Identifying Cranberry Juice Consumers with Predictive OPLS-DA Models of Plasma Metabolome and Validation of Cranberry Juice Intake Biomarkers in a Double-Blinded, Randomized, Placebo-Controlled, Cross-Over Study.
cranberries
metabolomics
orthogonal partial least squares-discriminant analysis
procyanidins
Journal
Molecular nutrition & food research
ISSN: 1613-4133
Titre abrégé: Mol Nutr Food Res
Pays: Germany
ID NLM: 101231818
Informations de publication
Date de publication:
06 2020
06 2020
Historique:
received:
05
12
2019
revised:
02
04
2020
pubmed:
14
4
2020
medline:
16
7
2021
entrez:
14
4
2020
Statut:
ppublish
Résumé
Methods to verify cranberry juice consumption are lacking. Predictive multivariate models built upon validated biomarkers may help to verify human consumption of a food using a nutrimetabolomics approach. A 21-day double-blinded, randomized, placebo-controlled, cross-over study was conducted among healthy young women aged 1829. Plasma was collected at baseline and after 3-day and 21-day consumption of cranberry or placebo juice. Plasma metabolome was analyzed using UHPLC coupled with high resolution mass spectrometry. 18 discriminant metabolites in positive mode and 18 discriminant metabolites in negative mode from a previous 3-day open-label study were re-discovered in the present blinded study. Predictive orthogonal partial least squares discriminant analysis (OPLS-DA) models were able to identify cranberry juice consumers over a placebo juice group with 96.9% correction rates after 3-day consumption in both positive and negative mode. This present study revealed 84 and 109 additional discriminant metabolites in positive and negative mode, respectively. Twelve of them were tentatively identified. Cranberry juice consumers were classified with high correction rates using predictive OPLS-DA models built upon validated plasma biomarkers. Additional biomarkers were tentatively identified. These OPLS-DA models and biomarkers provided an objective approach to verify participant compliance in future clinical trials.
Identifiants
pubmed: 32281738
doi: 10.1002/mnfr.201901242
doi:
Substances chimiques
Biomarkers
0
Placebos
0
Types de publication
Journal Article
Randomized Controlled Trial
Research Support, Non-U.S. Gov't
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1901242Informations de copyright
© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Références
A. Al-Badr, G. Al-Shaikh, Sultan Qaboos Univ. Med. J. 2013, 13, 359.
S. Zhao, H. Liu, L. Gu, J. Sci. Food Agric. 2018.
R. G. Jepson, G. Williams, J. C. Craig, Cochrane Database Syst. Rev. 2012, 10, CD001321.
M. M. Ulaszewska, C. H. Weinert, A. Trimigno, R. Portmann, C. A. Lacueva, R. Badertscher, L. Brennan, C. Brunius, A. Bub, F. Capozzi, M. C. Rosso, C. E. Cordero, H. Daniel, S. Durand, B. Egert, P. G. Ferrario, E. J. M. Feskens, P. Franceschi, M. Garcia-Aloy, F. Giacomoni, P. Giesbertz, R. Gonzalez-Dominguez, K. Hanhineva, L. Y. Hemeryck, J. Kopka, S. E. Kulling, R. Llorach, C. Manach, F. Mattivi, C. Migne et al., Mol. Nutr. Food Res. 2019, 63, e1800384.
P. S. C. S. Harsha, R. A. Wahab, C. Cuparencu, L. O. Dragsted, L. Brennan, Nutrients 2018, 10, 1911.
R. Llorach, I. Garrido, M. Monagas, M. Urpi-Sarda, S. Tulipani, B. Bartolome, C. Andres-Lacueva, J. Proteome Res. 2010, 9, 5859.
C. M. Rebholz, A. H. Lichtenstein, Z. Zheng, L. J. Appel, J. Coresh, Am. J. Clin. Nutr. 2018, 108, 243.
H. Y. Liu, T. J. Garrett, Z. H. Su, C. Khoo, L. W. Gu, J. Nutr. Biochem. 2017, 45, 67.
J. M. Lachin, J. P. Matts, L. J. Wei, Controlled Clin. Trials 1988, 9, 365.
M. C. Chambers, B. Maclean, R. Burke, D. Amodei, D. L. Ruderman, S. Neumann, L. Gatto, B. Fischer, B. Pratt, J. Egertson, K. Hoff, D. Kessner, N. Tasman, N. Shulman, B. Frewen, T. A. Baker, M. Y. Brusniak, C. Paulse, D. Creasy, L. Flashner, K. Kani, C. Moulding, S. L. Seymour, L. M. Nuwaysir, B. Lefebvre, F. Kuhlmann, J. Roark, P. Rainer, S. Detlev, T. Hemenway et al., Nat. Biotechnol. 2012, 30, 918.
