Chronic kidney disease promotes atrial fibrillation via inflammasome pathway activation
Bibliographic record
Abstract
Chronic kidney disease (CKD) is associated with a higher risk of atrial fibrillation (AF). The mechanistic link between CKD and AF remains elusive. IL-1β, a main effector of NLR family pyrin domain-containing 3 (NLRP3) inflammasome activation, is a key modulator of conditions associated with inflammation, such as AF and CKD. Circulating IL-1β levels were elevated in patients with CKD who had AF (versus patients with CKD in sinus rhythm). Moreover, NLRP3 activity was enhanced in atria of patients with CKD. To elucidate the role of NLRP3/IL-1β signaling in the pathogenesis of CKD-induced AF, Nlrp3-/- and WT mice were subjected to a 2-stage subtotal nephrectomy protocol to induce CKD. Four weeks after surgery, IL-1β levels in serum and atrial tissue were increased in WT CKD (WT-CKD) mice versus sham-operated WT (WT-sham) mice. The increased susceptibility to pacing-induced AF and the longer AF duration in WT-CKD mice were associated with an abbreviated atrial effective refractory period, enlarged atria, and atrial fibrosis. Genetic inhibition of NLRP3 in Nlrp3-/- mice or neutralizing anti-IL-1β antibodies effectively reduced IL-1β levels, normalized left atrial dimensions, and reduced fibrosis and the incidence of AF. These data suggest that CKD creates a substrate for AF development by activating the NLRP3 inflammasome in atria, which is associated with structural and electrical remodeling. Neutralizing IL-1β antibodies may be beneficial in preventing CKD-induced AF.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".