Contributors: Unité de Recherche sur les Maladies Cardiovasculaires, du Métabolisme et de la Nutrition = Research Unit on Cardiovascular and Metabolic Diseases (ICAN); Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Institut de Cardiométabolisme et Nutrition = Institute of Cardiometabolism and Nutrition CHU Pitié Salpêtrière (IHU ICAN); CHU Pitié-Salpêtrière AP-HP; Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-CHU Pitié-Salpêtrière AP-HP; Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU); Nutrition et obésités: approches systémiques (UMR-S 1269) (Nutriomics); Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU); Danone Nutricia Research Palaiseau, France; Centre Daniel Carasso Palaiseau, France; Unité de modélisation mathématique et informatique des systèmes complexes Bondy (UMMISCO); Université Gaston Berger de Saint-Louis Sénégal (UGB)-Université de Yaoundé I (UY1)-Institut de la francophonie pour l'informatique-Université Cadi Ayyad Marrakech (UCA)-Sorbonne Université (SU)-Institut de Recherche pour le Développement (IRD France-Nord )-Université Cheikh Anta Diop de Dakar Sénégal (UCAD); Imperial College London; Unité de Nutrition Humaine (UNH); Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne 2017-2020 (UCA 2017-2020 ); Nottingham Trent University; MICrobiologie de l'ALImentation au Service de la Santé (MICALIS); Institut National de la Recherche Agronomique (INRA)-AgroParisTech; AgroParisTech; This work was supported by Agence Nationale de la Recherche (ANR MICRO-Obes and ANR-10-IAHU-05) and the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement HEALTH-F4-2012-305312 (METACARDIS) and Fondation Leducq (to KC’s team). The clinical work received support from KOT-Ceprodi, Danone Nutricia Research and the Foundation Coeur et Artère. LH was in receipt of an MRC Intermediate Research Fellowship in Data Science (Grant No. MR/L01632X/1) and Heart and Stroke Foundation of Canada.; MICRO-Obes Consortium : Aurélie Cotillard, Sean P. Kennedy, Nicolas Pons, Emmanuelle Le Chatelier, Mathieu Almeida, Benoit Quinquis, Nathalie Galleron, Jean-Michel Batto, Pierre Renault, Stanislav Dusko Ehrlich, Hervé Blottière, Marion Leclerc, Tomas de Wouters, Patricia Lepage.; ANR-10-IAHU-0005,ICAN,Institut de Cardiologie-Métabolisme-Nutrition(2010); ANR-07-GMGE-0002,MICRO-Obes,Microbiome intestinal humain dans l'obésité et la transition nutritionnelle – initiative Franco-Chinoise(2007); European Project: 305312,EC:FP7:HEALTH,FP7-HEALTH-2012-INNOVATION-1,METACARDIS(2012)
نبذة مختصرة : International audience ; 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 range of data presented ...
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