Characteristics of the Gut Microbiota and Metabolism in Patients With Latent Autoimmune Diabetes in Adults: A Case-Control Study

Abstract

Objective: Type 1 and type 2 diabetes are associated with gut dysbiosis. However, the relationship between the gut microbiota and latent autoimmune diabetes in adults (LADA), sharing clinical and metabolic features with classic type 1 and type 2 diabetes, remains unclear. Here, we used a multiomics approach to identify the characteristics of the gut microbiota and metabolic profiles in patients with LADA.

Research design and methods: This age- and sex-matched case-control study included 30 patients with LADA, 31 patients with classic type 1 diabetes, 30 patients with type 2 diabetes, and 29 healthy individuals. The gut microbiota profiles were identified through the 16S rRNA gene, and fecal and serum metabolites were measured through untargeted liquid chromatography-mass spectrometry.

Results: Patients with LADA had a significantly different structure and composition of the gut microbiota and their metabolites as well as a severe deficiency of short-chain fatty acid-producing bacteria. The gut microbiota structure of the patients with LADA was more similar to that of patients with type 1 diabetes who were positive for GAD antibody. We identified seven serum metabolite modules and eight fecal metabolite modules that differed between the LADA group and the other groups.

Conclusions: The characteristic gut microbiota and related metabolites of patients with LADA are associated with autoantibodies, glucose metabolism, islet function, and inflammatory factors, which may contribute to the pathogenesis of LADA. Future longitudinal studies should explore whether modulating the gut microbiota and related metabolites can alter the natural course of autoimmune diabetes in the quest for new therapeutics.

© 2021 by the American Diabetes Association.

Figures

Figure 1

Identification of the major ASV modules in the context of LADA. A: PLS-DA of the gut microbiota composition of healthy subjects and patients with LADA, T1D, and T2D. B: Bar plot revealing the top physiological factors that were significantly associated with variations in the gut microbiota based on Bray-Curtis distances. The colors of the bars represent their clinical categories. Size effects and statistical significance were calculated by adonis/permutational MANOVA. The association was considered significant when P < 0.1. C: PLS-DA of the gut microbiota composition of the patients with LADA, T1D-A, T1D-B, and T2D. D: Between-sample Bray-Curtis distances of the gut microbiota of the T1D-A, T1D-B, and T2D groups compared with those of the LADA group. **P < 0.01. E: Network diagrams of the 12 CAGs. The taxonomy of bacteria phyla identified using the QIIME2 q2-feature-classifier is denoted on the nodes. The rectangular nodes represent Bacteroidetes, the triangular nodes represent Actinobacteria, the round nodes represent Firmicutes, the hexagonal nodes represent Proteobacteria, and the diamond-shaped nodes represent Tenericutes. Lines between nodes represent correlations; only correlations with magnitudes >0.3 are drawn. Red lines mean positive correlations, and blue lines mean negative correlations. F: Z-scores of the abundance of the 12 CAGs across the different groups. Z-scores were transformed by subtracting the average abundance and dividing by the SD of all samples. Blue represents negative Z-scores, and orange represents positive Z-scores. Comparison of the relative abundance of each CAG in the five groups was performed using the Kruskal-Wallis test followed by Dunn post hoc analysis. Significant differences (P < 0.05) are marked with arrows; arrows pointing up represent a significantly higher abundance, and arrows pointing down represent a significantly lower abundance. DBP, diastolic blood pressure; DPP4, dipeptidyl peptidase 4; GGT, glutamyl transpeptidase; RBC, red blood cell; TG, triglyceride; UA, uric acid.

Figure 2

Identification of major fecal and serum metabolite modules in the context of LADA. Distribution of the 12 fecal metabolite modules (A) and 11 serum metabolite modules (B) among the LADA, healthy, T1D-A, T1D-B, and T2D groups. The abundance profiles were transformed into Z-scores by subtracting the average abundance and dividing by the SD of all samples. Comparison of the abundances of each module in the five groups was performed using the Kruskal-Wallis test followed by Dunn post hoc analysis. P < 0.05 indicates a significant difference.

Figure 3

Correlations among gut microbiota, host fecal or serum metabolites, and host clinical phenotypes in the context of LADA. A: The Spearman correlation network of the gut microbiota, fecal metabolites, serum metabolites, and clinical phenotypes related to blood glucose. B: The Spearman correlation network of the gut microbiota, fecal metabolites, serum metabolites, and clinical phenotypes related to inflammation. C: The Spearman correlation network of the gut microbiota, fecal metabolites, serum metabolites, and autoimmunity antibodies. Red lines indicate positive correlations (FDR <0.05), and blue lines indicate negative correlations (FDR <0.05). 2h CP, 2-h postprandial C-peptide; 2h PG, 2-h postprandial glucose; FCP, fasting C-peptide.