Advancements in genomics have identified specific genetic markers that are closely linked to athletic performance traits. Genes such as PPARGC1A, which regulates mitochondrial biogenesis, and PPARD, involved in lipid metabolism and muscle fiber composition, are particularly associated with endurance capabilities (10–12). These markers underline the genetic basis that governs traits such as muscle efficiency and energy metabolism. By leveraging these insights, genomic profiling can inform training programs and dietary interventions that align with an athlete's genetic predispositions (13). However, the practical translation of this information into actionable recommendations requires further refinement to increase its accessibility and usability for both sports practitioners and athletes (14, 15).

The heritability of elite athletic performance, with estimates attributing up to 70% of variance in athlete status to genetic factors, emphasizes the crucial role of genomics in understanding athletic potential (16). This high degree of genetic influence supports the use of genomic screening for early talent identification and strategic athlete development. However, heritability studies often do not account for environmental influences, such as training and nutrition, which interact with genetic predispositions to shape performance outcomes. The complexity of these gene-environment interactions necessitates more integrative research approaches to contextualize heritability findings (17).

Genome-wide association studies (GWAS) have significantly contributed to our understanding of the genetic architecture of athletic traits by identifying hundreds of potential performance-related single nucleotide polymorphisms (SNPs) (18–20). However, these studies often face reproducibility issues due to small sample sizes, ethnic homogeneity, and confounding variables. Initiatives like the Athlome Project Consortium underscore the importance of large and diverse sample sizes to address these challenges. Despite technological advancements, the inability to consistently identify significant genetic variants highlights a limitation of current GWAS methodologies (21–23). This shortcoming points to the need for innovative study designs that go beyond statistical associations to elucidate causal relationships underlying athletic traits (17, 24).

The transition from candidate gene approaches to hypothesis-free GWAS methodologies has marked a significant evolution in sports genomics. By reducing bias and expanding the scope of inquiry, this shift holds promise for uncovering novel genetic factors linked to traits like VO₂max and injury susceptibility (25, 26). Nonetheless, the field must address challenges such as data interpretation and the functional validation of identified genetic variants to ensure meaningful applications in sports contexts (24).

The application of genomics raises important ethical considerations, particularly regarding data misuse, privacy concerns, and potential genetic discrimination (27). Athletes may face risks if sensitive information about their genetic predispositions is improperly disclosed or used to their detriment (28). Establishing clear ethical guidelines and robust policies is imperative for ensuring that genomics is applied responsibly and equitably in sports settings (29).

Nutrigenomics, which examines the interaction between genetic variations and dietary needs, has demonstrated the potential for optimizing nutritional strategies based on individual genotypes (30–32). For example, the APOA2 genotype influences fatty acid metabolism, and specific dietary fat intakes can modify its effects, thereby affecting athletic performance (33, 34). By identifying genotypes linked to nutrient processing efficiencies or sensitivities, practitioners can design diets that align with each athlete's metabolic profile. These approaches promise improvements in energy utilization, body composition, and overall performance, although robust validation through long-term, controlled studies remains necessary (35).

Another example of the value of nutrigenomics is seen in individuals with genetic lactase non-persistence, who exhibit altered gut microbiota and metabolite concentrations when consuming high amounts of milk (36, 37). Tailoring dietary recommendations to avoid or limit lactose in such individuals not only mitigates discomfort but also enhances metabolic health and nutrient absorption (38). This precision approach exemplifies how genetic information can be used to refine nutritional guidance in ways that support both health and athletic performance, particularly through the modulation of gut microbiome function. Yet, further investigation is needed to establish standardized protocols for such applications (35).

The identification of over 50,000 single nucleotide polymorphisms (SNPs) influencing cellular functions provides a vast dataset for crafting athlete-specific diets. These diets can address genetic predispositions impacting recovery, metabolism, and training adaptations. However, making this information actionable requires the development of user-friendly tools and platforms that can translate complex genetic data into practical recommendations for coaches and nutritionists (29, 30).

