Measuring Energy Availability and Energy Conservation in Endurance Athletes in the Pre-Race Period

Background: Low energy availability in male athletes has gained a lot of attention in the last years, but direct evidence of its effects on health and performance is lacking. Aim of this research was to objectively measure energy availability (EA) in healthy male endurance athletes without pre-existing relative energy deciency signs during pre-race season. Methods: 12 trained endurance athletes (performance level 3, 4 and 5) participated in the cross-sectional controlled laboratory study. Fat free mass, exercise energy expenditure and energy intake were measured to calculate EA. Resting energy expenditure was measured and estimated to assess energy conservation. Three specic performance tests were used to assess endurance, agility and explosive strength performance. For psychological evaluation, the Three Factor Eating Questionnaire and a short Well-being questionnaire were completed. Results: Mean EA was 29.5 kcal/kg FFM/day. The majority (66.6%) had EA under the threshold for low EA in females. Critical cognitive restriction ( ≥ 13) was reported by 75% of participants. There were no differences in performance, blood values or psychological evaluation when subjects were divided into two groups divided by EA=30kcal/kg FFM/day. Cognitive restriction was negatively associated with measured resting energy expenditure and energy conservation (r=-.578, p=.025 and r=-.549, p=.032, respectively). Conclusions: The mean EA measured in this study supports the theory that the threshold for low EA in endurance male athletes might be under the threshold for females. In addition, we conrmed cognitive restriction could be useful for early detection of energy conservation. The high cognitive restriction as measured in our sample stressed the need of eating behavior screening in endurance athletes in order to reduce risk of any disordered eating patterns. variables were rst checked for normality of distribution with Shapiro-Wilk’s test. Pearson’s correlation coecient was computed to assess the relationship between EA and obtained performance, laboratory, body composition and psychological parameters. Based on the EA value the subjects were later divided into two subgroups (with EA ≥ 30 kcal/kg FFM/day and with EA < 30 kcal/kg FFM/day). The possible differences in performance, blood, anthropometric, body composition and psychological parameters between those two groups were analyzed using the t-test for independent samples. The signicance level was set at p-values < 0.05 for all calculations.


Background
Endurance athletes are at risk for development of Relative Energy De ciency in Sport syndrome (RED-S) (1). Low Energy Availability (LEA) is the underlying cause for RED-S (2). Estimated prevalence of LEA is high (3) but methodology for assessment is not universal and often based on questionnaires and subjective estimation. The threshold for LEA is known in female athletes, but the equivalent in men is yet to be con rmed. While diagnosing athlete with obvious RED-S signs and symptoms is relatively straightforward, detecting LEA before detrimental health issues arise presents a greater challenge. It is also unclear how and when LEA affects performance. Clearly, performance is of the greatest interest to athletes and their coaches. Unfortunately, there is currently little research directly observing LEA's association with performance. Our current knowledge on performance effects is mostly theoretical (2,4). Objective methodology for measuring EA is the only way to discover the threshold of LEA in men. After a threshold (or a range for LEA) is con rmed, we will then be in a better position to elucidate the effects on performance more readily. There is speculation that performance effects could arise before clinical signs of poor well-being. This is why measuring EA status in apparently healthy athletes could provide an insight into the association of EA with performance.
It was previously reported that useful marker for detecting LEA could be energy conservation as detected by ratio of measured resting energy expenditure (mREE) and predicted resting energy expenditure (pREE)-the mREE/pREE ratio. In female athletes mREE/pREE ratio < 0.9 was associated with relative energy de ciency (5,6) and with poor aerobic performance in competitive female cyclists (7), but there were no reported cutoff values in men.
The primary endpoint of our study was to objectively measure energy availability (EA) in trained male endurance athletes without pre-existing RED-S signs during pre-race season and to evaluate and quantify possible relationships between measured EA and mREE/pREE, speci c blood marker and performance parameters. Our secondary endpoint was analysis of relationship between cognitive restriction and EA, as there is evidence in the literature suggesting that psychological questionnaires might be a better tool for RED-S screening than endocrine markers (8).

Study design
This was a cross-sectional controlled laboratory study. With unchanged living and training conditions subjects reported energy intake (EI) by completing dietary diaries for 7 consecutive days (9) (Fig. 1) During this period, exercise energy expenditure (EEE) was monitored during all training units. After 7 days, blood samples were drawn and after 1 day of rest (on day 9), body composition was assessed and REE was measured, followed by three performance tests for determining basal performance. At the end of the study participants completed psychological questionnaires.

