The role of nutrigenomics and biotechnology in personalized nutrition: Trends, challenges, and future directions
Keywords:
Personalized nutrition, Nutrigenomics, Multi-omics integration, Artificial intelligence, Precision healthAbstract
Personalized nutrition is redefining dietary science through the integration of genetic, metabolic, and microbiome data to enable targeted interventions. Nutrigenomics and nutrigenetics underpin this approach by elucidating gene–diet interactions and inter-individual variability in metabolic responses. Recent advances in high-throughput sequencing, polygenic risk modeling, and microbiome profiling, coupled with multi-omics integration and artificial intelligence, have improved the prediction of diet–health relationships and enabled data-driven nutritional strategies. However, clinical translation remains limited by inconsistent evidence, lack of standardized biomarkers, and insufficient external validation of predictive models, alongside challenges in cost and scalability. Ethical concerns, including data privacy, regulatory oversight, and equitable access, further constrain implementation. Future research should prioritize large, diverse cohorts, rigorous validation frameworks, and integration of AI-driven decision support with clinically actionable outcomes to advance personalized nutrition toward scalable and evidence-based practice.
References
1. Singar S, Nagpal R, Arjmandi BH, Akhavan NS. Personalized Nutrition: Tailoring Dietary Recommendations through Genetic Insights. Nutrients. Multidisciplinary Digital Publishing Institute (MDPI); 2024. doi:10.3390/nu16162673 PubMed PMID: 39203810.
2. Lagoumintzis G, Patrinos GP. Triangulating nutrigenomics, metabolomics and microbiomics toward personalized nutrition and healthy living. Human Genomics. BioMed Central Ltd; 2023. doi:10.1186/s40246-023-00561-w PubMed PMID: 38062537.
3. Nourazarain A, Vaziri Y. Nutrigenomics meets multi-omics: integrating genetic, metabolic, and microbiome data for personalized nutrition strategies. Genes and Nutrition. BioMed Central Ltd; 2025. doi:10.1186/s12263-025-00790-9
4. San-Cristobal R, de Toro-Martín J, Vohl MC. Appraisal of Gene-Environment Interactions in GWAS for Evidence-Based Precision Nutrition Implementation. Current Nutrition Reports. Springer; 2022. p. 563–73. doi:10.1007/s13668-022-00430-3 PubMed PMID: 35948824.
5. Mansour S, Alkhaaldi SMI, Sammanasunathan AF, Ibrahim S, Farhat J, Al-Omari B. Precision Nutrition Unveiled: Gene–Nutrient Interactions, Microbiota Dynamics, and Lifestyle Factors in Obesity Management. Nutrients. Multidisciplinary Digital Publishing Institute (MDPI); 2024. doi:10.3390/nu16050581 PubMed PMID: 38474710.
6. Kohlmeier M, De Caterina R, Ferguson LR, Görman U, Allayee H, Prasad C, et al. Guide and Position of the International Society of Nutrigenetics/Nutrigenomics on Personalized Nutrition: Part 2 - Ethics, Challenges and Endeavors of Precision Nutrition. In: Journal of Nutrigenetics and Nutrigenomics. S. Karger AG; 2016. p. 28–46. doi:10.1159/000446347 PubMed PMID: 27286972.
7. Farzand A, Rohin MAK, Awan SJ, Ahmad AMR, Akram H, Saleem T, et al. Nutrigenomics of Obesity: Integrating Genomics, Epigenetics, and Diet–Microbiome Interactions for Precision Nutrition. Life. Multidisciplinary Digital Publishing Institute (MDPI); 2025. doi:10.3390/life15111658
8. Lagoumintzis G, Patrinos GP. Triangulating nutrigenomics, metabolomics and microbiomics toward personalized nutrition and healthy living. Human Genomics. BioMed Central Ltd; 2023. doi:10.1186/s40246-023-00561-w PubMed PMID: 38062537.
9. Wang X, Sun Z, Xue H, An R. Artificial Intelligence Applications to Personalized Dietary Recommendations: A Systematic Review. Healthcare (Switzerland). Multidisciplinary Digital Publishing Institute (MDPI); 2025. doi:10.3390/healthcare13121417
10. Abeltino A, Riente A, Bianchetti G, Serantoni C, De Spirito M, Capezzone S, et al. Digital applications for diet monitoring, planning, and precision nutrition for citizens and professionals: a state of the art. Nutrition Reviews. Oxford University Press; 2025. p. e574–601. doi:10.1093/nutrit/nuae035 PubMed PMID: 38722240.
11. Ramos-Lopez O, Martinez JA, Milagro FI. Holistic Integration of Omics Tools for Precision Nutrition in Health and Disease. Nutrients. MDPI; 2022. doi:10.3390/nu14194074 PubMed PMID: 36235725.
12. Ramos Meyers G, Samouda H, Bohn T. Short Chain Fatty Acid Metabolism in Relation to Gut Microbiota and Genetic Variability. Nutrients. MDPI; 2022. doi:10.3390/nu14245361 PubMed PMID: 36558520.
