1. Deep Learning of the Retina Enables Phenome- and Genome-Wide Analyses of the Microvasculature
- Author
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Sarah Urbut, Ron A. Adelman, Pradeep Natarajan, Tobias Elze, Seyedeh M. Zekavat, Janey L. Wiggs, Declan P. O'Regan, Vineet K. Raghu, Mark Trinder, Yixuan Ye, Hongyu Zhao, Zhi Yu, Michael C. Honigberg, Ayellet V. Segrè, Sara Haidermota, Nazlee Zebardast, James F. Martone, Jay C. Wang, Akhil Pampana, Patrick T. Ellinor, Satoshi Koyama, Lucian V. Del Priore, Imperial College Healthcare NHS Trust- BRC Funding, and British Heart Foundation
- Subjects
Male ,retina ,microvessels ,Phenome ,Fundus (eye) ,Genome ,1117 Public Health and Health Services ,chemistry.chemical_compound ,Original Research Articles ,Physiology (medical) ,medicine ,Humans ,1102 Cardiorespiratory Medicine and Haematology ,Retina ,business.industry ,deep learning ,1103 Clinical Sciences ,Retinal ,Genomics ,Mendelian Randomization Analysis ,Middle Aged ,medicine.anatomical_structure ,Cardiovascular System & Hematology ,chemistry ,ComputingMethodologies_DOCUMENTANDTEXTPROCESSING ,Female ,epidemiology ,Cardiology and Cardiovascular Medicine ,business ,Neuroscience ,Genome-Wide Association Study - Abstract
Supplemental Digital Content is available in the text., Background: The microvasculature, the smallest blood vessels in the body, has key roles in maintenance of organ health and tumorigenesis. The retinal fundus is a window for human in vivo noninvasive assessment of the microvasculature. Large-scale complementary machine learning-based assessment of the retinal vasculature with phenome-wide and genome-wide analyses may yield new insights into human health and disease. Methods: We used 97 895 retinal fundus images from 54 813 UK Biobank participants. Using convolutional neural networks to segment the retinal microvasculature, we calculated vascular density and fractal dimension as a measure of vascular branching complexity. We associated these indices with 1866 incident International Classification of Diseases–based conditions (median 10-year follow-up) and 88 quantitative traits, adjusting for age, sex, smoking status, and ethnicity. Results: Low retinal vascular fractal dimension and density were significantly associated with higher risks for incident mortality, hypertension, congestive heart failure, renal failure, type 2 diabetes, sleep apnea, anemia, and multiple ocular conditions, as well as corresponding quantitative traits. Genome-wide association of vascular fractal dimension and density identified 7 and 13 novel loci, respectively, that were enriched for pathways linked to angiogenesis (eg, vascular endothelial growth factor, platelet-derived growth factor receptor, angiopoietin, and WNT signaling pathways) and inflammation (eg, interleukin, cytokine signaling). Conclusions: Our results indicate that the retinal vasculature may serve as a biomarker for future cardiometabolic and ocular disease and provide insights into genes and biological pathways influencing microvascular indices. Moreover, such a framework highlights how deep learning of images can quantify an interpretable phenotype for integration with electronic health record, biomarker, and genetic data to inform risk prediction and risk modification.
- Published
- 2022