Precision Nutrition and Its Emerging Role in Clinical Practice

Nutrition is widely recognized as a critical component of human health and disease management (Ayala-Germán et al., 2025). However, recent evidence suggests that the “one-size-fits-all” approaches may not be effective for everyone, as individuals following similar dietary patterns often experience markedly different health outcomes (Wu et al., 2025). This variability arises from differences including genetics, metabolism, environment, and gut microbiome composition (Guasch-Ferré et al., 2025), all of which influence how a person responds to food and complicate nutrition counseling. Most population-based dietary guidelines are derived from group averages and may not adequately account for individual biological differences or outliers (Adams et al., 2020). Precision nutrition has emerged to address this variability by tailoring dietary guidance to each person’s biological, behavioral, and environmental characteristics.

What is precision nutrition?

Precision nutrition is an emerging approach in nutrition and dietetics that personalizes dietary guidance based on an individual’s biology, lifestyle, and environment. It recognizes that individuals respond differently to food due to variations in genetics, gut microbiota, biological processes, and behaviors. It also considers that these responses can change over time, making nutrition a dynamic, personalized process (Zeisel, 2020). Precision nutrition builds on traditional dietetic practice by equipping registered dietitians with tools such as genetic testing, metabolomic and microbiome analyses to explain individual variability, support clinical decisions, and develop personalized, practical, and sustainable nutrition plans that optimize health outcomes.

Precision nutrition’s potential role in dietetics practice

Precision nutrition can be applied across diverse nutrition and dietetic practice settings when implemented thoughtfully and ethically. Key areas of application include:

Using genetic information in practice

Individualized nutrition care has long been central to dietetic practice, with dietitians tailoring recommendations based on clinical conditions, symptoms, and patient response (Mahan et al., 2012). Established examples of tailored nutrition include gluten-free diets for celiac disease (McDermid et al., 2023), lactose-free diets for those with lactose intolerance (Deng et al., 2015), and restriction of dietary phenylalanine for individuals with phenylketonuria (PKU)(MacLeod & Ney, 2010). Precision nutrition builds upon these established approaches by incorporating emerging biological data, such as genetic information, to further refine the understanding of individual variability in nutrient metabolism and dietary response. Genetic information can help dietitians understand how a person may respond to certain foods and adjust recommendations accordingly.  For example, genetic information regarding the CYP1A2 gene, which affects caffeine metabolism, may be used in tailoring caffeine intake recommendations (Low et al., 2024). Similarly, variants of the APOE gene are associated with differences in cholesterol and dietary fat metabolism, which can inform more targeted counseling on fat intake and management (Shatwan et al., 2017).

Gut microbiome-informed advice

The 2025–2030 Dietary Guidelines for Americans acknowledge the role of the gut microbiome in health and emphasize dietary patterns that support a diverse and healthy gut microbiota (U.S. Department of Agriculture & U.S. Department of Health and Human Services, 2025, 2026). Emerging research suggests that microbiome data may complement personalized nutrition planning (Nisa et al., 2025). Information about gut bacteria may help inform recommendations related to fiber intake, probiotics, and fermented foods to support digestion, immunity, and metabolism (Fu et al., 2022).  Although gut microbiome tests are becoming increasingly available, their use in dietetics practice remains limited (Williams et al., 2023), as the science is still evolving, and defined clinical guidelines for their application have not yet been established (Porcari et al., 2025).

Lifestyle, culture, and health-based personalization

Registered dietitians deliver personalized nutrition care by considering age, medical history, lab values, and lifestyle factors such as sleep, stress, and physical activity (Mahan et al., 2012). Precision nutrition builds on these principles, including cultural and behavioral personalization, which may enable dietitians to develop realistic, effective, and sustainable nutrition plans (Guasch-Ferré et al., 2025).  For example, a dietitian might adapt a DASH diet plan to include culturally familiar foods for a patient from East Asia (Kim et al., 2013), adjust meal composition and timing for an athlete to support training and recovery, or incorporate specific cooking methods based on the patient’s health condition, which has been shown to impact health outcomes (Han et al., 2024). These approaches ensure that nutrition plans are realistic, effective, and sustainable.

