Gut microbiome in schizophrenia and antipsychotic-induced metabolic alterations: a scoping review
Bibliographic record
Abstract
Schizophrenia (SCZ) is a severe mental disorder with high morbidity and lifetime disability rates. Patients with SCZ have a higher risk of developing metabolic comorbidities such as obesity and diabetes mellitus, leading to increased mortality. Antipsychotics (APs), which are the mainstay in the treatment of SCZ, increase the risk of these metabolic perturbations. Despite extensive research, the mechanism underlying SCZ pathophysiology and associated metabolic comorbidities remains unclear. In recent years, gut microbiota (GMB) has been regarded as a 'chamber of secrets', particularly in the context of severe mental illnesses such as SCZ, depression, and bipolar disorder. In this scoping review, we aimed to investigate the underlying role of GMB in the pathophysiology of SCZ and metabolic alterations associated with APs. Furthermore, we also explored the therapeutic benefits of prebiotic and probiotic formulations in managing SCZ and AP-induced metabolic alterations. A systematic literature search yielded 46 studies from both preclinical and clinical settings that met inclusion criteria for qualitative synthesis. Preliminary evidence from preclinical and clinical studies indicates that GMB composition changes are associated with SCZ pathogenesis and AP-induced metabolic perturbations. Fecal microbiota transplantation from SCZ patients to mice has been shown to induce SCZ-like behavioral phenotypes, further supporting the plausible role of GMB in SCZ pathogenesis. This scoping review recapitulates the preclinical and clinical evidence suggesting the role of GMB in SCZ symptomatology and metabolic adverse effects associated with APs. Moreover, this scoping review also discusses the therapeutic potentials of prebiotic/probiotic formulations in improving SCZ symptoms and attenuating metabolic alterations related to APs.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".