Deep Learning of the Retina Enables Phenome- and Genome-Wide Analyses of the Microvasculature.

Abstract

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.

• Authors: Seyedeh Maryam Zekavat, Vineet K Raghu, Mark Trinder, Yixuan Ye, Satoshi Koyama, Michael C Honigberg, Zhi Yu, Akhil Pampana, Sarah Urbut, Sara Haidermota, Declan P O'Regan, Hongyu Zhao, Patrick T Ellinor, Ayellet V Segrè, Tobias Elze, Janey L Wiggs, James Martone, Ron A Adelman, Nazlee Zebardast, Lucian Del Priore, Jay C Wang, Pradeep Natarajan
• Journal: Circulation (Circulation) Vol. 145 Issue 2 Pg. 134-150 (01 11 2022) ISSN: 1524-4539 [Electronic] United States
• PMID: 34743558 (Publication Type: Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't)

Topics

• Deep Learning (standards)
• Female
• Genome-Wide Association Study (methods)
• Genomics (methods)
• Humans
• Male
• Mendelian Randomization Analysis (methods)
• Microvessels (pathology)
• Middle Aged
• Retina (metabolism)