Biomarker-Guided Dietary Supplementation: A Narrative Review of Precision in Personalized Nutrition.
Background: Dietary supplements (DS) are widely used to address nutritional deficiencies and promote health, yet their indiscriminate use often leads to reduced efficacy, adverse effects, and safety concerns. Biomarker-driven approaches have emerged as a promising strategy to optimize DS prescriptio...
| Publicado en: | Nutrients Vol. 16; no. 23; pp. 4033 - 4049 |
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| Autores principales: | , , , , |
| Formato: | review Journal Article |
| Publicado: |
MDPI
Dec2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=181658697&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181658697 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726643 B0TT jtl: Nutrients issn: 20726643 maglogo: N pubinfo: dt: Dec2024 vid: 16 iid: 23 pid: 97109 pub: MDPI artinfo: ui: 181658697 181658697 181658697 10.3390/nu16234033 181658697 ppf: 4033 ppct: 16 formats: tig: atl: Biomarker-Guided Dietary Supplementation: A Narrative Review of Precision in Personalized Nutrition. aug: au: Pokushalov, Evgeny Ponomarenko, Andrey Shrainer, Evgenya Kudlay, Dmitry Miller, Richard affil: Center for New Medical Technologies, Novosibirsk 630090, Russia sug: subj: Biological Markers Analysis Dietary Supplementation Evaluation Individualized Medicine Nutrition Artificial Intelligence Utilization Disease Prevention and Control Health Promotion Genomics Proteomics Metabolomics Microbiota Oxidoreductases Blood Glucose Insulin Blood Metabolic Diseases Needs Assessment ab: Background: Dietary supplements (DS) are widely used to address nutritional deficiencies and promote health, yet their indiscriminate use often leads to reduced efficacy, adverse effects, and safety concerns. Biomarker-driven approaches have emerged as a promising strategy to optimize DS prescriptions, ensuring precision and reducing risks associated with generic recommendations. Methods: This narrative review synthesizes findings from key studies on biomarker-guided dietary supplementation and the integration of artificial intelligence (AI) in biomarker analysis. Key biomarker categories—genomic, proteomic, metabolomic, lipidomic, microbiome, and immunological—were reviewed, alongside AI applications for interpreting these biomarkers and tailoring supplement prescriptions. Results: Biomarkers enable the identification of deficiencies, metabolic imbalances, and disease predispositions, supporting targeted and safe DS use. For example, genomic markers like MTHFR polymorphisms inform folate supplementation needs, while metabolomic markers such as glucose and insulin levels guide interventions in metabolic disorders. AI-driven tools streamline biomarker interpretation, optimize supplement selection, and enhance therapeutic outcomes by accounting for complex biomarker interactions and individual needs. Limitations: Despite these advancements, AI tools face significant challenges, including reliance on incomplete training datasets and a limited number of clinically validated algorithms. Additionally, most current research focuses on clinical populations, limiting generalizability to healthier populations. Long-term studies remain scarce, raising questions about the sustained efficacy and safety of biomarker-guided supplementation. Regulatory ambiguity further complicates the classification of supplements, especially when combinations exhibit pharmaceutical-like effects. Conclusions: Biomarker-guided DS prescription, augmented by AI, represents a cornerstone of personalized nutrition. While offering significant potential for precision and efficacy, advancing these strategies requires addressing challenges such as incomplete AI data, regulatory uncertainties, and the lack of long-term studies. By overcoming these obstacles, clinicians can better meet individual health needs, prevent diseases, and integrate precision nutrition into routine care. pubtype: Academic Journal doctype: review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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