Imperial College London
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ProfessorDeclanO'Regan

Faculty of Medicine, Institute of Clinical Sciences

Professor of Imaging Sciences
 
 
 
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Contact

 

+44 (0)20 3313 1510declan.oregan

 
 
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Location

 

Imaging Sciences DepartmentHammersmith HospitalHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Zekavat:2021:10.1161/CIRCULATIONAHA.121.057709,
author = {Zekavat, SM and Raghu, VK and Trinder, M and Ye, Y and Koyama, S and Honigberg, MC and Yu, Z and Pampana, A and Urbut, S and Haidermota, S and O'Regan, DP and Zhao, H and Ellinor, PT and Segrè, AV and Elze, T and Wiggs, JL and Martone, J and Adelman, RA and Zebardast, N and Del, Priore L and Wang, JC and Natarajan, P},
doi = {10.1161/CIRCULATIONAHA.121.057709},
journal = {Circulation},
pages = {134--150},
title = {Deep learning of the retina enables phenome- and genome-wide analyses of the microvasculature.},
url = {http://dx.doi.org/10.1161/CIRCULATIONAHA.121.057709},
volume = {145},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Background: The microvasculature, the smallest blood vessels in the body, has key roles in maintenance of organ health as well as tumorigenesis. The retinal fundus is a window for human in vivo non-invasive 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 utilized 97,895 retinal fundus images from 54,813 UK Biobank participants. Using convolutional neural networks to segment the retinal microvasculature, we calculated fractal dimension (FD) as a measure of vascular branching complexity, and vascular density. We associated these indices with 1,866 incident ICD-based conditions (median 10y follow-up) and 88 quantitative traits, adjusting for age, sex, smoking status, and ethnicity. Results: Low retinal vascular FD 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 FD and density identified 7 and 13 novel loci respectively, which were enriched for pathways linked to angiogenesis (e.g., VEGF, PDGFR, angiopoietin, and WNT signaling pathways) and inflammation (e.g., 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 on 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 records, biomarker, and genetic data to inform risk prediction and risk modification.
AU  - Zekavat,SM
AU  - Raghu,VK
AU  - Trinder,M
AU  - Ye,Y
AU  - Koyama,S
AU  - Honigberg,MC
AU  - Yu,Z
AU  - Pampana,A
AU  - Urbut,S
AU  - Haidermota,S
AU  - O'Regan,DP
AU  - Zhao,H
AU  - Ellinor,PT
AU  - Segrè,AV
AU  - Elze,T
AU  - Wiggs,JL
AU  - Martone,J
AU  - Adelman,RA
AU  - Zebardast,N
AU  - Del,Priore L
AU  - Wang,JC
AU  - Natarajan,P
DO  - 10.1161/CIRCULATIONAHA.121.057709
EP  - 150
PY  - 2021///
SN  - 0009-7322
SP  - 134
TI  - Deep learning of the retina enables phenome- and genome-wide analyses of the microvasculature.
T2  - Circulation
UR  - http://dx.doi.org/10.1161/CIRCULATIONAHA.121.057709
UR  - https://www.ncbi.nlm.nih.gov/pubmed/34743558
UR  - https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.121.057709
UR  - http://hdl.handle.net/10044/1/93282
VL  - 145
ER  -
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