Gut Microbiome Is Related to Cognitive Impairment in Peritoneal Dialysis Patients
Bibliographic record
Abstract
Gut microbiota disturbances may influence cognitive function, increasing uremic toxins and inflammation in dialysis patients; therefore, we aimed to evaluate the association of the gut microbiota profile with cognitive impairment (CI) in patients on automated peritoneal dialysis (APD). In a cross-sectional study, cognitive function was evaluated using the Montreal Cognitive Assessment in 39 APD patients and classified as normal cognitive function and CI. The gut microbiota was analyzed using the 16S rRNA gene sequencing approach. All patients had clinical, biochemical and urea clearance evaluations. Eighty-two percent of patients were men, with a mean age of 47 ± 24 years and 11 (7–48) months on PD therapy; 64% had mild CI. Patients with CI were older (53 ± 16 vs. 38 ± 14, p = 0.006) and had a higher frequency of diabetes mellitus (56% vs. 21%, p = 0.04) and constipation (7% vs. 48%, p = 0.04) and lower creatinine concentrations (11.3 ± 3.7 vs. 14.9 ± 5.4, p = 0.02) compared to normal cognitive function patients. Patients with CI showed a preponderance of S24_7, Rikenellaceae, Odoribacteraceae, Odoribacter and Anaerotruncus, while patients without CI had a greater abundance of Dorea, Ruminococcus, Sutterella and Fusobacteria (LDA score (Log10) > 2.5; p < 0.05). After glucose and age adjustment, Odoribacter was still associated with CI. In conclusion, patients with CI had a different gut microbiota characterized by the higher abundance of indole-producing and mucin-fermenting bacteria compared to normal cognitive function patients.
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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 | 0.001 | 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".