Relative Energy Deficiency in Sport (REDs) and Its Effect on Health and Performance in Men: A Systematic Review of Cross-Sectional Studies.

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Alberto Valiño-Marques, José Manuel Jurado-Castro, Diego Domínguez-Balmaseda, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5571836/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background : Relative Energy Deficiency in Sport (REDs) poses a significant challenge to both health and performance in male athletes. This systematic review aimed to evaluate the effects of REDs on various health and performance parameters in male athletes. Methods : A comprehensive literature search was conducted up to April 2024, using four databases: PubMed, Scopus, Web of Science, and SPORTDiscus. A structured search was carried out following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines. Cross-sectional studies investigating the relationship between energy availability (EA) and metabolic and hormonal biomarkers, as well as athletic performance in male athletes, were included. The methodological quality of the selected studies was assessed using a modified version of the McMaster scale. Results : A total of 10 studies, comprising 308 participants, were included in this systematic review. Low energy availability was associated with significant reductions in resting metabolic rate (RMR) and RMR ratio, as well as increased cortisol levels and decreased athletic performance. However, no significant changes were observed in bone mineral density or in levels of testosterone, triiodothyronine, and insulin-like growth factor 1. Conclusions : REDs impacts male and female athletes differently, highlighting the need for further studies to determine the critical EA thresholds that trigger negative effects in male athletes. Registration: This systematic review was prospectively registered with the PROSPERO International prospective register of systematic reviews (PROSPERO registration ID number: CRD42024565897). male athlete low energy availability bone health performance endurance sport hypogonadism Figures Figure 1 Figure 2 Figure 3 Key Points - Relative Energy Deficiency in Sport (REDs) highlights how low energy availability (LEA) in athletes can disrupt key physiological systems, affecting health and performance. - LEA impacts metabolic and hormonal health in male athletes, leading to issues like reduced resting metabolic rate (RMR) and elevated cortisol, which can impair athletic recovery. - Differences in REDs between male and female athletes suggest unique thresholds and responses in men, underscoring the need for further research to establish LEA benchmarks specific to male athletes. BACKGROUND Participation in sports activities provides extensive health benefits [1]. However, some athletes may face adverse consequences if they fail to adequately meet their energy needs [2]. In this context, a state of low energy availability (LEA) may occur. Defined as the energy available for optimal physiological function after accounting for energy expended during exercise [2], energy availability (EA) is expressed in kcal/kg of fat-free mass (FFM) per day (Fig. 1 ). Table 1 Classification of Energy Availability (EA). Classification Considerations Male Female High EA For healthy body mass gain or body mass maintenance > 40 kcal/kg FFM > 45 kcal/kg FFM Optimal EA For body mass maintenance providing adequate energy for all physiological functions ≥ 40 kcal/kg FFM ≥ 45 kcal/kg FFM Subclinical LEA May be tolerated for short periods during a well-constructed weight-loss program 30–40 kcal/kg FFM 30–45 kcal/kg FFM Clinical LEA Health implications, including impairments in multiple body systems, training adaptation, and performance < 30 kcal/kg FFM < 30 kcal/kg FFM Adapted from Melin et al. (2019) [3]. Abbreviations: EA, Energy Availability; LEA, Low Energy Availability; FFM, Fat-Free Mass. Several levels of EA have been established based on mathematical calculations (Table 1 ) [3]. In women, the generally accepted LEA cutoff associated with Relative Energy Deficiency in Sport (REDs) outcomes is 30 kcal/kg FFM per day, though this threshold remains debated [3]. For men, however, the exact cutoff or range at which energy-related disorders occur is less clear but is thought to be lower, ranging between ~ 9 and 25 kcal/kg FFM [4]. There is considerable variability in the reported prevalence of LEA/REDs in both female (23-79.5%) and male (15–70%) athletes, according to the International Olympic Committee [2]. Other studies report prevalence rates between 22% and 58%, depending on the sport [5]. Ackerman and colleagues [6] found that 47.3% of female athletes presented with LEA, while another study reported a 47.2% prevalence in men [7]. Among national or world-class long-distance athletes, LEA was observed in 25% of men and 31% of women [8]. Despite similar prevalence rates, it is estimated that only 20% of studies conducted between 2018 and 2022 included male athletes as research subjects [2]. REDs is defined as a syndrome of physiological and/or psychological impairment affecting both female and male athletes, resulting from prolonged and/or severe problematic LEA [9]. Problematic LEA is associated with negative effects on the hypothalamic-pituitary-gonadal axis, metabolic hormones, immune function, bone health, performance, and lean body mass [2]. Thus, LEA has wide-ranging health consequences across multiple systems [2]. Compared to athletes with adequate EA, those with low EA are more likely to present metabolic, bone, cardiovascular, gastrointestinal, immunological, and hematological problems [6,10]. Despite the presence of narrative reviews on LEA and REDs in both men and women, there is a notable scarcity of systematic reviews. For instance, while McGuire et al. (2020) examined the prevalence of LEA in male athletes and its health effects [11], no systematic reviews have, to the best of the author’s knowledge, comprehensively evaluated the impact of REDs on both the health and performance of male athletes. Therefore, the main objective of this systematic review was to evaluate the effects of REDs on the health and performance of male athletes, specifically assessing metabolic and hormonal biomarkers. METHODS Search Strategy This systematic review evaluated the effects of REDs on health and performance through metabolic and hormonal biomarkers. A literature search was conducted from database inception up to April 16, 2024, using PubMed (MEDLINE), Scopus, Web of Science (WOS), and SPORTDiscus, with no year restriction applied to the search strategy. Search terms included a mix of Medical Subject Headings (MeSH) and free-text words for key concepts. The search terms were: ("relative energy deficienc*" OR "male athlete triad" OR "triad" OR "energy intake deficienc*" OR "low energy availability" OR "low energy intak*" OR "energy balance" OR "REDS" OR "LEA" OR "bone density" OR "bone mineral density" OR "stress fractur*" OR "hypogonadism" OR "hypothalamic-pituitary-gonadal axis" OR "disordered eating" OR "eating disorder") AND ("male athlet*") AND ("perfomanc*" OR "exercis*" OR "overtraining" OR "endurance" OR "running" OR "cycling" OR "swimming" OR "jockeys" OR "boxing" OR "rowers"). All titles and abstracts from the search were cross-checked for duplicates and missing studies. Titles and abstracts were screened for a subsequent full-text review. The search for published studies was independently performed by two different authors (AVM, FMO) and disagreements were resolved through discussions between them. Eligibility Criteria This systematic review followed the guidelines established by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 statement [12], and the protocol was prospectively registered in PROSPERO (CRD42024565897). The PICOS model [13] was used to determine the inclusion criteria: Participants: Adult men actively participating in sports disciplines. Intervention: Studies where EA was assessed, alongside measurements of metabolic and endocrine biomarkers or athletic performance tests. Comparator: When available, comparisons were made between metabolic and endocrine biomarkers or athletic performance tests across different EA levels. Outcomes: Metabolic biomarkers, such as resting metabolic rate (RMR), resting energy expenditure (REE), and bone mineral density (BMD). Endocrine biomarkers, including testosterone (TES), cortisol (COR), insulin-like growth factor 1 (IGF-1), and triiodothyronine (T3). Athletic performance, measured using jump tests, agility tests, and incremental exercise tests. Study design: Cross-sectional observational studies. Studies that did not meet the inclusion criteria or were review articles or grey literature were excluded. Study Selection and Data Extraction The screening and selection process for the articles obtained through the search was conducted independently by two authors (JJC, PVH) based on the inclusion criteria. Any potential disagreements were resolved by a third reviewer (JCG). Initally, duplicates were manually removed. Titles and abstracts of the articles were then reviewed, followed by a full-text evaluation of the studies that met the initial criteria. Data extraction from the selected articles was performed using a standardized table. Two reviewers (JJC, PVH) independently extracted data, with discrepancies resolved by a third reviewer (JCG). The following data were collected from eligible studies: authors and publication date, study duration, participant characteristics (age, body mass index (BMI), maximum aerobic capacity (VO 2 max), peak aerobic capacity (VO 2 peak), and exercise frequency), study objectives, outcome measures, and results. One author (JJC) extracted the mean (M), standard deviation (SD), and sample size data from tables in each paper. When necessary, authors were contacted to obtain missing data, and when this was not possible, mean and SD values were extrapolated from figures. Any disagreements were resolved by consensus between JJC and PVH or through third-party adjudication (JCG). Quality Assessment The methodological quality of the studies was evaluated using an adapted version of the McMaster University Risk of Bias Quality Form [15] by Sarmento et al. [14]. Two reviewers (LSN, ARS) independently assessed the studies, with a third reviewer (JCG) resolving any discrepancies. The articles were evaluated based on 16 quality criteria, including study purpose (item 1), relevance of the background literature (item 2), appropriateness of the study design (item 3), study sample (items 4 and 5), use of informed consent (item 6), clarity of outcome measures (items 7 and 8), description of the methods (item 9), results significance (item 10), data analysis (item 11), practical importance of the findings (item 12), description of dropouts (item 13), conclusions (item 14), practical implications (item 15), and identification of study limitations (item 16). The 16 criteria were evaluated using a binary scale (0/1). For two of these criteria (items 6 and 13) a " Not applicable" option was included, and in such cases, a score of 3 was assigned. A percentage score was calculated for each study to enable fair comparisons. Based on these scores, studies were categorized into one of three quality levels: (1) low methodological quality (score below 50%), (2) moderate methodological quality (score between 51% and 75%), or (3) excellent methodological quality (score above 75%). Table 2 presents the criteria used to assess the methodological quality of the studies included in this systematic review. TABLE 2. Quality criteria used to analyze the quantitative publications. 1 Was the study purpose stated clearly? 1=Yes 0=No 2 Was relevant background literature reviewed? 1=Yes 0=No 3 Was the design appropriate for the research question? 1=Yes 0=No 4 Was the sample described in detail? 1=Yes 0=No 5 Was sample size justified? 1=Yes 0=No 6 Was informed consent obtained? (if not described, assume No) 1=Yes 0=No (If not applicable, assume 3) 7 Were the outcome measures reliable? (if not described, assume No) 1=Yes 0=No 8 Were the outcome measures valid? (if not described, assume No) 1=Yes 0=No 9 Was method described in detail? 1=Yes 0=No 10 Were results reported in terms of statistical significance? 1=Yes 0=No 11 Were the analysis methods appropriate? 1=Yes 0=No 12 Was importance for the practice reported? 1=Yes 0=No 13 Were any dropouts reported? 1=Yes 0=No (If not applicable, assume 3) 14 Were conclusions appropriate given the study methods? 1=Yes 0=No 15 Are there any implications for practice given the results of the study? 1=Yes 0=No 16 Were limitations of the study acknowledged and described by the authors? 