Lad Eleonora M, Finger Robert P, Guymer Robyn
Department of Ophthalmology, Duke University Medical Center, Durham, NC, USA.
Department of Ophthalmology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Ophthalmol Ther. 2023 Dec;12(6):2917-2941. doi: 10.1007/s40123-023-00807-9. Epub 2023 Sep 29.
Age-related macular degeneration (AMD) is a leading cause of severe vision loss worldwide, with a global prevalence that is predicted to substantially increase. Identifying early biomarkers indicative of progression risk will improve our ability to assess which patients are at greatest risk of progressing from intermediate AMD (iAMD) to vision-threatening late-stage AMD. This is key to ensuring individualized management and timely intervention before substantial structural damage. Some structural biomarkers suggestive of AMD progression risk are well established, such as changes seen on color fundus photography and more recently optical coherence tomography (drusen volume, pigmentary abnormalities). Emerging biomarkers identified through multimodal imaging, including reticular pseudodrusen, hyperreflective foci, and drusen sub-phenotypes, are being intensively explored as risk factors for progression towards late-stage disease. Other structural biomarkers merit further research, such as ellipsoid zone reflectivity and choriocapillaris flow features. The measures of visual function that best detect change in iAMD and correlate with risk of progression remain under intense investigation, with tests such as dark adaptometry and cone-specific contrast tests being explored. Evidence on blood and plasma markers is preliminary, but there are indications that changes in levels of C-reactive protein and high-density lipoprotein cholesterol may be used to stratify patients and predict risk. With further research, some of these biomarkers may be used to monitor progression. Emerging artificial intelligence methods may help evaluate and validate these biomarkers; however, until we have large and well-curated longitudinal data sets, using artificial intelligence effectively to inform clinical trial design and detect outcomes will remain challenging. This is an exciting area of intense research, and further work is needed to establish the most promising biomarkers for disease progression and their use in clinical care and future trials. Ultimately, a multimodal approach may yield the most accurate means of monitoring and predicting future progression towards vision-threatening, late-stage AMD.
年龄相关性黄斑变性(AMD)是全球严重视力丧失的主要原因,预计全球患病率将大幅上升。识别指示进展风险的早期生物标志物将提高我们评估哪些患者从中期AMD(iAMD)进展为威胁视力的晚期AMD风险最高的能力。这是确保在发生实质性结构损伤之前进行个体化管理和及时干预的关键。一些提示AMD进展风险的结构生物标志物已得到充分证实,如彩色眼底照相术所见的变化,以及最近的光学相干断层扫描(玻璃膜疣体积、色素异常)。通过多模态成像确定的新兴生物标志物,包括网状假性玻璃膜疣、高反射灶和玻璃膜疣亚表型,正在作为晚期疾病进展的风险因素进行深入研究。其他结构生物标志物值得进一步研究,如椭圆体带反射率和脉络膜毛细血管血流特征。最能检测iAMD变化并与进展风险相关的视觉功能测量方法仍在深入研究中,正在探索暗适应测量和视锥细胞特异性对比测试等检查。关于血液和血浆标志物的证据是初步的,但有迹象表明,C反应蛋白和高密度脂蛋白胆固醇水平的变化可用于对患者进行分层并预测风险。随着进一步研究,其中一些生物标志物可能用于监测疾病进展。新兴的人工智能方法可能有助于评估和验证这些生物标志物;然而,在我们拥有大量精心整理的纵向数据集之前,有效利用人工智能为临床试验设计提供信息并检测结果仍将具有挑战性。这是一个令人兴奋的深入研究领域,需要进一步开展工作,以确定用于疾病进展的最有前景的生物标志物及其在临床护理和未来试验中的应用。最终,多模态方法可能产生监测和预测未来向威胁视力的晚期AMD进展的最准确手段。