Uremic Toxins and Extracellular Vesicles as Drivers of Cardiovascular Disease in CKD
Bibliographic record
Abstract
Background: Cardiovascular disease (CVD) is the main cause of death in chronic kidney disease (CKD). However, the pathogenesis of CVD in CKD remains incompletely understood. We hypothesized that microbiome-derived uremic toxins (UTs) trigger the release of endothelial (EC-) and immune cell (IC)-derived extracellular vesicles (EVs), promoting endothelial damage and CVD. Methods: We recruited a cohort of 94 children (mean age 10.9 years) at different stages of CKD, including patients on dialysis and after kidney transplantation (KTx), and agematched healthy donors, offering the unique opportunity to analyze cardiovascular effects of CKD and metabolite-EV interaction in the absence of age-related confounders like diabetes and metabolic syndrome. Plasma metabolomics for 31 tryptophan-derived UTs were performed. Plasma EVs were analyzed by nanoparticle tracking analysis, flow cytometry and small RNA sequencing. EV release from PBMCs was assessed upon UT exposure. Results: UTs of indole and kynurenine pathways were stage-dependently increased in children with CKD. Indoxyl sulfate (IS) increased 21-fold in peritoneal dialysis (PD) patients compared to healthy donors. Similar trends were seen in hemodialysis (HD), while more subtle increments were seen in CKD without dialysis and UT levels after KTx were almost normal. PD patients had elevated levels of total plasma EVs compared to healthy donors and KTx patients. Macrophage- (3-fold) and T-cell-derived EVs (6-fold) were increased in CKD without dialysis compared to healthy donors, while EC-EVs were reduced after KTx in longitudinal follow-ups and cross-sectionally comparing HD and KTx (3-fold). Sequencing revealed several differentially regulated microRNAs in EVs from CKD patients, including miR-16-5p, miR-19b-3p, miR-106a-5p, miR-451a and miR-4485. In vitro, IS dose-dependently increased EV release from PBMCs. Conclusions: Increased levels of microbiome-derived UTs and subsequent EV release from ICs and ECs may present both a biomarker and a pathomechanism in CKD that may drive or contribute to long-term CVD. Funding: Government Support - Non-U.S.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".