Personalized Nutrition for the Enhancement of Elite Athletic Performance

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Study Details

Authors
Sutehall, S., & Pitsiladis, Y
Published
2025
View the study
Citation
Sutehall, S., & Pitsiladis, Y. (2025). Personalized nutrition for the enhancement of elite athletic performance. Scandinavian Journal of Medicine & Science in Sports, 35(4), e70044. https://doi.org/10.1111/sms.70044

While sports nutrition has progressed significantly since the early 20th century, the field has reached a plateau in its ability to support elite performance using generalized strategies. This article advocates for a paradigm shift toward personalized nutrition, highlighting that a one-size-fits-all approach often fails to account for individual variability in physiology, metabolism, and response to interventions. Integrating real-time biometric data, wearable technologies, multi-omics analysis, and artificial intelligence, the authors outline the next frontier of performance nutrition.

Carbohydrate and Hydration Recommendations Are Evolving, but Still Controversial

  • Early studies identified carbohydrate (CHO) as a key performance fuel and hydration as a factor in endurance.
  • Despite popular guidelines (e.g., to avoid >2% dehydration), recent ecologically valid studies challenge this assumption, especially in elite athletes.
  • The field still grapples with low reproducibility, lab-based testing limitations, and industry bias in supplement research.
  • Individual characteristics like sweat rate, metabolic preference, and gastric emptying vary widely, complicating standard recommendations.

One-Size-Fits-All Interventions Underserve Diverse Athlete Needs

  • Even among elite athletes (e.g., Ethiopian Olympic runners), sweat rates can range from 0.8 to 3.6 L/hour, showing large interindividual variability.
  • Female athletes remain underrepresented in sports nutrition research, despite having distinct physiological needs.
  • Real-world performance demands differ from lab conditions, highlighting the need for more ecologically valid testing environments.
  • Wearable tech (e.g., CGMs, sweat patches) can support individualized recommendations by monitoring hydration, glucose, and thermoregulation in real time.

The Promise and Pitfalls of Genetic and Multi-Omics Personalization

  • Advances in genomics, metabolomics, proteomics, and transcriptomics enable individualized insights into fueling and recovery.
  • However, genetic testing for sports performance currently lacks clinical utility, and commercial offerings often outpace scientific validation.
  • A poly-omics approach, combined with AI, could identify individualized nutrition strategies, but data interpretation, standardization, and application remain major hurdles.

Artificial Intelligence Will Be Key to Scaling Personalized Nutrition

  • AI and machine learning offer the ability to process complex, real-time data and generate dynamic, evidence-based recommendations.
  • With the emergence of 6G networks, data from multiple devices can be integrated seamlessly, even in competition settings.
  • Future applications include real-time fuel and hydration adjustments based on individual metabolism, activity demands, and environmental conditions.
  • Still, these tools must be used ethically and be grounded in valid, interpretable data, not simply adopted for their novelty.

Conclusion and Forward-Looking Perspective

  • Personalized nutrition is not just about identifying the “best” supplement or macronutrient dose, but about capturing and responding to variability in athlete biology, context, and competition demands.
  • The field must move from generalized guidelines to real-time, context-sensitive, and individualized approaches, aided by bioinformatics and next-generation technologies.
  • Though focused largely on endurance sports, the implications extend to all disciplines, including team and strength-based sports.

Sutehall, S., & Pitsiladis, Y. (2025). Personalized nutrition for the enhancement of elite athletic performance. Scandinavian Journal of Medicine & Science in Sports, 35(4), e70044. https://doi.org/10.1111/sms.70044