Uremic Toxins in the Progression of Chronic Kidney Disease and Cardiovascular Disease: Mechanisms and Therapeutic Targets
Bibliographic record
Abstract
Chronic kidney disease (CKD) is a progressive loss of renal function. The gradual decline in kidney function leads to an accumulation of toxins normally cleared by the kidneys, resulting in uremia. Uremic toxins are classified into three categories: free water-soluble low-molecular-weight solutes, protein-bound solutes, and middle molecules. CKD patients have increased risk of developing cardiovascular disease (CVD), due to an assortment of CKD-specific risk factors. The accumulation of uremic toxins in the circulation and in tissues is associated with the progression of CKD and its co-morbidities, including CVD. Although numerous uremic toxins have been identified to date and many of them are believed to play a role in the progression of CKD and CVD, very few toxins have been extensively studied. The pathophysiological mechanisms of uremic toxins must be investigated further for a better understanding of their roles in disease progression and to develop therapeutic interventions against uremic toxicity. This review discusses the renal and cardiovascular toxicity of uremic toxins indoxyl sulfate, p-cresyl sulfate, hippuric acid, TMAO, ADMA, TNF-α, and IL-6. A focus is also placed on potential therapeutic targets against uremic toxicity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".