Distinct gut microbial profile in PIT1 lineage PitNETs: a potential link to cognitive impairment
Bibliographic record
Abstract
BACKGROUND: Patients with pituitary neuroendocrine tumors (PitNETs) frequently experience cognitive impairment (CI), yet the underlying mechanisms remain poorly understood. METHOD: In this study, we assessed cognitive function in 42 PitNETs patients and 42 healthy controls using the Montreal Cognitive Assessment (MoCA), evaluating the effects of tumor volume, invasiveness, pituitary hormone levels, lineage, and surgical intervention.Furthermore, 16S rRNA amplicon sequencing of fecal samples was performed to reveal alterations in gut microbiota composition. RESULTS: The results demonstrated significantly lower MoCA scores in PitNETs patients compared to controls. Patients with PIT1 lineage tumors exhibited more severe CI than those with SF-1 lineage tumors. Notably, surgical treatment led to improved cognitive performance. The sequencing revealed significant alterations in gut microbiota composition in PitNETs patients. Specifically, PIT1 lineage cases showed reduced levels of the butyrate-producing genus Agathobacter and increased abundance of UBA1819 and Alistipes indistinctus, taxa that have been implicated in pro-inflammatory states. DISCUSSION: These preliminary findings suggest that PIT1-lineage PitNETs may be associated with an increased susceptibility to cognitive impairment, potentially involving interactions between hormonal dysregulation and gut microbiota dysbiosis. This exploratory hypothesis provides a conceptual framework for future research to elucidate underlying mechanisms and explore potential interventions for cognitive impairment in PitNETs.
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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".