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Two complementary metabolomics studies to identify biomarkers of banana intake


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Two complementary metabolomics studies to identify biomarkers of banana intake

Natalia Vazquez Manjarrez, Bernard Lyan, Mélanie Petera, Céline Dalle, Estelle Pujos-Guillot, Stéphanie Durand, Christine Morand, Mathilde

Touvier, Laars Ove Dragsted, Claudine Manach

To cite this version:

Natalia Vazquez Manjarrez, Bernard Lyan, Mélanie Petera, Céline Dalle, Estelle Pujos-Guillot, et al.. Two complementary metabolomics studies to identify biomarkers of banana intake. 2.International Max Rubner Conference on Food Metabolomics, Oct 2016, Karlsruhe, Germany. 2016. �hal-01595284�


Two complementary metabolomics studies to identify biomarkers of banana intake

Untargeted metabolomics allows the discovery of biomarkers of intake of different foods that provide more reliable information about the dietary habits and compliance of volunteers in observational studies and clinical studies than that offered by FFQ or 24hr questionnaires. Banana is ranked amongst the most eaten fruits in the world however, there are no biomarkers of intake reported in the literature yet and little is known about the effect of banana intake on human health. Therefore, in order to elucidate novel biomarkers for acute intake of banana we applied an untargeted metabolomics approach on 24h urine samples of 12 volunteers that participated in an acute intervention study with banana, tomato and a control drink. In parallel, to identify long-term biomarkers of banana intake we applied the same methodology to morning spot urine samples of 45 high and low consumers of banana that participated in the SU.VI.MAX2 cohort study.


Boxplots of the two most significative ions in

univariate analysis; the p value of Student test with

BH Correction.


 The total number of extracted ions was 4092 of which 103 ions showed a high

contribution to the discrimination among groups on the PLSDA model (VIP>2).

0.778 0.947


Banana Control

Heat map of 78 most significative metabolites after banana intake

• After univariate analysis using Student T test with Benjamini Hochberg correction, we detected 85 ions that were significantly different among our groups of treatment. Of these ions, 78 ions were found in higher intensity in the banana intake group.

• We detected 38 metabolite clusters according to the RT of the 78 candidate biomarkers, will be now identified.

• Kinetic urine and plasma samples are available and will be further analyzed.

BioBanaTom Study

 In total 10,249 ions were extracted. Due the high number of ions detected given by the sensitivity of the instrument, we performed a pre selection of the significative ions among groups using a Wilcoxon Test (p value <0.05). We obtained 1308 significative ions on which we performed an OSC-PLSDA.

 With multivariate analysis (OSC-PLSDA), 111 ions were identified as discriminant markers (VIP>1) between high and low consumers of banana.

 We observed that only 3 metabolites (VIP>1 and p-value <0.05) were in higher intensity in the high consumer group.

 Most the discriminant ions in multivariate and univariate analysis, had higher intensities in the low consumers group which suggests that these may be markers of effect of the long term

consumption of banana rather than biomarkers of intake.


0.77 0.83


Through the application of an untargeted metabolomics approach in the 24h urine samples of the volunteers recruited in the BioBanaTom study, we were able to detect 78 discriminative ions for the acute intake of banana. The same approach applied to the SUVIMAX2 cohort study, revealed what mainly seem biomarkers of effect of the long term intake of banana. Currently, the identification of the markers selected in these two studies is in progress to later on determine their biological plausibility and validate their utility as biomarkers of banana intake.

Project funded by:


-Column BEH shield RP10 100x41x1.7 -25 min gradient -ESI(+) and (-) 2. Data pre-processing -Ion Extraction -Annotation of ions (CAMERA)

3. Multivariate and Univariate Analysis


-Student T test and Wilcoxon (BH) 4.Identification of Features -LTQ-Orbitrap MS -Database query. -Literature search.

BioBanaTom Study

n=12 (6F & 6M) 300g *

Test Food B Test Food C

2nd period 3rd period 1st period WO period Test Food A 240g * 250ml

Test food+ 150ml of control drink

 Blood Samples (6 collections in 24h)  Urine Samples (8 collections in 24h)

 24h urine samples (collected after

0h of food intake)

WO period

 Spot urine samples


High consumers n=23 159g/day Low consumers n=22 <1g/d FFQ and 24h recalls





N. Vázquez-Manjarrez


, B. Lyan


, M. Pétéra


, C. Dalle


, E. Pujos-Guillot


, S. Durand


, C. Morand


, M. Touvier



L.O. Dragsted3, C. Manach1

1Human Nutrition Unit, INRA Clermont-Ferrand, France, Email: natalia.vazquez-manjarrez@inra.fr; 2EREN, UMR U1153 Inserm, SMBH PARIS 13, France, 3Department of Nutrition, Exercise and Sports, University of Copenhagen, Denmark


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