Fatımah Mohammed Abbas Abbas
1
,
Rahime Evra Karakaya
2
1
Ankara Yıldırım Beyazıt University
2
Ankara Yıldırım Beyazıt University
Abstract
Type 2 Diabetes Mellitus (T2DM), characterized by chronic hyperglycemia, is a major global health problem associated with increased morbidity, mortality, and healthcare costs. This narrative review aimed to evaluate the clinical effects of personalized nutrition (PN) approaches supported by Digital Twin (DT) technology in the management of T2DM. A comprehensive literature review was conducted using indexed databases, including PubMed, Scopus, Web of Science, and Google Scholar, without time restrictions and limited to articles published in English. Relevant studies focusing on artificial intelligence (AI), DT technology, PN, continuous glucose monitoring (CGM), and diabetes management were analyzed. The findings indicate that DT-supported PN approaches, which integrate AI, Internet of Things (IoT)-based systems, CGM data, lifestyle factors, and physiological parameters, may contribute to improved glycemic control and individualized diabetes management. Studies reported reductions in HbA1c levels, insulin resistance, medication requirements, and improvements in certain metabolic and cardiovascular risk markers. In addition, DT technology enables the creation of virtual metabolic models that allow simulation of treatment outcomes and support early intervention strategies. However, important limitations remain, including challenges related to data integration, model bias, high implementation costs, and concerns regarding data privacy and security. In conclusion, DT-supported PN approaches represent a promising next-generation strategy for T2DM management by enhancing personalized care and supporting glycemic control. Further large-scale and long-term studies are needed to confirm their clinical effectiveness, cost-efficiency, and applicability in routine healthcare practice.
Keywords
Type 2 Diabetes Mellitus ,Digital Twin ,Personalized Nutrition, Artificial Intelligence ,Glycemic Control
How to Cite
Abbas, F. M. A., & Karakaya, R. E. (2026). An Innovative Approach in Healthcare: Personalized Nutrition Supported by Digital Twin Technology in the Management of Type 2 Diabetes. International Journal of Digital Health & Patient Care, 3(1), 44–53. https://doi.org/10.67015/ijdhpc.339
📄1. Lima, J. E., Moreira, N. C., & Sakamoto-Hojo, E. T. (2022). Mechanisms underlying the pathophysiology of type 2 diabetes: From risk factors to oxidative stress, metabolic dysfunction, and hyperglycemia. Mutation Research/Genetic Toxicology and Environmental Mutagenesis, 874, 503437 [CrossRef] [PubMed]
📄3. Dal Canto, E., Ceriello, A., Rydén, L., Ferrini, M., Hansen, T. B., Schnell, O., & Beulens, J. W. (2019). Diabetes as a cardiovascular risk factor: An overview of global trends of macro and micro vascular complications. European Journal of Preventive Cardiology, 26(2_suppl), 25-32. [CrossRef] [PubMed]
📄4. Liu, Y., Li, H., Zhao, Q., & Cui, W. (2025). Effects of 12 nutritional interventions on type 2 diabetes: a systematic review with network meta-analysis of randomized trials. Nutrition & Metabolism, 22(1), 94. [CrossRef] [PubMed]
📄5. Evert, A. B., Dennison, M., Gardner, C. D., Garvey, W. T., Lau, K. H. K., MacLeod, J., & Yancy Jr, W. S. (2019). Nutrition therapy for adults with diabetes or prediabetes: a consensus report. Diabetes care, 42(5), 731. [CrossRef] [PubMed]
