In a recent study published within the journal Nature, researchers utilize a comprehensive multi-omics strategy in humans to evaluate the association between gut microbial carbohydrate metabolism and insulin resistance (IR). Moreover, this study discovered links between fecal metabolites and metabolic syndrome (MetS), an IR-related illness.

Study: Gut microbial carbohydrate metabolism contributes to insulin resistance. Image Credit: Andrii Vodolazhskyi / Shutterstock.com

The gut microbiome and insulin resistance

The fundamental pathophysiology of type 2 diabetes and MetS is IR. Previous metagenomic investigations have detailed the features of gut microbes and their functions in IR-related food metabolism.

More specifically, commensal carbohydrate metabolism appears to contribute to the host’s overall energy extraction and, because of this, is involved within the etiology of prediabetes and obesity. Nevertheless, the basic process stays unknown.

In regards to the study

To analyze the role of the gut microbiota in IR, researchers integrated unbiased fecal metabolomes, metagenomes, and host transcriptomic data. The study comprised 306 individuals between the ages of 20 and 75 who enrolled within the study between 2014 and 2016 on the University of Tokyo Hospital during their regular yearly health visits.

In the conventional, obese, and prediabetic categories, the researchers enrolled 112, 100, and 101 people, respectively. Physical examinations, laboratory testing, fecal samples for fecal 16S ribosomal ribonucleic acid (rRNA) pyrosequencing and metabolomic evaluation, in addition to blood sampling for serological metabolomic analyses, were performed for all participants.

Exclusion criteria included confirmed diabetes diagnosis, habitual use of diabetes or intestinal disease drugs, use of antibiotics inside two weeks of sample collection, and lack of three kilograms of body weight within the three months preceding sample collection.

IR was determined using the homeostatic model assessment of IR (HOMA-IR) with scores of two.5 or greater. To evaluate differences in gut microbial activities, fecal metabolites, and anticipated genes were classified as co-abundance groupings (CAGs) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) categorizations, respectively.

The cap evaluation of genetic expression (CAGE) approach was used to collect transcriptomic data from peripheral blood mononuclear cells (PBMCs), which may quantify genetic expression with transcription-initiation-site resolution. Area under the curve (AUC) values of the receiver operative curve (ROC) curves using the random-forest classifier system to research ways through which omics data from fecal samples might predict insulin resistance.

Fecal 16S ribosomal ribonucleic acid (RNA) sequencing, metagenome, metabolome, and their combined datasets were used to discover estimator variables for modeling using the maximum-relevance minimum-redundancy algorithm.

Throughout the correlation and regression studies with clinical indicators, significant confounding variables, including gender and age, were corrected. Gas chromatography-tandem mass spectrometry (GC-MS/MS) techniques were used to extract and quantify hydrophilic metabolites in fecal and serological samples.

Study findings

The median body mass index (BMI) and glycated hemoglobin (HbA1c) levels were 25 kg/m² and 6%, respectively. Fecal carbohydrates, particularly host-accessible monosaccharides, were higher in insulin-resistant patients and were linked to microbial carbohydrate metabolism and host inflammatory cytokines.

Gut bacteria related to IR and insulin sensitivity exhibited a special pattern of carbohydrate metabolism. Furthermore, insulin-sensitivity-associated bacteria improved host phenotypes of IR in a murine model.

An untargeted metabolomics study utilizing two MS-based analytical platforms showed 195 and 100 annotated fecal and serological hydrophilic metabolites, respectively, in addition to 2,654 and 635 annotated fecal and serological lipid metabolites.

In predicting IR, the chosen characteristics of fecal metabolomic data surpassed those of 16S and metagenomics. This statement indicates that fecal metabolomics could also be helpful in investigating IR pathophysiology.

Amongst hydrophilic metabolites, most CAGs with significant correlations with IR were carbohydrate metabolites, mostly monosaccharides. IR was also related to increased short-chain fatty acid (SCFA) levels.

Metabolites within the IR-related hydrophilic co-abundance groups were related to carbohydrate metabolism, in accordance with a KEGG pathway enrichment study. Galactose, fructose, xylose, and mannose were significantly linked with IR.

Bacteroidal species absorb quite a lot of carbohydrates, which fuels the synthesis of their fermentation products. In mice, A. indistinctus reduced IR and altered intestinal glucose metabolites, which correlates with the findings within the human cohort.

Conclusions

The study findings reveal that gut microbial carbohydrate metabolism is crucial in IR and, because of this, could be considered a primary pathophysiological mechanism underlying MetS and sort 2 diabetes mellitus. Insulin-resistant individuals exhibit increased fecal carbohydrates, particularly host-accessible monosaccharides, that are linked to microbial carbohydrate metabolisms and pro-inflammatory cytokines.

Targeting gut microbial carbohydrate metabolism might be a possible therapeutic approach to ameliorate IR. Understanding the role of gut microbes in metabolizing carbohydrates could support the event of targeted interventions to administer IR and improve host IR phenotypes and metabolic well-being.