1=Yes 0=No RESULTS Participant and study characteristics A total of 834 results were identified after conducting searches in PubMed (n = 172), Scopus (n = 260), WOS (n = 284), and SPORTDiscus (n = 118). After removing duplicates (n = 436), these results were reduced to 398. Following title and abstract screening, 370 articles were excluded based on the inclusion criteria, leaving 28 articles for full-text review. After reviewing the full texts, 18 articles were excluded (see Supplementary Material), resulting in 10 articles [8,16–24] being included in this systematic review (Figure 2). Table 3 provides a detailed summary of the characteristics of the studies included in this review, covering aspects such as study duration, participant characteristics, study objectives, outcome measures, and results obtained. Of the 10 trials identified, all assessed EA in participants and measured metabolic and endocrine biomarkers, with two studies additionally conducting athletic performance tests [16,17]. TABLE 3. Characteristics of the studies investigating REDs in men (n = 10). Participant characteristics are presented as the mean ± standard deviation Reference Duration Participant characteristics Study objective Outcome measures Results Torstveit et al., (2018) [24] 4 days 31 highly trained endurance athletes (cyclists, triathletes, long-distance runners) Age: 34.7 ± 8.1 years old BMI: 22.3 ± 1.8 kg/m 2 Exercise: 8.7 ± 3.2 hours per week VO 2 peak: 66.4 ± 6.2 ml/kg/min Comparison of different parameters between groups with low or normal RMR Energy intake (weighing) Exercise energy expenditure (HR) Energy availability (EA) Body composition (DEXA) RMR (indirect calorimetry) Blood markers (GLU, COR, TES, T3) measured after fasting. No exercise in the previous 24 hours EA = 41 (normal RMR) and 37 (low RMR) kcal/kg FFM There were no significant differences in EA between groups, but the group with lower RMR had a lower within-day energy balance (WDEB), which was associated with higher cortisol levels and a decrease in the TES:COR ratio Heikura et al., (2018) [8] 7 days 24 middle- and long-distance runners (6 LEA and 18 moderate EA) Age: 26.9 ± 3.8 y 27.2 ± 4.2 years old Exercise: 130 ± 4.3 y 107 ± 25 km per week Evaluate EA, BMD, metabolic and reproductive hormonal function, and prevalence of injuries or illness during a precompetitive training period with high volume/intensity Energy intake (dietary record) Exercise energy expenditure (training log) Energy availability (EA) Body composition (DEXA) BMD (DEXA) of the whole body, femoral neck, and lumbar spine Blood markers (TES, IGF-1, T3, INS) measured after fasting. Previous exercise not specified Questionnaire on injuries and illness in the last 12 months EA = 21 (LEA) and 37 (moderate EA) kcal/kg FFM Lower TES in the LEA group All other parameters were the same between groups Torstveit et al., (2019) [23] 4 days 53 highly trained endurance athletes (cyclists, triathletes, runners) (12 LEA and 41 moderate EA) Age: 35.3 ± 8.3 years old BMI: 22.9 ± 1.9 kg/m 2 Exercise: 9.5 ± 3.4 hours per week VO 2 peak: 65.3 ± 5.9 ml/kg/min Evaluate the associations between exercise dependence and biomarkers of REDs Energy intake (weighing) Exercise energy expenditure (HR) Energy availability (EA) Body composition (DEXA) RMR (indirect calorimetry) Blood markers (GLU, INS, COR, TES, IGF-1, T3) measured after fasting. No intense exercise in the previous 24 hours Questionnaire on exercise dependence and eating disorders (EDE-Q) EA = 37.7 kcal/kg FFM Elevated COR in the group with higher exercise dependence All other parameters were the same between groups Lee et al., (2020) [19] 1 month 12 soccer players (5 LEA and 7 moderate EA) Age: 19-19.5 years old BMI: 22.5 ± 1.2 kg/m 2 VO 2 max: 54.9 ± 5.7 ml/kg/min Investigate the status of EA and its relationship with metabolic and hormonal status and bone metabolism Energy intake (dietary record) Exercise energy expenditure (HR) Energy availability (EA) Body composition (DEXA) BMD (DEXA) whole body REE (indirect calorimetry) Blood markers (FSH, LH, TES, E2, GH, IGF-1, COR, T3, LEP) measured after fasting. No intense exercise in the previous 24 hours Questionnaire on eating disorders (EAT-26) EA = 22.4 (LEA) and 38.7 (moderate EA) kcal/kg FFM Lower REE, IGF-1, and FSH levels in the LEA group All other parameters were the same between groups Taguchi et al., (2020) [22] 3 days 6 long-distance runners Age: 19-21 years old BMI: 19.2 ± 1.1 kg/m 2 Exercise: 132.2 ± 27.3 km per week VO 2 max: 58.6 ± 1.7 ml/kg/min Investigate the status of EA and its relationship with metabolic and hormonal status and bone metabolism Energy intake (food record) Exercise energy expenditure (HR) Energy availability (EA) Body composition (DEXA) BMD (DEXA) whole body REE (indirect calorimetry) Blood markers (T3, IGF-1, LH, E2, TES, Vit D) measured after fasting. Previous exercise not specified Questionnaire on eating disorders (EAT-26) EA = 18.9 kcal/kg FFM Low BMD and Vitamin D deficiency Jurov et al., (2021) [16] 7 days 12 highly trained endurance athletes Age: 27.5 ± 5.7 years old VO 2 max: 67.49 ± 6.74 ml/kg/min Measure EA in endurance athletes and its relationship with hormonal status and performance Energy intake (dietary record) Exercise energy expenditure (HR) Energy availability (EA) Body composition (BIA) REE (indirect calorimetry) Blood markers (FER, Fe, T3, TES, COR, INS, IGF-1) measured after fasting. Previous exercise not specified Psychological questionnaire (TFEQ-R18) Performance test (CMJ, T-test, incremental fatigue test) EA = 29.5 kcal/kg FFM There were no differences in performance, blood markers, or psychological assessment when dividing participants based on EA > or < 30 kcal/kg FFM Moore et al., (2021) [20] 14 days 14 recreational endurance athletes (triathletes, long-distance runners, and obstacle runners) Age: 26.4 ± 4.2 years old BMI: 21.9 ± 1.8 kg/m 2 Exercise: > 10 hours per week VO 2 max: 62.3 ± 6.9 ml/kg/min Evaluate EA, BMD, TES levels, and risk of eating disorders in endurance athletes Energy intake (dietary record) Exercise energy expenditure (HR) Energy availability (EA) Body composition (BIA) BMD (DEXA) whole body RMR (indirect calorimetry) Blood markers (TES) measured after fasting. No exercise in the previous 24 hours Questionnaire on eating disorders (EDI-3) EA = 27.6 kcal/kg FFM TES and BMD within normal values 64.3% presented with LEA 35% at risk of eating disorder Lane et al., (2021) [18] 7 days 60 recreational endurance athletes (cyclists, triathletes, runners) Age: 43.4 ± 11.6 years old Exercise: 10.9 ± 2.7 hours per week VO 2 max: 55.7 ± 8.0 ml/kg/min Evaluate EA and REDs risk factors in endurance athletes Energy intake (dietary record) Exercise energy expenditure (HR) Energy availability (EA) Body composition (DEXA) BMD (DEXA) whole body, femoral neck, and lumbar spine RMR (indirect calorimetry) Blood markers (GH, T3, TSH, TES, LH) measured after fasting. No exercise in the previous 24 hours EA = 28.7 kcal/kg FFM Blood markers and BMD within normal values Moris et al., (2022) [21] 7 days 44 athletes from various disciplines (cross country, soccer, wrestling, basketball, track, golf, baseball) Age: 20.4 ± 0.2 years old BMI: 25.3 ± 1.3 kg/m 2 Determine EA, BMD, and hormonal status Energy intake (dietary record) Exercise energy expenditure (accelerometer) Body composition (DEXA) BMD (DEXA) whole body, femoral neck, and lumbar spine RMR (indirect calorimetry) Blood markers (LH, INS, LEP, E2, TES) measured after fasting. No exercise in the previous 12 hours EA by sports (kcal/kg FFM): - Cross country: 42.03 - Soccer: 25.69 - Wrestling: 28.35 - Basketball: 27.37 - Track: 35.19 - Golf: 27.9 - Baseball: 29.88 15% had LEA, 0% had low BMD, 28% had low TES All other parameters were within normal values Kalpana et al., (2023) [17] 1 day 52 national level kho-kho players Age: 23.63 ± 3.77 years old Determine the prevalence of LEA and the impact of REDs on health and performance Dietary intake (dietary record) Exercise energy expenditure (METs) Energy availability (EA) Body composition (skinfold measurements) BMD (DEXA) whole body and lumbar spine RMR (Cunningham formula) Blood markers (Ca, Vit D, T3, Hb, ALB, CREA, SGOT, SGPT) measured after fasting. Previous exercise not specified Questionnaire on eating disorders (EAT-26) Performance test (vertical jump, agility test, sprint test) EA = 14.62 (LEA) and 51.69 (moderate EA) kcal/kg FFM 46% presented with LEA The LEA group had lower BMD, poorer sleep quality, and lower performance in agility tests All other parameters were the same between groups Legend: Abbreviations: ALB, Albumin; BIA, Bioelectrical Impedance Analysis; BMD, Bone Mineral Density; BMI, Body Mass Index; Ca, Calcium; CMJ, Countermovement Jump; COR, Cortisol; CREA, Creatinine; DEXA, Dual-Energy X-ray Absorptiometry; EA, Energy Availability; E2, Estradiol; EDs, Eating Disorders; Fe, Iron; FER, Ferritin; FSH, Follicle-Stimulating Hormone; GH, Growth Hormone; GLU, Glucose; Hb, Hemoglobin; HR, Heart Rate; IGF-1, Insulin-like Growth Factor 1; INS, Insulin; LEP, Leptin; LH, Luteinizing Hormone; METs, Metabolic Equivalent of Task; REE, Resting Energy Expenditure; RMR, Resting Metabolic Rate; SGOT, Aspartate Aminotransferase; SGPT, Alanine Aminotransferase; TES, Testosterone; T3, Triiodothyronine; TSH, Thyrotropin; VO 2 max , Maximum Aerobic Capacity; V O2 peak , Peak Aerobic Capacity; WDEB, Within-Day Energy Balance. In total, 308 participants were evaluated across the 10 cross-sectional studies included in this systematic review. All participants were active males involved in various sports disciplines such as cycling [18,23,24], triathlon [18,20,23,24], middle- and long-distance running [8,16,18,20,22–24], obstacle racing [20], soccer [19,21], basketball [21], cross country [21], wrestling [21], golf [21], baseball [21], and kho-kho [17]. The characteristics of the participants varied, with ages ranging from 19 [19,22] to 43.4 years [18], BMI ranging from 19.2 [22] to 25.3 kg/m² [21], VO 2 max ranging from 54.9 [19] to 67.49 mL/kg/min [16], and VO 2 peak ranging from 65.3 [23] to 66.4 mL/kg/min [24]. Reported weekly exercise duration ranged from 8.7 hours [24] to 10.9 hours [18], with training distances between 107 [8] and 132 km per week [22]. To evaluate EA, the 10 cross-sectional studies used different methodological techniques. Energy intake was estimated through dietary records in eight studies [8,16–22] and food weighing in two studies [23,24]. Exercise energy expenditure was measured using heart rate monitors in seven studies [16,18–20,22–24], training logs and Metabolic Equivalent of Task (METs) in two studies [8,17], and accelerometry in one study [21]. Body composition was assessed using dual-energy X-ray absorptiometry (DEXA) in seven studies [8,18,19,21–24], bioelectrical impedance analysis (BIA) in two studies [16,20], and anthropometry with skinfold measurements in one study [17]. Additionally, resting metabolic rate (RMR) was analyzed in six studies [17,18,20,21,23,24], and resting energy expenditure (REE) was measured in three studies [16,19,22]. Eight studies used indirect calorimetry to calculate these parameters [16,18–24], while one study estimated them theoretically using the Cunningham formula [17]. Bone mineral density (BMD) was evaluated using DEXA in seven studies [8,17–22], covering the whole body, lumbar spine, and femoral neck. Various biomarkers were analyzed through fasting blood tests to assess participants' health, including testosterone, cortisol, insulin-like growth factor 1 (IGF-1), triiodothyronine (T3), growth hormone (GH), glucose, insulin, and vitamin D. Additionally, several studies included questionnaires to evaluate eating disorders [17,19,20,22,23], psychological factors [16], exercise dependence [23], and incidence of injury and illness within the past 12 months [8]. Two studies further assessed athletic performance [16,17] using tests such as countermovement jumps, agility tests, and incremental exercise tests to exhaustion. Quality Assessment Table 4 provides the analysis of the methodological quality of the 10 studies included in this systematic review. Methodological quality scores ranged from 81.25% to 100%, with an average score of 90.6%. All 10 articles demonstrated excellent methodological quality, with none showing moderate or low quality. TABLE 4. Methodological quality analysis of the studies included in the systematic review (n = 10). Reference 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 Total score Torstveit et al., (2018) [24] 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 87.50% Heikura et al., (2018) [8] 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 87.50% Torstveit et al., (2019) [23] 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 93.75% Lee et al., (2020) [19] 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 93.75% Taguchi et al., (2020) [22] 1 1 1 1 0 1 1 1 1 0 1 1 0 1 1 1 81.25% Jurov et al., (2021) [16] 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 93.75% Moore et al., (2021) [20] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 100% Lane et al., (2021) [18] 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 87.50% Moris et al., (2022) [21] 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 93.75% Kalpana et al., (2023) [17] 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 87.50% Legend: The percentage score reflects the overall methodological rigor, evaluated using the adapted McMaster Risk of Bias Quality Form. DISCUSSION The primary objective of this systematic review was to synthesize the effects of REDs on various health and performance parameters in male athletes, aiming to provide a comprehensive understanding of the health and performance consequences of REDs within this population. LEA affects multiple systems as the body adapts to conserve energy, disrupting hormonal pathways and leading to a cascade of interrelated physiological consequences [5]. LEA has been identified as the primary driver of REDs syndrome [2], with its main effects on the health and performance of male athletes summarized in Figure 3. The EA across studies ranged from 14.6 to 51.6 kcal/kg FFM [17]. In seven out of the 10 studies, groups of athletes with LEA were identified [8,16–20,22]. These EA values are comparable to those reported in other observational studies involving different athletic populations, such as 11.09 kcal/kg FFM in endurance athletes [25], 18.8 kcal/kg FFM in competitive cyclists [26], 20 kcal/kg FFM over eight weeks in a Taekwondo competitor [27], 27.2 and 45.4 kcal/kg FFM in long-distance runners [28], and 36 kcal/kg FFM in race walkers, middle-, and long-distance runners [29]. The effects of REDs on athletes' health were