Literature DB >> 32281738

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.

Shaomin Zhao1, Haiyan Liu2, Zhihua Su3, Christina Khoo2, Liwei Gu1.   

Abstract

SCOPE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  cranberries; metabolomics; orthogonal partial least squares-discriminant analysis; procyanidins

Mesh:

Substances:

Year:  2020        PMID: 32281738     DOI: 10.1002/mnfr.201901242

Source DB:  PubMed          Journal:  Mol Nutr Food Res        ISSN: 1613-4125            Impact factor:   5.914


  3 in total

Review 1.  Nutritional Metabolomics and the Classification of Dietary Biomarker Candidates: A Critical Review.

Authors:  Talha Rafiq; Sandi M Azab; Koon K Teo; Lehana Thabane; Sonia S Anand; Katherine M Morrison; Russell J de Souza; Philip Britz-McKibbin
Journal:  Adv Nutr       Date:  2021-12-01       Impact factor: 8.701

Review 2.  Untargeted metabolomics analysis of esophageal squamous cell cancer progression.

Authors:  Tao Yang; Ruting Hui; Jessica Nouws; Maor Sauler; Tianyang Zeng; Qingchen Wu
Journal:  J Transl Med       Date:  2022-03-14       Impact factor: 5.531

3.  Metabolomics Coupled with Pathway Analysis Provides Insights into Sarco-Osteoporosis Metabolic Alterations and Estrogen Therapeutic Effects in Mice.

Authors:  Ziheng Wei; Fei Ge; Yanting Che; Si Wu; Xin Dong; Dianwen Song
Journal:  Biomolecules       Date:  2021-12-28
  3 in total

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