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      Metabolomics application for the design of an optimal diet

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          Summary

          Precision nutrition is an emerging branch of nutrition science that aims to use modern omics technologies (genomics, proteomics, and metabolomics) to assess an individual’s response to specific foods or dietary patterns and thereby determine the most effective diet or lifestyle interventions to prevent or treat specific diseases. Metabolomics is vital to nearly every aspect of precision nutrition. It can be targeted or untargeted, and it has many applications. Indeed, it can be used to comprehensively characterize the thousands of chemicals in foods, identify food by-products in human biofluids or tissues, characterize nutrient deficiencies or excesses, monitor biochemical responses to dietary interventions, track long- or short-term dietary habits, and guide the development of nutritional therapies. Indeed, metabolomics can be coupled with genomics and proteomics to study and advance the field of precision nutrition. Integrating omics with epidemiological and clinical data will begin to define the beneficial effects of human food metabolites. In this review, we present the metabolome and its relationship to precision nutrition. Moreover, we describe the different techniques used in metabolomics and present how metabolomics has been applied to advance the field of precision nutrition by providing notable examples and cases.

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          Most cited references74

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          HMDB 4.0: the human metabolome database for 2018

          Abstract The Human Metabolome Database or HMDB (www.hmdb.ca) is a web-enabled metabolomic database containing comprehensive information about human metabolites along with their biological roles, physiological concentrations, disease associations, chemical reactions, metabolic pathways, and reference spectra. First described in 2007, the HMDB is now considered the standard metabolomic resource for human metabolic studies. Over the past decade the HMDB has continued to grow and evolve in response to emerging needs for metabolomics researchers and continuing changes in web standards. This year's update, HMDB 4.0, represents the most significant upgrade to the database in its history. For instance, the number of fully annotated metabolites has increased by nearly threefold, the number of experimental spectra has grown by almost fourfold and the number of illustrated metabolic pathways has grown by a factor of almost 60. Significant improvements have also been made to the HMDB’s chemical taxonomy, chemical ontology, spectral viewing, and spectral/text searching tools. A great deal of brand new data has also been added to HMDB 4.0. This includes large quantities of predicted MS/MS and GC–MS reference spectral data as well as predicted (physiologically feasible) metabolite structures to facilitate novel metabolite identification. Additional information on metabolite-SNP interactions and the influence of drugs on metabolite levels (pharmacometabolomics) has also been added. Many other important improvements in the content, the interface, and the performance of the HMDB website have been made and these should greatly enhance its ease of use and its potential applications in nutrition, biochemistry, clinical chemistry, clinical genetics, medicine, and metabolomics science.
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            Host-gut microbiota metabolic interactions.

            The composition and activity of the gut microbiota codevelop with the host from birth and is subject to a complex interplay that depends on the host genome, nutrition, and life-style. The gut microbiota is involved in the regulation of multiple host metabolic pathways, giving rise to interactive host-microbiota metabolic, signaling, and immune-inflammatory axes that physiologically connect the gut, liver, muscle, and brain. A deeper understanding of these axes is a prerequisite for optimizing therapeutic strategies to manipulate the gut microbiota to combat disease and improve health.
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              Personalized Nutrition by Prediction of Glycemic Responses.

              Elevated postprandial blood glucose levels constitute a global epidemic and a major risk factor for prediabetes and type II diabetes, but existing dietary methods for controlling them have limited efficacy. Here, we continuously monitored week-long glucose levels in an 800-person cohort, measured responses to 46,898 meals, and found high variability in the response to identical meals, suggesting that universal dietary recommendations may have limited utility. We devised a machine-learning algorithm that integrates blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota measured in this cohort and showed that it accurately predicts personalized postprandial glycemic response to real-life meals. We validated these predictions in an independent 100-person cohort. Finally, a blinded randomized controlled dietary intervention based on this algorithm resulted in significantly lower postprandial responses and consistent alterations to gut microbiota configuration. Together, our results suggest that personalized diets may successfully modify elevated postprandial blood glucose and its metabolic consequences. VIDEO ABSTRACT.
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                Author and article information

                Journal
                J Prev Med Hyg
                J Prev Med Hyg
                JPMH
                Journal of Preventive Medicine and Hygiene
                Pacini Editore Srl
                1121-2233
                2421-4248
                17 October 2022
                June 2022
                : 63
                : 2 Suppl 3
                : E142-E149
                Affiliations
                [1 ] MAGI EUREGIO , Bolzano, Italy
                [2 ] ISB Ion Source & Biotechnologies srl , Italy, Bresso, Milano, Italy
                [3 ] MAGI’S LAB , Rovereto (TN), Italy
                [4 ] MAGI Group , San Felice del Benaco, Brescia, Italy
                [5 ] Total Lipedema Care , Beverly Hills California and Tucson Arizona, USA
                [6 ] UOSD Medicina Bariatrica, Fondazione Policlinico Agostino Gemelli IRCCS , Rome, Italy
                [7 ] Department of Cardiology, University of Brescia and ASST “Spedali Civili” Hospital , Brescia, Italy
                [8 ] Department of Cardiology and CardioLab, University of Rome Tor Vergata , Rome, Italy
                [9 ] Department of Vascular Surgery, University of Rome Tor Vergata , Rome Italy
                [10 ] Istituto di Medicina Genomica, Università Cattolica del Sacro Cuore , Rome, Italy
                [11 ] UOC Genetica Medica, Fondazione Policlinico Universitario “A. Gemelli” IRCCS , Rome, Italy
                [12 ] San Francisco Veterans Affairs Health Care System, Department of Oral & Maxillofacial Surgery, University of California , San Francisco, CA, USA
                [13 ] MAGISNAT , Peachtree Corners (GA), USA
                Author notes
                Correspondence: Kevin Donato, MAGI EUREGIO, Via Maso della Pieve 60/A, Bolzano (BZ), 39100, Italy. E-mail: kevin.donato@ 123456assomagi.org
                Article
                10.15167/2421-4248/jpmh2022.63.2S3.2755
                9710392
                36479478
                1215c467-bdb6-4887-a405-d64bc9de9f0a
                ©2022 Pacini Editore SRL, Pisa, Italy

                This is an open access article distributed in accordance with the CC-BY-NC-ND (Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International) license. The article can be used by giving appropriate credit and mentioning the license, but only for non-commercial purposes and only in the original version. For further information: https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en

                History
                Page count
                Figures: 0, Tables: 2, Equations: 0, References: 75, Pages: 8
                Categories
                Review

                metabolomics,mediterranean diet,precision nutrition,food metabolome,nmr

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