📄6. Goldenberg, J. Z., Day, A., Brinkworth, G. D., Sato, J., Yamada, S., Jönsson, T., & Johnston, B. C. (2021). Efficacy and safety of low and very low carbohydrate diets for type 2 diabetes remission: systematic review and meta-analysis of published and unpublished randomized trial data. BMJ, 372. [CrossRef] [PubMed]
📄7. Lean, M. E., Leslie, W. S., Barnes, A. C., Brosnahan, N., Thom, G., McCombie, L., & Taylor, R. (2018). Primary care-led weight management for remission of type 2 diabetes (DiRECT): an open-label, cluster-randomised trial. The Lancet, 391(10120), 541-551. [CrossRef] [PubMed]
📄8. Look AHEAD Research Group, & Wing, R. R. (2010). Long-term effects of a lifestyle intervention on weight and cardiovascular risk factors in individuals with type 2 diabetes mellitus: four-year results of the Look AHEAD trial. Archives of İnternal Medicine, 170(17), 1566–1575. [CrossRef] [PubMed]
📄9. Guasch-Ferré, M., & Willett, W. C. (2021). The Mediterranean diet and health: a comprehensive overview. Journal of İnternal Medicine, 290(3), 549–566. [CrossRef] [PubMed]
📄10. Yuan, X., Wang, J., Yang, S., Gao, M., Cao, L., Li, X., Hong, D., Tian, S., & Sun, C. (2020). Effect of the ketogenic diet on glycemic control, insulin resistance, and lipid metabolism in patients with T2DM: a systematic review and meta-analysis. Nutrition & Diabetes, 10(1), 38. [CrossRef] [PubMed]
📄11. 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. [CrossRef] [PubMed]
📄12. Papandonatos, G. D., Pan, Q., Pajewski, N. M., Delahanty, L. M., Peter, I., Erar, B., & Diabetes Prevention Program and the Look AHEAD Research Groups. (2015). Genetic predisposition to weight loss and regain with lifestyle intervention: analyses from the Diabetes Prevention Program and the Look AHEAD randomized controlled trials. Diabetes, 64(12), 4312-4321. [CrossRef] [PubMed]
📄13. Wang, D. D., & Hu, F. B. (2018). Precision nutrition for prevention and management of type 2 diabetes. The lancet Diabetes & endocrinology, 6(5), 416-426. [CrossRef] [PubMed]
📄14. Olawade, D. B., Owhonda, R. C., Alabi, J. O., Egbon, E., Daniel, R. I. A., & Bello, O. J. (2025). Digital twin paradigm in diabetes prediction and management. Diabetes Research and Clinical Practice, 113075. [CrossRef] [PubMed]
📄15. Joshi, S., Shamanna, P., Dharmalingam, M., Vadavi, A., Keshavamurthy, A., Shah, L., & Mechanick, J. I. (2023). Digital twin-enabled personalized nutrition improves metabolic dysfunction-associated fatty liver disease in type 2 diabetes: results of a 1-year randomized controlled study. Endocrine Practice, 29(12), 960-970. [CrossRef] [PubMed]
📄16. Shamanna, P., Saboo, B., Damodharan, S., Mohammed, J., Mohamed, M., Poon, T., & Thajudeen, M. (2020). Reducing HbA1c in type 2 diabetes using digital twin technology-enabled precision nutrition: a retrospective analysis. Diabetes Therapy, 11(11), 2703-2714. [CrossRef] [PubMed]
📄17. Arias-Marroquín, A. T., Del Razo-Olvera, F. M., Castañeda-Bernal, Z. M., Cruz-Juárez, E., Camacho-Ramírez, M. F., Elías-López, D., & Aguilar-Salinas, C. A. (2024). Personalized versus non-personalized nutritional recommendations/interventions for type 2 diabetes mellitus remission: a narrative review. Diabetes Therapy, 15(4), 749-761. [CrossRef] [PubMed]
📄18. Berry, S. E., Valdes, A. M., Drew, D. A., Asnicar, F., Mazidi, M., Wolf, J., & Spector, T. D. (2020). Human postprandial responses to food and potential for precision nutrition. Nature Medicine, 26(6), 964-973. [CrossRef] [PubMed]
