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ARS Home » Northeast Area » Boston, Massachusetts » Jean Mayer Human Nutrition Research Center On Aging » Research » Publications at this Location » Publication #359278
Title: A data integration multi-omics approach to study calorie restriction-induced changes in insulin sensitivity

Author
item DAO, CARLOTA - Jean Mayer Human Nutrition Research Center On Aging At Tufts University
item SOKOLOVSKA, NATALIYA - Sorbonne Universities, Paris
item BRAZEILLES, REMI - Danone Institute International
item AFFELDT, SEVERINE - Sorbonne Universities, Paris
item PELLOUX, VERONIQUE - Sorbonne Universities, Paris
item PRIFTI, EDI - Institute Of Cardiometabolism And Nutrition
item CHILLOUX, JULIEN - Imperial College
item VERGER, ERIC - Sorbonne Universities, Paris
item KAYSER, BRANDON - Sorbonne Universities, Paris
item ARON-WISNEWSKY, JUDITH - Sorbonne Universities, Paris
item ICHOU, FARID - Institute Of Cardiometabolism And Nutrition
item PUJOS-GUILLOT, ESTELLE - Clermont Universite, Universite D'Auvergne, Unite De Nutrition Humaine
item HOYLES, LESLEY - Imperial College
item JUSTE, CATHERINE - Institut National De La Recherche Agronomique (INRA)
item DORE, JOEL - Institut National De La Recherche Agronomique (INRA)
item DUMAS, MARC-EMMANUEL - Imperial College
item RIZKALLA, SALWA - Sorbonne Universities, Paris
item HOLMES, BRIDGET - Danone Institute International
item ZUCKER, JEAN-DANIEL - Sorbonne Universities, Paris
item CLEMENT, KARINE - Sorbonne Universities, Paris

Submitted to: Frontiers in Physiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/27/2018
Publication Date: 2/5/2019
Citation: Dao, C.M., Sokolovska, N., Brazeilles, R., Affeldt, S., Pelloux, V., Prifti, E., Chilloux, J., Verger, E.O., Kayser, B., Aron-Wisnewsky, J., Ichou, F., Pujos-Guillot, E., Hoyles, L., Juste, C., Dore, J., Dumas, M., Rizkalla, S.W., Holmes, B.A., Zucker, J., Clement, K. 2019. A data integration multi-omics approach to study calorie restriction-induced changes in insulin sensitivity. Frontiers in Physiology. 9:1958. https://doi.org/10.3389/fphys.2018.01958.
DOI: https://doi.org/10.3389/fphys.2018.01958

Interpretive Summary: Multiple factors, including diet and gut microbiota composition, are involved in the improvement of insulin sensitivity through weight loss interventions in obese individuals, yet the underlying mechanisms are only partially understood. Through deep phenotyping of patients, generation of 'omics' data, and data integration approaches we can gain greater insight on the association between lifestyle factors, gut microbiota and human physiology. This study is a 6-week calorie restriction intervention in 27 overweight and obese adults, where we integrated large data sets to investigate associations between changes in insulin sensitivity; lifestyle factors (diet and physical activity); subcutaneous adipose tissue (sAT) gene expression; metabolomics in serum, urine, and feces; and gut microbiota composition. Our results highlight associations between insulin sensitivity markers; serum branched chain amino acids; a host of metabolites from serum, urine, and feces; sAT genes involved in ER stress; fiber consumption; and abundance of gut microbial species. Thus, we have identified potential biomarkers and targets that could be used in individualized interventions to improve cardiometabolic health.

Technical Abstract: Background: The mechanisms responsible for weight loss-induced improvement in insulin sensitivity are partially understood. Greater insight can now be achieved through deep phenotyping and data integration. 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. 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, gut microbiota and host multiomics most associated with insulin sensitivity were identified. These analyses highlight associations between variations in insulin sensitivity, serum branched chain amino acids, sAT genes involved in endoplasmic reticulum stress and ubiquitination, and gut metagenomic species. Conclusions: This work has enhanced previous knowledge on mechanistic links between host glucose homeostasis, lifestyle factors and microbiota, and has identified modifiable factors and biomarkers that may be used to predict and improve individual response to weight-loss interventions.