evaluated using metabolic and hormonal biomarkers such as RMR or REE, BMD, testosterone, cortisol, T3, and IGF-1. RMR and REE assess resting energy expenditure, representing the minimal energy required for essential physiological functions [30]. The RMR ratio compares RMR measured through indirect calorimetry with a theoretical predicted RMR, calculated using the Cunningham formula [31,32]. In Torstveit et al. (2018), all participants had an RMR ratio < 0.9, with significant differences between low- and normal-RMR groups. No significant differences in EA were found between groups, but those with lower RMR had higher cortisol levels and a reduced testosterone:cortisol ratio [24]. In another study by Torstveit et al. (2019), 72% of participants had an RMR ratio < 0.9, despite an EA of 37 kcal/kg FFM. A low RMR ratio has been suggested as a marker of LEA in women [31,32] and may indicate adaptive thermogenesis resulting from dysregulated cellular thermogenesis [33]. Similarly, Lee et al. (2020) found that soccer players with LEA (22.4 kcal/kg FFM) had lower levels of REE compared to players with moderate EA (38.7 kcal/kg FFM) [19]. Meanwhile, Langan-Evans et al. (2020) observed no significant changes in RMR or RMR ratio over eight weeks at an EA of 20 kcal/kg FFM; however, during a five-day tapering phase with EA < 10 kcal/kg FFM, both RMR and RMR ratio were notably reduced (-257 kcal/day and < 0.9, respectively) [27]. In female athletes, LEA has been associated with menstrual dysfunction and impaired bone health [31,34]. Among male athletes, however, cross-sectional studies [35] suggests that low BMD is primarily found in those participating in sports that emphasize low body mass and who experience multiple LEA-related risk factors, such as low BMI, repeated episodes of rapid weight loss, or eating disorders. The American College of Sports Medicine defines low BMD as a Z-score between -1.0 and -2.0 relative to the reference population [34] . In Taguchi et al. (2020), long-distance runners with EA of 18.9 kcal/kg FFM exhibited low BMD (Z score = -1.1) and vitamin D deficiency [22]. In kho-kho players with LEA (14.62 kcal/kg FFM), BMD was significantly lower than in players with high EA (51.69 kcal/kg FFM) [17]. However, in the remaining studies included in this review where BMD was evaluated [8,18–21], no participants exhibited low values. Additionally, Lee et al. (2020) found no group differences in BMD after adjusting for EA [19]. Recent studies have further explored bone health and EA among athletes [26–29,36]. None of these studies found low BMD values, even at LEA levels of 20 kcal/kg FFM over eight weeks and < 10 kcal/kg FFM over five days [27]. For instance, runners with high weekly training loads (83 km/week) and LEA (27.2 kcal/kg FFM) were found to have low TES levels, yet most did not present with significantly reduced BMD, though individual cases of osteopenia in specific sites were noted [28]. Similarly, competitive cyclists maintained stable BMD despite experiencing chronic LEA (18.8 kcal/kg FFM) across a 10-month season, even with generally low micronutrient intake [26]. In Papageorgiou et al. (2017), LEA (15 kcal/kg FFM) did not affect bone formation and resorption markers in men, in contrast to findings in women where LEA was associated with reduced bone formation and increased resorption [36]. Testosterone plays a crucial role in anabolic processes and long-term adaptations in muscle mass and function [37]. Low testosterone under LEA conditions may contribute to a catabolic state, particularly during consecutive days of training. Despite this, eight of nine reviewed studies [16,18–24] reported testosterone within physiological ranges. However, Heikura et al. (2018), observed significantly lower testosterone levels in the LEA group (21 kcal/kg FFM) compared to the moderate EA group (37 kcal/kg FFM) [8]. Likewise, Moris et al. (2022) found low testosterone levels in 28% of participants [21]. While LEA-induced reductions in testosterone of 10-40% are well-documented, these values typically remain within normal clinical ranges [35]. Several intervention studies have further evaluated the effects of LEA on testosterone [4,33,38,39], with two short-term interventions finding lower testosterone when EA was halved over 14 days (17.3 kcal/kg FFM) [4] and when EA was set at 18.9 kcal/kg FFM for three days [39]. Elevated plasma cortisol is common in athletes with high training loads and inadequate recovery [28]. Four studies in this review measured plasma cortisol levels [16,19,23,24]. Torstveit et al. (2018) reported higher cortisol levels and a lower testosterone:cortisol ratio in participants with reduced RMR [24], potentially impairing recovery and increasing the risk of overtraining and injury [40]. In Torstveit et al. (2019), higher exercise dependence correlated with elevated cortisol, with 6% of participants showing increased levels [23]. In contrast, cortisol levels remained within normal ranges in the other two studies, with no significant intergroup differences [16,19]. Although several studies have explored the relationship between cortisol and EA [4,27,28,38], none reported significant group differences or elevated cortisol levels due to LEA alone. T3, a key regulator of energy metabolism, has been associated with low REE in female athletes with amenorrhea [41]. However, the eight studies in this review that examined T3 consistently found normal ranges [8,16–19,22–24]. Moreover, other intervention studies did not detect significant differences in T3 across EA groups [4,33,36,38]. Regarding IGF-1, several studies observed stable concentrations in male athletes [4,36,38,42]. However, Kojima et al. (2020) noted lower IGF-1 in athletes with LEA (18.9 kcal/kg FFM) [39], and Murphy and Koehler (2020) reported anabolic resistance under LEA (15 kcal/kg FFM), evidenced by increased in GH and reduced IGF-1 levels [42]. Nonetheless, the five studies included in this review that analyzed IGF-1 reported normal plasma levels [8,16,19,22,23]. In terms of athletic performance, the effects of LEA varied by exposure duration. Short- to medium-term LEA had neutral or positive impacts, while long-term LEA negatively affected performance [43]. Two studies assessed athletic performance alongside EA. Jurov et al. (2021) found no significant correlation between performance variables and EA [16], whereas Kalpana et al. (2024) reported poorer performance in agility tests in LEA participants (14.62 kcal/kg FFM), though no group differences were found in strength and speed tests [17]. Intervention studies that evaluated athletic performance demonstrated reductions in applied power, explosive power, anaerobic threshold, and respiratory compensation point after a 50% reduction in EA over 14 days (EA = 17.3 kcal/kg FFM) [4], and lower explosiveness in vertical jumps after a 25 % reduction in EA over 14 days (EA = 22.4 kcal/kg FFM) [38]. However, no differences in time to fatigue were observed during a treadmill test at 70 % VO 2 max when EA was set at 18.9 and 52.9 kcal/kg FFM for three days [39]. Limitations, Strengths and Future Recommendations. This systematic review has several limitations. First, only observational studies were included, which limits the ability to establish causal relationships between LEA and its effects on health and performance in male athletes. Methodological variability in estimating energy intake, energy expenditure, and body composition complicates direct comparisons. Additionally, data collection over just a few days may not adequately capture athletes' habitual practices, and reliance on self-reporting energy intake can introduce considerable potential for error. Most of the studies had short- to medium-term durations, emphasizing the need for long-term research to evaluate the chronic effects of different LEA levels on health and performance in male athletes. Small sample sizes and a lack of formal sample size calculations in some studies may also have affected statistical power and the robustness of the findings. While the studies included athletes from various sports, endurance sports were predominant, potentially limiting the generalizability of the findings. Furthermore, the thresholds used to define low and optimal EA ranged from 20 to 30 kcal/kg FFM, making it difficult to compare results across studies due to a lack of a standardized criterion. Another key limitation is the variability in the training levels of participants across studies. While some studies focused on elite or highly trained athletes, others included a broader range of athletic experience. This heterogeneity further limits the generalizability of the findings, as athletes with varying fitness levels may respond differently to LEA. Despite these limitations, this systematic review provides valuable insights into the current understanding of REDs in male athletes. There is a clear need for medium- and long-term studies using standardized methodologies to evaluate energy intake and expenditure, body composition, athletic performance, and metabolic and hormonal biomarkers. Such standardization will improve comparability across studies and ensure more robust conclusions about the impact of LEA on male athletes. Future research should also focus on identifying specific LEA thresholds that trigger adverse health and performance effects in men. Establishing precise cutoffs will help in early detection and intervention, which is crucial for developing effective strategies to prevent REDs-related issues in male athletes, particularly those in high-energy-demand sports. Practical applications of this review include the potential to inform coaching staff, nutritionists, and sports medicine professionals on the importance of monitoring energy availability, hormonal health, and performance markers across training cycles. By closely tracking these variables, practitioners can better protect athletes from the negative impacts of REDs and optimize performance outcomes. CONCLUSIONS In summary, LEA can have significant adverse effects on basal RMR, the RMR ratio, cortisol levels, and athletic performance. However, BMD and levels of testosterone, T3, and IGF-1 appeared stable across a wide range of EA values in the studies reviewed. This suggests that the effect of REDs on health and performance in male athletes may differ from those observed in female athletes, highlighting the need for further research to determine the specific thresholds at which LEA causes the most detrimental impacts in men. Given the growing focus on REDs in male athletes, future research should explore not only physiological biomarkers but also the psychological aspects of low energy availability, which may further influence both performance and overall well-being. Understanding these complex interactions will help shape more effective prevention and intervention strategies. Abbreviations REDs: Relative Energy Deficiency in Sport PRISMA: Preferred Reporting Items for Systematic Review and Meta-Analyses EA: Energy Availability LEA: Low Energy Availability RMR: Resting Metabolic Rate REE: Resting Energy Expenditure BMD: Bone Mineral Density TES: Testosterone COR: Cortisol IGF-1: Insulin-like growth factor 1 T3: Triiodothyronine VMI: Body Mass Index FFM: Fat-Free Mass WOS: Web of Science MsSH: Medical Subject Headings VO 2 max: Maximum aerobic capacity VO 2 peak: Peak aerobic capacity M: mean SD: Standard Deviation DEXA: dual-energy X-ray absorptiometry METs: Metabolic Equivalent of Task GH: Growth Hormone ALB: Albumin BIA: Bioelectrical Impedance Analysis Ca: Calcium CMJ: Countermovement Jump CREA: Creatinine E2: Estradiol EDs: Eating Disorders Fe: Iron FER: Ferritin FSH: Follicle-Stimulating Hormone GLU: Glucose Hb: Hemoglobin HR: Heart Rate INS: Insulin LEP: Leptin LH: Luteinizing Hormone SGOT: Aspartate Aminotransferase SGPT: Alanine Aminotransferase TSH: Thyrotropin WDEB: Within-Day Energy Balance. Declarations Ethics Approval and Consent to Participate Not applicable. Consent for Publication Not applicable. Availability of Data and Materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding No funds, grants, or other support was received. Author´s contributions All authors contributed to the conception of the systematic review. Study concept and design AVM and FMO. Database searches and article identification AVM and FMO. Data extraction was conducted by JJC and PVH, and subsequently checked by JCG. Methodological quality assessments were conducted by LSN and ARS, and then confirmed by JCG. Initial draft of the manuscript AVM, MLM, JCG, PVH, PLS, FMO. Critical revision of the manuscript PCB, AJSO, DDB, KRF, GMA, ABC and RLH. All authors read and approved the final manuscript. Acknowledgments Not applicable. 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Direct and indirect impact of low energy availability on sports performance. Scandinavian Med Sci Sports. 2024 Jan;34(1):e14327. Supplementary Files SupplementarymaterialFinalVersion.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 07 Apr, 2025 Reviewers invited by journal 11 Feb, 2025 Editor assigned by journal 04 Dec, 2024 First submitted to journal 03 Dec, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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depicting the identification, screening, and selection processes for studies included in the review. *Details of excluded publications and reasons for exclusion are provided in the supplementary material.