13. Lam HN, Lin SP, Nguyen DHN, Chen CM, Su CT, Fang TC, et al. Integrative Roles of Functional Foods, Microbiotics, Nutrigenetics, and Nutrigenomics in Managing Type 2 Diabetes and Obesity. Nutrients . Multidisciplinary Digital Publishing Institute (MDPI); 2025. doi:10.3390/nu17040608 PubMed PMID: 40004938.
14. Kassem H, Beevi AA, Basheer S, Lutfi G, Cheikh Ismail L, Papandreou D. Investigation and Assessment of AI’s Role in Nutrition—An Updated Narrative Review of the Evidence. Nutrients . Multidisciplinary Digital Publishing Institute (MDPI); 2025. doi:10.3390/nu17010190 PubMed PMID: 39796624.
15. Duarte GBS, Pascoal G de FL, Rogero MM. Polymorphisms Involved in Insulin Resistance and Metabolic Inflammation: Influence of Nutrients and Dietary Interventions. Metabolites. Multidisciplinary Digital Publishing Institute (MDPI); 2025. doi:10.3390/metabo15040245
16. Romero-Tapiador S, Lacruz-Pleguezuelos B, Tolosana R, Freixer G, Daza R, Fernández-Díaz CM, et al. AI4FoodDB: a database for personalized e-Health nutrition and lifestyle through wearable devices and artificial intelligence. Database. 2023;2023. doi:10.1093/database/baad049 PubMed PMID: 37465917.
17. Mohr AE, Ortega-Santos CP, Whisner CM, Klein-Seetharaman J, Jasbi P. Navigating Challenges and Opportunities in Multi-Omics Integration for Personalized Healthcare. Biomedicines. Multidisciplinary Digital Publishing Institute (MDPI); 2024. doi:10.3390/biomedicines12071496 PubMed PMID: 39062068.
18. Phalle A, Gokhale D. Navigating next-gen nutrition care using artificial intelligence-assisted dietary assessment tools—a scoping review of potential applications. Frontiers in Nutrition. Frontiers Media SA; 2025. doi:10.3389/fnut.2025.1518466
19. Investigation of Polymers with Differential Scanning Calorimetry. In: Physik von Makromolekülen. HUMBOLDT-UNIVERSITÄT ZU BERLIN. p. 1–17.
20. Sanna S, van Zuydam NR, Mahajan A, Kurilshikov A, Vich Vila A, Võsa U, et al. Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases. Nature Genetics. Nature Publishing Group; 2019. p. 600–5. doi:10.1038/s41588-019-0350-x PubMed PMID: 30778224.
21. Petre ML, Tsichla H, Pertesi ANK, Mpoulioglou OE, Kouvela M, Vamvakaris IN, et al. Precision nutrition: Is tailor-made dietary intervention a reality yet? (Review). Biomedical Reports. Spandidos Publications; 2025. doi:10.3892/br.2025.1964
22. Mundt C, Yusufoğlu B, Kudenko D, Mertoğlu K, Esatbeyoglu T. AI-Driven Personalized Nutrition: Integrating Omics, Ethics, and Digital Health. Molecular Nutrition and Food Research. John Wiley and Sons Inc; 2025. doi:10.1002/mnfr.70293 PubMed PMID: 41163310.
23. Verma M, Hontecillas R, Tubau-Juni N, Abedi V, Bassaganya-Riera J. Challenges in Personalized Nutrition and Health. Frontiers in Nutrition. Frontiers Media S.A.; 2018. doi:10.3389/fnut.2018.00117
24. Abdol Ghaffar Ebadi, Zeliha Selamoglu. Personalized Nutrition in the Genomics and Digital Age: Current Issues, Future Directions, and Evidence. Wah Academia Journal of Health and Nutrition. 2025 Mar 31;1(1):34–9. doi:10.63954/sns00e14
25. The Lancet Diabetes & Endocrinology. Diabetes behind bars: challenging inadequate care in prisons. The Lancet Diabetes and Endocrinology. Lancet Publishing Group; 2018. p. 347. doi:10.1016/S2213-8587(18)30103-7 PubMed PMID: 29680451.
26. Ludvik B, Frías JP, Tinahones FJ, Wainstein J, Jiang H, Robertson KE, et al. Dulaglutide as add-on therapy to SGLT2 inhibitors in patients with inadequately controlled type 2 diabetes (AWARD-10): a 24-week, randomised, double-blind, placebo-controlled trial. Lancet Diabetes Endocrinol. 2018 May 1;6(5):370–81. doi:10.1016/S2213-8587(18)30023-8 PubMed PMID: 29483060.
27. Sivaprasad S, Vasconcelos JC, Prevost AT, Holmes H, Hykin P, George S, et al. Clinical efficacy and safety of a light mask for prevention of dark adaptation in treating and preventing progression of early diabetic macular oedema at 24 months (CLEOPATRA): a multicentre, phase 3, randomised controlled trial. Lancet Diabetes Endocrinol. 2018 May 1;6(5):382–91. doi:10.1016/S2213-8587(18)30036-6 PubMed PMID: 29519744.
