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

Maria Carlota Dao1, Nataliya Sokolovska1, Rémi Brazeilles2

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Calorie restriction improves insulin sensitivity by altering gut microbes and host metabolism. This study identified key gut microbes and dietary fiber as major contributors to these improvements in overweight adults.

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Area of Science:

  • Metabolic Health
  • Microbiome Research
  • Nutritional Science

Background:

  • Mechanisms of calorie restriction (CR)-induced insulin sensitivity (IS) improvement are unclear.
  • Deep biological phenotyping and big data integration can elucidate CR's effects.
  • Previous studies lacked comprehensive multi-omics and lifestyle data integration.

Purpose of the Study:

  • Investigate associations between changes in IS and host, microbiota, and lifestyle factors after CR.
  • Identify key biological and lifestyle variables influencing IS improvement.
  • Reconstruct biological networks to understand CR's impact on glucose homeostasis.

Main Methods:

  • Integrative analysis of multi-omics data (gene expression, metabolomics) and lifestyle factors.
  • Partial least squares regression to associate changes in IS markers with various factors.
  • Network learning (ScaleNet with SCS) for biological network reconstruction.
  • Analysis of data from 27 overweight/obese adults over a 6-week CR period.

Main Results:

  • Identified significant associations between IS changes and 10 nutrients, 10 metagenomic species, 84 serum, 73 urine, 131 fecal metabolic features, and 257 sAT gene probes.
  • Network reconstruction revealed links between IS, serum branched-chain amino acids, ER stress/ubiquitination genes, and gut metagenomic species.
  • Linear regression highlighted gut metagenomic species and fiber intake as primary contributors to IS changes.

Conclusions:

  • Enhanced understanding of host glucose homeostasis, lifestyle, and gut microbiota interactions.
  • Identified potential biomarkers for predicting individual responses to weight-loss interventions.
  • First study to integrate diverse datasets, identifying 115 variables related to IS from 9,986 initial variables.