Autoimmune Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND Atrial fibrillation (AF) is by far the most common cardiac arrhythmia. In about 3% of individuals, AF develops as a primary disorder without any identifiable trigger (idiopathic or historically termed lone AF). In line with the emerging field of autoantibody-related cardiac arrhythmias, the objective of this study was to explore whether autoantibodies targeting cardiac ion channels can underlie unexplained AF. METHODS Peptide microarray was used to screen patient samples for autoantibodies. We compared patients with unexplained AF (n=37 pre-existent AF; n=14 incident AF on follow-up) to age- and sex-matched controls (n=37). Electrophysiological properties of the identified autoantibody were then tested in vitro with the patch clamp technique and in vivo with an experimental mouse model of immunization. RESULTS A common autoantibody response against K$_{ir}$3.4 protein was detected in patients with AF and even before the development of clinically apparent AF. K$_{ir}$3.4 protein forms a heterotetramer that underlies the cardiac acetylcholine-activated inwardly rectifying K$^{+}$ current, I$_{KACh}$. Functional studies on human induced pluripotent stem cell-derived atrial cardiomyocytes showed that anti-K$_{ir}$3.4 IgG purified from patients with AF shortened action potentials and enhanced the constitutive form of I$_{KACh}$, both key mediators of AF. To establish a causal relationship, we developed a mouse model of K$_{ir}$3.4 autoimmunity. Electrophysiological study in K$_{ir}$3.4-immunized mice showed that K$_{ir}$3.4 autoantibodies significantly reduced atrial effective refractory period and predisposed animals to a 2.8-fold increased susceptibility to AF. CONCLUSIONS To our knowledge, this is the first report of an autoimmune pathogenesis of AF with direct evidence of K$_{ir}$3.4 autoantibody-mediated 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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".