Personalized Health and Nutrition Approaches within the Framework of the Healthy Society Ideal: An Analysis Through Project Examples

Öz

In today's rapidly changing world, personalized health and nutrition have emerged as innovative solutions to meet the growing need for individual health management. These solutions often offer tailored recommendations on nutritional choices, exercise routines, and lifestyle changes based on individualized data such as genetics, disease history, and lifestyle habits. These advances in personalized health and nutrition aim to improve health outcomes by providing a more effective and customized approach to individuals' health needs. This study highlights the trends and advances of personalized health applications in developed countries through the literature review method. These applications are often supported by advanced technology and data analytics tools, offering a more in-depth and personalized approach to individuals' health and nutritional preferences. The results show that expanding personalized health and nutrition solutions is a crucial step towards a healthier society.

Anahtar Kelimeler

• Health
• evidence-based healthcare
• information management
• European Union (EU)

Sağlıklı Toplum İdeali Kapsamında Kişiselleştirilmiş Sağlık ve Beslenme Yaklaşımları: Proje Örnekleri Üzerinden Bir Analiz

Öz

Günümüz dünyasında hızlı toplumsal ve teknolojik değişimlere bağlı olarak, bireysel sağlık yönetimi giderek daha fazla önem kazanmaktadır. Bu çerçevede kişiye özel sağlık ve beslenme uygulamaları, bireylerin özgün ihtiyaçlarına yönelik yenilikçi çözümler olarak ortaya çıkmaktadır. Genetik özellikler, sağlık geçmişi ve yaşam alışkanlıkları gibi kişisel verilere dayanan bu uygulamalar; beslenme düzeni, fiziksel aktivite planı ve yaşam tarzı önerileri gibi alanlarda bireye özel rehberlik sağlamaktadır. Kişiselleştirilmiş sağlık ve beslenme alanındaki bu gelişmeler, bireylerin sağlık ihtiyaçlarına daha etkili ve özelleştirilmiş bir yaklaşım sunarak sağlık sonuçlarını pozitif yönde artırmayı hedeflemektedir. Bu çalışma, literatür taraması yöntemiyle yapılan kaynak araştırmalarına dayanarak, gelişmiş ülkelerdeki kişiselleştirilmiş sağlık uygulamalarının trendlerini ve ilerlemelerini vurgulamaktadır. Bu uygulamalar, genellikle ileri teknoloji ve veri analitiği araçlarıyla desteklenmektedir ve bireylerin sağlık ve beslenme tercihlerine yönelik daha derinlemesine ve özelleştirilmiş bir yaklaşım sunmaktadır. Sonuçlar, kişiye özel sağlık ve beslenme çözümlerinin yaygınlaştırılmasının, daha sağlıklı bir toplum için önemli bir adım olduğunu göstermektedir.

Anahtar Kelimeler

• Sağlık
• kanıta dayalı sağlık hizmeti
• bilgi yönetimi
• Avrupa Birliği (AB)

