n=21) Age (y) 38.6 13.8 40.1 14.6 38.0 13.1 36.1 13.5 Mass (kg) 74.9 8.6 76.9 8.2 73.3 7.7 72.7 10.4 Height (m) 1.80 0.10 1.80 0.05 1.79 0.07 1.80 0.09 BMI (kg/m 2 ) 23.1 2.3 23.7 2.3 22.8 2.0 22.4 2.3 Exercise per week (h) 12.2 5.4 12.0 3.6 12.0 4.1 13.0 9.8 Training years at current level (y) 6.9 8.7 6.5 9.4 8.3 9.5 5.3 4.9 Training break due to injury (last 12 months) ˆ33 (30.6%) 11 (21.6%) 12 (33.3%) 10 (47.6%) EA: Energy availability. No Risk: EA 45; Moderate Risk: EA 30—45; At Risk: EA 30. ˆ number of participants (percent); * signi cantly lower than At Risk group. As noted, prevalence for this study is categorized by EA risk status: (1) AR: EA 30 kcal/kg eLBM; (2) MR: EA 30—45 kcal/kg eLBM; and (3) NR: EA 45 kcal/kg eLBM. Based upon these criteria, within this sample of competitive, recreationally trained male endurance athletes, 47.2% (n=51; 95% CI (37.5, 57.1)) were classi ed AR, 33.3% (n=36; 95% CI (24.6, 43.1)) as MR, and 19.4% (n=21; 95% CI (12.5, 28.2)) as NR for low EA. Actual mean ( standard deviation (SD)) EA values (kcals/kg eLBM) are shown in Table for the risk categories were signi cantly di erent among the groupings (F2.105=152.443,p<0.001; all di ering from one another). Energy availability and

were classi ed AR, 33.3% (n=36; 95% CI (24.6, 43.1)) as MR, and 19.4% (n=21; 95% CI (12.5, 28.2)) as NR for low EA. Actual mean ( standard deviation (SD)) EA values (kcals/kg eLBM) are shown in Table for the risk categories were signi cantly di erent among the groupings (F2.105=152.443,p<0.001; all di ering from one another). Energy availability and its speci c components, both measured (energy intake (EI); exercise energy expenditure (EEE)) and estimated (lean body mass (eLBM) and resting metabolic rate (eRMR)) for the total sample can be found in Table. Table 2. Energy availability calculation variables and components by EA risk status groupings (n=108) (mean SD). Variables Total n=108 At Risk n=51 Moderate Risk n=36 No Risk n=21 Energy Availability (EA) (kcal/kg eLBM) 31.7 16.0 16.2 10.6 34.8 4.1 51.8 7.8 Energy Intake (EI) (kcals/day) 3086.7 810.1 2661.5 708.93134.5 474.3 4037.7 667.9 Exercise Energy Expenditure (EEE) (kcals/day) 1356.4 671.2 1676.9 756.81103.3 443.1 1011.9 364.4 Est. Resting Metabolic Rate (eRMR) kcals/day) 1 1804.4 102.31823.9 88.7 1786.8 96.8 1787.4 134.5 Est. Lean Body Mass (eLBM) (kg) 2 59.3 4.6 60.2 4.0 58.5 4.4 58.5 6.1 1 Cunningham equation, 2 Boer calculation. Energy availability risk status differed significantly by primary training mode (F2.105=4.089 , p=0.019). Post hoc analysis indicated that cyclists demonstrated significantly lower EA (26.9 17.4 kcal/kg eLBM,n=45) compared to runners (34.6 13.3 kcal/kg eLBM,n=55,p=0.016) and the others category (39.5 19.1 kcal/kg eLBM,n=8,p=0.037).

Medicina2019,55, 665 6 of 11 3.2. Exercise Training and Energy Availability Relative to exercise training, the risk groups did not di er for hours per week and years of training (p>0.05; see Table). Injury impact on training was highly variable, as 33 participants had experienced a break in their training of at least three weeks due to injury during the last 12 months (see Table). The prevalence of missed training due to injury tracked slightly higher in the MR (33.3%) and NR groups (47.6%), and lower in the AR group (21.5%) compared to the sample as a whole (30.6%). Subsequent analysis of how injury impacted prevalence categorization was conducted. From the entire sample, those having experienced an injury-induced training break in the previous 12 months had signi cantly higher EA (37.0 15.7 kcal/kg eLBM) than those without a training break (29.4 15.7, t106= 2.306 ,p=0.023). However, and most importantly, within each speci c risk category, the EA was una ected by the injury-induced training breaks (t49=0.523,p=0.603). Table 3.Duration of missed training due to injury in the last 12 months by EA risk status (n=33). EA Group n 3 wks–<5 wks 5 wks–<9 wks 9 wks–<12 wks 12 wks Totals (%) At Risk 1 51 7 2 1 1 11 (21.5) Moderate Risk 1 36 3 4 0 5 12 (33 /3) No Risk 1 21 7 2 0 1 10 (47.6) Total 1 108 17 (15.7) 8 (7.4) 1 (0.9) 7 (6.5) 33 (30.5) 1 Column values represents number of participants while ( ) numbers represent %, wks=weeks. 3.3. Nutritional Supplements and Energy Availability At least one nutritional supplement was consumed by 43.5% of participants (n=47). Categories of supplements consumed are shown in Table 22]. Notably, the total number of supplements consumed was greater than 47, as numerous participants consumed multiple supplements. Statistical analysis indicated supplement consumption did not signi cantly a ect EA values across the groups (F1.106=1.586,p=0.211). Table 4.Nutritional supplements consumed by participants (n=47). Supplement Category Number of Participants Consuming Vitamins 33 Minerals 31 Fish Oil/Flaxseed Oil 16 Protein/Amino Acids 31 Herbs/Botanicals/Extracts 14 Glucosamine Chondroitin 5 Enzymes 3

The study investigates low energy availability in male endurance athletes using an epidemiological survey approach.