Trial Outcomes & Findings for Effect of Bismuth Subsalicylate on the Gut Microbiome and Host Response in Healthy Adults (NCT NCT05930197)

Participants were recruited from June 2023 through October 2024. All recruitment took place at the National Institute of Allergy and Infectious Disease.

Baseline visits were completed after confirmation of eligibility. The overlap between the screening and baseline windows accounted for the required 30-day interval between the optional colonoscopies at baseline and day 8. Participants who did not undergo colonoscopy may have a shorter interval between screening, baseline, and day 0 once eligibility is confirmed. Some participants did not meet criteria to continue based on medication use, scheduling issues, and enrollment in other trials.

Effect of Bismuth Subsalicylate on the Gut Microbiome and Host Response in Healthy Adults

In the context of gut microbiome analysis, this measure represents the number of bacterial taxa that are significantly different between the two timepoints (baseline and 28 days post BSS) based on the read counts which represent abundance of taxa. We used shotgun metagenomic sequencing to analyze the gut microbiome. Sequenced reads were mapped to a reference database, and the read count abundance of each taxon was calculated. Statistical analysis was performed to identify the number of taxa that are significantly different between timepoints.

Difference of alpha diversity stool samples by Shannon index at baseline (before study drug administration) and 28 days after BSS. In the context of gut microbiome analysis, the Shannon Index represents a measure of alpha diversity (measure of diversity of a microbial community). The Shannon Index is a value greater than 0 with lower values indicating lower diversity and higher values indicating higher diversity, generally between 1.5 and 3.5, and usually \< 4.5. To evaluate changes in the gut microbiome, we compared the mean change in the Shannon Index at baseline and 28 days post-BSS. Shotgun metagenomic sequencing was performed, and sequenced reads were mapped to a reference database to identify and enumerate based on read count unique taxa present in each sample. Statistical analysis was then used to determine whether there were significant differences in the Shannon Index between the two timepoints, providing insight into shifts in microbial diversity following BSS administration.

Difference of beta diversity stool samples at baseline (before study drug administration) and 28 days after BSS. Beta diversity refers to the variation in bacterial composition among samples, which we measured utilizing the Bray-Curtis dissimilarity Index. The Bray-Curtis is a widely used metric to quantify beta diversity, reflecting the degree of dissimilarity between samples, and its range is from 0 to 1, with 0 meaning no differences and 1 meaning completely dissimilar. This measure is essential for understanding changes in the bacterial composition over time. We used shotgun metagenomic sequencing to analyze the gut microbiome. Sequenced reads were mapped to a reference database, and the abundance of each taxon was calculated. The Bray-Curtis dissimilarity index was then calculated using read counts per species per sample to quantify the differences in community composition between samples at baseline and 28 days post-BSS administration.

In the context of stool metabolome analysis, the number of differentially abundant metabolites refers to the count of metabolites whose levels significantly differ between baseline and 28 days post-BSS. To determine this, we performed broad targeted metabolomic profiling of stool samples collected at both timepoints. Metabolites were identified and quantified, and significant changes in abundance were calculated. The total number of differentially abundant metabolites provides a measure of the metabolic shifts in the gut environment following BSS administration.

In the context of gut microbiome analysis, this measure represents the number of bacterial taxa that are significantly different between the two timepoints (baseline and 2 days post BSS) based on the read counts which represent abundance of taxa. We used shotgun metagenomic sequencing to analyze the gut microbiome. Sequenced reads were mapped to a reference database, and the read count abundance of each taxon was calculated. Statistical analysis was performed to identify the number of taxa that are significantly different between timepoints.

Difference of alpha diversity stool samples by Shannon index at baseline (before study drug administration) and 2 days after BSS. In the context of gut microbiome analysis, the Shannon Index represents a measure of alpha diversity (measure of diversity of a microbial community). The Shannon Index is a value greater than 0 with lower values indicating lower diversity and higher values indicating higher diversity, generally between 1.5 and 3.5, and usually \< 4.5. To evaluate changes in the gut microbiome, we compared the mean change in the Shannon Index at baseline and 2 days post-BSS. Shotgun metagenomic sequencing was performed, and sequenced reads were mapped to a reference database to identify and enumerate based on read count unique taxa present in each sample. Statistical analysis was then used to determine whether there were significant differences in the Shannon Index between the two timepoints, providing insight into shifts in microbial diversity following BSS administration.

Beta diversity refers to the variation in bacterial composition among samples, which we measured utilizing the Bray-Curtis dissimilarity Index. Bray-Curtis is used to quantify beta diversity, reflecting the degree of dissimilarity between samples, and its range is from 0 to 1, with 0 meaning no differences and 1 meaning completely dissimilar. This measure is essential for understanding changes in the bacterial composition over time. We used shotgun metagenomic sequencing to analyze the gut microbiome. Sequenced reads were mapped to a reference database, and the abundance of each taxon was calculated. Bray-Curtis was then calculated using read counts per species per sample to quantify the differences in community composition between samples at baseline and 2 days post-BSS administration. Bray-Curtis dissimilarities were calculated between samples and visualized using principal coordinates analysis (PCoA); values along the second ordination axis (PCoA2) used for downstream analysis.

In the context of stool metabolome analysis, the number of differentially abundant metabolites refers to the count of metabolites whose levels significantly differ between baseline and 2 days post-BSS. To determine this, we performed broad targeted metabolomic profiling of stool samples collected at both timepoints. Metabolites were identified and quantified, and significant changes in abundance were calculated. The total number of differentially abundant metabolites provides a measure of the metabolic shifts in the gut environment following BSS administration.