AUTHOR=Dao Maria Carlota , Sokolovska Nataliya , Brazeilles Rémi , Affeldt Séverine , Pelloux Véronique , Prifti Edi , Chilloux Julien , Verger Eric O. , Kayser Brandon D. , Aron-Wisnewsky Judith , Ichou Farid , Pujos-Guillot Estelle , Hoyles Lesley , Juste Catherine , Doré Joël , Dumas Marc-Emmanuel , Rizkalla Salwa W. , Holmes Bridget A. , Zucker Jean-Daniel , Clément Karine ,  The MICRO-Obes Consortium , Cotillard Aurélie , Kennedy Sean P. , Pons Nicolas , Chatelier Emmanuelle Le , Almeida Mathieu , Quinquis Benoit , Galleron Nathalie , Batto Jean-Michel , Renault Pierre , Ehrlich Stanislav Dusko , Blottière Hervé , Leclerc Marion , de Wouters Tomas , Lepage Patricia 

TITLE=A Data Integration Multi-Omics Approach to Study Calorie Restriction-Induced Changes in Insulin Sensitivity

JOURNAL=Frontiers in Physiology

VOLUME=Volume 9 - 2018

YEAR=2019

URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2018.01958

DOI=10.3389/fphys.2018.01958

ISSN=1664-042X

ABSTRACT=Background: The mechanisms responsible for calorie restriction-induced improvement in insulin sensitivity have not been fully elucidated. Greater insight can be achieved through deep biological phenotyping of subjects undergoing calorie restriction, and integration of big data. 
Materials and Methods: An integrative approach was applied to investigate associations between change in insulin sensitivity and factors from host, microbiota and lifestyle after a 6-week calorie restriction 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 insulin sensitivity markers and lifestyle factors (diet and physical activity), subcutaneous adipose tissue (sAT) gene expression, metabolomics in 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 subcutaneous adipose tissue gene probes) most associated with insulin sensitivity were identified. Biological network reconstruction using SCS, highlighted links between changes in insulin sensitivity, serum branched chain amino acids, sAT genes involved in endoplasmic reticulum stress and ubiquitination, and gut metagenomic species. Linear regression analysis to model how changes of select variables over the calorie restriction period contribute to changes in insulin sensitivity, showed greatest contributions from gut metagenomic species and fiber intake.
Conclusions: This work has enhanced previous knowledge on links between host glucose homeostasis, lifestyle factors and 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 insulin sensitivity from the initial input, consisting of 9,986 variables.