Using metabolomics to investigate the relationship between the metabolomic profile of the intestinal microbiota derivatives and mental disorders in inflammatory bowel diseases: a narrative review
Bibliographic record
Abstract
Individuals with inflammatory bowel disease (IBD) are at a higher risk of developing mental disorders, such as anxiety and depression. The imbalance between the intestinal microbiota and its host, known as dysbiosis, is one of the factors, disrupting the balance of metabolite production and their signaling pathways, leading to disease progression. A metabolomics approach can help identify the role of gut microbiota in mental disorders associated with IBD by evaluating metabolites and their signaling comprehensively. This narrative review focuses on metabolomics studies that have comprehensively elucidated the altered gut microbial metabolites and their signaling pathways underlying mental disorders in IBD patients. The information was compiled by searching PubMed, Web of Science, Scopus, and Google Scholar from 2005 to 2023. The findings indicated that intestinal microbial dysbiosis in IBD patients leads to mental disorders such as anxiety and depression through disturbances in the metabolism of carbohydrates, sphingolipids, bile acids, neurotransmitters, neuroprotective, inflammatory factors, and amino acids. Furthermore, the reduction in the production of neuroprotective factors and the increase in inflammation observed in these patients can also contribute to the worsening of psychological symptoms. Analyzing the metabolite profile of the patients and comparing it with that of healthy individuals using advanced technologies like metabolomics, aids in the early diagnosis and prevention of mental disorders. This approach allows for the more precise identification of the microbes responsible for metabolite production, enabling the development of tailored dietary and pharmaceutical interventions or targeted manipulation of microbiota.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".