Advancements in precision nutrition: Targeted strategies for personalized health

Zheng Hua; Xuedi Zhang; Wentao Su; Ahmed A. Zaky; A. M. Abd El-Aty; Enbo Xu; Donghong Liu; Mingqian Tan

doi:10.59717/j.xinn-life.2026.100191

The Innovation Life > 2026 Vol. 4 > No. 1 > 100191

Next Article Previous Article

REVIEW Open Access

Cite PDF

Advancements in precision nutrition: Targeted strategies for personalized health

1.
State Key Laboratory of Marine Food Processing and Safety Control, Dalian Polytechnic University, Dalian 116034, China
2.
Academy of Food Interdisciplinary Science, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China
3.
National Engineering Research Center of Seafood, Dalian Polytechnic University, Dalian 116034, China
4.
Collaborative Innovation Center of Seafood Deep Processing, Dalian Polytechnic University, Dalian 116034, China
5.
Department of Radiology, Sir Run Run Shaw Hospital (SRRSH) of School of Medicine, Zhejiang University, Hangzhou 310016, China
6.
College of Biosystems Engineering and Food Science, Zhejiang Key Laboratory for Agro-Food Processing, Fuli Institute of Food Science, Zhejiang University, Hangzhou 310058, China
7.
Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314102, China
8.
Department of Food Technology, Food Industries and Nutrition Research Institute, National Research Centre, Dokki 12622, Egypt
9.
Department of Pharmacology, Faculty of Veterinary Medicine, Cairo University, 12211-Giza, Egypt
10.
Department of Medical Pharmacology, Medical Faculty, Ataturk University, Erzurum 25240, Turkey

Show all affliationsShow less

More Information

Corresponding author: mqtan@dlpu.edu.cn
Received Date: June 13, 2025

Accepted Date: January 06, 2026

Published Online: January 08, 2026

The Innovation Life 4(1), https://doi.org/10.59717/j.xinn-life.2026.100191 | Cite this article

GRAPHICAL ABSTRACT

GRAPHICAL ABSTRACT

DownLoad: Full size image

PUBLIC SUMMARY

PUBLIC SUMMARY
1. Individual biological differences and health status advocate precision nutrition.
  
  Wearable sensors and portable devices gain instant individual-specific data.
  
  Artificial intelligence helps to collect, handle, and evaluate abundant information.
  
  Targeted nutrition is a favorable approach for developing precision nutrition.

ABSTRACT

ABSTRACT

As the need for more effective nutritional interventions grows, precision nutrition has emerged as a strategy that leverages individual characteristics such as health status, genetic makeup, epigenetic profiles, and microbiota profiles to develop personalized nutrition plans tailored to treat and prevent disease. This strategy involves identifying an individual's health status through specific biomarkers, collecting personalized data via portable testing devices or digital tools, and managing individual-specific information via various algorithms. This review highlights advances in the biological underpinnings of precision nutrition, including understanding interindividual variation, designing target-specific nutrition plans, and addressing the challenges and opportunities of precision nutrition. Furthermore, targeted nutrition which focuses on delivering and releasing specific nutrients to particular sites in the body was explored as a recent innovation within precision nutrition. This review primarily examines targeted nutrition involving the controlled delivery of proteins/peptides, probiotics, fatty acids, polyphenols, and carotenoids, with a focus on the treatment of diseases affecting specific organs.
- precision nutrition /
- target-specific nutrition /
- biomarkers /
- nutrition delivery /
- individual information

FullText(HTML)

