Correlation between gut microbiome and cognitive impairment in patients undergoing peritoneal dialysis
Bibliographic record
Abstract
Abstract Background Growing evidence has demonstrated that patients undergoing peritoneal dialysis (PD) are more likely to experience cognitive impairment than patients with non-dialysis end-stage renal disease (ESRD); however, the underlying mechanisms remain unclear. This study aimed to identify the role and predictive significance of gut microbiome alterations in PD-associated cognitive impairment. Methods A total of 29 non-dialysis ESRD patients and 28 PD patients were enrolled in this study and divided into subgroups according to the Montreal Cognitive Assessment (MoCA). Faecal samples were analyzed using 16 S rRNA. Mini-Mental State Examination (MMSE) and MoCA scores were used to assess the degree of cognitive impairment in patients. Results The 16 S rRNA analysis demonstrated differences in gut microbiome abundance and structure between PD and non-dialysis ESRD patients and between PD patients with cognitive impairment (PCI) and PD patients with normal cognition (PNCI). At family and genus levels, Prevotellaceae exhibited the greatest structure difference, while Lactobacillus exhibited the greatest abundance difference between PCI and PNCI. Altered microbiota abundance significantly correlated with cognitive function and serum indicators in PD. In addition, different modules related to fatty acid, lipid, pantothenate, and coenzyme A biosynthesis, and tyrosine and tryptophan metabolism were inferred from 16 S rRNA data between PCI and PNCI. Both groups could be distinguished using models based on the abundance of Lactobacillaceae (Area under curve [AUC] = 0.83), Actinomycetaceae (AUC = 0.798), and Prevotellaceae (AUC = 0.778) families and Lactobacillus (AUC = 0.848) and Actinomyces (AUC = 0.798) genera. Conclusion Gut microbiome evaluation could aid early cognitive impairment diagnosis in patients undergoing PD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.025 | 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 teacher head, 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".