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Vol. 13 No. Continuous (2026)

Ordibehesht 2026

Precision NeuroNutrition: Toward Personalized Perioperative Care in Neurosurgery

  • Melika Hajimohammadebrahim-Ketabforoush
  • Alireza Zali
  • Sara Rahmati Roodsari

International Clinical Neuroscience Journal, Vol. 13 No. Continuous (2026), 10 Ordibehesht 2026 , Page 1-6
https://doi.org/10.22037/icnj.v13iContinuous.52881 Published: 2026-09-02

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Abstract

Precision medicine has transformed modern neurosurgery through advances in molecular diagnostics, advanced imaging, artificial intelligence (AI), and individualized therapeutic strategies. In contrast, perioperative nutritional care remains largely based on standardized guidelines that inadequately address the biological heterogeneity of neurosurgical patients. Growing evidence indicates that nutritional status is a modifiable determinant of postoperative outcomes, yet conventional assessment often fails to capture interindividual differences in metabolism, inflammation, body composition, and physiological reserve. This mini-review introduces Precision NeuroNutrition as a conceptual framework for personalized perioperative nutritional care in neurosurgery. It summarizes current evidence on nutritional vulnerability in neurosurgical patients and highlights how body composition analysis, inflammatory and metabolic biomarkers, metabolic phenotyping, microbiome science, multi-omics, and AI may enable biologically informed nutritional assessment and intervention. Precision NeuroNutrition complements, rather than replaces, established evidence-based recommendations such as ESPEN and Enhanced Recovery After Surgery (ERAS) by integrating patient-specific biological, metabolic, and molecular characteristics into nutritional decision-making. Although implementation remains limited by challenges in standardization, validation, cost, and data integration, advances in systems biology and computational medicine offer new opportunities for adaptive nutritional strategies. Precision NeuroNutrition offers a promising roadmap for future research and personalized perioperative nutritional management in modern neurosurgery.

Keywords:
  • Artificial Intelligence; Body Composition; Neurosurgery; Perioperative Nutrition; Precision Medicine; Precision NeuroNutrition; Precision Nutrition
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How to Cite

1.
Hajimohammadebrahim-Ketabforoush M, Zali A, Rahmati Roodsari S. Precision NeuroNutrition: Toward Personalized Perioperative Care in Neurosurgery. Int Clin Neurosci J [Internet]. 2026 Sep. 2 [cited 2026 Sep. 5];13(Continuous):1-6. Available from: https://journals.sbmu.ac.ir/neuroscience/article/view/52881
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References

1. Yan SC, Greenfield JP. The Emergence of Precision Medicine Within Neurological Surgery. Neurosurgery. 2019;84(2):348–356. doi: 10.1016/j.wneu.2024.06.143

2. Gilard V, et al. Precision Neurosurgery: A Path Forward. Neurosurgery. 2024. doi: 10.3390/jpm11101019

3. Ashley EA. The Precision Medicine Initiative: A New National Effort. N Engl J Med. 2015;372:793–795. doi: 10.1001/jama.2015.3595

4. Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med. 2019;25:24–29. doi: 10.1038/s41591-018-0316-z

5. Louis DN, Perry A, Wesseling P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol. 2021;23(8):1231–1251. doi: 10.1093/neuonc/noab106

6. Weimann A, Braga M, Carli F, et al. ESPEN practical guideline: Clinical nutrition in surgery. Clin Nutr. 2021;40(7):4745–4761. doi: 10.1016/j.clnu.2021.03.031

7. Singer P, Blaser AR, Berger MM. ESPEN guideline on clinical nutrition in the intensive care unit. Clin Nutr. 2019;38(1):48–79. doi: 10.1016/j.clnu.2018.08.037

8. Bellomo J, Blom V, Stumpo V. Level of evidence of enhanced recovery after surgery (ERAS) strategies for elective craniotomy: An updated systematic review. Neurosurg Rev. 2026;49(1):122. doi: 10.1007/s10143-025-03961-9

9. Peters EJ, Robinson M, Serletis D. Systematic Review of Enhanced Recovery After Surgery in Patients Undergoing Cranial Surgery. World Neurosurg. 2022;158:279–289.e1. doi: 10.1016/j.wneu.2021.10.176

