Deep Learning of the Retina Enables Phenome- and Genome-Wide Analyses of the Microvasculature.
deep learning
epidemiology
genomics
mendelian randomization analysis
microvessels
retina
Journal
Circulation
ISSN: 1524-4539
Titre abrégé: Circulation
Pays: United States
ID NLM: 0147763
Informations de publication
Date de publication:
11 01 2022
11 01 2022
Historique:
pubmed:
9
11
2021
medline:
23
2
2022
entrez:
8
11
2021
Statut:
ppublish
Résumé
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. 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 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). 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.
Sections du résumé
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
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.
Identifiants
pubmed: 34743558
doi: 10.1161/CIRCULATIONAHA.121.057709
pmc: PMC8746912
mid: NIHMS1759615
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
134-150Subventions
Organisme : Medical Research Council
ID : MC_PC_17228
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UP_1605/13
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : R01 HL151283
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001863
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL127564
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL142711
Pays : United States
Organisme : British Heart Foundation
ID : RG/19/6/34387
Pays : United Kingdom
Organisme : British Heart Foundation
ID : RE/18/4/34215
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : R01 HL148050
Pays : United States
Organisme : Medical Research Council
ID : MC_QA137853
Pays : United Kingdom
Organisme : British Heart Foundation
ID : NH/17/1/32725
Pays : United Kingdom
Organisme : NEI NIH HHS
ID : K23 EY032634
Pays : United States
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