First Episode Psychosis: The Commensal Gut Microbiota Perspective
Bibliographic record
Abstract
The rates of type 2 diabetes (T2D) in patients with schizophrenia (SCZ) are 3-5-fold higher than in the general population, contributing to a two-fold higher mortality due to cardiovascular disease (CVD). Antipsychotics, namely second-generation antipsychotics (SGAs), the cornerstone of treatment for this illness, induce weight gain and increase risk for diabetes. Accumulating research has demonstrated that the gut microbiome (GMB) plays a primary function in energy metabolism and could be a central factor in the pathophysiology of obesity and metabolic dysfunction. Antipsychotics are well known to contribute to metabolic dysregulation in patients with SCZ possibly through their impact on the GMB. This effect may be mediated by changes in dietary pattern induced by antipsychotics. Alterations in the GMB therefore may be contributing to both the etiology and concurrent metabolic dysregulation observed in schizophrenia spectrum disorders. In this review, we aim explore how GMB affect the pathophysiology and treatment in this difficult to treat condition. We will review the GMB in relation to patients with first episode psychosis and the changes that occur within the microbiota when antipsychotic medication is introduced. The focus will be on SGAs, given their high propensity to cause weight gain and other metabolic side effects (namely glucose dysfunction, insulin resistance). The interplay between the GMB and SGAs will be explored further by examining neurotransmitter modulations, endocrine system function, and dietary changes. This commentary highlights the need for more large scale, clinical studies investigating antipsychotic induced changes to the gut microbiome and the importance of making changes to a patient’s care pathway with the GMB in mind.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".