Decreased retinal vascular complexity is an early biomarker of MI supported by a shared genetic control
Bibliographic record
Abstract
Abstract There is increasing evidence that the complexity of the retinal vasculature (measured as fractal dimension, D f ) might offer earlier insights into the progression of coronary artery disease (CAD) before traditional biomarkers can be detected. This association could be partly explained by a common genetic basis; however, the genetic component of D f is poorly understood. We present here a genome-wide association study (GWAS) aimed to elucidate the genetic component of D f and to analyse its relationship with CAD. To this end, we obtained D f from retinal fundus images and genotyping information from ∼38,000 white-British participants in the UK Biobank. We discovered 9 loci associated with D f , previously reported in pigmentation, retinal width and tortuosity, hypertension, and CAD studies. Significant negative genetic correlation estimates endorse the inverse relationship between D f and CAD, and between D f and myocardial infarction (MI), one of CAD fatal outcomes. This strong association motivated us to developing a MI predictive model combining clinical information, D f , a CAD polygenic risk score and using a random forest algorithm. Internal cross validation evidenced a considerable improvement in the area under the curve (AUC) of our predictive model (AUC=0.770) when comparing with an established risk model, SCORE, (AUC=0.719). Our findings shed new light on the genetic basis of D f , unveiling a common control with CAD, and highlights the benefits of its application in individualised MI risk prediction.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".