The role of the intestinal microbiome in cognitive decline in patients with kidney disease
Bibliographic record
Abstract
Cognitive decline is frequently seen in patients with chronic kidney disease (CKD). The causes of cognitive decline in these patients are likely to be multifactorial, including vascular disease, uraemic toxins, blood-brain barrier leakage, and metabolic and endocrine changes. Gut dysbiosis is common in patients with CKD and contributes to the increase in uraemic toxins. However, the gut microbiome modulates local and systemic levels of several metabolites such as short-chain fatty acids or derivatives of tryptophan metabolism, neurotransmitters, endocannabinoid-like mediators, bile acids, hormones such as glucagon-like peptide 1 (GLP1) or cholecystokinin (CCK). These factors can affect gut function, immunity, autonomic nervous system activity and various aspects of brain function. Key areas include blood-brain barrier integrity, nerve myelination and survival/proliferation, appetite, metabolism and thermoregulation, mood, anxiety and depression, stress and local inflammation. Alterations in the composition of the gut microbiota and the production of biologically active metabolites in patients with CKD are well documented and are favoured by low-fiber diets, elevated urea levels, sedentary lifestyles, slow stool transit times and polypharmacy. In turn, dysbiosis can modulate brain function and cognitive processes, as discussed in this review. Thus, the gut microbiome may contribute to alterations in cognition in patients with CKD and may be a target for therapeutic interventions using diet, prebiotics and probiotics.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".