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5571836/v1/1e590aec23fec4cbbc35daaf.png"},{"id":73051553,"identity":"406ad0cd-2a12-4092-af91-e3c46418e083","added_by":"auto","created_at":"2025-01-06 09:28:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":130377,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of low energy availability (LEA) on hormonal and metabolic biomarkers and athletic performance in male athletes. Created using BioRender. Abbreviations: TES, Testosterone; COR, Cortisol; IGF-1, Insulin-like Growth Factor 1; T3, Triiodothyronine.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5571836/v1/ffae44c685e8687af6358eda.png"},{"id":73051841,"identity":"28fe1e24-3d27-4da7-ae3a-9d6ad3c68953","added_by":"auto","created_at":"2025-01-06 09:36:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1060196,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5571836/v1/72cdea0f-9ab9-4f61-9161-6b3ce2a6bad8.pdf"},{"id":73049652,"identity":"c71c3ddc-71ef-4d5f-bca5-d0c601cb41c7","added_by":"auto","created_at":"2025-01-06 09:20:41","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":41624,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialFinalVersion.docx","url":"https://assets-eu.researchsquare.com/files/rs-5571836/v1/afe71e242d8855480e6a0c57.docx"}],"financialInterests":"","formattedTitle":"Relative Energy Deficiency in Sport (REDs) and Its Effect on Health and Performance in Men: A Systematic Review of Cross-Sectional Studies.","fulltext":[{"header":"Key Points","content":"\u003cp\u003e- Relative Energy Deficiency in Sport (REDs) highlights how low energy availability (LEA) in athletes can disrupt key physiological systems, affecting health and performance.\u003c/p\u003e\u003cp\u003e- LEA impacts metabolic and hormonal health in male athletes, leading to issues like reduced resting metabolic rate (RMR) and elevated cortisol, which can impair athletic recovery.\u003c/p\u003e\u003cp\u003e- Differences in REDs between male and female athletes suggest unique thresholds and responses in men, underscoring the need for further research to establish LEA benchmarks specific to male athletes.\u003c/p\u003e"},{"header":"BACKGROUND","content":"\u003cp\u003eParticipation in sports activities provides extensive health benefits [1]. However, some athletes may face adverse consequences if they fail to adequately meet their energy needs [2]. In this context, a state of low energy availability (LEA) may occur. Defined as the energy available for optimal physiological function after accounting for energy expended during exercise [2], energy availability (EA) is expressed in kcal/kg of fat-free mass (FFM) per day (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification of Energy Availability (EA).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClassification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConsiderations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh EA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFor healthy body mass gain or body mass maintenance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;40 kcal/kg FFM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;45 kcal/kg FFM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOptimal EA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFor body mass maintenance providing adequate energy for all physiological functions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40 kcal/kg FFM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;45 kcal/kg FFM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubclinical LEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMay be tolerated for short periods during a well-constructed weight-loss program\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u0026ndash;40 kcal/kg FFM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u0026ndash;45 kcal/kg FFM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical LEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth implications, including impairments in multiple body systems, training adaptation, and performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;30 kcal/kg FFM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;30 kcal/kg FFM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eAdapted from Melin et al. (2019) [3]. Abbreviations: EA, Energy Availability; LEA, Low Energy Availability; FFM, Fat-Free Mass.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSeveral levels of EA have been established based on mathematical calculations (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) [3]. In women, the generally accepted LEA cutoff associated with Relative Energy Deficiency in Sport (REDs) outcomes is 30 kcal/kg FFM per day, though this threshold remains debated [3]. For men, however, the exact cutoff or range at which energy-related disorders occur is less clear but is thought to be lower, ranging between ~\u0026thinsp;9 and 25 kcal/kg FFM [4].\u003c/p\u003e \u003cp\u003e There is considerable variability in the reported prevalence of LEA/REDs in both female (23-79.5%) and male (15\u0026ndash;70%) athletes, according to the International Olympic Committee [2]. Other studies report prevalence rates between 22% and 58%, depending on the sport [5]. Ackerman and colleagues [6] found that 47.3% of female athletes presented with LEA, while another study reported a 47.2% prevalence in men [7]. Among national or world-class long-distance athletes, LEA was observed in 25% of men and 31% of women [8]. Despite similar prevalence rates, it is estimated that only 20% of studies conducted between 2018 and 2022 included male athletes as research subjects [2].\u003c/p\u003e \u003cp\u003eREDs is defined as a syndrome of physiological and/or psychological impairment affecting both female and male athletes, resulting from prolonged and/or severe problematic LEA [9]. Problematic LEA is associated with negative effects on the hypothalamic-pituitary-gonadal axis, metabolic hormones, immune function, bone health, performance, and lean body mass [2]. Thus, LEA has wide-ranging health consequences across multiple systems [2]. Compared to athletes with adequate EA, those with low EA are more likely to present metabolic, bone, cardiovascular, gastrointestinal, immunological, and hematological problems [6,10].\u003c/p\u003e \u003cp\u003eDespite the presence of narrative reviews on LEA and REDs in both men and women, there is a notable scarcity of systematic reviews. For instance, while McGuire et al. (2020) examined the prevalence of LEA in male athletes and its health effects [11], no systematic reviews have, to the best of the author\u0026rsquo;s knowledge, comprehensively evaluated the impact of REDs on both the health and performance of male athletes. Therefore, the main objective of this systematic review was to evaluate the effects of REDs on the health and performance of male athletes, specifically assessing metabolic and hormonal biomarkers.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cem\u003eSearch Strategy\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis systematic review evaluated the effects of REDs on health and performance through metabolic and hormonal biomarkers. A literature search was conducted from database inception up to April 16, 2024, using PubMed (MEDLINE), Scopus, Web of Science (WOS), and SPORTDiscus, with no year restriction applied to the search strategy. Search terms included a mix of Medical Subject Headings (MeSH) and free-text words for key concepts. The search terms were: (\u0026quot;relative energy deficienc*\u0026quot; OR \u0026quot;male athlete triad\u0026quot; OR \u0026quot;triad\u0026quot; OR \u0026quot;energy intake deficienc*\u0026quot; OR \u0026quot;low energy availability\u0026quot; OR \u0026quot;low energy intak*\u0026quot; OR \u0026quot;energy balance\u0026quot; OR \u0026quot;REDS\u0026quot; OR \u0026quot;LEA\u0026quot; OR \u0026quot;bone density\u0026quot; OR \u0026quot;bone mineral density\u0026quot; OR \u0026quot;stress fractur*\u0026quot; OR \u0026quot;hypogonadism\u0026quot; OR \u0026quot;hypothalamic-pituitary-gonadal axis\u0026quot; OR \u0026quot;disordered eating\u0026quot; OR \u0026quot;eating disorder\u0026quot;) AND (\u0026quot;male athlet*\u0026quot;) AND (\u0026quot;perfomanc*\u0026quot; OR \u0026quot;exercis*\u0026quot; OR \u0026quot;overtraining\u0026quot; OR \u0026quot;endurance\u0026quot; OR \u0026quot;running\u0026quot; OR \u0026quot;cycling\u0026quot; OR \u0026quot;swimming\u0026quot; OR \u0026quot;jockeys\u0026quot; OR \u0026quot;boxing\u0026quot; OR \u0026quot;rowers\u0026quot;). All titles and abstracts from the search were cross-checked for duplicates and missing studies. Titles and abstracts were screened for a subsequent full-text review. The search for published studies was independently performed by two different authors (AVM, FMO) and disagreements were resolved through discussions between them.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEligibility Criteria\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis systematic review followed the guidelines established by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 statement [12], and the protocol was prospectively registered in PROSPERO (CRD42024565897). The PICOS model [13] was used to determine the inclusion criteria:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eParticipants: Adult men actively participating in sports disciplines.\u003c/li\u003e\n \u003cli\u003eIntervention: Studies where EA was assessed, alongside measurements of metabolic and endocrine biomarkers or athletic performance tests.\u003c/li\u003e\n \u003cli\u003eComparator: When available, comparisons were made between metabolic and endocrine biomarkers or athletic performance tests across different EA levels.\u003c/li\u003e\n \u003cli\u003eOutcomes:\u0026nbsp;\u003cul\u003e\n \u003cli\u003eMetabolic biomarkers, such as resting metabolic rate (RMR), resting energy expenditure (REE), and bone mineral density (BMD).\u003c/li\u003e\n \u003cli\u003eEndocrine biomarkers, including testosterone (TES), cortisol (COR), insulin-like growth factor 1 (IGF-1), and triiodothyronine (T3).\u003c/li\u003e\n \u003cli\u003eAthletic performance, measured using jump tests, agility tests, and incremental exercise tests.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003eStudy design: Cross-sectional observational studies.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eStudies that did not meet the inclusion criteria or were review articles or grey literature were excluded. \u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStudy Selection and Data Extraction\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe screening and selection process for the articles obtained through the search was conducted independently by two authors (JJC, PVH) based on the inclusion criteria. Any potential disagreements were resolved by a third reviewer (JCG). Initally, duplicates were manually removed. Titles and abstracts of the articles were then reviewed, followed by a full-text evaluation of the studies that met the initial criteria.\u003c/p\u003e\n\u003cp\u003eData extraction from the selected articles was performed using a standardized table. Two reviewers (JJC, PVH) independently extracted data, with discrepancies resolved by a third reviewer (JCG). The following data were collected from eligible studies: authors and publication date, study duration, participant characteristics (age, body mass index (BMI), maximum aerobic capacity (VO\u003csub\u003e2\u003c/sub\u003e max), peak aerobic capacity (VO\u003csub\u003e2\u003c/sub\u003e peak), and exercise frequency), study objectives, outcome measures, and results.\u003cem\u003e\u0026nbsp;\u003c/em\u003eOne author (JJC) extracted the mean (M), standard deviation (SD), and sample size data from tables in each paper. When necessary, authors were contacted to obtain missing data, and when this was not possible, mean and SD values were extrapolated from figures. Any disagreements were resolved by consensus between JJC and PVH or through third-party adjudication (JCG).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eQuality Assessment\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe methodological quality of the studies was evaluated using an adapted version of the McMaster University Risk of Bias Quality Form [15] by Sarmento et al. [14]. Two reviewers (LSN, ARS) independently assessed the studies, with a third reviewer (JCG) resolving any discrepancies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe articles were evaluated based on 16 quality criteria, including study purpose (item 1), relevance of the background literature (item 2), appropriateness of the study design (item 3), study sample (items 4 and 5), use of informed consent (item 6), clarity of outcome measures (items 7 and 8), description of the methods (item 9), results significance (item 10), data analysis (item 11), practical importance of the findings (item 12), description of dropouts (item 13), conclusions (item 14), practical implications (item 15), and identification of study limitations (item 16).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe 16 criteria were evaluated using a binary scale (0/1). For two of these criteria (items 6 and 13) a \u0026quot;\u003cem\u003eNot applicable\u0026quot;\u003c/em\u003e option was included, and in such cases, a score of 3 was assigned. A percentage score was calculated for each study to enable fair comparisons. Based on these scores, studies were categorized into one of three quality levels: (1) low methodological quality (score below 50%), (2) moderate methodological quality (score between 51% and 75%), or (3) excellent methodological quality (score above 75%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 presents the criteria used to assess the methodological quality of the studies included in this systematic review.