T. Pluskal, S. Castillo, A. Villar-Briones, M. Oresic, BMC Bioinformatics 2010, 11, 395.
L. Eriksson, T. Byrne, E. Johansson, J. Trygg, C. Vikström, Multi- and Megavariate Data Analysis Basic Principles And Applications, Umetrics Academy, Umea, Sweden 2013.
D. S. Wishart, Y. D. Feunang, A. Marcu, A. C. Guo, K. Liang, R. Vazquez-Fresno, T. Sajed, D. Johnson, C. R. Li, N. Karu, Z. Sayeeda, E. Lo, N. Assempour, M. Berjanskii, S. Singhal, D. Arndt, Y. J. Liang, H. Badran, J. Grant, A. Serra-Cayuela, Y. F. Liu, R. Mandal, V. Neveu, A. Pon, C. Knox, M. Wilson, C. Manach, A. Scalbert, Nucleic Acids Res. 2018, 46, D608.
A. C. Schrimpe-Rutledge, S. G. Codreanu, S. D. Sherrod, J. A. McLean, J. Am. Soc. Mass Spectrom. 2016, 27, 1897.
S. Mahadevan, S. L. Shah, T. J. Marrie, C. M. Slupsky, Anal. Chem. 2008, 80, 7562.
R. D. Cook, S. Weisberg, Applied Regression Including Computing and Graphics, John Wiley & Sons, Weinheim, Germany 2009.
Q. Gao, G. Pratico, A. Scalbert, G. Vergeres, M. Kolehmainen, C. Manach, L. Brennan, L. A. Afman, D. S. Wishart, C. Andres-Lacueva, M. Garcia-Aloy, H. Verhagen, E. J. M. Feskens, L. O. Dragsted, Genes Nutr. 2017, 12, 34.
H. Y. Liu, F. Tayyari, C. Khoo, L. W. Gu, J. Funct. Foods 2015, 14, 76.
K. C. Yam, I. D'Angelo, R. Kalscheuer, H. Zhu, J. X. Wang, V. Snieckus, L. H. Ly, P. J. Converse, W. R. Jacobs, Jr., N. Strynadka, L. D. Eltis, PLoS Pathog. 2009, 5, e1000344.
V. E. Balderas-Hernandez, L. G. Trevino-Quintanilla, G. Hernandez-Chavez, A. Martinez, F. Bolivar, G. Gosset, Microb. Cell Fact. 2014, 13, 136.
T. Mimurai, K. Yazaki, K. Sawaki, T. Ozawa, M. Kawaguchi, Biomed. Res. 2005, 26, 139.
W. Zheng, S. Y. Wang, J. Agric. Food Chem. 2003, 51, 502.
J. D. Elsworth, R. H. Roth, D. E. Redmond, Jr., J. Neurochem. 1983, 41, 786.
H. Loo, B. Scatton, T. Dennis, C. Benkelfat, C. Gay, M. F. Poirier-Littre, M. Garreau, J. M. Vanelle, J. P. Olie, P. Deniker, Encephale 1983, 9, 297.
D. Cavallini, G. Ricci, S. Dupre, L. Pecci, M. Costa, R. M. Matarese, B. Pensa, A. Antonucci, S. P. Solinas, M. Fontana, Eur. J. Biochem. 1991, 202, 217.
D. Koehler, Z. A. Shah, K. Hensley, F. E. Williams, Neurochem. Int. 2018, 115, 61.
L. Servillo, A. Giovane, M. L. Balestrieri, G. Ferrari, D. Cautela, D. Castaldo, J. Agric. Food Chem. 2012, 60, 315.
Y. D. Feunang, Cheminformatics Tools for Enabling Metabolomics, University of Alberta, Alberta, Canada 2017.
A. Guerra, G. Folesani, P. Mena, A. Ticinesi, F. Allegri, A. Nouvenne, S. Pinelli, D. D. Rio, L. Borghi, T. Meschi, Int. J. Food Sci. Nutr. 2014, 65, 1033.
F. A. van Dorsten, C. H. Grun, E. J. van Velzen, D. M. Jacobs, R. Draijer, J. P. van Duynhoven, Mol. Nutr. Food Res. 2010, 54, 897.
A. L. Mayorga-Gross, P. Esquivel, Nutrients 2019, 11, 1163.