📄19. Mendes-Soares, H., Raveh-Sadka, T., Azulay, S., Edens, K., Ben-Shlomo, Y., Cohen, Y., & Nelson, H. (2019). Assessment of a personalized approach to predicting postprandial glycemic responses to food among individuals without diabetes. JAMA Network Open, 2(2), e188102-e188102. [CrossRef] [PubMed]
📄20. Mphasha, M., & Mothiba, T. (2025). Personalised nutrition approaches in the prevention and management of type 2 diabetes: A narrative review of evidence and practice. International Journal of Environmental Research and Public Health, 22(7), 1047. [CrossRef] [PubMed]
📄21. Tokpunar, M. (2026). Precision Nutrition in Certain Situations That Pose a Risk of Malnutrition: A Comprehensive Review. Genel Tıp Dergisi, 36(1). [CrossRef]
📄22. Agrawal, K., Goktas, P., Kumar, N., & Leung, M. F. (2025). Artificial intelligence in personalized nutrition and food manufacturing: a comprehensive review of methods, applications, and future directions. Frontiers in Nutrition, 12, 1636980. [CrossRef] [PubMed]
📄23. Ji, C., Jiang, T., Liu, L., Zhang, J., & You, L. (2025). Continuous glucose monitoring combined with artificial intelligence: redefining the pathway for prediabetes management. Frontiers in Endocrinology, 16, 1571362. [CrossRef] [PubMed]
📄24. Zhang, K., Qi, Y., Wang, W., Tian, X., Wang, J., Xu, L., & Zhai, X. (2025). Future horizons in diabetes: integrating AI and personalized care. Frontiers in Endocrinology, 16, 1583227. [CrossRef] [PubMed]
📄25. Fraser, R. A., Walker, R. J., Campbell, J. A., Ekwunife, O., & Egede, L. E. (2025). Integration of artificial intelligence and wearable technology in the management of diabetes and prediabetes. npj Digital Medicine, 8(1), 687. [CrossRef] [PubMed]
📄26. Munawaroh, E. F., Herawati, D. M. D., Megawati, G., Wijayakesuma, A., Apriani, L., Sofiatin, Y., & Dani, D. (2025). Effectiveness of personalized nutrition on management diabetes mellitus type 2 and prediabetes in adults: A systematic review. Diabetes, Metabolic Syndrome and Obesity, 2783-2796. [CrossRef] [PubMed]
📄27. Shamanna, P., Joshi, S., Thajudeen, M., Shah, L., Poon, T., Mohamed, M., & Mohammed, J. (2024). Personalized nutrition in type 2 diabetes remission: application of digital twin technology for predictive glycemic control. Frontiers in Endocrinology, 15, 1485464. [CrossRef] [PubMed]
📄28. Ramos-Lopez, O. (2024). Genotype-based precision nutrition strategies for the prediction and clinical management of type 2 diabetes mellitus. World Journal of Diabetes, 15(2), 142. [CrossRef] [PubMed]
📄29. Kallapura, G., Prakash, A. S., Sankaran, K., Manjappa, P., Chaudhary, P., Ambhore, S., & Dhar, D. (2024). Microbiota based personalized nutrition improves hyperglycaemia and hypertension parameters and reduces inflammation: a prospective, open label, controlled, randomized, comparative, proof of concept study. PeerJ, 12, e17583. [CrossRef] [PubMed]
📄30. Papakonstantinou, E., Oikonomou, C., Nychas, G., & Dimitriadis, G. D. (2022). Effects of diet, lifestyle, chrononutrition and alternative dietary interventions on postprandial glycemia and insulin resistance. Nutrients, 14(4), 823. [CrossRef] [PubMed]
📄31. Sarani Rad, F., Hendawi, R., Yang, X., & Li, J. (2024). Personalized diabetes management with digital twins: a patient-centric knowledge graph approach. Journal of personalized medicine, 14(4), 359. [CrossRef] [PubMed]
📄32. Grieves, M. (2016). Origins of the Digital Twin Concept. Florida Institute of Technology. NASA, Working Paper. [CrossRef]
📄33. Shafto, M., Conroy, M., Doyle, R., Glaessgen, E., Kemp, C., LeMoigne, J., & Wang, L. (2010). Draft modeling, simulation, information technology & processing roadmap. Technology area, 11, 1-32.