REFERENCES

[1]	Adams S. H., Anthony J. C., Carvajal R., et al. (2020). Perspective: Guiding principles for the implementation of personalized nutrition approaches that benefit health and function. Adv. Nutr. 11:25−34. DOI:10.1093/advances/nmz086 View in Article CrossRef Google Scholar
[2]	Bashiardes S., Abdeen S. K. and Elinav E. (2019). Personalized nutrition: Are we there yet. J. Pediatr. Gastroenterol. Nutr. 69:633. DOI:10.1097/MPG.0000000000002491 View in Article CrossRef Google Scholar
[3]	Virgili F. and Perozzi G. (2008). How does nutrigenomics impact human health. IUBMB Life 60:341−344. DOI:10.1002/iub.85 View in Article CrossRef Google Scholar
[4]	Raiten D. J., Combs G. F., Jr., Steiber A. L., et al. (2021). Perspective: Nutritional status as a biological variable (NABV): Integrating nutrition science into basic and clinical research and care. Adv. Nutr. 12:1599−1609. DOI:10.1093/advances/nmab046 View in Article CrossRef Google Scholar
[5]	Di Renzo L., Gualtieri P., Romano L., et al. (2019). Role of personalized nutrition in chronic-degenerative diseases. Nutrients 11:1707. DOI:10.3390/nu11081707 View in Article CrossRef Google Scholar
[6]	Bentley D. R. (2000). Decoding the human genome sequence. Hum. Mol. Genet. 9:2353−2358. DOI:10.1093/hmg/9.16.2353 View in Article CrossRef Google Scholar
[7]	Zeevi D., Korem T., Zmora N., et al. (2015). Personalized nutrition by prediction of glycemic responses. Cell 163:1079−1094. DOI:10.1016/j.cell.2015.11.001 View in Article CrossRef Google Scholar
[8]	Morabito A., De Simone G., Pastorelli R., et al. (2025). Algorithms and tools for data-driven omics integration to achieve multilayer biological insights: A narrative review. J. Transl. Med. 23:425. DOI:10.1186/s12967-025-06446-x View in Article CrossRef Google Scholar
[9]	Mbunge E., Muchemwa B., Jiyane S. e., et al. (2021). Sensors and healthcare 5.0: Transformative shift in virtual care through emerging digital health technologies. Glob. Health J. 5:169-177. DOI:10.1016/j.glohj.2021.11.008 View in Article Google Scholar
[10]	Shahidi F. and Pan Y. (2022). Influence of food matrix and food processing on the chemical interaction and bioaccessibility of dietary phytochemicals: A review. Crit. Rev. Food Sci. Nutr. 62:6421−6445. DOI:10.1080/10408398.2021.1901650 View in Article CrossRef Google Scholar
[11]	Turgeon S. L. and Rioux L.-E. (2011). Food matrix impact on macronutrients nutritional properties. Food Hydrocolloids 25:1915−1924. DOI:10.1016/j.foodhyd.2011.02.026 View in Article CrossRef Google Scholar
[12]	Weaver C. M. and Givens D. I. (2025). Overview: The food matrix and its role in the diet. Crit. Rev. Food Sci. Nutr.:1-18. DOI:10.1080/10408398.2025.2453074 View in Article Google Scholar
[13]	Hughes R. L., Kable M. E., Marco M., et al. (2019). The role of the gut microbiome in predicting response to diet and the development of precision nutrition models. Part II: Results. Adv. Nutr. 10:979−998. DOI:10.1093/advances/nmz049 View in Article CrossRef Google Scholar
[14]	Mills S., Stanton C., Lane J. A., et al. (2019). Precision nutrition and the microbiome, part I: Current state of the science. Nutrients 11:923. DOI:10.3390/nu11040923 View in Article CrossRef Google Scholar
[15]	Gan J., Siegel J. B. and German J. B. (2019). Molecular annotation of food—towards personalized diet and precision health. Trends Food Sci. Technol. 91:675−680. DOI:10.1016/j.jpgs.2019.07.016 View in Article CrossRef Google Scholar
[16]	Ivanova V. N., Nikitin I., Zhuchenko N. A., et al. (2019). Clustering of multidimensional objects in the formation of personalized diets. Int. J. Adv. Comput. Sci. Appl. 10:45−50. DOI:10.14569/ijacsa.2019.0100206 View in Article CrossRef Google Scholar
[17]	Newton R. S., Blumberg J. B., Reed D. G., et al. (2020). The American Nutrition Association®: Championing the science and practice of personalized nutrition. J. Am. Coll. Nutr. 39:1−4. DOI:10.1080/07315724.2020.1699380 View in Article CrossRef Google Scholar
[18]	Laing B. B., Lim A. G. and Ferguson L. R. (2019). A personalised dietary approach—a way forward to manage nutrient deficiency, effects of the Western diet, and food intolerances in inflammatory bowel disease. Nutrients 11:1532. DOI:10.3390/nu11071532 View in Article CrossRef Google Scholar
[19]	Michel M. and Burbidge A. (2019). Nutrition in the digital age—how digital tools can help to solve the personalized nutrition conundrum. Trends Food Sci. Technol. 90:194−200. DOI:10.1016/j.jpgs.2019.02.018 View in Article CrossRef Google Scholar
[20]	Bush C. L., Blumberg J. B., El-Sohemy A., et al. (2019). Toward the definition of personalized nutrition: A proposal by the American Nutrition Association. J. Am. Coll. Nutr. 39:5−15. DOI:10.1080/07315724.2019.1685332 View in Article CrossRef Google Scholar
