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Mostafa H, Cheok A, Meroño T, Andres-Lacueva C, Rodriguez-Mateos A. Biomarkers of Berry Intake: Systematic Review Update. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:11789-11805. [PMID: 37499164 PMCID: PMC10416351 DOI: 10.1021/acs.jafc.3c01142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 07/08/2023] [Accepted: 07/10/2023] [Indexed: 07/29/2023]
Abstract
Berries are rich in (poly)phenols, and these compounds may be beneficial to human health. Estimating berry consumption through self-reported questionnaires has been challenging due to compliance issues and a lack of precision. Estimation via food-derived biomarkers in biofluids was proposed as a complementary alternative. We aimed to review and update the existing evidence on biomarkers of intake for six different types of berries. A systematic literature search was performed to update a previous systematic review on PubMed, Web of Science, and Scopus from January 2020 until December 2022. Out of 42 papers, only 18 studies were eligible. A multimetabolite panel is suggested for blueberry and cranberry intake. Proposed biomarkers for blueberries include hippuric acid and malvidin glycosides. For cranberries, suggested biomarkers are glycosides of peonidin and cyanidin together with sulfate and glucuronide conjugates of phenyl-γ-valerolactone derivatives. No new metabolite candidates have been found for raspberries, strawberries, blackcurrants, and blackberries. Further studies are encouraged to validate these multimetabolite panels for improving the estimation of berry consumption.
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Affiliation(s)
- Hamza Mostafa
- Biomarkers
and Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences
and Gastronomy, Nutrition and Food Safety Research Institute (INSA),
Facultat de Farmàcia i Ciències de l’Alimentació, Universitat de Barcelona (UB), 08028 Barcelona, Spain
- Centro
de Investigación Biomédica en Red de Fragilidad y Envejecimiento
Saludable (CIBERFES), Instituto de Salud
Carlos III, Madrid 28029, Spain
| | - Alex Cheok
- Department
of Nutritional Sciences, School of Life Course and Population Sciences,
Faculty of Life Sciences and Medicine, King’s
College London, 150 Stamford
Street, SE1 9NH London, U.K.
| | - Tomás Meroño
- Biomarkers
and Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences
and Gastronomy, Nutrition and Food Safety Research Institute (INSA),
Facultat de Farmàcia i Ciències de l’Alimentació, Universitat de Barcelona (UB), 08028 Barcelona, Spain
- Centro
de Investigación Biomédica en Red de Fragilidad y Envejecimiento
Saludable (CIBERFES), Instituto de Salud
Carlos III, Madrid 28029, Spain
| | - Cristina Andres-Lacueva
- Biomarkers
and Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences
and Gastronomy, Nutrition and Food Safety Research Institute (INSA),
Facultat de Farmàcia i Ciències de l’Alimentació, Universitat de Barcelona (UB), 08028 Barcelona, Spain
- Centro
de Investigación Biomédica en Red de Fragilidad y Envejecimiento
Saludable (CIBERFES), Instituto de Salud
Carlos III, Madrid 28029, Spain
| | - Ana Rodriguez-Mateos
- Department
of Nutritional Sciences, School of Life Course and Population Sciences,
Faculty of Life Sciences and Medicine, King’s
College London, 150 Stamford
Street, SE1 9NH London, U.K.
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Renai L, Ulaszewska M, Mattivi F, Bartoletti R, Del Bubba M, van der Hooft JJJ. Combining Feature-Based Molecular Networking and Contextual Mass Spectral Libraries to Decipher Nutrimetabolomics Profiles. Metabolites 2022; 12:metabo12101005. [PMID: 36295906 PMCID: PMC9610267 DOI: 10.3390/metabo12101005] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 10/16/2022] [Accepted: 10/18/2022] [Indexed: 11/24/2022] Open
Abstract
Untargeted metabolomics approaches deal with complex data hindering structural information for the comprehensive analysis of unknown metabolite features. We investigated the metabolite discovery capacity and the possible extension of the annotation coverage of the Feature-Based Molecular Networking (FBMN) approach by adding two novel nutritionally-relevant (contextual) mass spectral libraries to the existing public ones, as compared to widely-used open-source annotation protocols. Two contextual mass spectral libraries in positive and negative ionization mode of ~300 reference molecules relevant for plant-based nutrikinetic studies were created and made publicly available through the GNPS platform. The postprandial urinary metabolome analysis within the intervention of Vaccinium supplements was selected as a case study. Following the FBMN approach in combination with the added contextual mass spectral libraries, 67 berry-related and human endogenous metabolites were annotated, achieving a structural annotation coverage comparable to or higher than existing non-commercial annotation workflows. To further exploit the quantitative data obtained within the FBMN environment, the postprandial behavior of the annotated metabolites was analyzed with Pearson product-moment correlation. This simple chemometric tool linked several molecular families with phase II and phase I metabolism. The proposed approach is a powerful strategy to employ in longitudinal studies since it reduces the unknown chemical space by boosting the annotation power to characterize biochemically relevant metabolites in human biofluids.
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Affiliation(s)
- Lapo Renai
- Department of Chemistry, University of Florence, Via della Lastruccia 3, Sesto Fiorentino, 50019 Florence, Italy
- Bioinformatics Group, Wageningen University, 6708 PB Wageningen, The Netherlands
- Correspondence: (L.R.); (M.U.); (J.J.J.v.d.H.)
| | - Marynka Ulaszewska
- Metabolomics Unit, Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via Mach 1, San Michele all’Adige, 38098 Trento, Italy
- Correspondence: (L.R.); (M.U.); (J.J.J.v.d.H.)
| | - Fulvio Mattivi
- Metabolomics Unit, Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via Mach 1, San Michele all’Adige, 38098 Trento, Italy
- Department of Cellular, Computational, and Integrative Biology (CIBIO), University of Trento, Via Mach 1, San Michele all’Adige, 38098 Trento, Italy
| | - Riccardo Bartoletti
- Department of Translational Research and New Technologies, University of Pisa, Via Risorgimento 36, 56126 Pisa, Italy
| | - Massimo Del Bubba
- Department of Chemistry, University of Florence, Via della Lastruccia 3, Sesto Fiorentino, 50019 Florence, Italy
| | - Justin J. J. van der Hooft
- Bioinformatics Group, Wageningen University, 6708 PB Wageningen, The Netherlands
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa
- Correspondence: (L.R.); (M.U.); (J.J.J.v.d.H.)
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