Management of chronic diseases

Precision nutrition is also applicable in the management of chronic diseases, where individualized blood sugar responses, cholesterol levels, inflammation markers, and phenotypic factors like insulin resistance can guide diet planning (Laddu & Hauser, 2019). Precision nutrition is also applicable in the management of chronic diseases, where individual variability in biomarkers such as blood glucose responses, lipid profiles, inflammatory markers, and phenotypic factors like insulin resistance can inform dietary planning (Laddu & Hauser, 2019). For example, individualized monitoring of postprandial glucose responses can guide carbohydrate distribution and meal composition in individuals with type 2 diabetes or gestational diabetes mellitus. Studies using continuous glucose monitoring have demonstrated substantial interindividual variability in glycemic responses to identical meals, supporting the need for personalized dietary recommendations (Zeevi et al., 2015). Patients may track glycemic responses to specific foods through self-monitoring of blood glucose or continuous glucose monitoring, allowing dietitians to adjust carbohydrate intake and meal timing based on individual metabolic responses. Similarly, lipid profiles may inform dietary fat recommendations for individuals with hyperlipidemia, while inflammatory markers may guide dietary strategies aimed at reducing cardiometabolic risk. In addition to traditional lipid biomarkers, such as plasma lipoproteins and standard lipid panels, emerging translational measures of lipoprotein functionality and metabolism, such as cellular cholesterol flux, are being investigated in precision nutrition research and may have clinical utility in assessing cardiovascular disease risk in the future (Andersen & Fernandez, 2025).

Sports and performance nutrition

Precision nutrition can help athletes perform at their best by using biomarkers and training data to create personalized plans for fueling, hydration, and recovery. This can help in adjusting what, when, and how much they eat or drink based on their body’s responses and training demands (Cooper & Heber, 2024). Biomarkers such as ferritin levels may be monitored to assess iron status and guide dietary or supplementation strategies for athletes at risk of iron deficiency (Solberg & Reikvam, 2023). Measures of hydration status, including urine specific gravity, sweat rate, and sodium losses, can inform individualized fluid and electrolyte replacement strategies during training and competition (Armstrong et al., 2025). Genetic information is also relevant in sports nutrition. Choline, a critical nutrient for athletes, supports nerve signaling through acetylcholine production and maintains muscle cell membrane integrity via phosphatidylcholine. Low choline intake can weaken muscle membranes, increasing susceptibility to exercise-induced damage and elevated creatine phosphokinase (CPK) levels. Certain genetic variants, such as the rs2771040 polymorphism in choline metabolism genes, can make some individuals more prone to choline deficiency. Monitoring intake and tailoring nutrition for these athletes illustrates how precision nutrition can optimize performance and reduce injury risk (Nieman, 2021).

What are some Practical Considerations for using precision nutrition and future directions?

Precision nutrition remains an emerging field, with ongoing research into its use for the most common chronic conditions. As interest in this field grows, dietitians face practical challenges in integrating precision nutrition into clinical practice.

Accessibility and cost: One major consideration for using precision nutrition is accessibility and cost. Advanced testing and personalized nutrition services remain expensive and unevenly accessible, which may limit who can benefit from these approaches. Genetic tests may not always be fully covered through public or private insurance programs or within public healthcare systems. Consequently, some patients may need to pay for testing out-of-pocket (Grant et al., 2021). In addition, microbiome testing and metabolomic profiling often require specialized labs and equipment, which can be expensive or locally unavailable. Precision nutrition tests require technicians and clinicians trained to interpret complex biological data, yet many regions have few specialists, which can limit access to these approaches in those regions (McGrath & Ghersi, 2016).

Responsible practice and interpretation of data: Another important consideration is the need for responsible interpretation of data from genetic testing and microbiome analysis. These should complement and not replace established evidence and clinical judgment (Braakhuis et al., 2021).

Research and evidence for clinical practice: Precision nutrition is an emerging field with limited evidence for definitive clinical guidelines. Stronger trials and clearer guidance for practice are needed (Braakhuis et al., 2021). Additionally, minority populations are often underrepresented in research (Roman, 2025), highlighting the need for interdisciplinary collaboration and attention to health equity to avoid worsening existing health disparities.

Ethical considerations: Precision nutrition often relies on sensitive personal data, including genetic information, microbiome profiles, and continuous health monitoring. Protecting client privacy, ensuring informed consent, and maintaining transparency about how data is used and stored are important considerations for its use (Zeisel, 2020).

Precision nutrition is a promising field to address interindividual variability in dietary response. Large-scale initiatives such as the National Institute of Health (NIH) Nutrition for Precision Health program highlight the growing commitment to understanding individual responses to diet and translating these insights into clinical nutrition practice (National Institute of Health, 2025). For dietitians, its value lies not in replacing foundational nutrition principles, but in strengthening their application through informed interpretation of emerging data.