[21]	Zeisel S. H. (2020). Precision (Personalized) nutrition: Understanding metabolic heterogeneity. Annu. Rev. Food Sci. Technol. 11:71−92. DOI:10.1146/annurev-food-032519-051736 View in Article CrossRef Google Scholar
[22]	Hausman-Cohen S. R., Hausman-Cohen L. J., Williams G. E., et al. (2020). Genomics of detoxification: How genomics can be used for targeting potential intervention and prevention strategies including nutrition for environmentally acquired illness. J. Am. Coll. Nutr. 39:94−102. DOI:10.1080/07315724.2020.1713654 View in Article CrossRef Google Scholar
[23]	Laddu D. and Hauser M. (2019). Addressing the nutritional phenotype through personalized nutrition for chronic disease prevention and management. Prog. Cardiovasc. Dis. 62:9−14. DOI:10.1016/j.pcad.2018.12.004 View in Article CrossRef Google Scholar
[24]	Yang X., Qiu M.-T., Hu J.-W., et al. (2013). GSTT1 null genotype contributes to lung cancer risk in Asian populations: A meta-analysis of 23 studies. PLOS ONE 8:e62181. DOI:10.1371/journal.pone.0062181 View in Article CrossRef Google Scholar
[25]	Ivanov I. V., Mappes T., Schaupp P., et al. (2018). Ultraviolet radiation oxidative stress affects eye health. J. Biophotonics 11:e201700377. DOI:10.1002/jbio.201700377 View in Article CrossRef Google Scholar
[26]	Sun H., Zhou Y., Jiang S., et al. (2023). Association between low-sodium salt intervention and long-term blood pressure changes is modified by ENaC genetic variation: A gene–diet interaction analysis in a randomized controlled trial. Food Funct. 14:9782−9791. DOI:10.1039/D3FO02393A View in Article CrossRef Google Scholar
[27]	Chen J., Dan L., Yuan S., et al. (2025). Dietary antioxidant capacity, genetic susceptibility and polymorphism, and inflammatory bowel disease risk in a prospective cohort. Clin. Gastroenterol. Hepatol. 23:1623−1632. DOI:10.1016/j.cgh.2024.09.033 View in Article CrossRef Google Scholar
[28]	Lin K., McCormick N., Yokose C., et al. (2023). Interactions between genetic risk and diet influencing risk of incident female gout: Discovery and replication analysis of four prospective cohorts. Arthritis Rheumatol. 75:1028−1038. DOI:10.1002/art.42419 View in Article CrossRef Google Scholar
[29]	Inbar-Feigenberg M., Choufani S., Butcher D. T., et al. (2013). Basic concepts of epigenetics. Fertil. Steril. 99:607−615. DOI:10.1016/j.fertnstert.2013.01.117 View in Article CrossRef Google Scholar
[30]	Chen L., Song H., Luo Z., et al. (2020). PHLPP2 is a novel biomarker and epigenetic target for the treatment of vitamin C in pancreatic cancer. Int. J. Oncol. 56:1294−1303. DOI:10.3892/ijo.2020.5001 View in Article CrossRef Google Scholar
[31]	Aruoma O. I., Hausman-Cohen S., Pizano J., et al. (2019). Personalized nutrition: Translating the science of nutrigenomics into practice: Proceedings from the 2018 American College of Nutrition meeting. J. Am. Coll. Nutr. 38:287−301. DOI:10.1080/07315724.2019.1582980 View in Article CrossRef Google Scholar
[32]	Wallace T. C., Bultman S., D’Adamo C., et al. (2019). Personalized nutrition in disrupting cancer—proceedings from the 2017 American College of Nutrition annual meeting. J. Am. Coll. Nutr. 38:1−14. DOI:10.1080/07315724.2018.1500499 View in Article CrossRef Google Scholar
[33]	Bordoni L. and Gabbianelli R. (2019). Primers on nutrigenetics and nutri(epi)genomics: Origins and development of precision nutrition. Biochimie 160:156−171. DOI:10.1016/j.biochi.2019.03.006 View in Article CrossRef Google Scholar
[34]	Johnson A. J., Vangay P., Al-Ghalith G. A., et al. (2019). Daily sampling reveals personalized diet-microbiome associations in humans. Cell Host Microbe 25:789−802. DOI:10.1016/j.chom.2019.05.005 View in Article CrossRef Google Scholar
[35]	Asnicar F., Berry S. E., Valdes A. M., et al. (2021). Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals. Nat. Med. 27:321−332. DOI:10.1038/s41591-020-01183-8 View in Article CrossRef Google Scholar
[36]	Janssen A. W. F. and Kersten S. (2017). Potential mediators linking gut bacteria to metabolic health: A critical view. J. Physiol. 595:477−487. DOI:10.1113/JP272476 View in Article CrossRef Google Scholar
[37]	Mann E. R., Lam Y. K. and Uhlig H. H. (2024). Short-chain fatty acids: Linking diet, the microbiome and immunity. Nat. Rev. Immunol. 24:577−595. DOI:10.1038/s41577-024-01014-8 View in Article CrossRef Google Scholar
[38]	Miller K. D., O’Connor S., Pniewski K. A., et al. (2023). Acetate acts as a metabolic immunomodulator by bolstering T-cell effector function and potentiating antitumor immunity in breast cancer. Nat. Cancer 4:1491−1507. DOI:10.1038/s43018-023-00636-6 View in Article CrossRef Google Scholar
[39]	Wang R., Yang X., Liu J., et al. (2022). Gut microbiota regulates acute myeloid leukaemia via alteration of intestinal barrier function mediated by butyrate. Nat. Commun. 13:2522. DOI:10.1038/s41467-022-30240-8 View in Article CrossRef Google Scholar
