dc.contributor.author
Vázquez Fresno, Rosa
dc.contributor.author
Llorach, Rafael
dc.contributor.author
Perera Lluna, Alexandre
dc.contributor.author
Mandal, Rupasri
dc.contributor.author
Feliz, M.
dc.contributor.author
Tinahones, Francisco J.
dc.contributor.author
Wishart, David S.
dc.contributor.author
Andrés Lacueva, Ma. Cristina
dc.date.issued
2019-02-18T14:56:36Z
dc.date.issued
2019-02-18T14:56:36Z
dc.date.issued
2015-10-26
dc.date.issued
2019-02-18T14:56:36Z
dc.identifier
https://hdl.handle.net/2445/128384
dc.description.abstract
This study aims to evaluate the robustness of clinical and metabolic phenotyping through, for the first time, the identification of differential responsiveness to dietary strategies in the improvement of cardiometabolic risk conditions. Clinical phenotyping of 57 volunteers with cardiovascular risk factors was achieved using k-means cluster analysis based on 69 biochemical and anthropometric parameters. Cluster validation based on Dunn and FOM analysis for internal coherence and external homogeneity were employed. k-means produced four clusters with particular clinical profiles. Differences on urine metabolomic profiles among clinical phenotypes were explored and validated by multivariate OSC-PLS-DA models. OSC-PLS-DA of 1H-NMR data revealed that model comparing 'obese and diabetic cluster' (OD-c) against 'healthier cluster' (H-c) showed the best predictability and robustness in terms of explaining the pairwise differences between clusters. Considering these two clusters, distinct groups of metabolites were observed following an intervention with wine polyphenol intake (WPI, 733 equivalents of gallic acid/day) per 28 days. Glucose was significantly linked to OD-c metabotype (p<0.01), and lactate, betaine and dimethylamine showed a significant trend. Whereas, associated to wine polyphenol intervention (OD-c_WPI and H-c_WPI) was tartrate (p<0.001), and mannitol, threonine methanol, fucose and 3-hydroxyphenylacetate showed a significant trend. Interestingly, 4-hydroxyphenylacetate significantly increased in H-c_WPI (p<0.05) compared to OD-c_WPI and to basal groups (gut microbial derived metabolite after polyphenol intake), thereby exhibiting a clear metabotypic intervention effect. Results revealed gut microbiota responsive phenotypes to wine polyphenols intervention. Overall, this study illustrates a novel metabolomic strategy for characterizing inter-individual responsiveness to dietary intervention and identification of health benefits.
dc.format
application/pdf
dc.format
application/pdf
dc.publisher
Elsevier B.V.
dc.relation
Versió postprint del document publicat a: https://doi.org/10.1016/j.jnutbio.2015.10.002
dc.relation
Journal of Nutritional Biochemistry, 2015, vol. 28, p. 114-120
dc.relation
https://doi.org/10.1016/j.jnutbio.2015.10.002
dc.rights
cc-by-nc-nd (c) Elsevier B.V., 2015
dc.rights
http://creativecommons.org/licenses/by-nc-nd/3.0/es
dc.rights
info:eu-repo/semantics/openAccess
dc.source
Articles publicats en revistes (Nutrició, Ciències de l'Alimentació i Gastronomia)
dc.subject
Hàbits alimentaris
dc.title
Clinical Phenotype Clustering in Cardiovascular risk patients for the identification of Responsive Metabotypes after red Wine Polyphenol intake
dc.type
info:eu-repo/semantics/article
dc.type
info:eu-repo/semantics/acceptedVersion