Nielsen Christopher, Wilms Matthias, Forkert Nils Daniel
Department of Radiology, University of Calgary, Calgary, Alberta, Canada.
Biomedical Engineering Graduate Program, University of Calgary, Calgary, Alberta, Canada.
Proc Biol Sci. 2025 May;292(2046):20242233. doi: 10.1098/rspb.2024.2233. Epub 2025 May 7.
Traditional biomarkers, such as those obtained from blood tests, are essential for early disease detection, improving health outcomes and reducing healthcare costs. However, they often involve invasive procedures, specialized laboratory equipment or special handling of biospecimens. The retinal age gap (RAG) has emerged as a promising new biomarker that can overcome these limitations, making it particularly suitable for disease screening in low- and middle-income countries. This study aimed to evaluate the potential of the RAG as a biomarker for broad disease screening across a vast spectrum of diseases. Fundus images were collected from 86 522 UK Biobank participants aged 40-83 (mean age: 56.2 ± 8.3 years). A deep learning model was trained to predict retinal age using 17 791 images from healthy participants. The remaining images were categorized into disease/injury groups based on clinical codes. Additionally, 8524 participants from the Brazilian Multilabel Ophthalmological Dataset (BRSET) were used for external validation. Among the 159 disease/injury groups from the 2019 Global Burden of Disease Study, 56 groups (35.2%) exhibited RAG distributions significantly different from healthy controls. Notable examples included chronic kidney disease, cardiovascular disease, blindness, vision loss and diabetes. Overall, the RAG shows great promise as a cost-effective, non-invasive biomarker for early disease screening.
传统生物标志物，如通过血液检测获得的那些，对于疾病早期检测、改善健康结果和降低医疗成本至关重要。然而，它们通常涉及侵入性操作、专门的实验室设备或生物样本的特殊处理。视网膜年龄差距（RAG）已成为一种有前景的新型生物标志物，能够克服这些局限性，使其特别适用于低收入和中等收入国家的疾病筛查。本研究旨在评估RAG作为一种用于广泛疾病筛查的生物标志物的潜力。从86522名年龄在40 - 83岁（平均年龄：56.2±8.3岁）的英国生物银行参与者中收集了眼底图像。使用来自健康参与者的17791张图像训练了一个深度学习模型来预测视网膜年龄。其余图像根据临床编码被分类到疾病/损伤组。此外，来自巴西多标签眼科数据集（BRSET）的8524名参与者被用于外部验证。在2019年全球疾病负担研究的159个疾病/损伤组中，56个组（35.2%）的RAG分布与健康对照组有显著差异。显著的例子包括慢性肾病、心血管疾病、失明、视力丧失和糖尿病。总体而言，RAG作为一种用于早期疾病筛查的经济高效、非侵入性生物标志物显示出巨大潜力。
