@article{Dao:2019:10.3389/fphys.2018.01958,
author = {Dao, MC and Sokolovska, N and Brazeilles, R and Affeldt, S and Pelloux, V and Prifti, E and Chilloux, J and Verger, EO and Kayser, BD and Aron-Wisnewsky, J and Ichou, F and Pujos-Guillot, E and Hoyles, L and Juste, C and Dore, J and Dumas, M-E and Rizkalla, SW and Holmes, BA and Zucker, J-D and Clement, K},
doi = {10.3389/fphys.2018.01958},
journal = {Frontiers in Physiology},
title = {A data integration multi-omics approach to study calorie restriction-induced changes in insulin sensitivity},
url = {http://dx.doi.org/10.3389/fphys.2018.01958},
volume = {9},
year = {2019}
}

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TY  - JOUR
AB  - Background: 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 ra
AU  - Dao,MC
AU  - Sokolovska,N
AU  - Brazeilles,R
AU  - Affeldt,S
AU  - Pelloux,V
AU  - Prifti,E
AU  - Chilloux,J
AU  - Verger,EO
AU  - Kayser,BD
AU  - Aron-Wisnewsky,J
AU  - Ichou,F
AU  - Pujos-Guillot,E
AU  - Hoyles,L
AU  - Juste,C
AU  - Dore,J
AU  - Dumas,M-E
AU  - Rizkalla,SW
AU  - Holmes,BA
AU  - Zucker,J-D
AU  - Clement,K
DO  - 10.3389/fphys.2018.01958
PY  - 2019///
SN  - 1664-042X
TI  - A data integration multi-omics approach to study calorie restriction-induced changes in insulin sensitivity
T2  - Frontiers in Physiology
UR  - http://dx.doi.org/10.3389/fphys.2018.01958
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000457808400001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/69276
VL  - 9
ER  - 

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