Between-sample dissimilarities were measured by (A) Bray-Curtis distance.

(B) CAP-plot ordinate distances. The statistical significance was assessed using permutational multivariate analysis of variance (PERMANOVA) (Pr = 0.015; F = 3.9359; N.Perm = 999). (C) weighted UniFrac distances.</p

Fig 4 -

(A) The Age of participants in the Active and Inactive cases of CD and the Healthy people. (B) box plots of α-diversity richness estimators between different Ages (1 = < 16, 2 = 17–40 and 3 = Above 40) in the gut microbiota of patients with Active CD compared with Inactive CD and, healthy controls. (C) The most abundant phylum presents in every group after we apply the mean for each group, between the different Ages of every group sense (1 = < 16, 2 = 17–40 and 3 = Above 40). (D) the most abundant order present in every group after we apply the mean for each group. We visualize the different levels of taxa among different groups using a bar-plots. The dataset is plotted with each sample mapped individually to the horizontal (x) axis, and abundance values mapped to the vertical (y) axis. The abundance values for each OTU are stacked in alphabetical order.</p

Fig 5 -

(A) Sex for participants with Active and Inactive Cases of Crohn’s Disease (CD) and Healthy People. (B) box plots of α-diversity richness estimators between different genders in the gut microbiota of each group. (C) bat plots show the most abundant phylum present in every group after we apply the mean from each group. (D) bat plots show the most abundant order present in every group. We visualize the different levels of taxa among different groups using a bar-plots. The dataset is plotted with each sample mapped individually to the horizontal (x) axis, and abundance values mapped to the vertical (y) axis. The abundance values for each OTU are stacked in alphabetical order.</p

S1 Data -

Crohn’s disease (CD) entails intricate interactions with gut microbiome diversity, richness, and composition. The relationship between CD and gut microbiome is not clearly understood and has not been previously characterized in Saudi Arabia. We performed statistical analysis about various factors influencing CD activity and microbiota dysbiosis, including diagnosis, treatment, and its impact on their quality of life as well as high-throughput metagenomic V3-V4 16S rRNA encoding gene hypervariable region of a total of eighty patients with CD, both in its active and inactive state with healthy controls. The results were correlated with the demographic and lifestyle information, which the participants provided via a questionnaire. α-diversity measures indicated lower bacterial diversity and richness in the active and inactive CD groups compared to the control group. Greater dysbiosis was observed in the active CD patients compared to the inactive form of the disease, showed by a reduction in microbial diversity. Specific pathogenic bacteria such as Filifactor, Peptoniphilus, and Sellimonas were identified as characteristic of CD groups. In contrast, anti-inflammatory bacteria like Defluviitalea, Papillibacter, and Petroclostridium were associated with the control group. Among the various factors influencing disease activity and microbiota dysbiosis, smoking emerged as the most significant, with reduced α-diversity and richness for the smokers in all groups, and proinflammatory Fusobacteria was more present (p.05). Opposite to the control group, microbial diversity and richness were lower in CD participants of older age compared to younger ones, and male CD participants showed less diversity compared to women participants from the same groups. Our results describe the first report on the relationship between microbiota and Crohn’s disease progress in Saudi Arabia, which may provide a theoretical basis for the application of therapeutic methods to regulate gut microbes in CD.</div

Patients’ characteristics and demographics.

Crohn’s disease (CD) entails intricate interactions with gut microbiome diversity, richness, and composition. The relationship between CD and gut microbiome is not clearly understood and has not been previously characterized in Saudi Arabia. We performed statistical analysis about various factors influencing CD activity and microbiota dysbiosis, including diagnosis, treatment, and its impact on their quality of life as well as high-throughput metagenomic V3-V4 16S rRNA encoding gene hypervariable region of a total of eighty patients with CD, both in its active and inactive state with healthy controls. The results were correlated with the demographic and lifestyle information, which the participants provided via a questionnaire. α-diversity measures indicated lower bacterial diversity and richness in the active and inactive CD groups compared to the control group. Greater dysbiosis was observed in the active CD patients compared to the inactive form of the disease, showed by a reduction in microbial diversity. Specific pathogenic bacteria such as Filifactor, Peptoniphilus, and Sellimonas were identified as characteristic of CD groups. In contrast, anti-inflammatory bacteria like Defluviitalea, Papillibacter, and Petroclostridium were associated with the control group. Among the various factors influencing disease activity and microbiota dysbiosis, smoking emerged as the most significant, with reduced α-diversity and richness for the smokers in all groups, and proinflammatory Fusobacteria was more present (p.05). Opposite to the control group, microbial diversity and richness were lower in CD participants of older age compared to younger ones, and male CD participants showed less diversity compared to women participants from the same groups. Our results describe the first report on the relationship between microbiota and Crohn’s disease progress in Saudi Arabia, which may provide a theoretical basis for the application of therapeutic methods to regulate gut microbes in CD.</div

We visualize the different levels of taxa among different groups using a bar-plots.

The dataset is plotted with each sample mapped individually to the horizontal (x) axis, and abundance values mapped to the vertical (y) axis. The abundance values for each OTU are stacked in alphabetical order. (A) α. diversity measure between samples of different groups (B) The most abundance phylum presents in every sample (C) boxplots of α-diversity richness estimators between different groups (D) The most abundance phylum presents in every group after we apply the mean for each group.</p

Fig 3 -

(A) Analysis of smoking and nonsmoking in Active CD, Inactive CD, and Control groups. (B) boxplots of α-diversity richness estimators between smokers and nonsmokers in the gut microbiota of patients with Active CD compared with Inactive CD and, healthy controls. (C) the most abundant phylum presents in every group, between smokers of each group. (D) the most abundant order present in every group, between smokers of each group. We visualize the different levels of taxa among different groups using a bar-plots. The dataset is plotted with each sample mapped individually to the horizontal (x) axis, and abundance values mapped to the vertical (y) axis. The abundance values for each OTU are stacked in alphabetical order.</p

Study flow diagram.

Study flow diagram.</p

Outcomes.

Outcomes.</p

CONSORT 2010 checklist of information to include when reporting a randomised trial*.

(DOC)</p