[40]	Tian P., Yang W., Guo X., et al. (2023). Early life gut microbiota sustains liver-resident natural killer cells maturation via the butyrate-IL-18 axis. Nat. Commun. 14:1710. DOI:10.1038/s41467-023-37419-7 View in Article CrossRef Google Scholar
[41]	Chen H.-C., Liu Y.-W., Chang K.-C., et al. (2023). Gut butyrate-producers confer post-infarction cardiac protection. Nat. Commun. 14:7249. DOI:10.1038/s41467-023-43167-5 View in Article CrossRef Google Scholar
[42]	Yang X., Zhang M., Liu Y., et al. (2023). Inulin-enriched Megamonas funiformis ameliorates metabolic dysfunction-associated fatty liver disease by producing propionic acid. npj Biofilms Microbiomes 9:84. DOI:10.1038/s41522-023-00451-y View in Article CrossRef Google Scholar
[43]	Sinha S. R., Haileselassie Y., Nguyen L. P., et al. (2020). Dysbiosis-induced secondary bile acid deficiency promotes intestinal inflammation. Cell Host Microbe 27:659−670. DOI:10.1016/j.chom.2020.01.021 View in Article CrossRef Google Scholar
[44]	Wang J. and Jia H. (2016). Metagenome-wide association studies: Fine-mining the microbiome. Nat. Rev. Microbiol. 14:508−522. DOI:10.1038/nrmicro.2016.83 View in Article CrossRef Google Scholar
[45]	Hou S., Yu J., Li Y., et al. (2025). Advances in fecal microbiota transplantation for gut dysbiosis-related diseases. Adv. Sci. 12:2413197. DOI:10.1002/advs.202413197 View in Article CrossRef Google Scholar
[46]	Kim J.-H., Kim K. and Kim W. (2021). Gut microbiota restoration through fecal microbiota transplantation: A new atopic dermatitis therapy. Exp. Mol. Med. 53:907−916. DOI:10.1038/s12276-021-00627-6 View in Article CrossRef Google Scholar
[47]	D’Auria E., Abrahams M., Zuccotti G. V., et al. (2019). Personalized nutrition approach in food allergy: Is it prime time yet. Nutrients 11:359. DOI:10.3390/nu11020359 View in Article CrossRef Google Scholar
[48]	Flanagan A., Bechtold D. A., Pot G. K., et al. (2021). Chrono-nutrition: From molecular and neuronal mechanisms to human epidemiology and timed feeding patterns. J. Neurochem. 157:53−72. DOI:10.1111/jnc.15246 View in Article CrossRef Google Scholar
[49]	Bennett G., Bardon L. A. and Gibney E. R. (2022). A comparison of dietary patterns and factors influencing food choice among ethnic groups living in one locality: A systematic review. Nutrients 14:941. DOI:10.3390/nu14050941 View in Article CrossRef Google Scholar
[50]	Wang Z., Cui L., Yu W., et al. (2025). Vitamin D nutritional status in China: A multicenter cross-sectional study. Nutr., Metab. Cardiovasc. Dis.36:104275. DOI:10.1016/j.numecd.2025.104275 View in Article Google Scholar
[51]	Palmnäs M., Brunius C., Shi L., et al. (2020). Perspective: Metabotyping—a potential personalized nutrition strategy for precision prevention of cardiometabolic disease. Adv. Nutr. 11:524−532. DOI:10.1093/advances/nmz121 View in Article CrossRef Google Scholar
[52]	Hjorth M. F., Astrup A., Zohar Y., et al. (2019). Personalized nutrition: Pretreatment glucose metabolism determines individual long-term weight loss responsiveness in individuals with obesity on low-carbohydrate versus low-fat diet. Int. J. Obes 43:2037−2044. DOI:10.1038/s41366-018-0298-4 View in Article CrossRef Google Scholar
[53]	Ritz C., Astrup A., Larsen T. M., et al. (2019). Weight loss at your fingertips: Personalized nutrition with fasting glucose and insulin using a novel statistical approach. Eur. J. Clin. Nutr. 73:1529−1535. DOI:10.1038/s41430-019-0423-z View in Article CrossRef Google Scholar
[54]	Welendorf C., Nicoletti C. F., Pinhel M. A. d. S., et al. (2019). Obesity, weight loss, and influence on telomere length: New insights for personalized nutrition. Nutrition 66:115−121. DOI:10.1016/j.nut.2019.05.002 View in Article CrossRef Google Scholar
[55]	Alvarenga L., Cardozo L. F. M. F., Lindholm B., et al. (2020). Intestinal alkaline phosphatase modulation by food components: Predictive, preventive, and personalized strategies for novel treatment options in chronic kidney disease. EPMA J. 11:565−579. DOI:10.1007/s13167-020-00228-9 View in Article CrossRef Google Scholar
[56]	Goecks J., Jalili V., Heiser L. M., et al. (2020). How machine learning will transform biomedicine. Cell 181:92−101. DOI:10.1016/j.cell.2020.03.022 View in Article CrossRef Google Scholar
[57]	Zhang Z., Azizi M., Lee M., et al. (2019). A versatile, cost-effective, and flexible wearable biosensor for in situ and ex situ sweat analysis, and personalized nutrition assessment. Lab Chip 19:3448−3460. DOI:10.1039/C9LC00734B View in Article CrossRef Google Scholar
[58]	Li M., Wang L., Liu R., et al. (2021). A highly integrated sensing paper for wearable electrochemical sweat analysis. Biosens. Bioelectron. 174:112828. DOI:10.1016/j.bios.2020.112828 View in Article CrossRef Google Scholar
[59]	Xiao J., Liu Y., Su L., et al. (2019). Microfluidic chip-based wearable colorimetric sensor for simple and facile detection of sweat glucose. Anal. Chem. 91:14803−14807. DOI:10.1021/acs.analchem.9b03110 View in Article CrossRef Google Scholar