Nutrigenomics has also shown promise in optimizing macronutrient and micronutrient strategies for athletes. For instance, by identifying genetic susceptibilities to nutrient deficiencies, such as specific vitamin or mineral needs, tailored supplementation can be offered, potentially improving performance and recovery. Athletes with polymorphisms affecting vitamin D, iron, or folate metabolism may require personalized intake strategies to maintain optimal physiological status (38, 39). This proactive approach can enhance immune function, reduce fatigue, and expedite recovery, offering competitive advantages. Nonetheless, the integration of these insights into sports nutrition programs is currently hindered by high testing costs and the lack of standardized implementation protocols across athletic organizations (40).

Proteomics analyzes molecular-level changes in response to exercise, shedding light on the physiological processes involved in adaptation and recovery (41, 42). The upregulation of proteins such as myoglobin and creatine kinase highlights their roles in muscle repair and immune response following intense activity (43). These findings provide a foundation for targeted nutritional and recovery interventions but require further research to elucidate how dietary factors can specifically modulate these protein responses (44).

Salivary proteomic profiling has revealed a substantial increase in total salivary proteins after prolonged exertion, presenting a non-invasive method for assessing physical stress and recovery states (45, 46). While promising, the applicability of salivary biomarkers in diverse athletic contexts needs further validation to establish their reliability and utility across different sporting disciplines (47). Exercise-induced modifications to protein structures, such as lysine acetylation, play crucial roles in regulating mitochondrial function and energy production, both essential for endurance performance (48). Understanding these molecular mechanisms can inform training programs aimed at enhancing mitochondrial efficiency; however, translating these insights into actionable recommendations for athletes is still in the early stages (49).

Proteomic data has demonstrated how environmental factors, such as altitude, influence protein expression, enabling tailored strategies for athletes exposed to variable conditions (50, 51). For instance, specific dietary or training adaptations can be designed to mitigate performance declines at high altitudes. Integrating such insights into practice necessitates more comprehensive research on the interaction between environmental stressors and proteomic responses (52). Nutrition-centered proteomic studies have linked specific dietary interventions to enhanced recovery pathways in endurance athletes. These findings underscore the potential of proteomics to optimize performance; however, the development of cost-effective and scalable methods for analyzing proteomic data is critical for broader application in sports nutrition (7).

Metabolomics has provided critical insights into metabolic responses during exercise, identifying key biomarkers like lactate and pyruvate that signal energy depletion (53, 54). Nutritional strategies based on these biomarkers can enhance recovery by replenishing energy reserves more efficiently. However, standardization of analytical methods is necessary to ensure consistency across studies and practical applications (55). Post-exercise metabolomic profiling has identified changes in tricarboxylic acid (TCA) cycle intermediates, ketone bodies, and lipid metabolites, which reflect recovery efficiency (56, 57). These findings can inform targeted dietary interventions, but challenges like small sample sizes and variability in metabolic responses limit the generalizability of these results (58).

The discovery of miR-532-5p as a biomarker for training adaptations demonstrates the potential of metabolomics to predict and monitor individual responses to exercise. While promising, the application of such biomarkers requires further validation to confirm their reliability and reproducibility in diverse athletic populations (59). Changes in metabolic pathways, such as shifts toward fatty acid metabolism and reduced glycolysis, are indicative of long-term adaptations to exercise that improve energy efficiency (60–63). Precision nutrition strategies can leverage these insights to design training and dietary programs that maximize these benefits, though continued research is needed to optimize their integration (64). Lipidomics, a subset of metabolomics, has highlighted differential tissue-specific lipid metabolism responses to exercise, emphasizing the importance of tailoring endurance strategies based on individual metabolic profiles (56, 65). However, the limited scalability of lipidomics due to cost and analytical requirements restricts its broader adoption (66).