Participants
Eighteen (N = 18) males were invited to participate in this research. Inclusion criteria for participation in the study are presented in Table 1 and owchart of enrollment in Fig. 2. At the time of procedures will refrain from alcohol consumption and any drug or other substance use Complete all procedures and report any factors that could in uence changes in blood values or performance (lack of motivation due to psychological factors, factors in between measurements that could in uence results etc.) All participants needed to sign an informed consent before commencing all protocols for allowing data to be gathered and analyzed anonymously. This research complied with the declaration of Helsinki. National medical ethical approval was acquired before the start of the study (No. 0120-202/2020/5).

Subject involvement
Subjects were invited to participate in the study through national cycling and triathlon organizations, professional cycling team's coaches. The information was also disseminated through faculty's laboratory, where national best endurance athletes regularly perform various testings.
Subjects were informed of all procedures and were selected based on inclusion criteria, high motivation and compliance.

Procedures
Energy availability calculation All procedures were carried during 9 days period ( Fig. 1). Participant EA was measured bycompleting dietary diaries for 7 consecutive days (9). All participants received detailed information on how to complete the diary and how to weight food or measure its quantity with the help of cups and other measuring tools.
They were asked to provide photographic evidence of all food and liquid ingested in that time. EI data was analyzed with Foodworks 9 Professional Edition (version 9.0.3973, Xyrix Software, Australia). During this same period EEE was estimated from heart rate using wearable heart rate monitors during all exercise sessions (Polar V800, Polar Electro, Kempele, Finland). EA was calculated as EA=(EI-EEE)/FFM (FFM = fat free mass).

Performance testing
To test performance, three different tests were chosen to assess vertical jump height (explosive power of lower extremity), motor task execution time (agility) and maximal aerobic capacity (aerobic endurance). The details of warm-up protocol, countermovement jump (CMJ), agility t-test and the incremental test can be found in the Additional le 1.
First, CMJ test was performed using a bilateral force plate system (Type 9260AA, Kistler Instrumente AG, Winterthur, Switzerland) with Kistler MARS software (S2P Ltd., Ljubljana, Slovenia) to acquire ground reaction force. Each subject has performed three to ve maximal counter movement jumps before the testing.
Second, to asses motor task execution time, validated modi ed agility t-test was used, as described by

Blood samples
On day 8, venous blood samples were drawn in the morning at 9am in a fasted state to assess complete blood count, ferritin, serum iron (Fe), triiodothyronine (T3), thyroid stimulating hormone (TSH), morning testosterone, fasting insulin, insulin like growth factor 1 (IGF-1) and 9am cortisol.
to body composition measurement, participants received instructions how to be adequately hydrated to enable precise measurement of FFM and body fat percentage that were used in further analysis.
Resting energy expenditure assessment REE was measured with indirect calorimetry (V2 mask (Hans Rudolph, USA), K5 (Cosmed, Albano Laziale, Rome, Italy) with Quark 8.1. PC software support) based on the Weir Eq. (12,13). The measurement was performed in a thermoneutral environment, in silence, between 6.00 and 9.00 a.m., after 12 hours of fasting (14). It lasted 30 min and the nal 20 min were used for REE measurement (15). During measurement respiratory quotient was monitored since measures under 0.70 or above 1 suggest protocol violations or inaccurate gas measurement (15). To obtain predicted REE (pREE), a Harris-Benedict equation was used (16). The mREE/pREE ratio was then calculated for further analysis.

Psychological assessment
The Three Factor Eating Questionnaire (TFEQ-R18) and Well-being questionnaire were used for psychological assessment (17,18). TFEQ-R18 was used to detect early changes in eating behaviors and has three subscales including cognitive restriction (CR), disinhibition and susceptibility to hunger, with higher scores indicating greater eating disturbances in participant. The subscale of interest was CR. General well-being was assessed by a simple questionnaire as recommended by Hooper and Mackinnon (1995) including six subjective ratings (fatigue, sleep, stress, muscle soreness, mood and morning erections) on a 1-5 scale. The last item about morning erection was to the original set as proposed by study on professional rugby players (20) (Additional le 2).