Kaynakça

1. Astarita, G., & Langridge, J. (2013). An Emerging Role for Metabolomics in Nutrition Science. Journal Of Nutrigenet and Nutrigenomics, 6(4-5), 181–200. https://doi:10.1159%2F000354403
2. Betts , J., & Gonzalez, J. (2016). Personalised Nutrition: What Makes You So Special? Nutrition Bullettin, 41(1), 353-359. https://doi:10.1111/nbu.12238
3. Bonneux, C., Mahmood, D., Scherrenberg, M., Falter, M., Ruiz, G., Kindermans, H., . . . Coninx, K. (2022). The CoroPrevention-SDM Approach: A Technology-supported Shared Decision Making Approach for a Comprehensive Secondary Prevention Program for Cardiac Patients. In Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2022), (s. 59-69). https://doi:10.5220/0011042300003188
4. Celis-Morales, C., Livingstone, K., Marsaux, C., Macready, A., Fallaize, R., O’Donovan, C., . . . Mathers, J. (2017). Effect Of Personalized Nutrition On Healthrelated Behaviour Change: Evidence from The Food4Me European Randomized Controlled Trial. International Journal of Epidemiology, 46(2), 578–588. https://doi:10.1093/ije/dyw186
5. Di Cesare, M., Bixby, H., Gaziano, T., Hadeed, L., Kabudula, C., McGhie, D., . . . Pinto, F. (2023). World Heart Report 2023. Geneva: Novartis Foundation. Retrieved May 14, 2024 from https://world-heart-federation.org/wp-content/uploads/World-Heart-Report-2023.pdf
6. Di Cesare, M., Khang, Y.-H., Asaria, P., Blakely, T., Cowan, M., Farzadfar, F., . . . Ezzati, M. (2013). Inequalities İn Non-Communicable Diseases And Effective Responses. Lancet, 381, 585–597. https://doi:10.1016/S0140-6736(12)61851-0
7. European Comission. (2014). EU Funded projects - EU Funding & Tenders Portal. Retrieved April 25, 2025 from https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/projects-results?order=DESC&pageNumber=1&pageSize=10&sortBy=es_SortDate&keywords=sustainability
8. European Comission. (2020). Personalized Prevention for Coronary Heart Disease. Retrieved June 26, 2025 from https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/projects-details/31045243/848056/H2020?keywords=personalized&isExactMatch=true&order=DESC&pageNumber=NaN&sortBy=relevance