28. Guasch-Ferré M, Wittenbecher C, Palmnäs M, Ben-Yacov O, Blaak EE, Dahm CC, et al. Precision nutrition for cardiometabolic diseases. Nature Medicine. Nature Research; 2025. p. 1444–53. doi:10.1038/s41591-025-03669-9 PubMed PMID: 40307513.
29. Priyadharshini D, Muthuvel I, Saraswathy S, Kavitha PS, Jegadeeswari V. Precision to plate: AI-driven innovations in fermentation and hyper-personalized diets. Frontiers in Nutrition. Frontiers Media SA; 2025. doi:10.3389/fnut.2025.1659511
30. Babu M, Snyder M. Multi-omics profiling for health. Molecular and Cellular Proteomics. 2023;22(6). doi:10.1016/j.mcpro.2023.100561 PubMed PMID: 37119971.
31. Mullins VA, Bresette W, Johnstone L, Hallmark B, Chilton FH. Genomics in personalized nutrition: Can you “eat for your genes”? Nutrients. MDPI AG; 2020. p. 1–23. doi:10.3390/nu12103118 PubMed PMID: 33065985.
32. Lagoumintzis G, Patrinos GP. Triangulating nutrigenomics, metabolomics and microbiomics toward personalized nutrition and healthy living. Human Genomics. BioMed Central Ltd; 2023. doi:10.1186/s40246-023-00561-w PubMed PMID: 38062537.
33. Lam HN, Lin SP, Nguyen DHN, Chen CM, Su CT, Fang TC, et al. Integrative Roles of Functional Foods, Microbiotics, Nutrigenetics, and Nutrigenomics in Managing Type 2 Diabetes and Obesity. Nutrients . Multidisciplinary Digital Publishing Institute (MDPI); 2025. doi:10.3390/nu17040608 PubMed PMID: 40004938.
34. Ben-Yacov O, Godneva A, Rein M, Shilo S, Lotan-Pompan M, Weinberger A, et al. Gut microbiome modulates the effects of a personalised postprandial-targeting (PPT) diet on cardiometabolic markers: a diet intervention in pre-diabetes. Gut. 2023 Aug 1;72(8):1486–96. doi:10.1136/gutjnl-2022-329201 PubMed PMID: 37137684.
35. Fekete M, Lehoczki A, Kryczyk-Poprawa A, Zábó V, Varga JT, Bálint M, et al. Functional Foods in Modern Nutrition Science: Mechanisms, Evidence, and Public Health Implications. Nutrients . Multidisciplinary Digital Publishing Institute (MDPI); 2025. doi:10.3390/nu17132153 PubMed PMID: 40647258.
36. Phillips CM. Nutrigenetics and metabolic disease: Current status and implications for personalised nutrition. Nutrients. MDPI AG; 2013. p. 32–57. doi:10.3390/nu5010032
37. Abeltino A, Hatem D, Serantoni C, Riente A, De Giulio MM, De Spirito M, et al. Unraveling the Gut Microbiota: Implications for Precision Nutrition and Personalized Medicine. Nutrients . Multidisciplinary Digital Publishing Institute (MDPI); 2024. doi:10.3390/nu16223806 PubMed PMID: 39599593.
38. Jardon KM, Canfora EE, Goossens GH, Blaak EE. Dietary macronutrients and the gut microbiome: a precision nutrition approach to improve cardiometabolic health. Gut. 2022;71(6):1214–26. doi:10.1136/gutjnl-2020-323715 PubMed PMID: 35135841.
39. Jardon KM, Canfora EE, Goossens GH, Blaak EE. Dietary macronutrients and the gut microbiome: a precision nutrition approach to improve cardiometabolic health. Gut. 2022;71(6):1214–26. doi:10.1136/gutjnl-2020-323715 PubMed PMID: 35135841.
40. Olutunde, Timothy, Ani, Chukwuemeka L, Adesue GA. Leveraging Machine Learning for Personalized Dietary Recommendations, Nutritional Patterns, and Health Outcome Predictions. JOSRAR. 1(2):43–56. doi:10.70882/josrar.2024.v1i2
41. Aydın SK, Ali RH, Faiz S, Khan TA. An Integrated AI Framework for Personalized Nutrition Using Machine Learning and Natural Language Processing for Dietary Recommendations. Applied Sciences (Switzerland). 2025 Sep 1;15(17). doi:10.3390/app15179283
42. Kassem H, Beevi AA, Basheer S, Lutfi G, Cheikh Ismail L, Papandreou D. Investigation and Assessment of AI’s Role in Nutrition—An Updated Narrative Review of the Evidence. Nutrients . Multidisciplinary Digital Publishing Institute (MDPI); 2025. doi:10.3390/nu17010190 PubMed PMID: 39796624.
43. Arshad Z, Shahid S, Hasnain A, Yaseen E, Rahimi M. Functional Foods Enriched With Bioactive Compounds: Therapeutic Potential and Technological Innovations. Food Science and Nutrition. John Wiley and Sons Inc; 2025. doi:10.1002/fsn3.71024
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