[60]	Sempionatto J. R., Khorshed A. A., Ahmed A., et al. (2020). Epidermal enzymatic biosensor for sweat vitamin C: Towards personalized nutrition. ACS Sensors 5:1804−1813. DOI:10.1021/acssensors.0c00604 View in Article CrossRef Google Scholar
[61]	Chen L., Xu J. and Li S. C. (2019). DeepMF: Deciphering the latent patterns in omics profiles with a deep learning method. BMC Bioinf. 20:648. DOI:10.1186/s12859-019-3291-6 View in Article CrossRef Google Scholar
[62]	Sundaravadivel P., Kesavan K., Kesavan L., et al. (2018). Smart-Log: A deep-learning based automated nutrition monitoring system in the IoT. IEEE Trans. Consum. Electron. 64:390−398. DOI:10.1109/TCE.2018.2867802 View in Article CrossRef Google Scholar
[63]	Azodi C. B., Tang J. and Shiu S.-H. (2020). Opening the black box: Interpretable machine learning for geneticists. Trends Genet. 36:442−455. DOI:10.1016/j.tig.2020.03.005 View in Article CrossRef Google Scholar
[64]	Sallinen R. J., Dethlefsen O., Ruotsalainen S., et al. (2020). Genetic risk score for serum 25-hydroxyvitamin D concentration helps to guide personalized vitamin D supplementation in healthy finnish adults. J. Nutr. 151:281−292. DOI:10.1093/jn/nxaa391 View in Article CrossRef Google Scholar
[65]	Yang Y., Shi C.-Y., Xie J., et al. (2020). Identification of potential dipeptidyl peptidase (DPP)-IV inhibitors among Moringa oleifera phytochemicals by virtual screening, molecular docking nalysis, ADME/T-based prediction, and in vitro analyses. Molecules 25:189. DOI:10.3390/molecules25010189 View in Article CrossRef Google Scholar
[66]	Xie L., Mo J., Ni J., et al. (2020). Structure-based design of human pancreatic amylase inhibitors from the natural anthocyanin database for type 2 diabetes. Food Funct. 11:2910−2923. DOI:10.1039/C9FO02885D View in Article CrossRef Google Scholar
[67]	Rasouli H., Hosseini-Ghazvini S. M.-B., Adibi H., et al. (2017). Differential α-amylase/α-glucosidase inhibitory activities of plant-derived phenolic compounds: A virtual screening perspective for the treatment of obesity and diabetes. Food Funct. 8:1942−1954. DOI:10.1039/C7FO00220C View in Article CrossRef Google Scholar
[68]	Guimarães R., Calhelha R. C., Froufe H. J. C., et al. (2016). Wild Roman chamomile extracts and phenolic compounds: Enzymatic assays and molecular modelling studies with VEGFR-2 tyrosine kinase. Food Funct. 7:79−83. DOI:10.1039/C5FO00586H View in Article CrossRef Google Scholar
[69]	Zhao W., Zhang D., Yu Z., et al. (2020). Novel membrane peptidase inhibitory peptides with activity against angiotensin converting enzyme and dipeptidyl peptidase IV identified from hen eggs. J. Funct. Foods 64:103649. DOI:10.1016/j.jff.2019.103649 View in Article CrossRef Google Scholar
[70]	Hebert P. R., Gaziano J. M., Chan K. S., et al. (1997). Cholesterol lowering with statin drugs, risk of stroke, and total mortality: An overview of randomized trials. JAMA 278:313−321. DOI:10.1001/jama.1997.03550040069040 View in Article CrossRef Google Scholar
[71]	Shi W., Hou T., Guo D., et al. (2019). Evaluation of hypolipidemic peptide (Val-Phe-Val-Arg-Asn) virtual screened from chickpea peptides by pharmacophore model in high-fat diet-induced obese rat. J. Funct. Foods 54:136−145. DOI:10.1016/j.jff.2019.01.001 View in Article CrossRef Google Scholar
[72]	Guo J.-F., Ning Z.-Q., Wu X., et al. (2019). Discovery of a natural PI3Kδ inhibitor through virtual screening and biological assay study. Biochem. Biophys. Res. Commun. 508:709−714. DOI:10.1016/j.bbrc.2018.12.009 View in Article CrossRef Google Scholar
[73]	Liu H., Liang J., Xiao G., et al. (2021). Dendrobine suppresses lipopolysaccharide-induced gut inflammation in a co-culture of intestinal epithelial Caco-2 cells and RAW264.7 macrophages. eFood 2:92-99. DOI:10.2991/efood.k.210409.001 View in Article Google Scholar
[74]	Liang D., Liu C., Li Y., et al. (2023). Engineering fucoxanthin-loaded probiotics’ membrane vesicles for the dietary intervention of colitis. Biomaterials 297:122107. DOI:10.1016/j.biomaterials.2023.122107 View in Article CrossRef Google Scholar
[75]	Hou S., Lai C., Song Y., et al. (2023). A food-grade and senescent cell-targeted fisetin delivery system based on whey protein isolate-galactooligosaccharides Maillard conjugate. Food Sci. Hum. Wellness 13:688−697. DOI:10.26599/FSHW.2022.9250058 View in Article CrossRef Google Scholar
[76]	Bao C., Liu B., Li B., et al. (2020). Enhanced transport of shape and rigidity-tuned α-lactalbumin nanotubes across intestinal mucus and cellular barriers. Nano Lett. 20:1352−1361. DOI:10.1021/acs.nanolett.9b04841 View in Article CrossRef Google Scholar
[77]	Wen S., Wang W., Huang K., et al. (2022). Novel capsaicin releasing system targeted protects ischemic brain from cardiac arrest. J. Drug Delivery Sci. Technol. 70:103229. DOI:10.1016/j.jddst.2022.103229 View in Article CrossRef Google Scholar