10. Cederholm T, Jensen GL, Correia MITD, et al. GLIM Criteria for the Diagnosis of Malnutrition. Clin Nutr. 2019;38(1):1–9. doi: 10.1016/j.clnu.2018.08.002

11. Cruz-Jentoft AJ, Bahat G, Bauer J. Sarcopenia: Revised European Consensus (EWGSOP2). Age Ageing. 2019;48(1):16–31. doi: 10.1093/ageing/afy169

12. Gillis C, Carli F. Promoting Perioperative Metabolic and Nutritional Care. Anesthesiology. 2015;123(6):1455–72. doi: 10.1097/ALN.0000000000000795

13. Wischmeyer PE, Carli F, Evans DC, et al. Consensus Statement on Nutrition Screening and Therapy Within an Enhanced Recovery Pathway. Anesth Analg. 2018;126(6):1883–95. doi: 10.1213/ANE.0000000000002743

14. Gillis C, Buhler K, Bresee L. Nutritional Prehabilitation: Systematic Review and Meta-analysis. Gastroenterology. 2018;155(2):391–410.e4. doi: c10.5812/ijcm.94542

15. Hajimohammadebrahim-Ketabforoush M, Vahdat SZ, Shahmohammadi M. In vitro and in vivo antitumor activity of vitamin D3 in malignant gliomas: a systematic review. Int J Cancer Manag. 2020;13(2). doi: 10.5812/ijcm.94542

16. Hajimohammadebrahim-Ketabforoush M, Shahmohammadi M, et al. Preoperative serum level of vitamin D is a possible protective factor for peritumoral brain edema of meningioma. Nutr Cancer. 2021;73(11–12):2842–2848. doi: 10.1080/01635581.2020.1861311

17. Arends J, Baracos V, Bertz H, et al. ESPEN expert group recommendations for action against cancer-related malnutrition. Clin Nutr. 2017;36(5):1187–1196. doi: 10.1016/j.clnu.2017.06.017

18. Carney N, Totten AM, O'Reilly C, et al. Guidelines for the Management of Severe Traumatic Brain Injury, Fourth Edition. Neurosurgery. 2017;80(1):6–15. doi: 10.1227/NEU.0000000000001432

19. Moskven E, Lasry O, Singh S, Flexman AM, Street JT, Dea N, et al. The role of frailty and sarcopenia in predicting major adverse events, length of stay and reoperation following en bloc resection of primary tumours of the spine. Global Spine J. 2024;14(8):2259-69. doi:10.1177/21925682231173360

20. Ordovas JM, Ferguson LR, Tai ES, Mathers JC. Personalised nutrition and health. BMJ. 2018;361:k2173. doi: 10.1136/bmj.k2173

21. Mathers JC. Personalised nutrition: Facts and fallacies. Proc Nutr Soc. 2023;82(4):357–66. doi: 10.1017/S0029665123004802

22. Pebes Vega JC, Mancin S, Vinciguerra G, Azzolini E, Colotta F, Pastore M, et al. Nutritional assessment and management of patients with brain neoplasms undergoing neurosurgery: a systematic review. Cancers (Basel). 2025;17(5):764. doi:10.3390/cancers17050764

23. Hajimohammadebrahim-Ketabforoush M, Emami Meybodi T, Mehmandoost M, Fallah M, Bahri A, Moftakhari Hajimirzaei S, et al. Immunonutrition for modifying inflammatory markers and improving clinical outcomes following traumatic brain injury: a systematic review and meta-analysis. BMC Neurol. 2025;26(1):16. doi:10.1186/s12883-025-04554-1

24. Wang Y, Zhang H, Li X, et al. The impact of sarcopenia on the incidence of postoperative outcomes following spine surgery: a systematic review and meta-analysis. PLoS One. 2024;19(8):e0302291. doi: 10.1371/journal.pone.0302291

25. Ferguson LR, De Caterina R, Görman U, Allayee H, Kohlmeier M, Prasad C, et al. Guide and position of the International Society of Nutrigenetics/Nutrigenomics on personalised nutrition: part 1—fields of precision nutrition. J Nutrigenet Nutrigenomics. 2016;9(1):12–27. doi: 10.1159/000445350

