We read with great interest the study by Asnicar and colleagues, describing the exploration of gut microbiota composition in relation to gut transit time using the ‘blue dye’ method. In agreement with previous research, the authors provided convincing evidence that longer gut transit times are linked to increasing relative abundances of microbial species (ie, Akkermansia muciniphila, Bacteroides spp. and Alistipes spp.). However, despite practical advantages, the ‘blue dye’ method does not allow to distinguish between segmental transit times. In a recent study, we investigated segmental transit time and passagerelated variation in pressure and pH in 22 individuals (11 subjects with normal weight and 11 subjects with obesity) using a wireless motility capsule (SmartPill). During capsule passage, participants were asked to collect a faecal sample. Samples were subjected to 16S rRNA gene amplicon sequencing according to VallesColomer et al. Associations between clinical and SmartPill variables and faecal microbiota community variation were assessed using single and stepwise multivariate distancebased redundancy analyses (dbRDA). Next, we explored associations between the microbiome covariates identified and participants’ enterotypes using KruskalWallis test with posthoc pairwise Dunn’s test. Finally, correlations between relative abundances of genera and significant variables were assessed using Spearman correlations. Twosided p values were adjusted for multiple testing using the BenjaminiHochberg method. As single explanatory variables, we identified a significant association between faecal community variation and body mass index (BMI), smallintestinal transit, smallintestinal pH and colonic transit (single dbRDA, p<0.05; figure 1A; table 1). However, only BMI and smallintestinal pH were observed to provide a significant, nonredundant contribution to genuslevel microbiome diversification of 11.78% and 3.36%, respectively (stepwise dbRDA, false discovery rate (FDR) <0.05). All significant covariates of community variation were found to be associated with enterotype classification of faecal samples (KruskalWallis test, FDR<0.05; figure 1B). More specifically, pairwise comparisons indicated that Ruminococcaceae (Rum)enterotyped samples were associated with longer colonic transit than their Prevotella (Prev)counterparts (Dunn, FDR=0.04). Additionally, individuals hosting a Bacteroides2 (Bact2)enterotyped microbiota were characterised by a higher BMI (Dunn, FDR=0.02) and shorter smallintestinal transit (Dunn, FDR=0.02) than Bacteroides1 (Bact1)-carriers. Sixteen genera were observed to be significantly associated with the covariates of microbiota community composition identified (Spearman, FDR<0.05; figure 1C). While relative abundances of 12 genera correlated with BMI, two were observed to be negatively associated with smallintestinal pH (Bacteroides, FDR=0.03 and Flavonifractor, FDR=0.03). While no taxa correlated significantly with smallintestinal transit, six genera were linked to colonic transit, five positively (Alistipes, FDR=0.03; Clostridium IV, FDR=0.003; Faecalicoccus, FDR=0.02; Methanobrevibacter, FDR=0.01; Phascolarctobacterium, FDR=0.03) and one negatively (Parasutterella, FDR=0.03). Among the former, Methanobrevibacter and Clostridium IV were additionally found to be associated with a lower BMI. In conclusion, we observed that faecal microbiota community variation was linked to BMI and gastrointestinal (GI) conditions. In our cohort, a shorter smallintestinal transit was associated with the Bact2enterotype, while a longer colonic transit was associated with the Rumcommunity type. Moreover, our findings revealed two genera to be related to smallintestinal pH and six genera to colonic transit. These findings are in agreement with the findings of Asnicar and colleagues, and prior publications. 3 However, while they link gut microbiota to wholegut transit, our findings provide insights into the link with segmental transit time. Moreover, these findings emphasise the importance Letter
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