Clinical Trial ARTICLE
A data integration multi-omics approach to study calorie restriction-induced changes in insulin sensitivity
- 1Sorbonne Universités, France
- 2Institut National de la Santé et de la Recherche Médicale (INSERM), France
- 3Danone (France), France
- 4Institut de Cardiométabolisme et Nutrition (ICAN), France
- 5Faculty of Medicine, Imperial College London, United Kingdom
- 6Assistance Publique Hopitaux De Paris (AP-HP), France
- 7Université Clermont Auvergne, France
- 8Nottingham Trent University, United Kingdom
- 9Institut Micalis, France
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.
Keywords: data integration, insulin sensitivity, lifestyle factors, microbiota, omics
Received: 01 Nov 2018;
Accepted: 27 Dec 2018.
Edited by:Jie YIN, Institute of Subtropical Agriculture (CAS), China
Copyright: © 2018 Dao, Sokolovska, Brazeilles, Affeldt, Pelloux, Prifti, Chilloux, Verger, Kayser, Aron-Wisnewsky, Ichou, Pujos-Guillot, Hoyles, Juste, Doré, Dumas, Rizkalla, Holmes, Zucker and Clément. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Dr. Maria Carlota Dao, Sorbonne Universités, Paris, France, firstname.lastname@example.org
Dr. Karine Clément, Sorbonne Universités, Paris, France, email@example.com