Dietary patterns drive loss of fiber-foraging species in the celiac disease patients gut microbiota compared to first-degree relatives

Abstract

Background: Celiac disease is an autoimmune disorder triggered by dietary gluten in genetically predisposed individuals that primarily affects the small intestine. Studies have reported differentially abundant bacterial taxa in the gut microbiota of celiac patients compared with non-celiac controls. However, findings across studies have inconsistencies and no microbial signature of celiac disease has been defined so far.

Results: Here, we showed, by comparing celiac patients with their non-celiac 1st-degree relatives, that bacterial communities of related individuals have similar species occurrence and abundance compared with non-relatives, regardless the disease status. We also found in celiac patients a loss of bacterial species associated with fiber degradation, and host metabolic and immune modulation, as ruminiclostridia, ruminococci, Prevotella, and Akkermansia muciniphila species. We demonstrated that the differential abundance of bacterial species correlates to different dietary patterns observed between the two groups. For instance, Ruminiclostridium siraeum, Ruminococcus bicirculans, and Bacteroides plebeious, recognized as fiber-degraders, appear more abundant in non-celiac 1st-degree relatives, which have a vegetable consumption pattern higher than celiac patients. Pattern of servings per day also suggests a possible link between these species' abundance and daily calorie intake.

Conclusions: Overall, we evidenced that a kinship approach could be valuable in unveiling potential celiac disease microbial traits, as well as the significance of dietary factors in shaping microbial profiles and their influence on disease development and progression. Our results pave the way for designing and adopting novel dietary strategies based on gluten-free fiber-enriched ingredients to improve disease management and patients' quality of life.

Keywords: Akkermansia; Celiac disease; Fiber-degraders; Gluten-free diet; Gut microbiota; Ruminococci bacteria.

Fig. 1

Study recruiting and exclusion processes. Flow diagram showing the number of participants included: 143 celiac patients (CeD) and their non-celiac 1st-degree relatives (NC1R) volunteered to participate in the current study; out of the 143, 30 were excluded based on inclusion and exclusion study criteria; the final 113 participants were included, but 12 participants failed to provide a stool sample for microbiota assessment

Fig. 2

Microbiota assessment on celiac (CeD) subjects and non-celiac 1st-degree relatives (NC1R). Red color legend shows values and distributions for CeD and blue color legend for NC1R (N = 101, CeD = 42, NC1R = 59). A Distribution of individual richness alpha diversity descriptor. Statistical comparison based on generalized linear mixed models (GLM, lme4:glmer function) with covariate adjustment. B Non-metric dimensional scaling (NMDS) analysis of microbiota multivariate data. A permutation-based comparison (Adonis, vegan::adonis2 function) was used to evaluate the microbial variability attributed to disease condition; statistical estimates are shown in the inbox scatter plot. Ellipses show value distribution and confidence interval at 95%. C Aitchison distance (compositional) between relative and non-relative pairs is shown in a boxplot manner. The non-relative distances outnumber several hundred times the obtained for relative pairs. For homogeneous comparison aims, there was a resampling to obtain 250 non-relative random distances to compare with < 100 retrieved from relative pairs. This procedure was tenfold, and statistical comparison was achieved every time, always significantly different (Wilcoxon Rank Sum test). D Bacterial species found to be more abundant in non-celiac controls. E Species retrieved to be more abundant in CeD subjects. Distribution of clr-based abundance is shown as violin plots; medians appear as solid lines with respective color legends. Statistical comparisons in D and E are based on generalized linear mixed models (GLM, lme4:glmer function) with covariate adjustment

Fig. 3

Integration of gut microbiota data with host variables. Logistic regression (stats::glm function with binomial distribution assessment) with covariate control was used to evaluate correlation between dietary patterns and disparate gut microbiota traits between celiac (CeD) (red) and non-celiac 1st-degree relatives (NC1R) (blue). Statistical assessment supports interaction between Ruminiclostridium siraeum and nutritional supplementation (A), and with vegetable consumption (B). C The pattern of 3 servings a day was correlated with 4 out of 9 categories detected to be differentially abundant between CeD and NC1R (Fig. 1D, E). Comparison between subgroups was achieved using Wilcoxon Rank Sum test with multiple testing (FDR). Chi-squared test (stats:chi.sq function with Monte Carlo simulation) was computed to evaluate contingency tables resulting from subject grouping and dietary patterns as categorical and dichotomous variables