Whole Blood Gene Expression Differentiates between Atrial Fibrillation and Sinus Rhythm after Cardioversion
Bibliographic record
Abstract
BACKGROUND: Treatment to restore sinus rhythm among patients with atrial fibrillation (AF) has limited long-term success rates. Gene expression profiling may provide new insights into AF pathophysiology. OBJECTIVE: To identify biomarkers and improve our understanding of AF pathophysiology by comparing whole blood gene expression before and after electrical cardioversion (ECV). METHODS: In 46 patients with persistent AF that underwent ECV, whole blood samples were collected 1-2 hours before and 4 to 6 weeks after successful cardioversion. The paired samples were sent for microarray and plasma biomarker comparison. RESULTS: Of 13,942 genes tested, expression of SLC25A20 and PDK4 had the strongest associations with AF. Post-cardioversion, SLC25A20 and PDK4 expression decreased by 0.8 (CI 0.7-0.8, p = 2.0x10-6) and 0.7 (CI 0.6-0.8, p = 3.0x10-5) fold respectively. Median N-terminal pro B-type natriuretic peptide (NT-proBNP) concentrations decreased from 127.7 pg/mL to 44.9 pg/mL (p = 2.3x10-13) after cardioversion. AF discrimination models combining NT-proBNP and gene expression (NT-proBNP + SLC25A20 area under the curve = 0.88, NT-proBNP + PDK4 AUC = 0.86) had greater discriminative capacity as compared with NT-proBNP alone (AUC = 0.82). Moreover, a model including NT-proBNP, SLC25A20 and PDK4 significantly improved AF discrimination as compared with other models (AUC = 0.87, Net Reclassification Index >0.56, p<5.8x10-3). We validated the association between SLC25A20 and PDK4 with AF in an independent sample of 17 patients. CONCLUSION: This study demonstrates that SLC25A20, PDK4, and NT-proBNP have incremental utility as biomarkers discriminating AF from sinus rhythm. Elevated SLC25A20 and PDK4 expression during AF indicates an important role for energy metabolism in 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 | 0.000 | 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".