Data analysis
All data were analyzed using the IBM SPSS Software for Windows (version 21, SPSS Inc., Armonk, New York, USA). Categorical variables are displayed as numbers and percentages, and numeric variables are presented as means and standard deviations. All numeric variables were rst checked for normality of distribution with Shapiro-Wilk's test. Pearson's correlation coe cient was computed to assess the relationship between EA and obtained performance, laboratory, body composition and psychological parameters. Based on the EA value the subjects were later divided into two subgroups (with EA ≥ 30 kcal/kg FFM/day and with EA < 30 kcal/kg FFM/day). The possible differences in performance, blood, anthropometric, body composition and psychological parameters between those two groups were analyzed using the t-test for independent samples. The signi cance level was set at p-values < 0.05 for all calculations.

Results
The means and standard deviations of all obtained parameters are presented in Table 2.
We report that results support that this was a sample of well-trained healthy endurance athletes. Average training time was 2 hours and 4 minutes (80.6% spent cycling, 9.3% running and 10.1% swimming). Furthermore, mean VO 2max showed that 25% of participants are at the performance level 3 (VO 2max between 55.0 and 64.9 ml/min/kg), 33.3% at the performance level 4 (VO 2max between 65-71 ml/min/kg) and 41.6% are professional athletes with performance level 5 (VO 2max > 71 ml/min/kg) (Additional le 3). In addition to endurance performance, we report good jumping capacity as well as the agility with motor task execution times within the normal range expected for the sex and age of the participants (mean time 6.49 seconds). Hormone levels were within the normal range without any pathological ndings, with only one participant with testosterone levels in the lower quartile reference range. Serum iron levels were also in the healthy range, and there were no pathological ndings in the complete blood count (not presented in Table 2).
Our main ndings are related to energy and metabolic parameters in this healthy, well-trained sample of endurance athletes. EI was 3078 kcal and EEE was 1173 kcal. Calculated energy availability was 29.5 kcal kg FFM (95% CI 25.6 to 33.4).
Pearson correlation analysis did not show any signi cant correlations between anthropometric parameters, performance parameters, hormone levels or any other blood parameter and EA. However, we found that EA has signi cant negative correlation with EEE (r = − .618, p = .016) and that EI had signi cant positive correlation with CR subscale of TFEQ (r = .559, p = .03), while it was negatively correlated with mREE (r = − .578, p = .025) and mREE/pREE ratio (r = − .549, p = .032). Nine (n = 9; 75%) participants reported critical CR, which is any value ≥ 13 indicating possible LEA presence.
A t-test for independent samples was used to compare subgroups of subjects with EA ≥ 30 kcal/kg FFM/day (n = 6) and subjects with EA < 30 kcal/kg FFM/day (n = 6). There were no signi cant differences in any of the compared parameters.

Discussion
This study design was set to measure actual EA in healthy endurance athletes in the pre-race period. The mean EA measured in this study supports the theory that the threshold for LEA in endurance male athletes might be below the threshold set for females. In addition, we con rmed cognitive restriction could be useful for early detection of energy conservation. The high cognitive restriction as measured in our sample stresses the need of eating behavior screening in endurance athletes in order to reduce the risk of disordered eating patterns and eating disorders.