[78]	Tian C., Asghar S., Hu Z., et al. (2019). Understanding the cellular uptake and biodistribution of a dual-targeting carrier based on redox-sensitive hyaluronic acid-ss-curcumin micelles for treating brain glioma. Int. J. Biol. Macromol. 136:143−153. DOI:10.1016/j.ijbiomac.2019.06.060 View in Article CrossRef Google Scholar
[79]	Agwa M. M., Abdelmonsif D. A., Khattab S. N., et al. (2020). Self-assembled lactoferrin-conjugated linoleic acid micelles as an orally active targeted nanoplatform for Alzheimer's disease. Int. J. Biol. Macromol. 162:246−261. DOI:10.1016/j.ijbiomac.2020.06.058 View in Article CrossRef Google Scholar
[80]	Monaco A., Ferrandino I., Boscaino F., et al. (2018). Conjugated linoleic acid prevents age-dependent neurodegeneration in a mouse model of neuropsychiatric lupus via the activation of an adaptive response. J. Lipid Res. 59:48−57. DOI:10.1194/jlr.M079400 View in Article CrossRef Google Scholar
[81]	Lu Y., Guo Z., Zhang Y., et al. (2019). Microenvironment remodeling micelles for Alzheimer's disease therapy by early modulation of activated microglia. Adv. Sci. 6:1801586. DOI:10.1002/advs.201801586 View in Article CrossRef Google Scholar
[82]	Wei X., Yang D., Xing Z., et al. (2021). Quercetin loaded liposomes modified with galactosylated chitosan prevent LPS/D-GalN induced acute liver injury. Mater. Sci. Eng.: C 131:112527. DOI:10.1016/j.msec.2021.112527 View in Article CrossRef Google Scholar
[83]	Zhao F.-Q., Wang G.-F., Xu D., et al. (2021). Glycyrrhizin mediated liver-targeted alginate nanogels delivers quercetin to relieve acute liver failure. Int. J. Biol. Macromol. 168:93−104. DOI:10.1016/j.ijbiomac.2020.11.204 View in Article CrossRef Google Scholar
[84]	Hua Z., Zhang X., Zhao X., et al. (2023). Hepatic-targeted delivery of astaxanthin for enhanced scavenging free radical scavenge and preventing mitochondrial depolarization. Food Chem. 406:135036. DOI:10.1016/j.foodchem.2022.135036 View in Article CrossRef Google Scholar
[85]	Hua Z., Zhang X., Chen Y., et al. (2023). A bifunctional hepatocyte-mitochondrion targeting nanosystem for effective astaxanthin delivery to the liver. Food Chem. 424:136439. DOI:10.1016/j.foodchem.2023.136439 View in Article CrossRef Google Scholar
[86]	Teng W., Zhao L., Yang S., et al. (2019). The hepatic-targeted, resveratrol loaded nanoparticles for relief of high fat diet-induced nonalcoholic fatty liver disease. J. Controlled Release 307:139−149. DOI:10.1016/j.jconrel.2019.06.023 View in Article CrossRef Google Scholar
[87]	Chen Z., Li W., Shi L., et al. (2020). Kidney-targeted astaxanthin natural antioxidant nanosystem for diabetic nephropathy therapy. Eur. J. Pharm. Biopharm. 156:143−154. DOI:10.1016/j.ejpb.2020.09.005 View in Article CrossRef Google Scholar
[88]	Shu G., Lu C., Wang Z., et al. (2021). Fucoidan-based micelles as P-selectin targeted carriers for synergistic treatment of acute kidney injury. Nanomed.: Nanotechnol. Biol. Med. 32:102342. DOI:10.1016/j.nano.2020.102342 View in Article CrossRef Google Scholar
[89]	Liu D., Jin F., Shu G., et al. (2019). Enhanced efficiency of mitochondria-targeted peptide SS-31 for acute kidney injury by pH-responsive and AKI-kidney targeted nanopolyplexes. Biomaterials 211:57−67. DOI:10.1016/j.biomaterials.2019.04.034 View in Article CrossRef Google Scholar
[90]	Zhang S., Kang L., Hu S., et al. (2021). Carboxymethyl chitosan microspheres loaded hyaluronic acid/gelatin hydrogels for controlled drug delivery and the treatment of inflammatory bowel disease. Int. J. Biol. Macromol. 167:1598−1612. DOI:10.1016/j.ijbiomac.2020.11.117 View in Article CrossRef Google Scholar
[91]	Yang C., Zhang M., Lama S., et al. (2020). Natural-lipid nanoparticle-based therapeutic approach to deliver 6-shogaol and its metabolites M2 and M13 to the colon to treat ulcerative colitis. J. Controlled Release 323:293−310. DOI:10.1016/j.jconrel.2020.04.032 View in Article CrossRef Google Scholar
[92]	Zhang X., Zhao X., Tie S., et al. (2022). A smart cauliflower-like carrier for astaxanthin delivery to relieve colon inflammation. J. Controlled Release 342:372−387. DOI:10.1016/j.jconrel.2022.01.014 View in Article CrossRef Google Scholar
[93]	Zhang X., Zhao X., Hua Z., et al. (2023). ROS-triggered self-disintegrating and pH-responsive astaxanthin nanoparticles for regulating the intestinal barrier and colitis. Biomaterials 292:121937. DOI:10.1016/j.biomaterials.2022.121937 View in Article CrossRef Google Scholar
[94]	Chen Y., Su W., Tie S., et al. (2023). Orally deliverable sequence-targeted astaxanthin nanoparticles for colitis alleviation. Biomaterials 293:121976. DOI:10.1016/j.biomaterials.2022.121976 View in Article CrossRef Google Scholar