26. de Toro-Martín J, Arsenault BJ, Després JP, Vohl MC. Precision nutrition: a review of personalized nutritional approaches for the prevention and management of metabolic syndrome. Nutrients. 2017;9(8):913. doi: 10.3390/nu9080913

27. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56. doi: 10.1038/s41591-018-0300-7

28. Flores M, Glusman G, Brogaard K, Price ND, Hood L. P4 medicine: how systems medicine will transform the healthcare sector and society. Per Med. 2013;10(6):565–76. doi: 10.2217/pme.13.57

29. Hood L, Flores M. A personal view on systems medicine and the emergence of proactive P4 medicine. FEBS Lett. 2012;586(17):2813–2815. doi: 10.1016/j.nbt.2012.03.004

30. Prado CM, Heymsfield SB. Lean tissue imaging: a new era for nutritional assessment and intervention. JPEN J Parenter Enteral Nutr. 2014;38(8):940–53. doi: 10.1177/0148607114550189

31. Aleixo GFP, Shachar SS, Nyrop KA, Muss HB, Malpica L, Williams GR. Myosteatosis and prognosis in cancer: a systematic review and meta-analysis. Crit Rev Oncol Hematol. 2020;145:102839. doi: 10.1016/j.critrevonc.2019.102839

32. Shen W, Punyanitya M, Wang Z, Gallagher D, St-Onge MP, Albu J, et al. Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image. J Appl Physiol (1985). 2004;97(6):2333–8. doi: 10.1152/japplphysiol.00744.2004

33. Paris MT, Mourtzakis M. Assessment of skeletal muscle mass in clinical practice: emerging imaging technologies and automated body composition analysis. Curr Opin Clin Nutr Metab Care. 2016;19(2):125-30. doi: 10.1097/MCO.0000000000000259

34. Templeton AJ, McNamara MG, Šeruga B, Vera-Badillo FE, Aneja P, Ocaña A, et al. Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: a systematic review and meta-analysis. J Natl Cancer Inst. 2014;106(6):dju124. doi: 10.1093/jnci/dju124

35. Dolan RD, McSorley ST, Horgan PG, Laird B, McMillan DC. The role of the systemic inflammatory response in predicting outcomes in patients with cancer: systematic review and meta-analysis. Crit Rev Oncol Hematol. 2017;116:134–46. doi: 10.1016/j.critrevonc.2017.06.002

36. Wischmeyer PE. Tailoring nutrition therapy to illness and recovery. Crit Care. 2017;17(7):407-420. doi: 10.1186/s13054-017-1906-8

37. Puchades-Carrasco L, Pineda-Lucena A. Metabolomics applications in precision medicine: an oncological perspective. Curr Top Med Chem. 2017;17(24):2740–51. doi: 10.2174/1568026617666170707120034

38. van der Poll T, Shankar-Hari M, Wiersinga WJ. The immunology of sepsis and potential therapeutic targets. Nat Rev Immunol. 2017;17(7):407-420. doi: 10.1038/nri.2017.36

39. Valdes AM, Walter J, Segal E, Spector TD. Role of the gut microbiota in nutrition and health. BMJ. 2018;361:k2179. doi: 10.1136/bmj.k2179

40. Wishart DS. Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov. 2016;15(7):473–84. doi: 10.1038/nrd.2016.32

41. Senders JT, Staples PC, Karhade AV, Zaki MM, Gormley WB, Broekman MLD, et al. Machine learning and neurosurgical outcome prediction: a systematic review. World Neurosurg. 2018;109:476–86.e1. doi: 10.1016/j.wneu.2017.09.149

42. Björnsson B, Borrebaeck C, Elander N, Gasslander T, Gawel DR, Gustafsson M, et al. Digital twins to personalize medicine. Genome Med. 2020;12(1):4. doi: 10.1186/s13073-019-0701-3

43. Collins GS, Moons KGM. Reporting of artificial intelligence prediction models. Lancet. 2019;393(10181):1577–9. doi: 10.1016/S0140-6736(19)30037-6

44. Dzau VJ, Ginsburg GS, Van Nuys K, Agus D, Goldman D. Aligning incentives to fulfill the promise of personalized medicine. Lancet. 2015;385(9982):2118–9. doi: 10.1016/S0140-6736(15)60722-X

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