Energy markers
As expected, anthropometric measurements showed the subjects were lean (body fat M = 10.2%±2.5%). They trained more than 2 hours per day, expending 1173 kcal on daily average. According to physiological parameters obtained at the incremental test, subjects were trained (n = 3), well-trained (n = 4) and professional athletes (n = 5) (10). Their mean EA was low -it was 29.5 kcal/kg FFM/day and according to Melin et al. (21) it would be considered clinically relevant and symptomatic. Two third of subjects (66.6%) had EA < 30, which is in accordance with studies showing prevalence of LEA is high in athletes (3).
However, Fagerberg (2018) has suggested that in male athletes a prolonged EA < 25 kcal/kg FFM/day could be the critical value, although this paper was referring to strength and not endurance athletes. Our ndings support the Fagerberg proposal, but the true cut-off value of EA will have to be investigated more thoroughly in future studies, so that these predictions are supported by relevant evidence. Greater EEE was associated with lower EA (r = -618, p = .016). This situation raises concern as in pre-race season EEE should be coupled with su cient EI to ensure optimization of adaptation to training.
Mean mREE/pREE was 1.03, which was unexpected when considering mean EA was under proposed optimal EA value (≥ 40 kcal/kg FFM/day) for men (Melin et al., 2019). The ratio mREE/pREE has been suggested to be a potential screening marker for LEA. In this study, we suggest there are two possible reasons why we failed to show this association. The rst is that while mREE/pREE might indeed be correlated to LEA, we do not know the threshold for LEA. From our study we suspect the mean EA values were simply not low enough for the marker mREE/pREE to show energy conservation (i.e. mREE/pREE < 0.9 or even lower (23)). The second reason could be that greater sample size might reveal more since there was only one subject with mREE/pREE < 0.9 in our sample.
Cognitive restraint CR means that a person consumes less than they like to and does not tell anything about energy balance (24). Gibbs et al. (2013) showed that cognitive restriction ≥ 13 might be a useful marker for LEA in exercising women. We were not able to show an association between CR ≥ 13 and lower EA in our sample of male endurance athletes. This might be due to high mean CR in our sample. 75% of our participants had bigger values than 13, which is worrying since this is their eating behavior in the pre-race season preparation period when the pressure on optimal body mass is not yet high. The lowest body mass should be achieved in the competitive season. Secondly, subjects with greater EI had higher cognitive restriction (r = .599, p = .03). Expressing high CR could lead to disordered eating. It is known that when entering pre-race season preparation period, endurance athletes have high energy demands due to high training volume. This should be coupled with satisfactory EI in order to achieve good training adaptation and recovery. High CR in our sample suggests athletes might intentionally restrict their EI in order to achieve optimal body composition. Finally, the bigger cognitive restriction, the lower were mREE and mREE/pREE (r=-.578, p = .025 and r=-.549, p = .032, respectively). Cognitive restriction could thus be associated with energy conservation as measured by mREE and expressed by mREE/pREE.
What is the threshold for LEA?
In order to detect a possible threshold, we divided subjects into two groups separated by EA = 30 kcal/kg FFM/day. We failed to nd any differences in endurance, strength, agility performance or blood parameters. In addition, there were no differences in testosterone's functional health effects (morning erection). This indicates that EA = 30 kcal/kg FFM/day might not be the actual threshold for LEA in men as proposed before by Viner et al. (17). A lower threshold might be more accurate. Unfortunately, dividing subjects based on a threshold of 15 kcal/kg FFM/day as suggested by Koehler et al. (2016) was not possible for analysis since only one of the subjects reached EA < 15 kcal/kg FFM/day. The possibility that the threshold in men could be lower is also supported by blood test results. All measured parameters (full blood count, ferritin, serum Fe, T3, TSH, morning testosterone, fasting insulin, IGF-1, 9am cortisol) were normal in all participants, despite mean EA was lower than 30 kcal/kg FFM/day. In further research, inducing lower EA in the same participants should be the next step in order to nd if a lower threshold indeed shows differences in hormones and/or performance.

Limitatons
Authors would like to acknowledge that a more homogenous sample of only one endurance discipline would be more optimal. As known in this research area, nding participants that are willing to accept the high burden of EA measurement is challenging. Still, this is one of the largest sample sizes of trained endurance athletes when measured with objective EA methodology. This is why we believe this paper is a contribution to current knowledge. In addition, other individual characteristics of athletes in uence performance. A study comparing different EA in same individuals would be a more appropriate way to compare performance.

Conclusions
This paper suggests that psychological evaluation such as cognitive restriction in the TFEQ might be more appropriate where there are no signs of RED-S for assessing energy conservation and possible suboptimal EA. This study adds to our knowledge that threshold for LEA in men is indeed probably lower than 30 kcal/kg FFM/day. Further studies with objective measurement of EA in athletes with apparent LEA or laboratory induced LEA should be performed to determine the threshold for LEA or refute its existence. Practical application of this study is that coaches and their athletes might bene t from cognitive restriction assessment in order to prevent energy conservation.

List Of Abbreviations
[La] 5min -lactate concentration 5 minutes after the end of the test Ethics approval and consent to participate: National medical ethical approval was acquired before the start of the study (No. 0120-202/2020/5). All participants signed a written consent for their data to be analyzed and published anonymously.

Consent for publication: not applicable
Availability of data and materials: Data are available upon reasonable request Competing interests: The authors declare that they have no competing interests Funding: The study was funded by the Slovenian Research Agency (research grant: P5-0147). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript Figures Figure 1 The timeline of all procedures and measurements Figure 1 The timeline of all procedures and measurements   Scatterplots of performance parameters (PO -peak power output, RPO -relative power output, VO2max -maximal oxygen uptake, CMJ -countermovement jump)