9. European Comission. (2021). Developing tools for the PREDICTion, at individual level, of the CArdiometabolic REsponse to the consumption of dietary (poly)phenols. Universita Degli Studi Di Parma, Italy. https://doi:10.3030/950050
10. European Comission. (2022a). A PeRsOnalized Prevention roadmap for the future HEalThcare. Retrieved June 24, 2025 from https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/projects-details/43108390/101057721/HORIZON?keywords=personalized&isExactMatch=true&order=DESC&pageNumber=NaN&sortBy=relevance
11. European Comission. (2022b). Fact-Based Personalised Nutrition For The Young. ALPES LASERS SA. Switzerland: Publication Office of the European Union. https://doi:10.3030/818110
12. European Comission. (2022c). Innovative Solution For Personalised Nutrition Advice. ETHNIKO KENTRO EREVNAS KAI TECHNOLOGIKIS ANAPTYXIS. Publication Office of the European Union. https://doi:10.3030/817732
13. European Comission. (2023a). FOod and nutritiOn Data-driven innovation respectful of citizen's Data SovereIgnTY. F6s Network Ireland Limited, Ireland. https://doi:10.3030/101086105
14. European Comission. (2023b). Personalised Medicine. Retrieved July 20, 2025 from https://research-and-innovation.ec.europa.eu/research-area/health/personalised-medicine_en
15. Foodity. (2023). Retrieved May 19, 2024 from https://foodity.eu/datalake/
16. Franks, P., Pearson, E., & Florez, J. (2013). Gene-Environment and Gene-Treatment Interactions in Type 2 Diabetes. Diabetes Care, 36(5), 1413-1421. https://doi:10.2337/dc12-2211
17. IDF. (2021). Diabetes Data Report 2000-2045. International Diabetes Federation. Retrieved May 13, 2024 from https://diabetesatlas.org/data/en/world/
18. IHME. (2022a). GBD Cancer Compare Data Visualization. Seattle: Institute for Health Metrics and Evaluation, University of Washington. Retrieved May 17, 2024 from https://vizhub.healthdata.org/gbd-compare/cancer
19. IHME. (2022b). GBD CVD Compare Data Visualization. Seattle: Institute for Health Metrics and Evaluation, University of Washington. Retrieved May 17, 2024 from https://vizhub.healthdata.org/gbd-compare/
20. Mabry, P., & Bures, R. (2014). Systems Science for Obesity-Related Research Questions: An Introduction to the Theme Issue. American Journal of Public Health (AJPH), 104(7), 1157-1159. https://doi:10.2105/AJPH.2014.302083
21. Nutrishield. (2019). Outcomes. Retrieved May 18, 2024 from https://nutrishield-project.eu/outcomes
22. Pastorinoa, R., Pezzullo, A., Osti, T., Adany, R., Borry, P., Barnhoorn, F., . . . Boccia, S. (2024). The PROPHET project paves the way for personalized prevention in the future healthcare. European Journal of Cancer Prevention, 33(5), 387–389. https://doi:10.1097/CEJ.0000000000000873
23. Popkin, B., & Hawkes, C. (2016). Sweetening of the global diet, particularly beverages: patterns, trends, and policy responses. The Lancet, 4(2), 174-186.
24. Roman, S., Campos-Medina, L., & Leal-Mercado, L. (2024). Personalized Nutrition: The End Of The One-Diet-Fits-All Era. Frontiers in Nutrition, 01-08. https://doi.org/10.3389/fnut.2024.1370595
25. Stefanidis, K., Tsatsou, D., Konstantinidis, D., Gymnopoulos, L., Daras, P., Wilson-Barnes, S., . . . Dimitropoulos, K. (2022). PROTEIN AI Advisor: A Knowledge-Based Recommendation Framework Using Expert-Validated Meals for Healthy Diets. Nutrients, 14, 1-28. doi:10.3390/nu14204435
26. Theodore Armand, T., Nfor, K., Kim, J.-I., & Kim, H.-C. (2024). Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review. Nutrients, 16(7), 1-24. https://doi:10.3390/nu16071073
27. Verma, M., Hontecillas, R., Tubau-Juni, N., Abedi, V., & Bassaganya-Riera, J. (2018). Challenges in Personalized Nutrition and Health. Frontiers İn Nutrition, 5(1), 1-10. https://doi:10.3389/fnut.2018.00117
28. Wang, Y., Wang, X., Wang, C., & Zhou, J. (2024). Global, Regional, and National Burden of Cardiovascular Disease, 1990-2021: Results From the 2021 Global Burden of Disease Study. Cureus, 16(11), 1-32. https://doi:10.7759/cureus.74333
29. WHO. (2020). Healthy diet. Retrieved June 24, 2025 from https://www.who.int/news-room/fact-sheets/detail/healthy-diet
30. WHO. (2024a). Noncommunicable diseases. Retrieved July 31, 2025 from https://www.who.int/news-room/fact-sheets/detail/communicable-diseases
31. WHO. (2024b). Prevalence of overweight among adults, BMI ≥ 25 (age-standardized estimate) (%). Retrieved May 12, 2024, from https://www.who.int/data/gho/data/indicators/indicator-details/GHO/prevalence-of-overweight-among-adults-bmi--25-(age-standardized-estimate)-(-)
32. WHO. (2024c). Prevalence of underweight among adults, BMI < 18 (age-standardized estimate) (%). Retrieved May 12, 2024, from https://www.who.int/data/gho/data/indicators/indicator-details/GHO/prevalence-of-underweight-among-adults-bmi-18-(age-standardized-estimate)-(-)
33. Wilson-Barnes, S., Gymnopoulos, L., Dimitropoulos, K., Solachidis, V., Rouskas, K., Russell, D., . . . Hart, K. (2021). PeRsOnalised nutriTion for hEalthy livINg:The PROTEIN project. Nutrition Bulletin, 46(1), 77-87. https://doi:10.1111/nbu.12482
34. Zeevi, D., Korem, T., Zmora, N., Israeli, D., Rothschild, D., Weinberger, A., . . . Segal, E. (2015). Personalized Nutrition by Prediction of Glycemic Responses. Cell, 163(5), 1079–1094. https://doi:10.1016/j.cell.2015.11.001