References

Adams, S. H., Anthony, J. C., Carvajal, R., Chae, L., Khoo, C. S. H., Latulippe, M. E., Matusheski, N. V., McClung, H. L., Rozga, M., Schmid, C. H., Wopereis, S., & Yan, W. (2020). Perspective: Guiding principles for the implementation of personalized nutrition approaches that benefit health and function. Advances in Nutrition, 11(1), 25–34. https://doi.org/10.1093/advances/nmz086

Andersen, C. J., & Fernandez, M. L. (2025). Emerging biomarkers and determinants of lipoprotein profiles to predict CVD risk: Implications for precision nutrition. Nutrients, 17(1), 42. https://doi.org/10.3390/nu17010042

Armstrong, L. E., Stearns, R. L., Huggins, R. A., Sekiguchi, Y., Mershon, A. J., & Casa, D. J. (2025). Reference values for hydration biomarkers: Optimizing athletic performance and recovery. Open Access Journal of Sports Medicine, 16, 31–50. https://doi.org/10.2147/OAJSM.S508656

Ayala-Germán, A. G., Pandey, A., & Duro, D. (2025). The role of functional nutrition in disease prevention and management. Gastroenterology Clinics of North America, 54(4), 791–803. https://doi.org/10.1016/j.gtc.2025.08.011

Braakhuis, A., Monnard, C. R., Ellis, A., & Rozga, M. (2021). Consensus report of the Academy of Nutrition and Dietetics: Incorporating genetic testing into nutrition care. Journal of the Academy of Nutrition and Dietetics, 121(3), 545–552. https://doi.org/10.1016/j.jand.2020.04.002

Cooper, C., & Heber, D. (2024). Precision nutrition in exercise and sports performance. In Precision Nutrition (pp. 333–354). Elsevier.

Fu, J., Yan Zheng, Gao, Y., & Xu, W. (2022). Dietary fiber intake and gut microbiota in human health. Microorganisms, 10(12), 2507. https://doi.org/10.3390/microorganisms10122507

Grant, P., Langlois, S., Lynd, L. D., Study, G., Austin, J. C., & Elliott, A. M. (2021). Out-of-pocket and private pay in clinical genetic testing: A scoping review. Clinical Genetics, 100(5), 504–521. https://doi.org/10.1111/cge.14006

Guasch-Ferré, M., Wittenbecher, C., Palmnäs, M., Ben-Yacov, O., Blaak, E. E., Dahm, C. C., Fall, T., Heitmann, B. L., Licht, T. R., Löf, M., Loos, R., Patel, C. J., Quarta, C., Redman, L. M., Segal, E., Segata, N., Snyder, M., Sun, Q., Tobias, D. K., … Merino, J. (2025). Precision nutrition for cardiometabolic diseases. Nature Medicine, 31(5), 1444–1453. https://doi.org/10.1038/s41591-025-03669-9

Guess, N., Pollard, D., Brennan, A., & Breeze, P. (2026). Precision nutrition must consider cost-effectiveness to deliver benefits to patients. Nature Medicine, 1–2. https://doi.org/10.1038/s41591-026-04231-x

Han, T., Wei, W., Jiang, W., Geng, Y., Liu, Z., Yang, R., Jin, C., Lei, Y., Sun, X., Xu, J., Chen, J., & Sun, C. (2024). The future landscape and framework of precision nutrition. Engineering, 42, 15–25. https://doi.org/10.1016/j.eng.2024.01.020

Kim, H., Song, H.-J., Han, H.-R., Kim, K. B., & Kim, M. T. (2013). Translation and validation of the Dietary Approaches to Stop Hypertension for Koreans intervention. The Journal of Cardiovascular Nursing, 28(6), 514–523. https://doi.org/10.1097/JCN.0b013e318262c0c1

Laddu, D., & Hauser, M. (2019). Addressing the nutritional phenotype through personalized nutrition for chronic disease prevention and management. Progress in Cardiovascular Diseases, 62(1), 9–14. https://doi.org/10.1016/j.pcad.2018.12.004

Low, J. J.-L., Tan, B. J.-W., Yi, L.-X., Zhou, Z.-D., & Tan, E.-K. (2024). Genetic susceptibility to caffeine intake and metabolism: A systematic review. Journal of Translational Medicine, 22(1), 961. https://doi.org/10.1186/s12967-024-05737-z