Multi-omics integration offers a holistic approach by combining datasets from genomics, proteomics, and metabolomics, among other fields, to derive a comprehensive understanding of athletic physiology (67–71). This approach supports the identification of precise, personalized interventions aimed at improving performance and health outcomes. The adoption of integrative platforms and emerging tools, such as OmicsAnalyst and artificial intelligence, facilitates the analysis of complex datasets, enabling improved precision in tailoring nutrition and training strategies (72, 73). Yet, challenges such as ethical concerns, logistical hurdles, and data integration complexities persist, limiting the broader implementation of multi-omics approaches in real-world settings (74, 75).

Further highlighting the relevance of multi-omics, its role in individualized athlete monitoring is amplified by advancements in wearable technology and biosensors, which allow the real-time collection of metabolic and physiological data. Such technological innovations bridge the gap between laboratory-based analyses and everyday training environments, offering unprecedented opportunities for personalized interventions. However, the high costs and need for multidisciplinary cooperation to interpret these data remain substantial obstacles (76, 77).

The scalability of omics-based interventions remains a pressing issue, particularly given the limited accessibility of sophisticated technologies for amateur or under-resourced athletic groups. However, the potential for technological advancements, such as low-cost real-time monitoring systems, may gradually resolve these limitations, making precision nutrition more widely available. Continued efforts to improve the usability and affordability of multi-omics platforms will be vital in democratizing access to these tools, thereby broadening their impact across diverse athletic populations (78–80).

Omics technologies offer immense potential for advancing sports nutrition, but addressing challenges related to cost, data integration, and ethical considerations is essential for their widespread application. Further research aimed at overcoming these limitations will be critical to maximizing the utility of omics-based precision nutrition in sports science.

The application of precision nutrition extends to addressing specific challenges faced by athletes, such as menstrual dysfunction in female athletes. Tailored macronutrient modulation and supplementation strategies have been shown to optimize hormonal health and overall performance in this context (111–115). These interventions are particularly valuable for reducing the risk of stress-induced illnesses and supporting recovery from intensive training loads. However, gender-sensitive research in sports nutrition remains underrepresented, highlighting the need for more diverse and inclusive studies to refine these strategies further (116).

Long-term exercise has been shown to induce metabolic adaptations, such as improved beta-oxidation of fatty acids and reduced glycolysis reliance, enhancing energy efficiency. These shifts not only improve immediate athletic performance but also contribute to systemic benefits, such as reduced inflammation and better cardiovascular health. Insights into these adaptations can guide more effective training and nutritional strategies tailored to individual metabolic profiles. Moreover, research indicates that the benefits of exercise extend beyond metabolic efficiency, influencing neurological health and reducing the risk of neurodegenerative diseases. For instance, sustained physical activity has been shown to enhance brain-derived neurotrophic factor expression, which plays a protective role in conditions such as Alzheimer's disease. These findings, derived from multi-omics studies, demonstrate the far-reaching physiological advantages of long-term exercise and further emphasize the importance of holistic approaches to athlete training plans (117, 118).

Urinary metabolome changes occurring after strenuous exercise provide valuable indicators of recovery states and potential pathways for intervention. For example, reduced concentrations of specific metabolites highlight the physiological stress imposed by intensive activity, offering markers for targeted recovery strategies (56). Leveraging these biomarkers can enhance the precision of post-competition nutritional protocols, allowing for faster and more effective recovery. However, the transient nature of these metabolic shifts raises questions about their reliability as long-term indicators, necessitating further exploration of their clinical utility (119).

Genetic markers associated with endurance traits and injury susceptibility offer critical insights for developing proactive training and nutrition plans. These markers facilitate the customization of workloads and recovery schedules to align with an athlete's genetic predispositions, thereby reducing the risk of overtraining and related injuries (120, 121). Such targeted strategies not only enhance training outcomes but also safeguard long-term physical health. Despite this potential, challenges in translating genetic findings into actionable recommendations persist, underscoring the need for ongoing research to refine their practical applications (7).

In conclusion, applying omics technologies to athletic performance and recovery offers significant potential for advancing personalized nutrition strategies (Figure 3). By addressing existing challenges, such as cost, scalability, and data integration, future research can unlock new opportunities to enhance both immediate and long-term athletic outcomes.