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Examining the Interaction of the Gut Microbiome with Host Metabolism and Cardiometabolic Health in Metabolic Syndrome.
Galié, Serena; Papandreou, Christopher; Arcelin, Pierre; Garcia, David; Palau-Galindo, Antoni; Gutiérrez-Tordera, Laia; Folch, Àlex; Bulló, Mònica.
Afiliação
  • Galié S; Department of Biochemistry and Biotechnology, Faculty of Medicine and Health Sciences, University RoviraiVirgili (URV), 43201 Reus, Spain.
  • Papandreou C; Institute of Health Pere Virgili-IISPV, University Hospital Sant Joan, 43202 Reus, Spain.
  • Arcelin P; Institute of Health Pere Virgili-IISPV, University Hospital Sant Joan, 43202 Reus, Spain.
  • Garcia D; CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
  • Palau-Galindo A; Institute of Health Pere Virgili-IISPV, University Hospital Sant Joan, 43202 Reus, Spain.
  • Gutiérrez-Tordera L; Atención Basica de Salut (ABS) Reus V. Centre d'Assistència Primària Marià Fortuny, SAGESSA, 43204 Reus, Spain.
  • Folch À; ABS Alt Camp Oest, Centre d'Atenció Primària, 43460 Alcover, Spain.
  • Bulló M; Department of Biochemistry and Biotechnology, Faculty of Medicine and Health Sciences, University RoviraiVirgili (URV), 43201 Reus, Spain.
Nutrients ; 13(12)2021 Nov 29.
Article em En | MEDLINE | ID: mdl-34959869
(1) Background: The microbiota-host cross-talk has been previously investigated, while its role in health is not yet clear. This study aimed to unravel the network of microbial-host interactions and correlate it with cardiometabolic risk factors. (2) Methods: A total of 47 adults with overweight/obesity and metabolic syndrome from the METADIET study were included in this cross-sectional analysis. Microbiota composition (151 genera) was assessed by 16S rRNA sequencing, fecal (m = 203) and plasma (m = 373) metabolites were profiled. An unsupervised sparse generalized canonical correlation analysis was used to construct a network of microbiota-metabolite interactions. A multi-omics score was derived for each cluster of the network and associated with cardiometabolic risk factors. (3) Results: Five multi-omics clusters were identified. Thirty-one fecal metabolites formed these clusters and were correlated with plasma sphingomyelins, lysophospholipids and medium to long-chain acylcarnitines. Seven genera from Ruminococcaceae and a member from the Desulfovibrionaceae family were correlated with fecal and plasma metabolites. Positive correlations were found between the multi-omics scores from two clusters with cholesterol and triglycerides levels. (4) Conclusions: We identified a correlated network between specific microbial genera and fecal/plasma metabolites in an adult population with metabolic syndrome, suggesting an interplay between gut microbiota and host lipid metabolism on cardiometabolic health.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Síndrome Metabólica / Microbioma Gastrointestinal / Lipídeos / Obesidade Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Síndrome Metabólica / Microbioma Gastrointestinal / Lipídeos / Obesidade Idioma: En Ano de publicação: 2021 Tipo de documento: Article