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"702\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003eTABLE 2. Quality criteria used to analyze the quantitative publications.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.9886%;\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60.3989%;\"\u003e\n \u003cp\u003eWas the study purpose stated clearly?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.98006%;\"\u003e\n \u003cp\u003e1=Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6325%;\"\u003e\n \u003cp\u003e0=No\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.9886%;\"\u003e\n \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60.3989%;\"\u003e\n \u003cp\u003eWas relevant background literature reviewed?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.98006%;\"\u003e\n \u003cp\u003e1=Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6325%;\"\u003e\n \u003cp\u003e0=No\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.9886%;\"\u003e\n \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60.3989%;\"\u003e\n \u003cp\u003eWas the design appropriate for the research question?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.98006%;\"\u003e\n \u003cp\u003e1=Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6325%;\"\u003e\n \u003cp\u003e0=No\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.9886%;\"\u003e\n \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60.3989%;\"\u003e\n \u003cp\u003eWas the sample described in detail?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.98006%;\"\u003e\n \u003cp\u003e1=Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6325%;\"\u003e\n \u003cp\u003e0=No\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.9886%;\"\u003e\n \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60.3989%;\"\u003e\n \u003cp\u003eWas sample size justified?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.98006%;\"\u003e\n \u003cp\u003e1=Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6325%;\"\u003e\n \u003cp\u003e0=No\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.9886%;\"\u003e\n \u003cp\u003e\u003cem\u003e6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60.3989%;\"\u003e\n \u003cp\u003eWas informed consent obtained? (if not described, assume No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.98006%;\"\u003e\n \u003cp\u003e1=Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6325%;\"\u003e\n \u003cp\u003e0=No (If not applicable, assume 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.9886%;\"\u003e\n \u003cp\u003e\u003cem\u003e7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60.3989%;\"\u003e\n \u003cp\u003eWere the outcome measures reliable? (if not described, assume No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.98006%;\"\u003e\n \u003cp\u003e1=Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6325%;\"\u003e\n \u003cp\u003e0=No\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.9886%;\"\u003e\n \u003cp\u003e\u003cem\u003e8\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60.3989%;\"\u003e\n \u003cp\u003eWere the outcome measures valid? (if not described, assume No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.98006%;\"\u003e\n \u003cp\u003e1=Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6325%;\"\u003e\n \u003cp\u003e0=No\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.9886%;\"\u003e\n \u003cp\u003e\u003cem\u003e9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60.3989%;\"\u003e\n \u003cp\u003eWas method described in detail?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.98006%;\"\u003e\n \u003cp\u003e1=Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6325%;\"\u003e\n \u003cp\u003e0=No\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.9886%;\"\u003e\n \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60.3989%;\"\u003e\n \u003cp\u003eWere results reported in terms of statistical significance?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.98006%;\"\u003e\n \u003cp\u003e1=Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6325%;\"\u003e\n \u003cp\u003e0=No\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.9886%;\"\u003e\n \u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60.3989%;\"\u003e\n \u003cp\u003eWere the analysis methods appropriate?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.98006%;\"\u003e\n \u003cp\u003e1=Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6325%;\"\u003e\n \u003cp\u003e0=No\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.9886%;\"\u003e\n \u003cp\u003e\u003cem\u003e12\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60.3989%;\"\u003e\n \u003cp\u003eWas importance for the practice reported?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.98006%;\"\u003e\n \u003cp\u003e1=Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6325%;\"\u003e\n \u003cp\u003e0=No\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.9886%;\"\u003e\n \u003cp\u003e\u003cem\u003e13\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60.3989%;\"\u003e\n \u003cp\u003eWere any dropouts reported?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.98006%;\"\u003e\n \u003cp\u003e1=Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6325%;\"\u003e\n \u003cp\u003e0=No (If not applicable, assume 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.9886%;\"\u003e\n \u003cp\u003e\u003cem\u003e14\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60.3989%;\"\u003e\n \u003cp\u003eWere conclusions appropriate given the study methods?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.98006%;\"\u003e\n \u003cp\u003e1=Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6325%;\"\u003e\n \u003cp\u003e0=No\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.9886%;\"\u003e\n \u003cp\u003e\u003cem\u003e15\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60.3989%;\"\u003e\n \u003cp\u003eAre there any implications for practice given the results of the study?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.98006%;\"\u003e\n \u003cp\u003e1=Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6325%;\"\u003e\n \u003cp\u003e0=No\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.9886%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60.3989%;\"\u003e\n \u003cp\u003eWere limitations of the study acknowledged and described by the authors?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.98006%;\"\u003e\n \u003cp\u003e1=Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6325%;\"\u003e\n \u003cp\u003e0=No\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cem\u003eParticipant and study characteristics\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 834 results were identified after conducting searches in PubMed (n = 172), Scopus (n = 260), WOS (n = 284), and SPORTDiscus (n = 118). After removing duplicates (n = 436), these results were reduced to 398. Following title and abstract screening, 370 articles were excluded based on the inclusion criteria, leaving 28 articles for full-text review. After reviewing the full texts, 18 articles were excluded (see Supplementary Material), resulting in 10 articles [8,16\u0026ndash;24]\u0026nbsp;being included in this systematic review (Figure 2).\u003c/p\u003e\n\u003cp\u003eTable 3 provides a detailed summary of the characteristics of the studies included in this review, covering aspects such as study duration, participant characteristics, study objectives, outcome measures, and results obtained. Of the 10 trials identified, all assessed EA in participants and measured metabolic and endocrine biomarkers, with two studies additionally conducting athletic performance tests [16,17].\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 39.5383%;\"\u003e\n \u003cp\u003eTABLE 3. Characteristics of the studies investigating REDs in men (n = 10). Participant characteristics are presented as the mean \u0026plusmn; standard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.453%;\" colspan=\"2\"\u003eReference\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 1.7272%;\"\u003e\u003cspan style='color: rgb(0, 0, 0); font-family: \"Times New Roman\"; font-size: medium; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; white-space: normal; background-color: rgb(255, 255, 255); text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;'\u003eDuration\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 45.5556%;\" colspan=\"3\"\u003eParticipant characteristics\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.174%;\"\u003e\u003cspan style='color: rgb(0, 0, 0); font-family: \"Times New Roman\"; font-size: medium; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; white-space: normal; background-color: rgb(255, 255, 255); text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;'\u003eStudy objective\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8.7799%;\"\u003eOutcome measures\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 5.1096%;\"\u003eResults\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 5.0736%;\"\u003e\n \u003cp\u003eTorstveit et al., (2018)\u0026nbsp;[24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.7272%;\"\u003e\n \u003cp\u003e4 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 16.9481%;\"\u003e\n \u003cp\u003e31 highly trained endurance athletes (cyclists, triathletes, long-distance runners)\u003c/p\u003e\n \u003cp\u003eAge: 34.7 \u0026plusmn; 8.1 years old\u003c/p\u003e\n \u003cp\u003eBMI: 22.3 \u0026plusmn; 1.8 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eExercise: 8.7 \u0026plusmn; 3.2 hours per week\u003c/p\u003e\n \u003cp\u003eVO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003epeak: 66.4 \u0026plusmn; 6.2 ml/kg/min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.174%;\"\u003e\n \u003cp\u003eComparison of different parameters between groups with low or normal RMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7799%;\"\u003e\n \u003cul\u003e\n \u003cli\u003eEnergy intake (weighing)\u003c/li\u003e\n \u003cli\u003eExercise energy expenditure (HR)\u003c/li\u003e\n \u003cli\u003eEnergy availability (EA)\u003c/li\u003e\n \u003cli\u003eBody composition (DEXA)\u003c/li\u003e\n \u003cli\u003eRMR (indirect calorimetry)\u003c/li\u003e\n \u003cli\u003eBlood markers (GLU, COR, TES, T3) measured after fasting. No exercise in the previous 24 hours\u0026nbsp;\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1096%;\"\u003e\n \u003cp\u003eEA = 41 (normal RMR) and 37 (low RMR) kcal/kg FFM\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eThere were no significant differences in EA between groups, but the group with lower RMR had a lower within-day energy balance (WDEB), which was associated with higher cortisol levels and a decrease in the TES:COR ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 5.0736%;\"\u003e\n \u003cp\u003eHeikura et al., (2018)\u0026nbsp;[8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.7272%;\"\u003e\n \u003cp\u003e7 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 16.9481%;\"\u003e\n \u003cp\u003e24 middle- and long-distance runners (6 LEA and 18 moderate EA)\u003c/p\u003e\n \u003cp\u003eAge: 26.9 \u0026plusmn; 3.8 y 27.2 \u0026plusmn; 4.2 years old\u003c/p\u003e\n \u003cp\u003eExercise: 130 \u0026plusmn; 4.3 y 107 \u0026plusmn; 25 km per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.174%;\"\u003e\n \u003cp\u003eEvaluate EA, BMD, metabolic and reproductive hormonal function, and prevalence of injuries or illness during a precompetitive training period with high volume/intensity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7799%;\"\u003e\n \u003cul\u003e\n \u003cli\u003eEnergy intake (dietary record)\u003c/li\u003e\n \u003cli\u003eExercise energy expenditure (training log)\u003c/li\u003e\n \u003cli\u003eEnergy availability (EA)\u003c/li\u003e\n \u003cli\u003eBody composition (DEXA)\u003c/li\u003e\n \u003cli\u003eBMD (DEXA) of the whole body, femoral neck, and lumbar spine\u003c/li\u003e\n \u003cli\u003eBlood markers (TES, IGF-1, T3, INS) measured after fasting. Previous exercise not specified\u003c/li\u003e\n \u003cli\u003eQuestionnaire on injuries and illness in the last 12 months\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1096%;\"\u003e\n \u003cp\u003eEA = 21 (LEA) and 37 (moderate EA) kcal/kg FFM\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLower TES in the LEA group\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAll other parameters were the same between groups\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 5.0736%;\"\u003e\n \u003cp\u003eTorstveit et al., (2019)\u0026nbsp;[23]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.7272%;\"\u003e\n \u003cp\u003e4 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 16.9481%;\"\u003e\n \u003cp\u003e53 highly trained endurance athletes (cyclists, triathletes, runners) (12 LEA and 41 moderate EA)\u003c/p\u003e\n \u003cp\u003eAge: 35.3 \u0026plusmn; 8.3 years old\u003c/p\u003e\n \u003cp\u003eBMI: 22.9 \u0026plusmn; 1.9 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eExercise: 9.5 \u0026plusmn; 3.4 hours per week\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eVO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003epeak: 65.3 \u0026plusmn; 5.9 ml/kg/min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.174%;\"\u003e\n \u003cp\u003eEvaluate the associations between exercise dependence and biomarkers of REDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7799%;\"\u003e\n \u003cul\u003e\n \u003cli\u003eEnergy intake (weighing)\u003c/li\u003e\n \u003cli\u003eExercise energy expenditure (HR)\u003c/li\u003e\n \u003cli\u003eEnergy availability (EA)\u003c/li\u003e\n \u003cli\u003eBody composition (DEXA)\u003c/li\u003e\n \u003cli\u003eRMR (indirect calorimetry)\u003c/li\u003e\n \u003cli\u003eBlood markers (GLU, INS, COR, TES, IGF-1, T3) measured after fasting. No intense exercise in the previous 24 hours\u003c/li\u003e\n \u003cli\u003eQuestionnaire on exercise dependence and eating disorders (EDE-Q)\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1096%;\"\u003e\n \u003cp\u003eEA = 37.7 kcal/kg FFM\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eElevated COR in the group with higher exercise dependence\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAll other parameters were the same between groups\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 5.0736%;\"\u003e\n \u003cp\u003eLee et al., (2020)\u0026nbsp;[19]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.7272%;\"\u003e\n \u003cp\u003e1 month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 16.9481%;\"\u003e\n \u003cp\u003e12 soccer players (5 LEA and 7 moderate EA)\u003c/p\u003e\n \u003cp\u003eAge: 19-19.5 years old\u003c/p\u003e\n \u003cp\u003eBMI: 22.5 \u0026plusmn; 1.2 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eVO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003emax: 54.9 \u0026plusmn; 5.7 ml/kg/min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.174%;\"\u003e\n \u003cp\u003eInvestigate the status of EA and its relationship with metabolic and hormonal status and bone metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7799%;\"\u003e\n \u003cul\u003e\n \u003cli\u003eEnergy intake (dietary record)\u003c/li\u003e\n \u003cli\u003eExercise energy expenditure (HR)\u003c/li\u003e\n \u003cli\u003eEnergy availability (EA)\u003c/li\u003e\n \u003cli\u003eBody composition (DEXA)\u003c/li\u003e\n \u003cli\u003eBMD (DEXA) whole body\u003c/li\u003e\n \u003cli\u003eREE (indirect calorimetry)\u003c/li\u003e\n \u003cli\u003eBlood markers (FSH, LH, TES, E2, GH, IGF-1, COR, T3, LEP) measured after fasting. No intense exercise in the previous 24 hours\u003c/li\u003e\n \u003cli\u003eQuestionnaire on eating disorders (EAT-26)\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1096%;\"\u003e\n \u003cp\u003eEA = 22.4 (LEA) and 38.7 (moderate EA) kcal/kg FFM\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLower REE, IGF-1, and FSH levels in the LEA group\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAll other parameters were the same between groups\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 5.0736%;\"\u003e\n \u003cp\u003eTaguchi et al., (2020)\u0026nbsp;[22]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.7272%;\"\u003e\n \u003cp\u003e3 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 16.9481%;\"\u003e\n \u003cp\u003e6 long-distance runners\u003c/p\u003e\n \u003cp\u003eAge: 19-21 years old\u003c/p\u003e\n \u003cp\u003eBMI: 19.2 \u0026plusmn; 1.1 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eExercise: 132.2 \u0026plusmn; 27.3 km per week\u003c/p\u003e\n \u003cp\u003eVO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003emax: 58.6 \u0026plusmn; 1.7 ml/kg/min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.174%;\"\u003e\n \u003cp\u003eInvestigate the status of EA and its relationship with metabolic and hormonal status and bone metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7799%;\"\u003e\n \u003cul\u003e\n \u003cli\u003eEnergy intake (food record)\u003c/li\u003e\n \u003cli\u003eExercise energy expenditure (HR)\u003c/li\u003e\n \u003cli\u003eEnergy availability (EA)\u003c/li\u003e\n \u003cli\u003eBody composition (DEXA)\u003c/li\u003e\n \u003cli\u003eBMD (DEXA) whole body\u003c/li\u003e\n \u003cli\u003eREE (indirect calorimetry)\u003c/li\u003e\n \u003cli\u003eBlood markers (T3, IGF-1, LH, E2, TES, Vit D) measured after fasting. Previous exercise not specified\u003c/li\u003e\n \u003cli\u003eQuestionnaire on eating disorders (EAT-26)\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1096%;\"\u003e\n \u003cp\u003eEA = 18.9 kcal/kg FFM\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLow BMD and Vitamin D deficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 5.0736%;\"\u003e\n \u003cp\u003eJurov et al., (2021)\u0026nbsp;[16]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.7272%;\"\u003e\n \u003cp\u003e7 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 16.9481%;\"\u003e\n \u003cp\u003e12 highly trained endurance athletes\u003c/p\u003e\n \u003cp\u003eAge: 27.5 \u0026plusmn; 5.7 years old\u003c/p\u003e\n \u003cp\u003eVO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003emax: 67.49 \u0026plusmn; 6.74 ml/kg/min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.174%;\"\u003e\n \u003cp\u003eMeasure EA in endurance athletes and its relationship with hormonal status and performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7799%;\"\u003e\n \u003cul\u003e\n \u003cli\u003eEnergy intake (dietary record)\u003c/li\u003e\n \u003cli\u003eExercise energy expenditure (HR)\u003c/li\u003e\n \u003cli\u003eEnergy availability (EA)\u003c/li\u003e\n \u003cli\u003eBody composition (BIA)\u003c/li\u003e\n \u003cli\u003eREE (indirect calorimetry)\u003c/li\u003e\n \u003cli\u003eBlood markers (FER, Fe, T3, TES, COR, INS, IGF-1) measured after fasting. Previous exercise not specified\u003c/li\u003e\n \u003cli\u003ePsychological questionnaire (TFEQ-R18)\u003c/li\u003e\n \u003cli\u003ePerformance test (CMJ, T-test, incremental fatigue test)\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1096%;\"\u003e\n \u003cp\u003eEA = 29.5 kcal/kg FFM\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eThere were no differences in performance, blood markers, or psychological assessment when dividing participants based on EA \u0026gt; or \u0026lt; 30 kcal/kg FFM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 5.0736%;\"\u003e\n \u003cp\u003eMoore et al., (2021)\u0026nbsp;[20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.7272%;\"\u003e\n \u003cp\u003e14 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 16.9481%;\"\u003e\n \u003cp\u003e14 recreational endurance athletes (triathletes, long-distance runners, and obstacle runners)\u003c/p\u003e\n \u003cp\u003eAge: 26.4 \u0026plusmn; 4.2 years old\u003c/p\u003e\n \u003cp\u003eBMI: 21.9 \u0026plusmn; 1.8 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eExercise: \u0026gt; 10 hours per week\u003c/p\u003e\n \u003cp\u003eVO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003emax: 62.3 \u0026plusmn; 6.9 ml/kg/min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.174%;\"\u003e\n \u003cp\u003eEvaluate EA, BMD, TES levels, and risk of eating disorders in endurance athletes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7799%;\"\u003e\n \u003cul\u003e\n \u003cli\u003eEnergy intake (dietary record)\u003c/li\u003e\n \u003cli\u003eExercise energy expenditure (HR)\u003c/li\u003e\n \u003cli\u003eEnergy availability (EA)\u003c/li\u003e\n \u003cli\u003eBody composition (BIA)\u003c/li\u003e\n \u003cli\u003eBMD (DEXA) whole body\u003c/li\u003e\n \u003cli\u003eRMR (indirect calorimetry)\u003c/li\u003e\n \u003cli\u003eBlood markers (TES) measured after fasting. No exercise in the previous 24 hours\u003c/li\u003e\n \u003cli\u003eQuestionnaire on eating disorders (EDI-3)\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1096%;\"\u003e\n \u003cp\u003eEA = 27.6 kcal/kg FFM\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTES and BMD within normal values\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e64.3% presented with LEA\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e35% at risk of eating disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 5.0736%;\"\u003e\n \u003cp\u003eLane et al., (2021)\u0026nbsp;[18]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.7272%;\"\u003e\n \u003cp\u003e7 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 16.9481%;\"\u003e\n \u003cp\u003e60 recreational endurance athletes (cyclists, triathletes, runners)\u003c/p\u003e\n \u003cp\u003eAge: 43.4 \u0026plusmn; 11.6 years old\u003c/p\u003e\n \u003cp\u003eExercise: 10.9 \u0026plusmn; 2.7 hours per week\u003c/p\u003e\n \u003cp\u003eVO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003emax: 55.7 \u0026plusmn; 8.0 ml/kg/min\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.174%;\"\u003e\n \u003cp\u003eEvaluate EA and REDs risk factors in endurance athletes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7799%;\"\u003e\n \u003cul\u003e\n \u003cli\u003eEnergy intake (dietary record)\u003c/li\u003e\n \u003cli\u003eExercise energy expenditure (HR)\u003c/li\u003e\n \u003cli\u003eEnergy availability (EA)\u003c/li\u003e\n \u003cli\u003eBody composition (DEXA)\u003c/li\u003e\n \u003cli\u003eBMD (DEXA) whole body, femoral neck, and lumbar spine\u003c/li\u003e\n \u003cli\u003eRMR (indirect calorimetry)\u003c/li\u003e\n \u003cli\u003eBlood markers (GH, T3, TSH, TES, LH) measured after fasting. No exercise in the previous 24 hours\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1096%;\"\u003e\n \u003cp\u003eEA = 28.7 kcal/kg FFM\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBlood markers and BMD within normal values\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 5.0736%;\"\u003e\n \u003cp\u003eMoris et al., (2022)\u0026nbsp;[21]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.7272%;\"\u003e\n \u003cp\u003e7 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 16.9481%;\"\u003e\n \u003cp\u003e44 athletes from various disciplines (cross country, soccer, wrestling, basketball, track, golf, baseball)\u003c/p\u003e\n \u003cp\u003eAge: 20.4 \u0026plusmn; 0.2 years old\u003c/p\u003e\n \u003cp\u003eBMI: 25.3 \u0026plusmn; 1.3 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.174%;\"\u003e\n \u003cp\u003eDetermine EA, BMD, and hormonal status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7799%;\"\u003e\n \u003cul\u003e\n \u003cli\u003eEnergy intake (dietary record)\u003c/li\u003e\n \u003cli\u003eExercise energy expenditure (accelerometer)\u003c/li\u003e\n \u003cli\u003eBody composition (DEXA)\u003c/li\u003e\n \u003cli\u003eBMD (DEXA) whole body, femoral neck, and lumbar spine\u003c/li\u003e\n \u003cli\u003eRMR (indirect calorimetry)\u003c/li\u003e\n \u003cli\u003eBlood markers (LH, INS, LEP, E2, TES) measured after fasting. No exercise in the previous 12 hours\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1096%;\"\u003e\n \u003cp\u003eEA by sports (kcal/kg FFM):\u003c/p\u003e\n \u003cp\u003e- Cross country: 42.03\u003c/p\u003e\n \u003cp\u003e- Soccer: 25.69\u003c/p\u003e\n \u003cp\u003e- Wrestling: 28.35\u003c/p\u003e\n \u003cp\u003e- Basketball: 27.37\u003c/p\u003e\n \u003cp\u003e- Track: 35.19\u003c/p\u003e\n \u003cp\u003e- Golf: 27.9\u003c/p\u003e\n \u003cp\u003e- Baseball: 29.88\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15% had LEA, 0% had low BMD, 28% had low TES\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAll other parameters were within normal values\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 5.0736%;\"\u003e\n \u003cp\u003eKalpana et al., (2023) [17]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.7272%;\"\u003e\n \u003cp\u003e1 day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 16.9481%;\"\u003e\n \u003cp\u003e52 national level kho-kho players\u003c/p\u003e\n \u003cp\u003eAge: 23.63 \u0026plusmn; 3.77 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.174%;\"\u003e\n \u003cp\u003eDetermine the prevalence of LEA and the impact of REDs on health and performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7799%;\"\u003e\n \u003cul\u003e\n \u003cli\u003eDietary intake (dietary record)\u003c/li\u003e\n \u003cli\u003eExercise energy expenditure (METs)\u003c/li\u003e\n \u003cli\u003eEnergy availability (EA)\u003c/li\u003e\n \u003cli\u003eBody composition (skinfold measurements)\u003c/li\u003e\n \u003cli\u003eBMD (DEXA) whole body and lumbar spine\u003c/li\u003e\n \u003cli\u003eRMR (Cunningham formula)\u003c/li\u003e\n \u003cli\u003eBlood markers (Ca, Vit D, T3, Hb, ALB, CREA, SGOT, SGPT) measured after fasting. Previous exercise not specified\u003c/li\u003e\n \u003cli\u003eQuestionnaire on eating disorders (EAT-26)\u003c/li\u003e\n \u003cli\u003ePerformance test (vertical jump, agility test, sprint test)\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1096%;\"\u003e\n \u003cp\u003eEA = 14.62 (LEA) and 51.69 (moderate EA) kcal/kg FFM\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e46% presented with LEA\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eThe LEA group had lower BMD, poorer sleep quality, and lower performance in agility tests\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAll other parameters were the same between groups\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLegend: Abbreviations: ALB, Albumin; BIA, Bioelectrical Impedance Analysis; BMD, Bone Mineral Density; BMI, Body Mass Index; Ca, Calcium; CMJ, Countermovement Jump; COR, Cortisol; CREA, Creatinine; DEXA, Dual-Energy X-ray Absorptiometry; EA, Energy Availability; E2, Estradiol; EDs, Eating Disorders; Fe, Iron; FER, Ferritin; FSH, Follicle-Stimulating Hormone; GH, Growth Hormone; GLU, Glucose; Hb, Hemoglobin; HR, Heart Rate; IGF-1, Insulin-like Growth Factor 1; INS, Insulin; LEP, Leptin; LH, Luteinizing Hormone; METs, Metabolic Equivalent of Task; REE, Resting Energy Expenditure; RMR, Resting Metabolic Rate; SGOT, Aspartate Aminotransferase; SGPT, Alanine Aminotransferase; TES, Testosterone; T3, Triiodothyronine; TSH, Thyrotropin; VO \u003csub\u003e2 max\u003c/sub\u003e, Maximum Aerobic Capacity; V \u003csub\u003eO2 peak\u003c/sub\u003e, Peak Aerobic Capacity; WDEB, Within-Day Energy Balance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn total, 308 participants were evaluated across the 10 cross-sectional studies included in this systematic review. All participants were active males involved in various sports disciplines such as cycling [18,23,24], triathlon\u0026nbsp;[18,20,23,24], middle- and long-distance running\u0026nbsp;[8,16,18,20,22\u0026ndash;24], obstacle racing\u0026nbsp;[20], soccer\u0026nbsp;[19,21], basketball\u0026nbsp;[21], cross country\u0026nbsp;[21], wrestling\u0026nbsp;[21], golf\u0026nbsp;[21], baseball\u0026nbsp;[21], and kho-kho\u0026nbsp;[17]. The characteristics of the participants varied, with ages ranging from 19\u0026nbsp;[19,22]\u0026nbsp;to 43.4 years\u0026nbsp;[18], BMI ranging from 19.2\u0026nbsp;[22]\u0026nbsp;to 25.3 kg/m\u0026sup2;\u0026nbsp;[21], VO\u003csub\u003e2\u003c/sub\u003e max ranging from 54.9 [19] to 67.49 mL/kg/min [16], and VO\u003csub\u003e2\u003c/sub\u003e peak ranging from 65.3 [23] to 66.4 mL/kg/min [24]. Reported weekly exercise duration ranged from 8.7 hours [24] to 10.9 hours [18], with training distances between 107 [8] and 132 km per week [22].