Altered microbial carbohydrate metabolism is associated with anxiety and gastrointestinal symptoms in patients with Generalized Anxiety Disorder
Bibliographic record
Abstract
Abstract Background Generalized anxiety disorder (GAD) is a common psychiatric condition, with unknown etiology and pathophysiology. Recent studies have suggested alterations in the microbiota-gut-brain axis may be involved in the development of GAD. We aimed to explore the interactions between the gut microbiota, gastrointestinal and psychiatric symptoms, neuroimmune markers and dietary patterns in patients with GAD. Methods We recruited 83 GAD patients and 98 age- and sex-matched healthy controls (HC) and assessed their psychiatric and gastrointestinal symptoms, and long-term diet using validated questionnaires. We measured serum and stool neuroimmune markers and metabolites by ELISA and LC-MS, microbiota was analyzed using 16S rRNA gene sequencing with functional predictions by PICRUSt2. Microbial carbohydrate degradation capacity was assessed ex vivo . The data was analyzed using classical statistics and machine learning (XGBoost). Results GAD patients exhibited higher BMI, gastrointestinal symptoms and inflammatory markers, while reporting reduced intake of fiber and other macro- and micronutrients compared to HC. Gastrointestinal symptoms were the most predictive feature separating GAD from HC. GAD patients had a distinct microbiota profile, dominated by Bacteroides , compared with a Prevotella -dominated microbiota in HC. Carbohydrate degradation pathways were enriched in GAD and strongly associated with Bacteroides abundance. Anxiety scores correlated with Bacteroides abundance, carbohydrate degradation pathways and gastrointestinal symptoms, while negatively correlating with dietary fiber intake. Ex vivo mucin-to-inulin degradation ratio was higher in GAD and correlated with inflammatory markers. Conclusions GAD patients exhibited marked gastrointestinal symptoms, elevated immune markers, reduced fiber intake and a Bacteroides -dominated microbiota that preferentially degrades mucin. These data suggest that their microbiota adapted to utilize host-derived carbohydrates that may affect the mucus barrier, altering immune homeostasis and leading to gastrointestinal symptoms and anxiety. Dietary interventions, such as gradually increasing fiber intake, could reprogram bacterial carbohydrate metabolism, thus ameliorating gut barrier function and alleviating anxiety and gastrointestinal symptoms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".