Elevated Lipoprotein(a) and Risk of Atrial Fibrillation
Bibliographic record
Abstract
Atrial fibrillation (AF) is a cardiac arrhythmia associated with an elevated risk of stroke, heart failure, and mortality. However, preventative therapies are needed with ancillary benefits on its cardiovascular comorbidities. Lipoprotein(a) (Lp[a]) is a recognized risk factor for atherosclerotic cardiovascular disease (ASCVD), which itself increases AF risk, but it remains unknown whether Lp(a) is a causal mediator of AF independent of ASCVD. This study investigated the role of Lp(a) in AF and whether it is independent of ASCVD. Measured and genetically predicted Lp(a) levels were tested for association with 20,432 cases of incident AF in the UK Biobank (N = 435,579). Mendelian randomization analyses were performed by using summary-level data for AF from publicly available genome-wide association studies (N = 1,145,375). In the UK Biobank, each 50 nmol/L (23 mg/dL) increase in Lp(a) was associated with an increased risk of incident AF using measured Lp(a) (HR: 1.03; 95% CI: 1.02-1.04 ; P = 1.65 × 10–8) and genetically predicted Lp(a) (OR: 1.03; 95% CI: 1.02-1.05; P = 1.33 × 10–5). Mendelian randomization analyses using independent data replicated the effect (OR: 1.04 per 50 nmol/L Lp[a] increase; 95% CI: 1.03-1.05 per 50 nmol/L Lp[a] increase; P = 9.23 × 10–10). There was no evidence of risk-conferring effect from low-density lipoprotein cholesterol or triglycerides, and only 39% (95% CI: 27%-73%) of Lp(a) risk was mediated through ASCVD, suggesting that Lp(a) partly influences AF independent of its known effects on ASCVD. Our findings implicate Lp(a) as a potential causal mediator in the development of AF which show that the effects of Lp(a) extend across myocardial tissues. Ongoing clinical trials for Lp(a)-lowering therapies should evaluate effects on AF prevention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".