\u003c/p\u003e\n\u003cp\u003eTo evaluate EA, the 10 cross-sectional studies used different methodological techniques. Energy intake was estimated through dietary records in eight studies [8,16\u0026ndash;22] and food weighing in two studies [23,24]. Exercise energy expenditure was measured using heart rate monitors in seven studies\u0026nbsp;[16,18\u0026ndash;20,22\u0026ndash;24], training logs and Metabolic Equivalent of Task\u003cem\u003e\u0026nbsp;\u003c/em\u003e(METs) in two studies\u0026nbsp;[8,17], and accelerometry in one study\u0026nbsp;[21]. Body composition was assessed using dual-energy X-ray absorptiometry (DEXA) in seven studies\u0026nbsp;[8,18,19,21\u0026ndash;24], bioelectrical impedance analysis (BIA) in two studies\u0026nbsp;[16,20], and anthropometry with skinfold measurements in one study\u0026nbsp;[17].\u003c/p\u003e\n\u003cp\u003eAdditionally, resting metabolic rate (RMR) was analyzed in six studies [17,18,20,21,23,24], and resting energy expenditure (REE) was measured in three studies [16,19,22]. Eight studies used indirect calorimetry to calculate these parameters\u0026nbsp;[16,18\u0026ndash;24], while one study estimated them theoretically using the Cunningham formula\u0026nbsp;[17]. Bone mineral density (BMD) was evaluated using DEXA in seven studies\u0026nbsp;[8,17\u0026ndash;22], covering the whole body, lumbar spine, and femoral neck.\u003c/p\u003e\n\u003cp\u003eVarious biomarkers were analyzed through fasting blood tests to assess participants\u0026apos; health, including testosterone, cortisol, insulin-like growth factor 1 (IGF-1), triiodothyronine (T3), growth hormone (GH), glucose, insulin, and vitamin D. Additionally, several studies included questionnaires to evaluate eating disorders [17,19,20,22,23], psychological factors [16], exercise dependence [23], and incidence of injury and illness within the past 12 months [8]. Two studies further assessed athletic performance [16,17] using tests such as countermovement jumps, agility tests, and incremental exercise tests to exhaustion. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eQuality Assessment\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 provides the analysis of the methodological quality of the 10 studies included in this systematic review. Methodological quality scores ranged from 81.25% to 100%, with an average score of 90.6%. All 10 articles demonstrated excellent methodological quality, with none showing moderate or low quality.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"709\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"18\" style=\"width: 709px;\"\u003e\n \u003cp\u003eTABLE 4. Methodological quality analysis of the studies included in the systematic review (n = 10).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eTotal score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eTorstveit et al., (2018)\u0026nbsp;[24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e87.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eHeikura et al., (2018)\u0026nbsp;[8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e87.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eTorstveit et al., (2019)\u0026nbsp;[23]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e93.75%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eLee et al., (2020)\u0026nbsp;[19]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e93.75%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eTaguchi et al., (2020)\u0026nbsp;[22]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e81.25%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eJurov et al., (2021)\u0026nbsp;[16]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e93.75%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eMoore et al., (2021)\u0026nbsp;[20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eLane et al., (2021)\u0026nbsp;[18]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e87.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eMoris et al., (2022)\u0026nbsp;[21]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e93.75%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eKalpana et al., (2023)\u0026nbsp;[17]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e87.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eLegend: The percentage score reflects the overall methodological rigor, evaluated using the adapted McMaster Risk of Bias Quality Form.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe primary objective of this systematic review was to synthesize the effects of REDs on various health and performance parameters in male athletes, aiming to provide a comprehensive understanding of the health and performance consequences of REDs within this population.\u003c/p\u003e\n\u003cp\u003eLEA affects multiple systems as the body adapts to conserve energy, disrupting hormonal pathways and leading to a cascade of interrelated physiological consequences [5]. LEA has been identified as the primary driver of REDs syndrome [2], with its main effects on the health and performance of male athletes summarized in Figure 3.\u003c/p\u003e\n\u003cp\u003eThe EA across studies ranged from 14.6 to 51.6 kcal/kg FFM [17]. In seven out of the 10 studies, groups of athletes with LEA were identified [8,16\u0026ndash;20,22]. These EA values are comparable to those reported in other observational studies involving different athletic populations, such as 11.09 kcal/kg FFM in endurance athletes [25], 18.8 kcal/kg FFM in competitive cyclists [26], 20 kcal/kg FFM over eight weeks in a Taekwondo competitor [27], 27.2 and 45.4 kcal/kg FFM in long-distance runners [28], and 36 kcal/kg FFM in race walkers, middle-, and long-distance runners [29].\u003c/p\u003e\n\u003cp\u003eThe effects of REDs on athletes\u0026apos; health were evaluated using metabolic and hormonal biomarkers such as RMR or REE, BMD, testosterone, cortisol, T3, and IGF-1. RMR and REE assess resting energy expenditure, representing the minimal energy required for essential physiological functions [30]. The RMR ratio compares RMR measured through indirect calorimetry with a theoretical predicted RMR, calculated using the Cunningham formula [31,32]. In Torstveit et al. (2018), all participants had an RMR ratio \u0026lt; 0.9, with significant differences between low- and normal-RMR groups. No significant differences in EA were found between groups, but those with lower RMR had higher cortisol levels and a reduced testosterone:cortisol ratio [24]. In another study by Torstveit et al. (2019), 72% of participants had an RMR ratio \u0026lt; 0.9, despite an EA of 37 kcal/kg FFM. A low RMR ratio has been suggested as a marker of LEA in women [31,32] and may indicate adaptive thermogenesis resulting from dysregulated cellular thermogenesis [33]. Similarly, Lee et al. (2020) found that soccer players with LEA (22.4 kcal/kg FFM) had lower levels of REE compared to players with moderate EA (38.7 kcal/kg FFM) [19]. Meanwhile, Langan-Evans et al. (2020) observed no significant changes in RMR or RMR ratio over eight weeks at an EA of 20 kcal/kg FFM; however, during a five-day tapering phase with EA \u0026lt; 10 kcal/kg FFM, both RMR and RMR ratio were notably reduced (-257 kcal/day and \u0026lt; 0.9, respectively) [27].\u003c/p\u003e\n\u003cp\u003eIn female athletes, LEA has been associated with menstrual dysfunction and impaired bone health [31,34]. Among male athletes, however, cross-sectional studies [35] suggests that low BMD is primarily found in those participating in sports that emphasize low body mass and who experience multiple LEA-related risk factors, such as low BMI, repeated episodes of rapid weight loss, or eating disorders. The American College of Sports Medicine defines low BMD as a Z-score between -1.0 and -2.0 relative to the reference population [34]\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eIn Taguchi et al. (2020), long-distance runners with EA of 18.9 kcal/kg FFM exhibited low BMD (Z score = -1.1) and vitamin D deficiency [22]. In kho-kho players with LEA (14.62 kcal/kg FFM), BMD was significantly lower than in players with high EA (51.69 kcal/kg FFM) [17]. However, in the remaining studies included in this review where BMD was evaluated [8,18\u0026ndash;21], no participants exhibited low values. Additionally, Lee et al. (2020) found no group differences in BMD after adjusting for EA [19].\u003c/p\u003e\n\u003cp\u003eRecent studies have further explored bone health and EA among athletes [26\u0026ndash;29,36]. None of these studies found low BMD values, even at LEA levels of 20 kcal/kg FFM over eight weeks and \u0026lt; 10 kcal/kg FFM over five days [27]. For instance, runners with high weekly training loads (83 km/week) and LEA (27.2 kcal/kg FFM) were found to have low TES levels, yet most did not present with significantly reduced BMD, though individual cases of osteopenia in specific sites were noted [28]. Similarly, competitive cyclists maintained stable BMD despite experiencing chronic LEA (18.8 kcal/kg FFM) across a 10-month season, even with generally low micronutrient intake [26]. In Papageorgiou et al. (2017), LEA (15 kcal/kg FFM) did not affect bone formation and resorption markers in men, in contrast to findings in women where LEA was associated with reduced bone formation and increased resorption [36].\u003c/p\u003e\n\u003cp\u003eTestosterone plays a crucial role in anabolic processes and long-term adaptations in muscle mass and function [37]. Low testosterone under LEA conditions may contribute to a catabolic state, particularly during consecutive days of training. Despite this, eight of nine reviewed studies [16,18\u0026ndash;24] reported testosterone within physiological ranges. However, Heikura et al. (2018), observed significantly lower testosterone levels in the LEA group (21 kcal/kg FFM) compared to the moderate EA group (37 kcal/kg FFM) [8]. Likewise, Moris et al. (2022) found low testosterone levels in 28% of participants [21]. While LEA-induced reductions in testosterone of 10-40% are well-documented, these values typically remain within normal clinical ranges [35]. Several intervention studies have further evaluated the effects of LEA on testosterone [4,33,38,39], with two short-term interventions finding lower testosterone when EA was halved over 14 days (17.3 kcal/kg FFM) [4] and when EA was set at 18.9 kcal/kg FFM for three days [39].\u003c/p\u003e\n\u003cp\u003eElevated plasma cortisol is common in athletes with high training loads and inadequate recovery [28]. Four studies in this review measured plasma cortisol levels [16,19,23,24]. Torstveit et al. (2018) reported higher cortisol levels and a lower testosterone:cortisol ratio in participants with reduced RMR [24], potentially impairing recovery and increasing the risk of overtraining and injury [40]. In Torstveit et al. (2019), higher exercise dependence correlated with elevated cortisol, with 6% of participants showing increased levels [23]. In contrast, cortisol levels remained within normal ranges in the other two studies, with no significant intergroup differences [16,19]. Although several studies have explored the relationship between cortisol and EA [4,27,28,38], none reported significant group differences or elevated cortisol levels due to LEA alone.\u003c/p\u003e\n\u003cp\u003eT3, a key regulator of energy metabolism, has been associated with low REE in female athletes with amenorrhea [41]. However, the eight studies in this review that examined T3 consistently found normal ranges [8,16\u0026ndash;19,22\u0026ndash;24]. Moreover, other intervention studies did not detect significant differences in T3 across EA groups [4,33,36,38].\u003c/p\u003e\n\u003cp\u003eRegarding IGF-1, several studies observed stable concentrations in male athletes [4,36,38,42]. However, Kojima et al. (2020) noted lower IGF-1 in athletes with LEA (18.9 kcal/kg FFM) [39], and Murphy and Koehler (2020) reported anabolic resistance under LEA (15 kcal/kg FFM), evidenced by increased in GH and reduced IGF-1 levels [42]. Nonetheless, the five studies included in this review that analyzed IGF-1 reported normal plasma levels [8,16,19,22,23].\u003c/p\u003e\n\u003cp\u003eIn terms of athletic performance, the effects of LEA varied by exposure duration. Short- to medium-term LEA had neutral or positive impacts, while long-term LEA negatively affected performance [43]. Two studies assessed athletic performance alongside EA. Jurov et al. (2021) found no significant correlation between performance variables and EA [16], whereas Kalpana et al. (2024) reported poorer performance in agility tests in LEA participants (14.62 kcal/kg FFM), though no group differences were found in strength and speed tests [17].