Wu Ning, Xu Mingze, Chen Shuohua, Wu Shouling, Li Jing, Hui Ying, Li Xiaoshuai, Wang Zhenchang, Lv Han
Department of Medical Imaging, Yanjing Medical College, Capital Medical University, Beijing, China.
Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
Research (Wash D C). 2025 Mar 6;8:0633. doi: 10.34133/research.0633. eCollection 2025.
Cerebral small vessel disease (SVD) involves ischemic white matter damage and choroid plexus (CP) dysfunction for cerebrospinal fluid (CSF) production. Given the vascular and CSF links between the eye and brain, this study explored whether retinal vascular morphology can indicate cerebrovascular injury and CP dysfunction in SVD. We assessed SVD burden using imaging phenotypes like white matter hyperintensities (WMH), perivascular spaces, lacunes, and microbleeds. Cerebrovascular injury was quantified by WMH volume and peak width of skeletonized mean diffusivity (PSMD), while CP volume measured its dysfunction. Retinal vascular markers were derived from fundus images, with associations analyzed using generalized linear models and Pearson correlations. Path analysis quantified contributions of cerebrovascular injury and CP volume to retinal changes. Support vector machine models were developed to predict SVD severity using retinal and demographic data. Among 815 participants, 578 underwent ocular imaging. Increased SVD burden markedly correlated with both cerebral and retinal biomarkers, with retinal alterations equally influenced by cerebrovascular damage and CP enlargement. Machine learning models showed robust predictive power for severe SVD burden (AUC was 0.82), PSMD (0.81), WMH volume (0.77), and CP volume (0.80). These findings suggest that retinal imaging could serve as a cost-effective, noninvasive tool for SVD screening based on vascular and CSF connections.
脑小血管病(SVD)涉及缺血性白质损伤和脉络丛(CP)功能障碍,后者会影响脑脊液(CSF)的生成。鉴于眼与脑之间存在血管和脑脊液联系,本研究探讨了视网膜血管形态是否能够提示SVD中的脑血管损伤和CP功能障碍。我们使用白质高信号(WMH)、血管周围间隙、腔隙和微出血等影像学表型评估SVD负担。通过WMH体积和骨架化平均扩散率峰值宽度(PSMD)对脑血管损伤进行量化,而通过CP体积衡量其功能障碍。视网膜血管标志物源自眼底图像,使用广义线性模型和Pearson相关性分析关联。路径分析量化了脑血管损伤和CP体积对视网膜变化的贡献。开发了支持向量机模型,以使用视网膜和人口统计学数据预测SVD严重程度。在815名参与者中,578人接受了眼部成像。SVD负担增加与脑和视网膜生物标志物均显著相关,视网膜改变同样受到脑血管损伤和CP增大的影响。机器学习模型对严重SVD负担(AUC为0.82)、PSMD(0.81)、WMH体积(0.77)和CP体积(0.80)显示出强大的预测能力。这些发现表明,基于血管和脑脊液联系,视网膜成像可作为一种经济高效的SVD筛查非侵入性工具。