Background/Objectives: Sports performance is influenced by a complex interplay of physical, physiological, and psychological factors, which differ across disciplines. Thus, this study aims to identify and compare the distinct multidimensional profiles (i.e., body composition, diet, resting metabolic rate, physical activity, sleep quality, and subjective well-being) of athletes from different sports disciplines (i.e., endurance, strength, and intermittent sports). Methods: This study included 77 athletes (28 women) classified as endurance (n = 40), strength (n = 12), or intermittent (n = 25). Body composition was assessed by dual-energy X-ray absorptiometry, bioimpedance, and anthropometry, diet was determined using dietary recalls, resting metabolic rate was assessed by indirect calorimetry, physical activity and sleep quality were determined by a wrist-worn accelerometer, and subjective well-being was determined through validated questionnaires. Results: Strength and intermittent athletes had greater body weight, lean mass, and bone parameters than endurance athletes, whereas intermittent athletes showed higher adiposity than endurance levels (all p ≤ 0.008). The endurance group did not exhibit a higher intake of fats or proteins compared to the strength and intermittent groups; however, it did demonstrate a higher consumption of carbohydrates (p ≤ 0.016). No differences were observed in resting metabolic rate, sleep quality, and subjective well-being, though endurance athletes demonstrated higher levels of vigorous physical activity than strength athletes (p = 0.014). Conclusions: In conclusion, we reveal a distinct physiological phenotype between strength, intermittent, and endurance athletes in terms of body composition, dietary intake, and physical activity levels. These findings underscore the necessity for tailored training and nutrition protocols depending on the sports discipline.

Background/Objectives: Energy availability (EA), defined as the dietary energy remaining after exercise energy expenditure (EEE), is a central determinant of both health and performance in athletes. Chronic insufficient EA leads to low energy availability (LEA), which is an underlying mechanism of Relative Energy Deficiency in Sport (REDs). This narrative review critically explores the conceptual evolution of EA and LEA, summarizes current physiological evidence, and discusses methodological and practical challenges in their assessment and application in free-living athletes. Methods: Evidence from experimental and observational studies was reviewed to describe the hormonal, metabolic, and performance outcomes associated with LEA. Screening tools, including the Low Energy Availability in Females Questionnaire (LEAF-Q) and the Low Energy Availability in Males Questionnaire (LEAM-Q), were also evaluated for their validity and applicability in different sports contexts. Results: LEA is associated with alterations in thyroid and reproductive hormones, which, in turn, contribute to reduced resting metabolic rate, lower bone mineral density, and delayed recovery. While screening questionnaires can help identify athletes at risk, their accuracy varies by sport and individual characteristics. Incorporating hormonal and metabolic biomarkers provides a more direct and sensitive method for detecting physiological stress. Measuring dietary intake, EEE, endocrine balance and body composition in real-world settings remains a major methodological challenge. Combining hormonal, metabolic, and behavioral indicators may improve the identification of athletes experiencing LEA. Conclusions: EA plays a central role in the interaction between nutrition, exercise, and athlete health, but methodological limitations in its assessment may compromise accurate diagnosis. Improving measurement techniques and adopting integrated monitoring strategies are essential to improve early detection, guide individualized nutrition, and prevent RED-related health and performance impairments.

Low energy availability (LEA), defined as an imbalance between dietary intake and exercise energy expenditure, has been associated with physiological and psychological dysfunction in athletes. The resting metabolic rate ratio (RMR _ratio ), calculated as measured RMR divided by predicted RMR, is increasingly used as a surrogate marker for chronic energy deficiency when values fall below 0.9. Therefore, this study aimed to (1) compare the RMR _ratio across different predictive equations, (2) assess the association between energy availability (EA) and both RMR and RMR _ratio , and (3) explore the relationship between the RMR _ratio and phenotypical characteristics, including body composition, dietary intake, physical activity, sleep, and subjective well-being in Spanish competitive athletes. A total of 49 competitive athletes (33 males; 25.2 ± 5.6 years) from various sports were evaluated in this cross-sectional study. RMR was measured using indirect calorimetry and predicted using eleven validated equations. EA was estimated through dietary recalls and training logs. The prevalence of athletes classified as having metabolic suppression (RMR _ratio < 0.9) ranged from 34.7% to 93.9%, depending on the predictive equation used. No significant associations were observed between EA and either RMR or RMR _ratio . However, athletes with RMR _ratio ≥ 0.9 had significantly higher visceral adipose tissue mass, bone mineral content, and density. They also showed more favorable behavioral and psychological profiles, including greater sleep regularity, fewer sustained inactivity bouts, higher self-perceived performance, and lower general stress scores. In conclusion, while no relationship was observed between RMR or RMR _ratio and EA, the RMR _ratio was associated with different physiological, behavioral, and psychological parameters.

Energy availability (EA) is a critical factor in athletic performance and overall well-being, influencing athletes’ physiological functions, training adaptations, and recovery. This study examined the impact of different body composition assessment methods (dual-energy X-ray absorptiometry (DXA), bioelectrical impedance analysis (BIA), and anthropometry) for assessing fat-free mass (FFM) on calculating and classifying EA in elite and pre-elite athletes. A total of 75 elite and pre-elite athletes (27 women; 24.9 ± 5.9 years) were included in this study. EA was computed using each method’s FFM estimate, and comparisons were conducted to evaluate differences in EA values, intermethod agreement, and shifts in EA categorization. The results revealed significant variations in EA depending on the FFM method employed ( p < 0.001), with anthropometry yielding higher EA estimates than both BIA and DXA. Although strong correlations and high consistency were observed between methods (intraclass correlation coefficient = 0.982), discrepancies in classification were evident, particularly for individuals with EA values near clinical thresholds (i.e., 30 kcal/day/kg FFM or 45 kcal/day/kg FFM). BIA tended to overclassify low EA compared to DXA, while anthropometry more often identified individuals as having adequate EA. These findings highlight that choosing the FFM assessment method substantially influences EA estimation and its interpretation. Caution is advised when using BIA or anthropometry in applied settings, and the development of method-specific thresholds may enhance the accuracy and reliability of EA monitoring in athletic populations.