[95]	Tie S. and Tan M. (2022). Current advances in multifunctional nanocarriers based on marine polysaccharides for colon delivery of food polyphenols. J. Agric. Food Chem. 70:903−915. DOI:10.1021/acs.jafc.1c05012 View in Article CrossRef Google Scholar
[96]	Tie S., Zhang L., Li B., et al. (2023). Effect of dual targeting procyanidins nanoparticles on metabolomics of lipopolysaccharide-stimulated inflammatory macrophages. Food Sci. Hum. Wellness 12:2252−2262. DOI:10.1016/j.fshw.2023.03.045 View in Article CrossRef Google Scholar
[97]	Tie S., Su W., Chen Y., et al. (2022). Dual targeting procyanidin nanoparticles with glutathione response for colitis treatment. Chem. Eng. J. 441:136095. DOI:10.1016/j.cej.2022.136095 View in Article CrossRef Google Scholar
[98]	Gou S., Huang Y., Wan Y., et al. (2019). Multi-bioresponsive silk fibroin-based nanoparticles with on-demand cytoplasmic drug release capacity for CD44-targeted alleviation of ulcerative colitis. Biomaterials 212:39−54. DOI:10.1016/j.biomaterials.2019.05.012 View in Article CrossRef Google Scholar
[99]	Zhang X., Ma Y., Ma L., et al. (2019). Oral administration of chondroitin sulfate-functionalized nanoparticles for colonic macrophage-targeted drug delivery. Carbohydr. Polym. 223:115126. DOI:10.1016/j.carbpol.2019.115126 View in Article CrossRef Google Scholar
[100]	Lee T. and Chang Y. H. (2020). Structural, physicochemical, and in-vitro release properties of hydrogel beads produced by oligochitosan and de-esterified pectin from yuzu (Citrus junos) peel as a quercetin delivery system for colon target. Food Hydrocolloids 108:106086. DOI:10.1016/j.foodhyd.2020.106086 View in Article CrossRef Google Scholar
[101]	Shishir M. R. I., Karim N., Xie J., et al. (2020). Colonic delivery of pelargonidin-3-O-glucoside using pectin-chitosan-nanoliposome: Transport mechanism and bioactivity retention. Int. J. Biol. Macromol. 159:341−355. DOI:10.1016/j.ijbiomac.2020.05.076 View in Article CrossRef Google Scholar
[102]	Liang D., Su W., Zhao X., et al. (2022). Microfluidic fabrication of pH-responsive nanoparticles for encapsulation and colon-target release of fucoxanthin. J. Agric. Food Chem. 70:124−135. DOI:10.1021/acs.jafc.1c05580 View in Article CrossRef Google Scholar
[103]	Qin X.-S., Luo Z.-G. and Li X.-L. (2021). An enhanced pH-sensitive carrier based on alginate-Ca-EDTA in a set-type W1/O/W2 double emulsion model stabilized with WPI-EGCG covalent conjugates for probiotics colon-targeted release. Food Hydrocolloids 113:106460. DOI:10.1016/j.foodhyd.2020.106460 View in Article CrossRef Google Scholar
[104]	Irwin C., Desbrow B., Khalesi S., et al. (2019). Challenges following a personalised diet adhering to dietary guidelines in a sample of Australian university students. Nutr. Health 25:185−194. DOI:10.1177/0260106019841247 View in Article CrossRef Google Scholar
[105]	Kohlenberg-Müller K., Ramminger S., Kolm A., et al. (2019). Nutrition assessment in process-driven, personalized dietetic intervention–the potential importance of assessing behavioural components to improve behavioural change: Results of the EU-funded IMPECD project. Clin. Nutr. ESPEN 32:125−134. DOI:10.1016/j.clnesp.2019.03.017 View in Article CrossRef Google Scholar
[106]	Vallée Marcotte B., Cormier H., Garneau V., et al. (2018). Nutrigenetic testing for personalized nutrition: An evaluation of public perceptions, attitudes, and concerns in a population of French Canadians. Lifestyle Genom. 11:155−162. DOI:10.1159/000499626 View in Article CrossRef Google Scholar
[107]	Franco R. Z., Fallaize R., Hwang F., et al. (2019). Strategies for online personalised nutrition advice employed in the development of the eNutri web app. Proc. Nutr. Soc. 78:407−417. DOI:10.1017/S0029665118002707 View in Article CrossRef Google Scholar
[108]	Grigorian A., Tabatabaeyan A., Salesi M., et al. (2025). Astaxanthin supplement improves clinical outcomes, quality of life, and inflammatory factors in patients with rheumatoid arthritis: A randomized clinical trial. Food Funct. 16:5850−5858. DOI:10.1039/D5FO00949A View in Article CrossRef Google Scholar
[109]	Li N., Cui C., Xu J., et al. (2024). Quercetin intervention reduced hepatic fat deposition in patients with nonalcoholic fatty liver disease: A randomized, double-blind, placebo-controlled crossover clinical trial. Am. J. Clin. Nutr. 120:507−517. DOI:10.1016/j.ajcnut.2024.07.013 View in Article CrossRef Google Scholar
[110]	Musso G., Pinach S., Mariano F., et al. (2025). Effect of phospholipid curcumin Meriva on liver histology and kidney disease in nonalcoholic steatohepatitis: A randomized, double-blind, placebo-controlled trial. Hepatology 81:560−575. DOI:10.1097/HEP.0000000000000937 View in Article CrossRef Google Scholar
[111]	Mathers J. C. (2019). Paving the way to better population health through personalised nutrition. EFSA J. 17:e170713. DOI:10.2903/j.efsa.2019.e170713 View in Article CrossRef Google Scholar

