Evaluation of Genes Encoding for the Transient Outward Current (Ito) Identifies the <i>KCND2</i> Gene as a Cause of J-Wave Syndrome Associated With Sudden Cardiac Death
Bibliographic record
Abstract
BACKGROUND: J-wave ECG patterns are associated with an increased risk of sudden arrhythmic death, and experimental evidence supports a transient outward current (I(to))-mediated mechanism of J-wave formation. This study aimed to determine the frequency of genetic mutations in genes encoding the I(to) in patients with J waves on ECG. METHODS AND RESULTS: Comprehensive mutational analysis was performed on I(to)-encoding KCNA4, KCND2, and KCND3 genes, as well as the previously described J-wave-associated KCNJ8 gene, in 51 unrelated patients with ECG evidence defining a J-wave syndrome. Only patients with a resuscitated cardiac arrest or type 1 Brugada ECG pattern were included for analysis. A rare genetic mutation of the KCND2 gene, p.D612N, was identified in a single patient. Co-expression of mutant and wild-type KCND2 with KChIP2 in HEK293 cells demonstrated a gain-of-function phenotype, including an increase in peak I(to) density of 48% (P<0.05) in the heterozygous state. Using computer modeling, this increase in Ito resulted in loss of the epicardial action potential dome, predicting an increased ventricular transmural Ito gradient. The previously described KCNJ8-S422L mutation was not identified in this cohort of patients with ECG evidence of J-wave syndrome. CONCLUSIONS: These findings are the first to implicate the KCND2 gene as a novel cause of J-wave syndrome associated with sudden cardiac arrest. However, genetic defects in I(to)-encoding genes seem to be an uncommon cause of sudden cardiac arrest in patients with apparent J-wave syndromes.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".