Background: An abnormal faecal microbiota could be a causal factor for disease. This study evaluated a new method for faecal microbiota analysis in subjects with obesity and irritable bowel syndrome. Methods: The study had a matched case-control design. Forty-six subjects with morbid obesity (defined as BMI > 40 or >35 kg/m2 with obesity-related complications) of whom 23 had irritable bowel syndrome (IBS), were compared with 46 healthy volunteers. The faecal microbiota was analysed with Precision Microbiome Profiling (PMP™) which quantified 104 bacteria species. The primary aim was comparisons between the cases and controls. Results: Two men and 44 women with a mean age of 43.6 years were included in each of the groups; BMI in the groups was (mean and SD) 41.9 (3.5) and 22.5 (1.5) kg/m2, respectively. Seventeen bacterial species showed statistically significant differences between the groups after adjusting for multiple testing. In a post hoc analysis, the sensitivity and specificity were 78%. Alpha diversity was lower in the group with obesity. In subjects with morbid obesity, no clinically significant differences were seen between subjects with and without IBS or from before to six months after bariatric surgery. Conclusions: The results encourage further evaluation of the new microbiome profiling tool.

Background Inflammatory bowel disease (IBD) in South-Eastern Norway (IBSEN) III is a population-based inception cohort including patients with suspected IBD between 2017 and 2019. The present study aimed to evaluate the diagnostic and prognostic properties of baseline microbiota profiling, particularly regarding future disease course within the first year from diagnosis. Methods Stool samples were collected in preservative before index colonoscopy or as close after as possible. Patients with suspected IBD but no evidence of inflammation were categorized as symptomatic controls. Patients <18 years and patients treated with antibiotics the previous three months were excluded. A severe disease course was defined as the need for one or more of the following within one year of diagnosis: IBD-related surgery, hospitalizations due to IBD, exposure to ≥2 biologics, exposure to >2 steroid courses, or development of complicated disease behaviour. The V3-V4 region of the 16S rRNA gene was amplified and sequenced on an Illumina platform. Statistics were performed in R version 4.2.0, using MaAsLin2 with confounder correction to test for differential abundance of bacteria. XGBoost with 5-fold cross validation was used with area under the curve (AUC) analysis to produce predictive machine learning (ML) models. Results 970 patients were included: n=569 Ulcerative colitis (UC, 53% male), n=287 Crohn’s disease (CD, 41% male), n=114 symptomatic controls (51% male). Baseline microbiota was associated with a severe disease course in UC-patients when compared to those with an indolent disease course, characterized by a reduced α-diversity at baseline (Shannon diversity index, p = 0.0001, beta = -1.42 [-1.79, -1.05]) and a distinct microbial profile identified by differential abundance analyses (q < 0.05, 42 taxa). UC and CD had unique microbial profiles compared to each other (q < 0.05, 35 taxa), and both presented with decreased α-diversity when baseline disease severity increased. Further, UC and CD were different from symptomatic controls (q < 0.05, 12 and 4 taxa respectively), but only UC had a reduced α-diversity compared to systematic controls. Using ML-models and AUC analyses, microbial profiles were better prognostic markers than calprotectin and CRP for predicting both IBD subtype, i.e. UC vs CD, and a severe disease course in UC (p < 0.00001, Table). Conclusion In this large IBD inception cohort followed for one year, baseline microbial profiles show potential as both a prognostic and diagnostic tool. The microbial profile outperformed traditional biochemical markers in predicting diagnosis as well as a severe disease course, which may have clinical implications.

The relationship between the gut microbiota, short chain fatty acid (SCFA) metabolism, and obesity remains unclear due to conflicting reports from studies with limited statistical power. Additionally, this association has rarely been explored in large scale diverse populations. Here, we investigated associations between fecal microbial composition, predicted metabolic potential, SCFA concentrations, and obesity in a large (N = 1,934) adult cohort of African-origin spanning the epidemiologic transition, from Ghana, South Africa, Jamaica, Seychelles, and the United States (US). The greatest gut microbiota diversity and total fecal SCFA concentration was found in the Ghanaian population, while the lowest levels were found in the US population, respectively representing the lowest and the highest end of the epidemiologic transition spectrum. Country-specific bacterial taxa and predicted-functional pathways were observed, including an increased prevalence ofPrevotella,Butyrivibrio,WeisellaandRomboutsiain Ghana and South Africa, whileBacteroidesandParabacteroideswere enriched in Jamaican and the US populations. Importantly, 'VANISH' taxa, includingButyricicoccusandSuccinivibrio, were significantly enriched in the Ghanaian cohort, reflecting the participants' traditional lifestyles. Obesity was significantly associated with lower SCFA concentrations, a decrease in microbial richness, and dissimilarities in community composition, and reduction in the proportion of SCFA synthesizing bacteria includingOscillospira,Christensenella,Eubacterium,Alistipes,ClostridiumandOdoribacter. Further, the predicted proportions of genes in the lipopolysaccharide (LPS) synthesis pathway were enriched in obese individuals, while genes associated with butyrate synthesis via the dominant pyruvate pathway were significantly reduced in obese individuals. Using machine learning, we identified features predictive of metabolic state and country of origin. Country of origin could accurately be predicted by the fecal microbiota (AUC = 0.97), whereas obesity could not be predicted as accurately (AUC = 0.65). Participant sex (AUC = 0.75), diabetes status (AUC = 0.63), hypertensive status (AUC = 0.65), and glucose status (AUC = 0.66) could all be predicted with different success. Interestingly, within country, the predictive accuracy of the microbiota for obesity was inversely correlated to the epidemiological transition, being greatest in Ghana (AUC = 0.57). Collectively, our findings reveal profound variation in the gut microbiota, inferred functional pathways, and SCFA synthesis as a function of country of origin. While obesity could be predicted accurately from the microbiota, the variation in accuracy in parallel with the epidemiological transition suggests that differences in the microbiota between obesity and non-obesity may be larger in low-to-middle countries compared to high-income countries. Further examination of independent study populations using multi-omic approaches will be necessary to determine the factors that drive this association.

The relationship between gut microbiota, short chain fatty acid (SCFA) metabolism, and obesity is still not well understood. Here we investigated these associations in a large (n=1904) African origin cohort from Ghana, South Africa, Jamaica, Seychelles, and the US. Fecal microbiota diversity and SCFA concentration were greatest in Ghanaians, and lowest in the US population, representing the lowest and highest end of the epidemiologic transition spectrum, respectively. Obesity was significantly associated with a reduction in SCFA concentration, microbial diversity and SCFA synthesizing bacteria. Country of origin could be accurately predicted from the fecal microbiota (AUC=0.97), while the predictive accuracy for obesity was inversely correlated to the epidemiological transition, being greatest in Ghana (AUC = 0.57). The findings suggest that the microbiota differences between obesity and non-obesity may be larger in low-to-middle-income countries compared to high-income countries. Further investigation is needed to determine the factors driving this association.