Dysbiosis, inflammation, and response to treatment: a longitudinal study of pediatric subjects with newly diagnosed inflammatory bowel disease

Abstract

Background: Gut microbiome dysbiosis has been demonstrated in subjects with newly diagnosed and chronic inflammatory bowel disease (IBD). In this study we sought to explore longitudinal changes in dysbiosis and ascertain associations between dysbiosis and markers of disease activity and treatment outcome.

Methods: We performed a prospective cohort study of 19 treatment-naïve pediatric IBD subjects and 10 healthy controls, measuring fecal calprotectin and assessing the gut microbiome via repeated stool samples. Associations between clinical characteristics and the microbiome were tested using generalized estimating equations. Random forest classification was used to predict ultimate treatment response (presence of mucosal healing at follow-up colonoscopy) or non-response using patients' pretreatment samples.

Results: Patients with Crohn's disease had increased markers of inflammation and dysbiosis compared to controls. Patients with ulcerative colitis had even higher inflammation and dysbiosis compared to those with Crohn's disease. For all cases, the gut microbial dysbiosis index associated significantly with clinical and biological measures of disease severity, but did not associate with treatment response. We found differences in specific gut microbiome genera between cases/controls and responders/non-responders including Akkermansia, Coprococcus, Fusobacterium, Veillonella, Faecalibacterium, and Adlercreutzia. Using pretreatment microbiome data in a weighted random forest classifier, we were able to obtain 76.5 % accuracy for prediction of responder status.

Conclusions: Patient dysbiosis improved over time but persisted even among those who responded to treatment and achieved mucosal healing. Although dysbiosis index was not significantly different between responders and non-responders, we found specific genus-level differences. We found that pretreatment microbiome signatures are a promising avenue for prediction of remission and response to treatment.

Keywords: Crohn’s disease; Dysbiosis; Inflammatory bowel disease; Microbiome.

Figures

Fig. 1

Log10(calprotectin + 1) values for all study subjects used in analysis. Larger circle size reflects higher measured calprotectin. Time points where calprotectin was <100 μg/g are shown in blue; time points where calprotectin was >100 μg/g are shown in red. CD Crohn’s disease, UC ulcerative colitis, R responder to treatment, NR non-responder to treatment, F, familial control, U unrelated control. (See also Table 1 and Additional file 2: Table S1.)

Fig. 2

Clinical characteristics for all study subjects. a–c Characteristics for control subjects (black), Crohn’s disease patients (CD, red), and ulcerative colitis patients (UC, blue) are plotted over time with unadjusted regression lines in black and 95 % confidence intervals in gray. For patients with CD and UC, calprotectin decreases (a), alpha diversity increases (b), and gut microbial dysbiosis decreases (c) over time, reflecting overall improvement following treatment. Additionally, calprotectin and microbial dysbiosis were significantly higher in our UC patients than in CD. (See also Additional file 2: Figures S1 and S2, Tables S3 and S4.)

Fig. 3

Genera with significant differences between cases and controls, non-responders and responders. a –Log10(p value) from testing difference in abundance of each genus in cases compared to controls and non-responders compared to responders. Blue bars indicate taxa negatively associated with case or non-responder status, and red bars indicate a positive association. The line below 2 represents the threshold for nominal significance; the higher line is the significance level after Bonferroni adjustment for multiple tests. The asterisks denote taxa that also appear in the results of our random forest classifier. b–d Example patterns representative of each of the three categories: b significant in both comparisons, c significant only between cases and controls, and d significant only between non-responders and responders. (See also Additional file 2: Table S6.)

Fig. 4

Use of genera to predict eventual response to treatment in pretreatment samples. a Our classifier classifies response status significantly better than random guess with AUC = 0.75 and overall accuracy of 76.5 % for predicting treatment response/non-response. b Box plots of the log10 relative abundance plus pseudocount (1E-05) of the 15 genera with highest importance scores in random forest analysis in responders and non-responders. The asterisks denote taxa also identified as significant in our generalized estimating equations analysis. (See also Additional file 2: Figures S4 and S6, Additional file 2: Tables S7 and S9.)