4.7 Article

Fine-mapping of retinal vascular complexity loci identifies Notch regulation as a shared mechanism with myocardial infarction outcomes

Journal

COMMUNICATIONS BIOLOGY
Volume 6, Issue 1, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s42003-023-04836-9

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A genome-wide association study reveals an inverse relationship between the complexity of retinal vasculature (measured as fractal dimension) and the progression of coronary artery disease. The study also validates a predictive model for myocardial infarction.
A genome-wide association study finds an inverse relationship between progression of coronary artery disease and retinal vasculature's complexity measured as fractal dimension, and validates a predictive model for myocardial infarction. 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 a genome-wide association study (GWAS) of 38,000 individuals with white British ancestry from the UK Biobank aimed to comprehensively study the genetic component of D-f and analyse its relationship with CAD. We replicated 5 D-f loci and found 4 additional loci with suggestive significance (P < 1e-05) to contribute to D-f variation, which previously were reported in retinal tortuosity and complexity, hypertension, and CAD studies. Significant negative genetic correlation estimates support the inverse relationship between D-f and CAD, and between D-f and myocardial infarction (MI), one of CAD's fatal outcomes. Fine-mapping of D-f loci revealed Notch signalling regulatory variants supporting a shared mechanism with MI outcomes. We developed a predictive model for MI incident cases, recorded over a 10-year period following clinical and ophthalmic evaluation, combining clinical information, D-f, and a CAD polygenic risk score. Internal cross-validation demonstrated a considerable improvement in the area under the curve (AUC) of our predictive model (AUC = 0.770 +/- 0.001) when comparing with an established risk model, SCORE, (AUC = 0.741 +/- 0.002) and extensions thereof leveraging the PRS (AUC = 0.728 +/- 0.001). This evidences that D-f provides risk information beyond demographic, lifestyle, and genetic risk factors. Our findings shed new light on the genetic basis of D-f, unveiling a common control with MI, and highlighting the benefits of its application in individualised MI risk prediction.

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