Mahan, L. K., Escott-Stump, S., Raymond, J. L., & Krause, M. V. (2012). Krause’s food & the nutrition care process (13th ed). Elsevier/Saunders. https://cir.nii.ac.jp/crid/1970304959953475080

McGrath, S., & Ghersi, D. (2016). Building towards precision medicine: Empowering medical professionals for the next revolution. BMC Medical Genomics, 9, 23. https://doi.org/10.1186/s12920-016-0183-8

National Institute of Health. (2025). Nutrition for Precision Health, powered by the All of Us Research Program. https://commonfund.nih.gov/nutritionforprecisionhealth

Nieman, D. C. (2021). Multiomics approach to precision sports nutrition: Limits, challenges, and possibilities. Frontiers in Nutrition, 8. https://doi.org/10.3389/fnut.2021.796360

Nisa, P., Kirthi, A. V., & Sinha, P. (2025). Microbiome-based approaches to personalized nutrition: From gut health to disease prevention. Folia Microbiologica, 70(5), 961–978. https://doi.org/10.1007/s12223-025-01337-6

Porcari, S., Mullish, B. H., Asnicar, F., Ng, S. C., Zhao, L., Hansen, R., O’Toole, P. W., Raes, J., Hold, G., Putignani, L., Hvas, C. L., Zeller, G., Koren, O., Tun, H., Valles-Colomer, M., Collado, M. C., Fischer, M., Allegretti, J., Iqbal, T., … Ianiro, G. (2025). International consensus statement on microbiome testing in clinical practice. The Lancet Gastroenterology & Hepatology, 10(2), 154–167. https://doi.org/10.1016/S2468-1253(24)00311-X

Roman, Y. (2025). Bridging the United States population diversity gaps in clinical research: Roadmap to precision health and reducing health disparities. Personalized Medicine, 22(3), 193–203. https://doi.org/10.1080/17410541.2025.2504329

Shatwan, I. M., Weech, M., Jackson, K. G., Lovegrove, J. A., & Vimaleswaran, K. S. (2017). Apolipoprotein E gene polymorphism modifies fasting total cholesterol concentrations in response to replacement of dietary saturated with monounsaturated fatty acids in adults at moderate cardiovascular disease risk. Lipids in Health and Disease, 16(1), 222. https://doi.org/10.1186/s12944-017-0606-3

Solberg, A., & Reikvam, H. (2023). Iron status and physical performance in athletes. Life, 13(10), 2007. https://doi.org/10.3390/life13102007

U.S. Department of Agriculture & U.S. Department of Health and Human Services, 2025. (2026). Dietary Guidelines for Americans, 2025–2030. https://www.dietaryguidelines.gov/

Williams, G. M., Tapsell, L. C., & Beck, E. J. (2023). Dietitians’ perspectives on the role of dietetics practice in “gut health.” Nutrition & Dietetics: The Journal of the Dietitians Association of Australia, 80(1), 95–103. https://doi.org/10.1111/1747-0080.12778

Wu, Y., Ehlert, B., Metwally, A. A., Perelman, D., Park, H., Brooks, A. W., Abbasi, F., Michael, B., Celli, A., Bejikian, C., Ayhan, E., Lu, Y., Lancaster, S. M., Hornburg, D., Ramirez, L., Bogumil, D., Pollock, S., Wong, F., Bradley, D., … Snyder, M. P. (2025). Individual variations in glycemic responses to carbohydrates and underlying metabolic physiology. Nature Medicine, 31(7), 2232–2243. https://doi.org/10.1038/s41591-025-03719-2

Zeevi, D., Korem, T., Zmora, N., Israeli, D., Rothschild, D., Weinberger, A., Ben-Yacov, O., Lador, D., Avnit-Sagi, T., Lotan-Pompan, M., Suez, J., Mahdi, J. A., Matot, E., Malka, G., Kosower, N., Rein, M., Zilberman-Schapira, G., Dohnalová, L., Pevsner-Fischer, M., … Segal, E. (2015). Personalized nutrition by prediction of glycemic responses. Cell, 163(5), 1079–1094. https://doi.org/10.1016/j.cell.2015.11.001

Zeisel, S. H. (2020). Precision (personalized) nutrition: Understanding metabolic heterogeneity. Annual Review of Food Science and Technology, 11, 71–92. https://doi.org/10.1146/annurev-food-032519-051736

Image: Google DeepMind via Pexels