Cargando…

Integration of Traditional and Metabolomics Biomarkers Identifies Prognostic Metabolites for Predicting Responsiveness to Nutritional Intervention against Oxidative Stress and Inflammation

Various statistical approaches can be applied to integrate traditional and omics biomarkers, allowing the discovery of prognostic markers to classify subjects into poor and good prognosis groups in terms of responses to nutritional interventions. Here, we performed a prototype study to identify meta...

Descripción completa

Detalles Bibliográficos
Autores principales: Kim, You Jin, Huh, Iksoo, Kim, Ji Yeon, Park, Saejong, Ryu, Sung Ha, Kim, Kyu-Bong, Kim, Suhkmann, Park, Taesung, Kwon, Oran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5372896/
https://www.ncbi.nlm.nih.gov/pubmed/28273855
http://dx.doi.org/10.3390/nu9030233
_version_ 1782518713217449984
author Kim, You Jin
Huh, Iksoo
Kim, Ji Yeon
Park, Saejong
Ryu, Sung Ha
Kim, Kyu-Bong
Kim, Suhkmann
Park, Taesung
Kwon, Oran
author_facet Kim, You Jin
Huh, Iksoo
Kim, Ji Yeon
Park, Saejong
Ryu, Sung Ha
Kim, Kyu-Bong
Kim, Suhkmann
Park, Taesung
Kwon, Oran
author_sort Kim, You Jin
collection PubMed
description Various statistical approaches can be applied to integrate traditional and omics biomarkers, allowing the discovery of prognostic markers to classify subjects into poor and good prognosis groups in terms of responses to nutritional interventions. Here, we performed a prototype study to identify metabolites that predict responses to an intervention against oxidative stress and inflammation, using a data set from a randomized controlled trial evaluating Korean black raspberry (KBR) in sedentary overweight/obese subjects. First, a linear mixed-effects model analysis with multiple testing correction showed that four-week consumption of KBR significantly changed oxidized glutathione (GSSG, q = 0.027) level, the ratio of reduced glutathione (GSH) to GSSG (q = 0.039) in erythrocytes, malondialdehyde (MDA, q = 0.006) and interleukin-6 (q = 0.006) levels in plasma, and seventeen NMR metabolites in urine compared with those in the placebo group. A subsequent generalized linear mixed model analysis showed linear correlations between baseline urinary glycine and N-phenylacetylglycine (PAG) and changes in the GSH:GSSG ratio (p = 0.008 and 0.004) as well as between baseline urinary adenine and changes in MDA (p = 0.018). Then, receiver operating characteristic analysis revealed that a two-metabolite set (glycine and PAG) had the strongest prognostic relevance for future interventions against oxidative stress (the area under the curve (AUC) = 0.778). Leave-one-out cross-validation confirmed the accuracy of prediction (AUC = 0.683). The current findings suggest that a higher level of this two-metabolite set at baseline is useful for predicting responders to dietary interventions in subjects with oxidative stress and inflammation, contributing to the emergence of personalized nutrition.
format Online
Article
Text
id pubmed-5372896
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-53728962017-04-05 Integration of Traditional and Metabolomics Biomarkers Identifies Prognostic Metabolites for Predicting Responsiveness to Nutritional Intervention against Oxidative Stress and Inflammation Kim, You Jin Huh, Iksoo Kim, Ji Yeon Park, Saejong Ryu, Sung Ha Kim, Kyu-Bong Kim, Suhkmann Park, Taesung Kwon, Oran Nutrients Article Various statistical approaches can be applied to integrate traditional and omics biomarkers, allowing the discovery of prognostic markers to classify subjects into poor and good prognosis groups in terms of responses to nutritional interventions. Here, we performed a prototype study to identify metabolites that predict responses to an intervention against oxidative stress and inflammation, using a data set from a randomized controlled trial evaluating Korean black raspberry (KBR) in sedentary overweight/obese subjects. First, a linear mixed-effects model analysis with multiple testing correction showed that four-week consumption of KBR significantly changed oxidized glutathione (GSSG, q = 0.027) level, the ratio of reduced glutathione (GSH) to GSSG (q = 0.039) in erythrocytes, malondialdehyde (MDA, q = 0.006) and interleukin-6 (q = 0.006) levels in plasma, and seventeen NMR metabolites in urine compared with those