Objective <p>The predictive role of baseline gut microbiota in type 2 diabetes (T2D) remission after bariatric surgery remains unexplored. This study aimed to identify specific gut microbiota profiles prior to surgery associated with T2D remission following sleeve gastrectomy (SG).</p> Methods <p>This was an observational study including participants with a body mass index (BMI) <b>≥</b> 40&#xa0;kg/m<sup>2</sup> and T2D who underwent SG, and had preoperative stool samples available. Gut microbiota was analayzed by 16&#xa0;S rRNA sequencing. Participants were classified into remission and non-remission groups based on T2D status one year after SG.</p> Results <p>A total of forty-six participants were included. After adjusting for baseline confounders (i.e., age, HbA1c levels, T2D duration, and insulin treatment), preoperative gut microbiota diversity showed no statistically significant differences between groups, except for Pielou’s evenness index, which was significantly higher in the non-remission group (<i>p</i> = 0.01). ANCOM-BC2 analysis identified an enrichment in <i>Fusicatenibacter</i>, <i>Holdemanella</i> and <i>Senegalimassilia</i> in the non-remission group, whereas <i>Eggerthella</i>,<i> Flavonifractor</i>,<i> Ruminococcaceae g__Incertae Sedis</i> and <i>Ruminococcus gnavus group</i> were enriched in the remission group. Furthermore, insulin treatment and the gut microbial taxa <i>Ruminococcaceae g__Incertae Sedis</i>, <i>Fusicatenibacter</i>, and <i>Eggerthella</i> emerged as potential predictors of T2D remission. Functional analysis using PICRUSt2 revealed increased carbohydrate metabolism pathways in the remission group.</p> Conclusions <p>Baseline gut microbiota composition may serve as an independent predictor of T2D remission in patients undergoing SG, and could become a potentially relevant biomarker, complementing other existing clinical predictors.</p> Graphical Abstract <p></p>

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Gut Microbiota-specific Profile Prior to Surgery for Predicting Type 2 Diabetes Remission in Patients Undergoing Sleeve Gastrectomy

  • José Ignacio Martínez-Montoro,
  • Raquel Sancho-Marín,
  • Lourdes Garrido-Sánchez,
  • Luis Ocaña-Wilhelmi,
  • Rocío Soler-Humanes,
  • Nerea Ruiz-Campos,
  • María José García-López,
  • Francisco J Tinahones,
  • Carolina Gutiérrez-Repiso

摘要

Objective

The predictive role of baseline gut microbiota in type 2 diabetes (T2D) remission after bariatric surgery remains unexplored. This study aimed to identify specific gut microbiota profiles prior to surgery associated with T2D remission following sleeve gastrectomy (SG).

Methods

This was an observational study including participants with a body mass index (BMI)  40 kg/m2 and T2D who underwent SG, and had preoperative stool samples available. Gut microbiota was analayzed by 16 S rRNA sequencing. Participants were classified into remission and non-remission groups based on T2D status one year after SG.

Results

A total of forty-six participants were included. After adjusting for baseline confounders (i.e., age, HbA1c levels, T2D duration, and insulin treatment), preoperative gut microbiota diversity showed no statistically significant differences between groups, except for Pielou’s evenness index, which was significantly higher in the non-remission group (p = 0.01). ANCOM-BC2 analysis identified an enrichment in Fusicatenibacter, Holdemanella and Senegalimassilia in the non-remission group, whereas Eggerthella, Flavonifractor, Ruminococcaceae g__Incertae Sedis and Ruminococcus gnavus group were enriched in the remission group. Furthermore, insulin treatment and the gut microbial taxa Ruminococcaceae g__Incertae Sedis, Fusicatenibacter, and Eggerthella emerged as potential predictors of T2D remission. Functional analysis using PICRUSt2 revealed increased carbohydrate metabolism pathways in the remission group.

Conclusions

Baseline gut microbiota composition may serve as an independent predictor of T2D remission in patients undergoing SG, and could become a potentially relevant biomarker, complementing other existing clinical predictors.

Graphical Abstract