Department of Ophthalmology and Visual Science, Yale School of Medicine, New Haven, CT (S.M.Z., J.M., R.A.A., L.D.P., J.C.W.).
Computational Biology & Bioinformatics Program (S.M.Z., Y.Y., H.Z.), Yale University, New Haven, CT.
Circulation. 2022 Jan 11;145(2):134-150. doi: 10.1161/CIRCULATIONAHA.121.057709. Epub 2021 Nov 8.
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 -based conditions (median 10-year follow-up) and 88 quantitative traits, adjusting for age, sex, smoking status, and ethnicity.
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.
人体最小的血管——微血管,在维持器官健康和肿瘤发生方面起着关键作用。眼底是人类对活体非侵入性评估微血管的窗口。基于机器学习的大规模视网膜血管评估与表型全基因组和基因组分析相结合,可能会为人类健康和疾病提供新的见解。
我们使用了来自 54813 名英国生物库参与者的 97895 张眼底图像。使用卷积神经网络对视网膜微血管进行分割,我们计算了血管密度和分形维数作为血管分支复杂性的度量。我们将这些指数与 1866 项基于事件的情况(中位数 10 年随访)和 88 项定量特征相关联,调整了年龄、性别、吸烟状况和种族。
低视网膜血管分形维数和密度与更高的死亡率、高血压、充血性心力衰竭、肾衰竭、2 型糖尿病、睡眠呼吸暂停、贫血和多种眼部疾病的发病风险以及相应的定量特征显著相关。血管分形维数和密度的全基因组关联分析分别确定了 7 个和 13 个新的基因座,这些基因座富集了与血管生成(如血管内皮生长因子、血小板衍生生长因子受体、血管生成素和 WNT 信号通路)和炎症(如白细胞介素、细胞因子信号)相关的通路。
我们的研究结果表明,视网膜血管可能作为未来代谢和眼部疾病的生物标志物,并为影响微血管指数的基因和生物学途径提供了新的见解。此外,这种框架突出了深度学习图像如何量化可解释的表型,以与电子健康记录、生物标志物和遗传数据相结合,为风险预测和风险修正提供信息。