The fecal microbiota transplantation from drug-naïve schizophrenia patients distinctively changes gut microbiome and metabolic profiles in male and female mice
Bibliographic record
Abstract
Abstract Background Emerging evidence suggests a role for the gut microbiome in schizophrenia (SCZ) and antipsychotic-induced metabolic perturbations. Using human fecal microbiota transplantation (FMT) in mice, this study investigated the role of gut microbiome in metabolic changes related to SCZ and antipsychotic (olanzapine) treatment. Methods 5-6 weeks old germ-free NIH Swiss mice of both sexes received microbiota from either SCZ patients (SCZ-FMT) or healthy controls (HC-FMT) followed by a diet with or without olanzapine for six-weeks. Food intake and body weight were monitored weekly, and an intraperitoneal glucose tolerance test and open field test were performed. Serum glucose, and insulin were measured. Gut microbiome characterization and short-chain fatty acids (SCFAs) quantification were performed in the cecal samples using 16S rRNA gene sequencing and gas chromatography-mass spectrometry, respectively. Results Olanzapine treatment decreased the locomotor activity in the open field test, irrespective of sex or microbiota. Female SCZ-FMT recipient mice exhibited insulin resistance compared to HC-FMT, irrespective of olanzapine treatment. Female SCZ-FMT mice showed significantly lower alpha-diversity compared to HC-FMT, whereas olanzapine treatment increased alpha-diversity. SCZ-FMT and olanzapine treatment differentially altered the microbial abundances, and metabolic pathways in male and female mice. Interestingly, cecal SCFAs, mainly acetate levels, were significantly decreased in female SCZ-FMT mice compared to HC-FMT, while olanzapine treatment increased acetate levels in male mice. Both male and female SCZ-FMT mice showed elevated levels of isovaleric acid compared to HC-FMT. Conclusion These preliminary findings suggest that gut microbiome could be a predisposing factor contributing to the intrinsic risk of developing type 2 diabetes associated with SCZ in females. Graphical abstract
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".