📄34. Singh, M., Fuenmayor, E., Hinchy, E. P., Qiao, Y., Murray, N., & Devine, D. (2021). Digital twin: Origin to future. Applied System Innovation, 4(2), 36. [CrossRef]
📄35. Vallée, A. (2023). Digital twin for healthcare systems. Frontiers in Digital Health, 5, 1253050. [CrossRef] [PubMed]
📄36. Shamanna, P., Joshi, S., Shah, L., Dharmalingam, M., Saboo, B., Mohammed, J., & Keshavamurthy, A. (2021). Type 2 diabetes reversal with digital twin technology-enabled precision nutrition and staging of reversal: a retrospective cohort study. Clinical Diabetes and Endocrinology, 7(1), 21. [CrossRef] [PubMed]
📄37. Wang, S., Song, S., Gao, J., Wu, W., Fu, Y., Yuan, T., & Zhao, W. (2025). Dynamic Prediction of Postprandial Glycemic Response and Personalized Dietary Interventions Based on Machine Learning. The Journal of Nutrition, 155(12), 4193–4208. [CrossRef] [PubMed]
📄38. Modak, S. K. S., & Jha, V. K. (2024). Diabetes prediction model using machine learning techniques. Multimedia Tools and Applications, 83(13), 38523-38549. [CrossRef]
📄39. Carrillo-Moreno, J., Pérez-Gandía, C., Sendra-Arranz, R., García-Sáez, G., Hernando, M. E., & Gutiérrez, Á. (2021). Long short-term memory neural network for glucose prediction. Neural Computing and Applications, 33(9), 4191-4203. [CrossRef]
📄40. Bruynseels, K., Santoni de Sio, F., & Van den Hoven, J. (2018). Digital twins in health care: ethical implications of an emerging engineering paradigm. Frontiers in genetics, 9, 31. [CrossRef] [PubMed]
📄41. Zhang, Y., Qin, G., Aguilar, B., Rappaport, N., Yurkovich, J. T., Pflieger, L., & Shmulevich, I. (2024). A framework towards digital twins for type 2 diabetes. Frontiers in Digital Health, 6, 1336050. [CrossRef] [PubMed]
📄42. Joshi, S. R., Shah, L., Mohamed, M., Mohammed, J., Poon, T., Dharmalingam, M., & Shamanna, P. (2022). 26-OR: metabolic benefits beyond glycemic control with artificial intelligence, internet of things, and whole-body digital twin: initial six months results of a randomized control trial. Diabetes, 71(Supplement_1), 26-OR. [CrossRef]
📄43. Shamanna, P., Dharmalingam, M., Vadavi, A., Keshavamurthy, A., Bhonsley, S., Thajudeen, M., & Joshi, S. R. (2024). 20-OR: digital twin (DT) technology in type 2 diabetes remission and glycemic improvement Eighteen months results from a randomized controlled trial. Diabetes, 73(Supplement_1), 20-OR. [CrossRef]
📄44. Shah, L., Joshi, S., Dharmalingam, M., Shamanna, P., Damodaran, S., Mohammed, J., & Willis, B. (2022). IDF21-0598 Remission of T2DM by digital twin technology across Indian and American cohorts–Initial insights from real-world study. Diabetes Research and Clinical Practice, 186. [CrossRef]
📄45. Shamanna, P., Joshi, S., Shah, L., Dharmalingam, M., Vadavi, A., Damodaran, S., & Bhonsley, S. (2022). Remission of T2DM by digital twin technology with reduction of cardiovascular risk: interim results of randomised controlled clinical trial. European Heart Journal, 43(Supplement_1), ehab849-177. [CrossRef]
📄46. Shamanna, P., Dharmalingam, M., Vadavi, A., Keshavamurthy, A., Thajudeen, M., Bhonsley, S., & Joshi, S. (2024). # 1706999 The Impact of Digital Twin Technology on HbA1c Reduction and ASCVD Risk in Participants Enrolled for Type 2 Diabetes Remission: Outcomes of RCCT At 1 Year. Endocrine Practice, 30(5), S78. [CrossRef]