in the placebo group. A subsequent generalized linear mixed model analysis showed linear correlations between baseline urinary glycine and N-phenylacetylglycine (PAG) and changes in the GSH:GSSG ratio (p = 0.008 and 0.004) as well as between baseline urinary adenine and changes in MDA (p = 0.018). Then, receiver operating characteristic analysis revealed that a two-metabolite set (glycine and PAG) had the strongest prognostic relevance for future interventions against oxidative stress (the area under the curve (AUC) = 0.778). Leave-one-out cross-validation confirmed the accuracy of prediction (AUC = 0.683). The current findings suggest that a higher level of this two-metabolite set at baseline is useful for predicting responders to dietary interventions in subjects with oxidative stress and inflammation, contributing to the emergence of personalized nutrition. MDPI 2017-03-04 /pmc/articles/PMC5372896/ /pubmed/28273855 http://dx.doi.org/10.3390/nu9030233 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kim, You Jin
Huh, Iksoo
Kim, Ji Yeon
Park, Saejong
Ryu, Sung Ha
Kim, Kyu-Bong
Kim, Suhkmann
Park, Taesung
Kwon, Oran
Integration of Traditional and Metabolomics Biomarkers Identifies Prognostic Metabolites for Predicting Responsiveness to Nutritional Intervention against Oxidative Stress and Inflammation
title Integration of Traditional and Metabolomics Biomarkers Identifies Prognostic Metabolites for Predicting Responsiveness to Nutritional Intervention against Oxidative Stress and Inflammation
title_full Integration of Traditional and Metabolomics Biomarkers Identifies Prognostic Metabolites for Predicting Responsiveness to Nutritional Intervention against Oxidative Stress and Inflammation
title_fullStr Integration of Traditional and Metabolomics Biomarkers Identifies Prognostic Metabolites for Predicting Responsiveness to Nutritional Intervention against Oxidative Stress and Inflammation
title_full_unstemmed Integration of Traditional and Metabolomics Biomarkers Identifies Prognostic Metabolites for Predicting Responsiveness to Nutritional Intervention against Oxidative Stress and Inflammation
title_short Integration of Traditional and Metabolomics Biomarkers Identifies Prognostic Metabolites for Predicting Responsiveness to Nutritional Intervention against Oxidative Stress and Inflammation
title_sort integration of traditional and metabolomics biomarkers identifies prognostic metabolites for predicting responsiveness to nutritional intervention against oxidative stress and inflammation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5372896/
https://www.ncbi.nlm.nih.gov/pubmed/28273855
http://dx.doi.org/10.3390/nu9030233
work_keys_str_mv AT kimyoujin integrationoftraditionalandmetabolomicsbiomarkersidentifiesprognosticmetabolitesforpredictingresponsivenesstonutritionalinterventionagainstoxidativestressandinflammation
AT huhiksoo integrationoftraditionalandmetabolomicsbiomarkersidentifiesprognosticmetabolitesforpredictingresponsivenesstonutritionalinterventionagainstoxidativestressandinflammation
AT kimjiyeon integrationoftraditionalandmetabolomicsbiomarkersidentifiesprognosticmetabolitesforpredictingresponsivenesstonutritionalinterventionagainstoxidativestressandinflammation
AT parksaejong integrationoftraditionalandmetabolomicsbiomarkersidentifiesprognosticmetabolitesforpredictingresponsivenesstonutritionalinterventionagainstoxidativestressandinflammation
AT ryusungha integrationoftraditionalandmetabolomicsbiomarkersidentifiesprognosticmetabolitesforpredictingresponsivenesstonutritionalinterventionagainstoxidativestressandinflammation
AT kimkyubong integrationoftraditionalandmetabolomicsbiomarkersidentifiesprognosticmetabolitesforpredictingresponsivenesstonutritionalinterventionagainstoxidativestressandinflammation
AT kimsuhkmann integrationoftraditionalandmetabolomicsbiomarkersidentifiesprognosticmetabolitesforpredictingresponsivenesstonutritionalinterventionagainstoxidativestressandinflammation
AT parktaesung integrationoftraditionalandmetabolomicsbiomarkersidentifiesprognosticmetabolitesforpredictingresponsivenesstonutritionalinterventionagainstoxidativestressandinflammation
AT kwonoran integrationoftraditionalandmetabolomicsbiomarkersidentifiesprognosticmetabolitesforpredictingresponsivenesstonutritionalinterventionagainstoxidativestressandinflammation