Cited by

ABOUT THIS ARTICLE

Cite this article:

Hua Z., Zhang X., Su W., et al. (2026). Advancements in precision nutrition: Targeted strategies for personalized health. The Innovation Life 4:100191. https://doi.org/10.59717/j.xinn-life.2026.100191

Hua Z., Zhang X., Su W., et al. (2026). Advancements in precision nutrition: Targeted strategies for personalized health. The Innovation Life 4:100191. https://doi.org/10.59717/j.xinn-life.2026.100191

Welcome!

To request copyright permission to republish or share portions of our works, please visit Copyright Clearance Center's (CCC) Marketplace website at marketplace.copyright.com.

PDF

Figures(5) Tables(1)

Article Metrics

Article views(7166) PDF downloads(1019)

Advancements in precision nutrition: Targeted strategies for personalized health

GRAPHICAL ABSTRACT

PUBLIC SUMMARY

ABSTRACT

REFERENCES

ABOUT THIS ARTICLE

Cite this article:

Welcome!

Share

Article Metrics

Relative Articles

Cited by

Catalog

{{newsColumn.name}}

Official Account

{{newsColumn.name}}

Stay connected

{{newsColumn.name}}

Advancements in precision nutrition: Targeted strategies for personalized health

GRAPHICAL ABSTRACT

PUBLIC SUMMARY

ABSTRACT

REFERENCES

ABOUT THIS ARTICLE

Cite this article:

Welcome!

Share

Article Metrics

Relative Articles

Cited by

Catalog

Export File

Citation

Format

Content

{{newsColumn.name}}

Official Account

{{newsColumn.name}}

Stay connected

{{newsColumn.name}}