\u003c/p\u003e\n\u003cp\u003eIntervention studies that evaluated athletic performance demonstrated reductions in applied power, explosive power, anaerobic threshold, and respiratory compensation point after a 50% reduction in EA over 14 days (EA = 17.3 kcal/kg FFM) [4], and lower explosiveness in vertical jumps after a 25 % reduction in EA over 14 days (EA = 22.4 kcal/kg FFM) [38]. However, no differences in time to fatigue were observed during a treadmill test at 70 % VO\u003csub\u003e2\u003c/sub\u003e max when EA was set at 18.9 and 52.9 kcal/kg FFM for three days [39].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLimitations, Strengths and Future Recommendations.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis systematic review has several limitations. First, only observational studies were included, which limits the ability to establish causal relationships between LEA and its effects on health and performance in male athletes.\u003c/p\u003e\n\u003cp\u003eMethodological variability in estimating energy intake, energy expenditure, and body composition complicates direct comparisons. Additionally, data collection over just a few days may not adequately capture athletes\u0026apos; habitual practices, and reliance on self-reporting energy intake can introduce considerable potential for error.\u003c/p\u003e\n\u003cp\u003eMost of the studies had short- to medium-term durations, emphasizing the need for long-term research to evaluate the chronic effects of different LEA levels on health and performance in male athletes. Small sample sizes and a lack of formal sample size calculations in some studies may also have affected statistical power and the robustness of the findings.\u003c/p\u003e\n\u003cp\u003eWhile the studies included athletes from various sports, endurance sports were predominant, potentially limiting the generalizability of the findings. Furthermore, the thresholds used to define low and optimal EA ranged from 20 to 30 kcal/kg FFM, making it difficult to compare results across studies due to a lack of a standardized criterion.\u003c/p\u003e\n\u003cp\u003eAnother key limitation is the variability in the training levels of participants across studies. While some studies focused on elite or highly trained athletes, others included a broader range of athletic experience. This heterogeneity further limits the generalizability of the findings, as athletes with varying fitness levels may respond differently to LEA.\u003c/p\u003e\n\u003cp\u003eDespite these limitations, this systematic review provides valuable insights into the current understanding of REDs in male athletes. There is a clear need for medium- and long-term studies using standardized methodologies to evaluate energy intake and expenditure, body composition, athletic performance, and metabolic and hormonal biomarkers. Such standardization will improve comparability across studies and ensure more robust conclusions about the impact of LEA on male athletes.\u003c/p\u003e\n\u003cp\u003eFuture research should also focus on identifying specific LEA thresholds that trigger adverse health and performance effects in men. Establishing precise cutoffs will help in early detection and intervention, which is crucial for developing effective strategies to prevent REDs-related issues in male athletes, particularly those in high-energy-demand sports.\u003c/p\u003e\n\u003cp\u003ePractical applications of this review include the potential to inform coaching staff, nutritionists, and sports medicine professionals on the importance of monitoring energy availability, hormonal health, and performance markers across training cycles. By closely tracking these variables, practitioners can better protect athletes from the negative impacts of REDs and optimize performance outcomes.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn summary, LEA can have significant adverse effects on basal RMR, the RMR ratio, cortisol levels, and athletic performance. However, BMD and levels of testosterone, T3, and IGF-1 appeared stable across a wide range of EA values in the studies reviewed. This suggests that the effect of REDs on health and performance in male athletes may differ from those observed in female athletes, highlighting the need for further research to determine the specific thresholds at which LEA causes the most detrimental impacts in men.\u003c/p\u003e \u003cp\u003eGiven the growing focus on REDs in male athletes, future research should explore not only physiological biomarkers but also the psychological aspects of low energy availability, which may further influence both performance and overall well-being. Understanding these complex interactions will help shape more effective prevention and intervention strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eREDs: Relative Energy Deficiency in Sport\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePRISMA: Preferred Reporting Items for Systematic Review and Meta-Analyses\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEA: Energy Availability\u003c/p\u003e\n\u003cp\u003eLEA: Low Energy Availability\u003c/p\u003e\n\u003cp\u003eRMR: Resting Metabolic Rate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eREE: Resting Energy Expenditure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBMD: Bone Mineral Density\u003c/p\u003e\n\u003cp\u003eTES: Testosterone\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCOR: Cortisol\u003c/p\u003e\n\u003cp\u003eIGF-1: Insulin-like growth factor 1\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eT3: Triiodothyronine\u003c/p\u003e\n\u003cp\u003eVMI: Body Mass Index\u003c/p\u003e\n\u003cp\u003eFFM: Fat-Free Mass\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWOS: Web of Science\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMsSH: Medical Subject Headings\u003c/p\u003e\n\u003cp\u003eVO\u003csub\u003e2\u003c/sub\u003e max: Maximum aerobic capacity\u003c/p\u003e\n\u003cp\u003eVO\u003csub\u003e2\u003c/sub\u003e peak: Peak aerobic capacity\u003c/p\u003e\n\u003cp\u003eM: mean\u003c/p\u003e\n\u003cp\u003eSD: Standard Deviation\u003c/p\u003e\n\u003cp\u003eDEXA: dual-energy X-ray absorptiometry\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMETs: Metabolic Equivalent of Task\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGH: Growth Hormone\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eALB: Albumin\u003c/p\u003e\n\u003cp\u003eBIA: Bioelectrical Impedance Analysis\u003c/p\u003e\n\u003cp\u003eCa: Calcium\u003c/p\u003e\n\u003cp\u003eCMJ: Countermovement Jump\u003c/p\u003e\n\u003cp\u003eCREA: Creatinine\u003c/p\u003e\n\u003cp\u003eE2: Estradiol\u003c/p\u003e\n\u003cp\u003eEDs: Eating Disorders\u003c/p\u003e\n\u003cp\u003eFe: Iron\u003c/p\u003e\n\u003cp\u003eFER: Ferritin\u003c/p\u003e\n\u003cp\u003eFSH: Follicle-Stimulating Hormone\u003c/p\u003e\n\u003cp\u003eGLU: Glucose\u003c/p\u003e\n\u003cp\u003eHb: Hemoglobin\u003c/p\u003e\n\u003cp\u003eHR: Heart Rate\u003c/p\u003e\n\u003cp\u003eINS: Insulin\u003c/p\u003e\n\u003cp\u003eLEP: Leptin\u003c/p\u003e\n\u003cp\u003eLH: Luteinizing Hormone\u003c/p\u003e\n\u003cp\u003eSGOT: Aspartate Aminotransferase\u003c/p\u003e\n\u003cp\u003eSGPT: Alanine Aminotransferase\u003c/p\u003e\n\u003cp\u003eTSH: Thyrotropin\u003c/p\u003e\n\u003cp\u003eWDEB: Within-Day Energy Balance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics Approval and Consent to Participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConsent for Publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of Data and Materials\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNo funds, grants, or other support was received.\u003c/p\u003e\n\u003cp\u003eAuthor\u0026acute;s contributions\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the conception of the systematic review. Study concept and design AVM and FMO. Database searches and article identification AVM and FMO. Data extraction was conducted by JJC and PVH, and subsequently checked by JCG. Methodological quality assessments were conducted by LSN and ARS, and then confirmed by JCG. Initial draft of the manuscript AVM, MLM, JCG, PVH, PLS, FMO. Critical revision of the manuscript\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ePCB, AJSO, DDB, KRF, GMA, ABC and RLH. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eQiu Y, Fern\u0026aacute;ndez-Garc\u0026iacute;a B, Lehmann HI, Li G, Kroemer G, L\u0026oacute;pez-Ot\u0026iacute;n C, et al. Exercise sustains the hallmarks of health. 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Bone. 2017 Dec;105:191\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eGeesmann B, Gibbs JC, Mester J, Koehler K. Association Between Energy Balance and Metabolic Hormone Suppression During Ultraendurance Exercise. International Journal of Sports Physiology and Performance. 2017 Aug;12(7):984\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eJurov I, Keay N, Spudić D, Rauter S. Inducing low energy availability in trained endurance male athletes results in poorer explosive power. Eur J Appl Physiol. 2022 Feb;122(2):503\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003eKojima C, Ishibashi A, Tanabe Y, Iwayama K, Kamei A, Takahashi H, et al. Muscle Glycogen Content during Endurance Training under Low Energy Availability. Medicine \u0026amp; Science in Sports \u0026amp; Exercise. 2020 Jan;52(1):187\u0026ndash;95.\u003c/li\u003e\n\u003cli\u003eBanfi G, Dolci A. Free testosterone/cortisol ratio in soccer: usefulness of a categorization of values. Journal of Sports Medicine and Physical Fitness. 2006;46(4):611.\u003c/li\u003e\n\u003cli\u003eLoucks AB, Callister R. Induction and prevention of low-T3 syndrome in exercising women. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology. 1993 May 1;264(5):R924\u0026ndash;30.\u003c/li\u003e\n\u003cli\u003eMurphy C, Koehler K. Caloric restriction induces anabolic resistance to resistance exercise. Eur J Appl Physiol. 2020 May;120(5):1155\u0026ndash;64.\u003c/li\u003e\n\u003cli\u003eMelin AK, Areta JL, Heikura IA, Stellingwerff T, Torstveit MK, Hackney AC. Direct and indirect impact of low energy availability on sports performance. Scandinavian Med Sci Sports. 2024 Jan;34(1):e14327.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"sports-medicine-open","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"smoa","sideBox":"Learn more about [Sports Medicine-Open](http://sportsmedicine-open.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/smoa/default.aspx","title":"Sports Medicine-Open","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"male athlete, low energy availability, bone health, performance, endurance sport, hypogonadism","lastPublishedDoi":"10.21203/rs.3.rs-5571836/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5571836/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Relative Energy Deficiency in Sport (REDs) poses a significant challenge to both health and performance in male athletes. This systematic review aimed to evaluate the effects of REDs on various health and performance parameters in male athletes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A comprehensive literature search was conducted up to April 2024, using four databases: PubMed, Scopus, Web of Science, and SPORTDiscus. A structured search was carried out following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines. Cross-sectional studies investigating the relationship between energy availability (EA) and metabolic and hormonal biomarkers, as well as athletic performance in male athletes, were included. The methodological quality of the selected studies was assessed using a modified version of the McMaster scale.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A total of 10 studies, comprising 308 participants, were included in this systematic review. Low energy availability was associated with significant reductions in resting metabolic rate (RMR) and RMR ratio, as well as increased cortisol levels and decreased athletic performance. However, no significant changes were observed in bone mineral density or in levels of testosterone, triiodothyronine, and insulin-like growth factor 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: REDs impacts male and female athletes differently, highlighting the need for further studies to determine the critical EA thresholds that trigger negative effects in male athletes.\u003c/p\u003e\n\u003cp\u003eRegistration: This systematic review was prospectively registered with the PROSPERO International prospective register of systematic reviews (PROSPERO registration ID number: CRD42024565897).\u003c/p\u003e","manuscriptTitle":"Relative Energy Deficiency in Sport (REDs) and Its Effect on Health and Performance in Men: A Systematic Review of Cross-Sectional Studies.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-06 09:20:36","doi":"10.21203/rs.3.rs-5571836/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-04-07T08:37:13+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-02-11T07:00:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-12-04T12:44:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Sports Medicine-Open","date":"2024-12-03T06:23:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"sports-medicine-open","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"smoa","sideBox":"Learn more about [Sports Medicine-Open](http://sportsmedicine-open.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/smoa/default.aspx","title":"Sports Medicine-Open","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"baecb003-13a0-4566-abfa-63f89829564d","owner":[],"postedDate":"January 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-01-06T09:20:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-06 09:20:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5571836","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5571836","identity":"rs-5571836","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}