Gut microbiota composition is influenced by environmental factors and has been shown to impact body metabolism. Objective: To assess the gut microbiota profile before and after Roux-en-Y gastric bypass (RYGB) and the correlation with food intake and postoperative type 2 diabetes remission (T2Dr). Design: Gut microbiota profile from obese diabetic women was evaluated before (n = 25) and 3 (n = 20) and 12 months (n = 14) after RYGB, using MiSeq Illumina-based V4 bacterial 16S rRNA gene profiling. Data on food intake (7-day record) and T2Dr (American Diabetes Association (ADA) criteria) were recorded. Results: Preoperatively, the abundance of five bacteria genera differed between patients with (57%) and without T2Dr (p < 0.050). Preoperative gut bacteria genus signature was able to predict the T2Dr status with 0.94 accuracy ROC curve (receiver operating characteristic curve). Postoperatively (vs. preoperative), the relative abundance of some gut bacteria genera changed, the gut microbial richness increased, and the Firmicutes to Bacteroidetes ratio (rFB) decreased (p < 0.05) regardless of T2Dr. Richness levels was correlated with dietary profile pre and postoperatively, mainly displaying positive and inverse correlations with fiber and lipid intakes, respectively (p < 0.05). Conclusions: Gut microbiota profile was influenced by RYGB and correlated with diet and T2Dr preoperatively, suggesting the possibility to assess its composition to predict postoperative T2Dr.

Inflammatory bowel diseases (IBD) are chronic conditions arising from an intricate interplay of genetics and environmental factors, and are associated with gut dysbiosis, inflammation, and gut permeability. In this study, we investigated whether the inflammatory potential of the diet is associated with the gut microbiota profile, inflammation, and permeability in forty patients with IBD in clinical remission. The dietary inflammatory index (DII) score was used to assess the inflammatory potential of the diet. The fecal microbiota profile was analyzed using 16SrRNA (V3–V4) gene sequencing, while fecal zonulin and calprotectin levels were measured with enzyme-linked immunosorbent assays. We found a positive correlation between the DII score and elevated calprotectin levels (Rho = 0.498; p = 0.001), but not with zonulin levels. Although α- and β-diversity did not significantly differ across DII quartiles, the most pro-inflammatory diet group exhibited a higher fecal abundance of Veillonella rogosae (p = 0.026). In addition, the abundance of some specific bacteria sequences showed an exponential behavior across DII quartiles and a correlation with calprotectin or zonulin levels (p ≤ 0.050). This included a positive correlation between sq702. Veillonella rogosae and fecal calprotectin levels (Rho = 0.419, p = 0.007). DII, calprotectin, and zonulin levels were identified as significant predictors of 6-month disease relapse (p ≤ 0.050). Our findings suggest a potential relationship of a pro-inflammatory diet intake with Veillonella rogosae and calprotectin levels in IBD patients in clinical remission, which may contribute to disease relapse.

Gut microbiota has been implicated in various clinical conditions, yet the substantial heterogeneity in gut microbiota research results necessitates a more sophisticated approach than merely identifying statistically different microbial taxa between healthy and unhealthy individuals. Our study seeks to not only select microbial taxa but also explore their synergy with phenotypic host variables to develop novel predictive models for specific clinical conditions. We assessed 50 healthy and 152 unhealthy individuals for phenotypic variables (PV) and gut microbiota (GM) composition by 16S rRNA gene sequencing. The entire modeling process was conducted in the R environment using the Random Forest algorithm. Model performance was assessed through ROC curve construction. We evaluated 52 bacterial taxa and pre-selected PV (p < 0.05) for their contribution to the final models. Across all diseases, the models achieved their best performance when GM and PV data were integrated. Notably, the integrated predictive models demonstrated exceptional performance for rheumatoid arthritis (AUC = 88.03%), type 2 diabetes (AUC = 96.96%), systemic lupus erythematosus (AUC = 98.4%), and type 1 diabetes (AUC = 86.19%). Our findings underscore that the selection of bacterial taxa based solely on differences in relative abundance between groups is insufficient to serve as clinical markers. Machine learning techniques are essential for mitigating the considerable variability observed within gut microbiota. In our study, the use of microbial taxa alone exhibited limited predictive power for health outcomes, while the integration of phenotypic variables into predictive models substantially enhanced their predictive capabilities.

The etiology of systemic lupus erythematosus (SLE) remains unclear, with both genetic and environmental factors potentially contributing. This study aimed to explore the relationship among gut microbiota (GM), intestinal permeability, and food intake with inflammatory markers in inactive SLE patients. A total of 22 women with inactive SLE and 20 healthy volunteers were enrolled, and dietary intake was assessed through 24-h dietary recalls. Plasma zonulin was used to evaluate intestinal permeability, while GM was determined by 16S rRNA sequencing. Regression models were used to analyze laboratory markers of lupus disease (C3 and C4 complement and C-reactive protein). Our results showed that the genus Megamonas was significantly enriched in the iSLE group (p < 0.001), with Megamonas funiformis associated with all evaluated laboratory tests (p < 0.05). Plasma zonulin was associated with C3 levels (p = 0.016), and sodium intake was negatively associated with C3 and C4 levels (p < 0.05). A combined model incorporating variables from each group (GM, intestinal permeability, and food intake) demonstrated a significant association with C3 complement levels (p < 0.01). These findings suggest that increased Megamonas funiformis abundance, elevated plasma zonulin, and higher sodium intake may contribute to reduced C3 complement levels in women with inactive SLE.

Roux-en-Y Gastric bypass (RYGB) promotes improvement in type 2 diabetes (T2D) shortly after surgery, with metabolic mechanisms yet to be elucidated. This study aimed to investigate the relationship between food intake, tryptophan metabolism, and gut microbiota on the glycemic control of obese T2D women after RYGB surgery. Twenty T2D women who underwent RYGB were evaluated before and three months after surgery. Food intake data were obtained by a seven-day food record and a food frequency questionnaire. Tryptophan metabolites were determined by untargeted metabolomic analysis, and the gut microbiota was determined by 16S rRNA sequencing. The glycemic outcomes were fasting blood glucose, HbA1C, HOMA-IR, and HOMA-beta. Linear regression models were applied to assess the associations between the changes in food intake, tryptophan metabolism, and gut microbiota on glycemic control after RYGB. All variables changed after RYGB (p < 0.05), except for tryptophan intake. Jointly, the variation in red meat intake, plasma indole-3-acetate, and Dorea longicatena was associated with postoperative HOMA-IR {R² 0.80, R² adj 0.74; p < 0.01}. Red meat intake decreased three months after bariatric surgery while indole-3-acetate and Dorea longicatena increased in the same period. These combined variables were associated with better insulin resistance in T2D women after RYGB.