Abundance of Prevotella copri in gut microbiota is inversely related to a healthy diet in patients with type 2 diabetes

Abstract

While the gut microbiota is known to be influenced by habitual food intake, this relationship is seldom explored in type 2 diabetes patients. This study aims to investigate the relationship between dietary patterns and gut microbial species abundance in 113 type 2 diabetes patients (mean age, 58 years; body mass index, 29.1; glycohemoglobin [HbA1c], 8.1%). We analyzed the gut microbiota using 16S amplicon sequencing, and all patients were categorized into either the Bacteroides enterotype (57.5%, n = 65) or the Prevotella enterotype (42.5%, n = 48) using the partitioning around medoids clustering algorithm, based on the most representative genera. Patients with the Bacteroides enterotype showed better glycemic control with a 2.71 odds of HbA1c ≤ 7.0% compared to the Prevotella enterotype (95% confidence interval, 1.02-7.87; P, 0.034). Dietary habits and the nutrient composition of all patients were assessed using a validated food frequency questionnaire. It was observed that the amounts of dietary fiber consumed were suboptimal, with an average intake of 16 g per day. Additionally, we extracted four dietary patterns through factor analysis: eating-out, high-sugar foods, fish-vegetable, and fermented foods patterns. Patients with the Bacteroides enterotype had higher scores for the fish-vegetable pattern compared to the Prevotella enterotype (0.17 ± 0.13 versus -0.23 ± 0.09; P, 0.010). We further investigated the relationship between the microbiota and the four dietary patterns and found that only the fish-vegetable dietary pattern scores were correlated with principal coordinate values. A lower pattern score was associated with the accumulated abundance of the 31 significant microbial features. Among these features, Prevotella copri was identified as the most significant by using a random forest model, with an area under the receiver operating characteristic of 0.93 (95% confidence interval, 0.88-0.98). To validate these results, we conducted a custom quantitative polymerase chain reaction assay. This assay confirmed the presence of P. copri (sensitivity, 0.96; specificity, 0.97) in our cohort, with a prevalence of 47.8%, and a mean relative abundance of 21.0% in subjects harboring P. copri. In summary, type 2 diabetes patients with the Prevotella enterotype demonstrated poorer glycemic control and deviations from a healthy dietary pattern. The abundance of P. copri, as a major contributing microbial feature, was associated with the severity in the deficiency in dietary fish and vegetables. Emphasis should be placed on promoting a healthy dietary pattern and understanding the microbial correlations.

Fig. 1

A) The loading values of 38 dietary factors are presented by the color gradient in the heatmap. Dietary patterns are named according to representative foods components or behaviors. *Cells with loading values ≥ 0.2 B) Pearson’s correlation coefficients of dietary patterns versus macronutrients and clinical parameters are shown by a color gradient. *P < 0.05; **P < 0.01; ***P < 0.001. ALT, alanine aminotransferase; BMI, body mass index; eGFR, estimated glomerular filtration rate; FPG, fasting plasma glucose; HbA1c, glycohemoglobin; HDL-C, HDL-cholesterol; LDL-C, LDL-cholesterol; T-C, total-cholesterol; T2D, type 2 diabetes; UACR, urinary albumin-creatinine ratio.

Fig. 2

A–C) The alpha diversity indexes of microbiota differed weakly between patients with diverse enterotypes. D) The dissimilarity between the two enterotype groups in beta diversity is shown. The fish–vegetable pattern score is shown as an arrow towards Bacteroides; ΔCt for the Prevotella copri assay is shown as an opposite arrow. E, F) The fish–vegetable pattern scores correlated with PCoA 2 and accumulated log-abundance of the 31 positive PCoA 2-associated KTUs. ΔCt, delta cycle threshold; KTU, k-mer taxonomic unit; PCoA, principal coordinate analysis.

Fig. 3

A) The top 10 KTUs are plotted in the order of their correlation coefficient values. Samples are grouped by enterotypes in sequences of the fish–vegetable pattern scores. The color gradient in cells represents the log-transformed abundance of microbial features. B) Top 10 KTUs arranged by their mean decreasing accuracy scores in a random forest model. C) The ROC curves illustrate the diagnostic power of the top microbial features (blue, top 10; red, top 3). D) Comparison of 16S ribosomal RNA amplicon sequencing, enterotyping, and the Prevotella copri assay. All samples are arranged in accordance with ΔCt values on the x-axis. The top 5 genera are shown by relative abundance on the y-axis. ΔCt of −2.71 indicates the presence of Prevotella copri (> 0.1%), whereas the balance between Bacteroides and Prevotella contributes to the determination of enterotypes. AUROC, area under the receiver operating characteristic; r, correlation coefficient value; ΔCt, delta cycle threshold; ET, enterotype; KTU, k-mer taxonomic unit; ROC, Receiver operating characteristic.