Multi-omics signatures in new-onset diabetes predict metabolic response to dietary inulin: findings from an observational study followed by an interventional trial

Abstract

Aim: The metabolic performance of the gut microbiota contributes to the onset of type 2 diabetes. However, targeted dietary interventions are limited by the highly variable inter-individual response. We hypothesized (1) that the composition of the complex gut microbiome and metabolome (MIME) differ across metabolic spectra (lean-obese-diabetes); (2) that specific MIME patterns could explain the differential responses to dietary inulin; and (3) that the response can be predicted based on baseline MIME signature and clinical characteristics.

Method: Forty-nine patients with newly diagnosed pre/diabetes (DM), 66 metabolically healthy overweight/obese (OB), and 32 healthy lean (LH) volunteers were compared in a cross-sectional case-control study integrating clinical variables, dietary intake, gut microbiome, and fecal/serum metabolomes (16 S rRNA sequencing, metabolomics profiling). Subsequently, 27 DM were recruited for a predictive study: 3 months of dietary inulin (10 g/day) intervention.

Results: MIME composition was different between groups. While the DM and LH groups represented opposite poles of the abundance spectrum, OB was closer to DM. Inulin supplementation was associated with an overall improvement in glycemic indices, though the response was very variable, with a shift in microbiome composition toward a more favorable profile and increased serum butyric and propionic acid concentrations. The improved glycemic outcomes of inulin treatment were dependent on better baseline glycemic status and variables related to the gut microbiota, including the abundance of certain bacterial taxa (i.e., Blautia, Eubacterium halii group, Lachnoclostridium, Ruminiclostridium, Dialister, or Phascolarctobacterium), serum concentrations of branched-chain amino acid derivatives and asparagine, and fecal concentrations of indole and several other volatile organic compounds.

Conclusion: We demonstrated that obesity is a stronger determinant of different MIME patterns than impaired glucose metabolism. The large inter-individual variability in the metabolic effects of dietary inulin was explained by differences in baseline glycemic status and MIME signatures. These could be further validated to personalize nutritional interventions in patients with newly diagnosed diabetes.

Fig. 1. Fecal microbiome composition.

A 2D PCA scores plot on genera level after clr transformation. The explained variance of each component is included in the axis labels. The large points represent the centroids of each group. B Abundances of all significant genera (FDR <0.1). Proportional data were used. Each cell then represents the mean in each group for the corresponding genera. Rows were z-scaled. Core genera are defined by the condition abundance >0.05% and prevalence >75% at least in one group. Genera marked by * are confirmed butyrate producers, and genera marked by (*) are potential butyrate producers.

Fig. 2. Fecal metabolome composition.

A 2D PCA scores plot on VOCs abundances after clr transformation. Only VOCs meeting condition AUC_x,p ≥ 0.1% AUC_total,p are shown. The explained variance of each component is included in the axis labels. The large points represent the centroids of each group. B Abundances of significant metabolites. Proportional data were used. Each cell then represents the median in each group for the corresponding metabolite. Rows were z-scaled.

Fig. 3. Serum metabolome composition.

A 2D PCA scores plot. The explained variance of each component is included in the axis labels. The large points represent the centroids of each group. B Abundances of significant metabolites. Each cell then represents the median in each group for the corresponding metabolite. Rows were z-scaled.

Fig. 4. Correlation chord diagrams between variables of different datasets.

Spearman correlations were calculated for each group (LH, OB, DM) separately. Only correlations among variables from different datasets (clinical variables, microbiome, serum, and fecal metabolome) and characterized by |ρ| > 0.5 are presented. Positive (A, C, E) and negative (B, D, F) correlations are shown separately. The colors on the circuit code individual datasets, the color of the edges corresponds to one of the datasets that are linked by the edge. Blue: microbiome; green: fecal metabolome; yellow: clinical variables; violet: serum metabolome.

Fig. 5. Effect of inulin on fecal microbiome composition.

A Alpha diversity calculated on rarefied ASV data, p value represents the result of Wilcoxon test; B 2D PCA scores plot on genera level. The explained variance of each component is included in the axis labels. The large points represent the centroids of each group. C Biomarker bacterial genera. Prior to all calculations, data were clr transformed. Biomarkers were generated from univariable discriminant analysis (FDR ≥0.1), with effect size estimated by Cliff’s delta with a 95% confidence interval. A, time point prior to intervention; B, time point post-intervention. Core genera (bold) are defined by the condition abundance >0.05% and prevalence >75% at least in one group. Genera marked by * are confirmed butyrate producers, and genera marked by (*) are potential butyrate producers. ASV, amplicon sequence variant.

Fig. 6. Effect of inulin supplementation on selected markers of glucose metabolism.

Data are expressed as the percentual change baseline to post-intervention. A, time point prior to intervention; B, time point post-intervention. Dashed line, 0%; dotted lines, ±10% range.