The gut microbiome's influence on brain function and behavior, potentially impacting feeding circuits and overeating, is a growing area of research. While studies in mice have demonstrated a causal link between specific microbiota profiles and weight changes, human evidence is limited. Dysbiosis, an imbalance in the gut microbiota composition, has been associated with psychiatric eating disorders and obesity. However, the specific mechanisms by which nutrition- or body weight-related microbial changes affect human eating behavior and weight remain unclear. Short-chain fatty acids (SCFAs), produced by gut bacteria during carbohydrate fermentation, are known to stimulate the secretion of anorexigenic hormones (PYY and GLP-1) in the colon, influencing appetite regulation through hypothalamic signaling. SCFAs can also directly influence appetite by crossing the blood-brain barrier. Dietary fiber, a prebiotic nutrient, is crucial in nourishing SCFA-producing bacteria. This study aimed to investigate whether specific gut microbial genera are linked to eating behavior via dietary intake and SCFA metabolism in humans. The researchers hypothesized that gut microbial diversity and genera abundance would be related to eating behavior and SCFA metabolites in the colon (feces) and blood in a sample of young overweight adults, and that these relationships would be correlated with weight status and treatment success in RYGB patients.

Existing literature suggests a complex interplay between the gut microbiome, diet, and eating behavior. Studies in mice demonstrated that microbiota changes after gastric bypass surgery can induce weight loss. In humans, bariatric surgery often leads to increased microbiota diversity and changes in the relative abundance of certain bacterial genera, such as *Bacteroides* and *Prevotella*. The *Bacteroides*-to-*Prevotella* ratio has been shown to predict dietary weight loss success. Furthermore, fecal microbiota transplantation (FMT) experiments have shown potential for improving insulin sensitivity and preventing weight regain. However, mechanistic understanding of how specific gut bacteria modulate human eating behavior and weight remains limited. SCFAs, produced by gut bacteria, are believed to be key mediators, influencing appetite through both hormonal and direct neural pathways. Dietary fiber intake is crucial as it supports the growth of SCFA-producing bacteria. Previous studies have indicated the beneficial effects of butyrate and *Akkermansia* spp. on body weight and brain function.

This study involved two cross-sectional samples: Sample 1 comprised 27 young overweight adults (BMI 25–31 kg/m²) who were thoroughly screened to ensure a typical Western omnivorous diet. Sample 2 consisted of 23 patients two years post-RYGB surgery (divided into 'good' and 'bad' responders based on weight loss) and 17 BMI-matched controls. Anthropometric measurements, dietary fiber intake, eating behavior questionnaires (TFE-Q and EDE-Q), 16S rRNA-derived microbiota profiles, and fecal and serum SCFA levels were assessed. In Sample 1, 16S rRNA gene sequencing was performed to assess microbiota composition and relative abundances of bacterial genera. Eating behavior was assessed using the Three-Factor Eating Questionnaire (TFE-Q) and Eating Disorder Examination Questionnaire (EDE-Q). Dietary fiber intake was measured using a quantitative food frequency questionnaire, hunger ratings were assessed after a standardized meal, and SCFAs were quantified in blood and fecal samples using liquid chromatography-tandem mass spectrometry. For sample 2, the EDE-Q questionnaire was administered, and microbiota profiles were determined using the same 16S rRNA method. Statistical analysis including correlation analysis (Pearson's and Spearman's), Kruskal-Wallis tests, and mediation analysis was performed to investigate the relationships between microbiota composition, eating behavior, dietary fiber intake, SCFA levels, and weight status. Multiple testing corrections were applied using the false discovery rate (FDR). Sum scores were calculated for 'health-related' and 'inversely health-related' genera to investigate the combined effects of groups of bacterial genera.

In Sample 1 (young overweight adults), seven bacterial genera (*Alistipes*, *Blautia*, Clostridiales cluster XVIII, *Gemmiger*, *Roseburia*, *Ruminococcus*, and *Streptococcus*) correlated with healthier eating behavior, while five genera (Clostridiales cluster IV and XIVb, *Collinsella*, *Fusicatenibacter*, and *Parabacteroides*) correlated with unhealthier eating (all |r| > 0.4, FDR-corrected p < 0.05). *Collinsella* abundance correlated with higher body fat mass, and *Streptococcus* abundance with lower systolic blood pressure. Three genera (*Collinsella*, *Parabacteroides*, and Clostridium XVIII) showed relationships with dietary fiber intake. Higher dietary fiber intake correlated with healthier eating behavior and lower body fat mass. SCFA concentrations in feces were much higher than in serum. Some inversely health-related genera correlated with higher fecal SCFA levels. The negative sumscore of inversely health-related genera significantly correlated with cognitive restraint and disinhibition in eating behavior. Exploratory mediation analysis suggested a potential mediating role of fecal acetate between *Parabacteroides* abundance and hunger ratings. In Sample 2, *Parabacteroides* abundance was significantly lower in good responders compared to bad responders after RYGB surgery. The negative sumscore of inversely health-related genera correlated with unhealthier eating and less weight loss after surgery. Exploratory analysis adjusting for body fat mass showed that associations with inversely health-related genera remained largely significant, but positive health-related correlations did not. No significant effects of common confounders (time of day, seasonality, coffee intake) on alpha diversity were found.

This study's findings support the hypothesis that specific gut bacterial genera are associated with both healthy and unhealthy eating behaviors. The identified bacterial genera show potential links with dietary fiber intake, SCFA metabolism, and weight status. The association of 'health-related' genera with healthier eating behaviors and favorable anthropometric measures, and the opposite pattern for 'inversely health-related' genera, suggests a potential mechanistic role of the gut microbiota in the regulation of eating behavior and energy homeostasis. The observed correlations between specific genera, dietary fiber intake, SCFA levels, and weight outcomes in both samples provide preliminary evidence suggesting that the gut microbiome's influence on eating behavior may be mediated, at least in part, through dietary fiber and SCFA metabolism. The lack of strong correlations with alpha diversity highlights the need for investigation at the genus level, as the functional capacity of bacterial genera can vary.

This exploratory cross-sectional study reveals significant associations between specific gut bacterial genera and eating behaviors, potentially mediated by dietary fiber intake and SCFA metabolism. The identification of potentially beneficial and unfavorable genera provides valuable targets for future interventional studies to establish causality and understand the complex interactions between the gut microbiome, diet, and eating behavior. Longitudinal studies and randomized controlled trials are needed to confirm these associations and determine if modifying the gut microbiome through dietary interventions or other means can impact eating behavior and weight management.

The cross-sectional design prevents definitive conclusions about causality. The relatively small sample sizes, particularly in Sample 2, limit the statistical power and generalizability of the findings. The lack of detailed species-level analysis may have obscured finer-grained relationships. Additional potential confounders not measured in this study could influence the observed associations. Future research should address these limitations by employing larger, longitudinal, and interventional study designs with detailed species-level characterization and a wider range of covariate measurements.