📄47. Paramesh, S., Mohammed, J., Mohamed, M., Poon, T., Thajudeen, M., Keshavamurthy, A., ... & Joshi, S. (2023). Remission of hypertension at six months of whole body digital twin technology. Journal of Hypertension, 41(Suppl 3), e122. [CrossRef]
📄48. Shamanna, P., Joshi, S., Dharmalingam, M., Vadavi, A., Keshavamurthy, A., Thajudeen, M., & Bhonsley, S. (2024). Impact of Digital Twin Intervention on Microalbuminuria Progression in Participants Enrolled for Type 2 Diabetes Remission: Outcomes of RCCT At 1 Year. Endocrine Practice, 30(5), S50-S51. [CrossRef]
📄49. Shamanna, P., Dharmalingam, M., Vadavi, A., Keshavamurthy, A., Bhonsley, S., Mohamed, M., & Joshi, S. R. (2024). 1806-LB: Improvement in Sensory Function and Achieving Remission of Type 2 Diabetes Mellitus through Digital Twin Technology A One-Year Intervention Study. Diabetes, 73(Supplement_1), 1806-LB. [CrossRef]
📄50. Joshi, S., Dharmalingam, M., Vadavi, A., Keshavamurthy, A., Bhonsley, S., Mohamed, M., ... & Shamanna, P. (2024). 1811-LB: Improvement in Diabetic Retinopathy and Achieving Remission of Type 2 Diabetes Mellitus through Digital Twin Technology A One-Year Intervention Study. Diabetes, 73(Supplement_1), 1811-LB. [CrossRef]
📄51. Shamanna, P., Shah, L., Mohammed, J., Mohamed, M., Poon, T., Dharmalingam, M., & Joshi, S. R. (2022). 4-LB: remission of type 2 diabetes and improvement in metabolic markers with the twin precision treatment technology (TPT) a multicenter, randomized, controlled trial. Diabetes, 71(Supplement_1), 4-LB. [CrossRef]
📄52. Shah, L., Joshi, S. R., Mohammed, J., Mohamed, M., Thajudeen, M., Kaufman, F. R., & Willis, B. (2024). 851-P: whole body digital twin (WBDT) enabled diabetes reversal US real-world findings. Diabetes, 73(Supplement_1), 851-P. [CrossRef]
📄53. Joshi, S. R., Dharmalingam, M., Vadavi, A., Keshavamurthy, A., Shamanna, P., Shah, L., & Mohamed, M. (2025). OR20-07 Digital Twin Technology For T2D Remission And ASCVD Risk Reduction: 2-Year RCT Outcomes. Journal of the Endocrine Society, 9(Supplement_1), bvaf149-1032. [CrossRef]
📄54. Joshi, S., Dharmalingam, M., Vadavi, A., Damodharan, S., Erukulapati, R. S., Shukla, A., Shah, L., Thajudeen, M., Kovil, R., Baxi, R., Seshadri, K. G., & Murthy, S. (2025). Digital twin technology for sustained remission of T2DM: 3-year results of a prospective randomized controlled clinical trial. Endocrine Practice, 31, S203. [CrossRef]
📄55. Mahr, D., Hebich, M., & Weinberger, N. (2025). Toward demographic robustness in digital patient twins: Addressing the gender data gap. Digital Health, 11, 20552076251386660. [CrossRef] [PubMed]
📄56. Akbarialiabad, H., Pasdar, A., Murrell, D. F., Mostafavi, M., Shakil, F., Safaee, E., & Grada, A. (2025). Enhancing randomized clinical trials with digital twins. NPJ Systems Biology and Applications, 11(1), 110. [CrossRef] [PubMed]
📄57. Cáceres-Gutiérrez, D. A., Bonilla-Bonilla, D. M., Liscano, Y., & Díaz Vallejo, J. A. (2025). From Architecture to Outcomes: Mapping the Landscape of Digital Twins for Personalized Diabetes Care A Scoping Review. Journal of Personalized Medicine, 15(11), 504. [CrossRef] [PubMed]
📄58. Attaran, M., & Celik, B. G. (2023). Digital Twin: Benefits, use cases, challenges, and opportunities. Decision Analytics Journal, 6, 100165. [CrossRef]
,
Rahime Evra Karakaya
2