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Dao M. C., Sokolovska N., Brazeilles R., Affeldt S., Pelloux V., Prifti E., Chilloux J., Verger E., Kayser B. D., Aron-Wisnewsky J., Ichou F., Pujos-Guillot E., Hoyles L., Juste C., Dore J., Dumas M. E., Rizkalla S. W., Holmes B. A., Zucker Jean-Daniel, Clement K., Micro-Obes Consortium. (2019). A data integration multi-omics approach to study calorie restriction-induced changes in insulin sensitivity. Frontiers in Physiology, 9, art. 1958 [14 p.]. ISSN 1664-042X

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Lien direct chez l'éditeur doi:10.3389/fphys.2018.01958

Titre
A data integration multi-omics approach to study calorie restriction-induced changes in insulin sensitivity
Année de publication2019
Type de documentArticle référencé dans le Web of Science WOS:000457808400001
AuteursDao M. C., Sokolovska N., Brazeilles R., Affeldt S., Pelloux V., Prifti E., Chilloux J., Verger E., Kayser B. D., Aron-Wisnewsky J., Ichou F., Pujos-Guillot E., Hoyles L., Juste C., Dore J., Dumas M. E., Rizkalla S. W., Holmes B. A., Zucker Jean-Daniel, Clement K., Micro-Obes Consortium.
SourceFrontiers in Physiology, 2019, 9, p. art. 1958 [14 p.]. p. art. 1958 [14 p.] ISSN 1664-042X
RésuméBackground: The mechanisms responsible for calorie restriction (CR)-induced improvement in insulin sensitivity (IS) have not been fully elucidated. Greater insight can be achieved through deep biological phenotyping of subjects undergoing CR, and integration of big data. Materials and Methods: An integrative approach was applied to investigate associations between change in IS and factors from host, microbiota, and lifestyle after a 6-week CR period in 27 overweight or obese adults (ClinicalTrials.gov: NCT01314690). Partial least squares regression was used to determine associations of change (week 6 - baseline) between IS markers and lifestyle factors (diet and physical activity), subcutaneous adipose tissue (sAT) gene expression, metabolomics of serum, urine and feces, and gut microbiota composition. ScaleNet, a network learning approach based on spectral consensus strategy (SCS, developed by us) was used for reconstruction of biological networks. Results: A spectrum of variables from lifestyle factors (10 nutrients), gut microbiota (10 metagenomics species), and host multi-omics (metabolic features: 84 from serum, 73 from urine, and 131 from feces; and 257 sAT gene probes) most associated with IS were identified. Biological network reconstruction using SCS, highlighted links between changes in IS, serum branched chain amino acids, sAT genes involved in endoplasmic reticulum stress and ubiquitination, and gut metagenomic species (MGS). Linear regression analysis to model how changes of select variables over the CR period contribute to changes in IS, showed greatest contributions from gut MGS and fiber intake. Conclusion: This work has enhanced previous knowledge on links between host glucose homeostasis, lifestyle factors and the gut microbiota, and has identified potential biomarkers that may be used in future studies to predict and improve individual response to weight-loss interventions. Furthermore, this is the first study showing integration of the wide range of data presented herein, identifying 115 variables of interest with respect to IS from the initial input, consisting of 9,986 variables.
Plan de classementSanté : généralités [050] ; Nutrition, alimentation [054] ; Biotechnologies [084]
Descr. géo.FRANCE
LocalisationFonds IRD [F B010074955]
Identifiant IRDfdi:010074955
Lien permanenthttp://www.documentation.ird.fr/hor/fdi:010074955

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