Taxonomic and Functional Fecal Microbiota Signatures Associated With Insulin Resistance in Non-Diabetic Subjects With Overweight/Obesity Within the Frame of the PREDIMED-Plus Study

Abstract

Objective: An altered gut microbiota has been associated with insulin resistance, a metabolic dysfunction consisting of cellular insulin signaling impairment. The aim of the present study is to determine the taxonomic and functional fecal microbiota signatures associated with HOMA-IR index in a population with high cardiovascular risk.

Methods: A total of 279 non-diabetic individuals (55-75 years aged) with overweight/obesity and metabolic syndrome were stratified according to tertiles of HOMA-IR index. Blood biochemical parameters, anthropometric measurements and fecal samples were collected at baseline. Fecal microbial DNA extraction, 16S amplicon sequencing and bioinformatics analysis were performed.

Results: Desulfovibrio, Odoribacter and Oscillospiraceae UCG-002 were negatively associated with HOMA-IR index, whereas predicted total functional abundances revealed gut metabolic modules mainly linked to amino acid degradation. Butyricicoccus, Erysipelotrichaceae UCG-003, Faecalibacterium were positively associated with HOMA-IR index, whereas predicted total functional abundances revealed gut metabolic modules mainly linked to saccharide degradation. These bacteria contribute differentially to the gut metabolic modules, being the degree of contribution dependent on insulin resistance. Both taxa and gut metabolic modules negatively associated to HOMA-IR index were linked to mechanisms involving sulfate reducing bacteria, improvement of intestinal gluconeogenesis and production of acetate. Furthermore, both taxa and gut metabolic modules positively associated to HOMA-IR index were linked to production and mechanisms of action of butyrate.

Conclusions: Specific taxonomic and functional fecal microbiota signatures associated with insulin resistance were identified in a non-diabetic population with overweight/obesity at high cardiovascular risk. These findings suggest that tailoring therapies based on specific fecal microbiota profiles could be a potential strategy to improve insulin sensitivity.

Keywords: 16S sequencing; HOMA-IR; fecal microbiota; gut metabolic modules; insulin resisitance.

Figure 1

Workflow showing the fecal samples analytic process and bioinformatics pipeline. Bacterial DNA was extracted from frozen fecal and 16S amplicon sequencing performed. Resulting raw sequences in fastq format were imported into R environment and processed with DADA2 package. Output files followed 3 different pipelines: (1) processed in order to obtain information about alpha and beta diversity and differential abundant taxa; (2) processed with PICRUSt2 in order to obtain the predicted total functional abundances, then gut metabolic modules (GMM) were computed and the association with HOMA-IR index determined; (3) processed with PICRUSt2 in order to obtain the predicted contribution per genus of each GMM previously computed.

Figure 2

Potential mechanisms explaining the association between fecal microbiome and IR. The negative association with IR seems to be linked with glucose homeostasis, induced by an increase in amino acids breakdown and by an increase in sulfate-reducing bacteria, with consequent promotion of intestinal gluconeogenesis, acetate synthesis and H₂S production, in addition to an improved succinate metabolism. The positive association with IR seems to be linked to an increase in saccharides degradation that can induce the growth of butyrate-producing bacteria and bring to a disproportion in butyrate synthesis and an impairment in glycolipid metabolism.