@article{fdi:010074955,
  title = {{A} data integration multi-omics approach to study calorie restriction-induced changes in insulin sensitivity},
  author = {{D}ao, {M}. {C}. and {S}okolovska, {N}. and {B}razeilles, {R}. and {A}ffeldt, {S}. and {P}elloux, {V}. and {P}rifti, {E}. and {C}hilloux, {J}. and {V}erger, {E}. and {K}ayser, {B}. {D}. and {A}ron-{W}isnewsky, {J}. and {I}chou, {F}. and {P}ujos-{G}uillot, {E}. and {H}oyles, {L}. and {J}uste, {C}. and {D}ore, {J}. and {D}umas, {M}. {E}. and {R}izkalla, {S}. {W}. and {H}olmes, {B}. {A}. and {Z}ucker, {J}ean-{D}aniel and {C}lement, {K}. and {M}icro-{O}bes {C}onsortium,},
  editor = {},
  language = {{ENG}},
  abstract = {{B}ackground: {T}he mechanisms responsible for calorie restriction ({CR})-induced improvement in insulin sensitivity ({IS}) have not been fully elucidated. {G}reater insight can be achieved through deep biological phenotyping of subjects undergoing {CR}, and integration of big data. {M}aterials and {M}ethods: {A}n 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 ({C}linical{T}rials.gov: {NCT}01314690). {P}artial 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 (s{AT}) gene expression, metabolomics of serum, urine and feces, and gut microbiota composition. {S}cale{N}et, a network learning approach based on spectral consensus strategy ({SCS}, developed by us) was used for reconstruction of biological networks. {R}esults: {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 s{AT} gene probes) most associated with {IS} were identified. {B}iological network reconstruction using {SCS}, highlighted links between changes in {IS}, serum branched chain amino acids, s{AT} genes involved in endoplasmic reticulum stress and ubiquitination, and gut metagenomic species ({MGS}). {L}inear 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. {C}onclusion: {T}his 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. {F}urthermore, 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.},
  keywords = {data integration ; insulin sensitivity ; lifestyle factors ; microbiota ; omics ; {FRANCE}},
  booktitle = {},
  journal = {{F}rontiers in {P}hysiology},
  volume = {9},
  numero = {},
  pages = {art. 1958 [14 p.]},
  ISSN = {1664-042{X}},
  year = {2019},
  DOI = {10.3389/fphys.2018.01958},
  URL = {https://www.documentation.ird.fr/hor/fdi:010074955},
}