Fecal microbiota composition differs between children with β-cell autoimmunity and those without

Abstract

The role of the intestinal microbiota as a regulator of autoimmune diabetes in animal models is well-established, but data on human type 1 diabetes are tentative and based on studies including only a few study subjects. To exclude secondary effects of diabetes and HLA risk genotype on gut microbiota, we compared the intestinal microbiota composition in children with at least two diabetes-associated autoantibodies (n = 18) with autoantibody-negative children matched for age, sex, early feeding history, and HLA risk genotype using pyrosequencing. Principal component analysis indicated that a low abundance of lactate-producing and butyrate-producing species was associated with β-cell autoimmunity. In addition, a dearth of the two most dominant Bifidobacterium species, Bifidobacterium adolescentis and Bifidobacterium pseudocatenulatum, and an increased abundance of the Bacteroides genus were observed in the children with β-cell autoimmunity. We did not find increased fecal calprotectin or IgA as marker of inflammation in children with β-cell autoimmunity. Functional studies related to the observed alterations in the gut microbiome are warranted because the low abundance of bifidobacteria and butyrate-producing species could adversely affect the intestinal epithelial barrier function and inflammation, whereas the apparent importance of the Bacteroides genus in development of type 1 diabetes is insufficiently understood.

Trial registration: ClinicalTrials.gov NCT00570102 NCT01055080.

FIG. 1.

The association of PC1 with autoantibody positivity was demonstrated as an inverse correlation between PC1 and number of β-cell autoantibodies in the study cohort (P = 0.018; Spearman ρ test) and as a difference in PC1 score between the children positive for four autoantibodies and control children negative for autoantibodies (P = 0.008; Mann-Whitney test). Control children are indicated by being positive for no autoantibodies and cases are positive for two, three, or four autoantibodies (x-axis). Correlations between PC1 and bacterial groups are shown in right panel.

FIG. 2.

The distribution of children positive for β-cell autoantibodies (case subjects with open symbols) and autoantibody-negative children (control subjects with filled symbols) according to PC2 (x-axis) and PC3 (y-axis). PC2 shows an inverse correlation with the abundance of B. adolescentis and a positive correlation with B. pseudocatenulatum, whereas PC3 shows an inverse correlation with both B. adolescentis and B. pseudocatenulatum. Indicated percentages indicate the prevalence of B. adolescentis and B. pseudocatenulatum, respectively. Age of the children is associated with PC2, whereas autoantibody positivity is associated with PC3. The children 3 to 7 years of age (circle) from the FINDIA pilot have higher numbers of B. pseudocatenulatum and lower numbers of B. adolescentis than the children from the TRIGR pilot 11 to 14 years of age (square). The main correlations with PC2 and PC3 are depicted with vectors in the top left. Children with a dearth of both B. adolescentis and B. pseudocatenulatum (sum <12%) are encompassed by the dashed circle near the top and the number of autoantibody-positive children is higher than the number of control subjects at the apex (10/18 vs. 4/18; P = 0.040; χ² test).