Deep Learning of the Retina Enables Phenome- and Genome-Wide Analyses of the Microvasculature.
Deep Learning of the Retina Enables Phenome- and Genome-Wide Analyses of the Microvasculature.
复制标题
DOI：
10.1161/circulationaha.121.057709
复制
发表时间：
2022-01-11
期刊：
影响因子：
37.8
通讯作者：
Natarajan P
中科院分区：
文献类型：
--
作者：
Zekavat SM;Raghu VK;Trinder M;Ye Y;Koyama S;Honigberg MC;Yu Z;Pampana A;Urbut S;Haidermota S;O'Regan DP;Zhao H;Ellinor PT;Segrè AV;Elze T;Wiggs JL;Martone J;Adelman RA;Zebardast N;Del Priore L;Wang JC;Natarajan P
文本中提供了补充数字内容。微血管系统是体内最小的血管，在维持器官健康和肿瘤发生中起着关键作用。视网膜眼底是人类在体无创评估微血管的窗口。通过全表型和全基因组分析对视网膜血管进行大规模的基于互补机器学习的评估，可能会对人类健康和疾病产生新的见解。我们使用了来自54813名英国生物银行参与者的97895张视网膜眼底图像。使用卷积神经网络分割视网膜微血管，我们计算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例基于国际疾病分类的事件（中位10年随访）和88个数量性状相关联，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较高的意外死亡率、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以及相应的数量性状的风险显著相关。血管分形维数和密度的全基因组关联分别确定了7个和13个新位点，这些位点富集了与血管生成（例如，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（例如，白细胞介素、细胞因子信号通路）相关的通路。我们的研究结果表明，视网膜血管系统可能作为未来的心脏代谢和眼部疾病的生物标志物，并提供影响微血管指数的基因和生物途径的见解。此外，这样的框架强调了图像的深度学习如何量化可解释的表型，以便与电子健康记录，生物标志物和遗传数据集成，从而为风险预测和风险修改提供信息。
Supplemental Digital Content is available in the text. 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 International Classification of Diseases–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.