通过视网膜图谱连接糖尿病视网膜病变的局部病变与全身病理通路。
Causal-RetiGraph: Cross-Cohort Retinal Support and Same-Subject Pathway Analysis for Diabetic Retinopathy
- 构建视网膜图谱表型X1234，融合血管结构、病灶证据与生物标志物。
- 在稀疏不平衡数据下，诊断准确率达0.9055，AUROC达0.9711。
- 揭示血糖-肾功能与血糖-血流动力学为关键中介路径，适合临床机制研究。
糖尿病视网膜病变（DR）是局部视网膜病变过程，也是系统性微血管损伤的可见表现。现代视网膜AI可精准分级图像，但常无法回答局部病灶证据、视网膜血管结构与全身疾病通路之间的关联。本文提出Causal-RetiGraph，一个紧凑的生物信息学框架，将视网膜图谱表型与基于NHANES的通路建模相结合。视网膜部分通过空间分支$X_{12}$与雅可比分支$X_{34}$，整合血管图、病灶证据、图像嵌入与AutoMorph生物标志物，构建可解释的$X1234$表型。NHANES部分建模系统暴露因素、协变量、同一受试者的视网膜中介家族$R^*$及下游结果。$X1234$用于视网膜支持与通路优先级排序，$R^*$用于个体层面通路摘要。在视网膜部分，$X1234$实现0.9055的二分类DR准确率与0.9711的AUROC，分级DR的QWK为0.8312。结果表明，在数据稀缺且不平衡条件下，病灶与生物标志物流显著提升视网膜表征上下文信息。在NHANES中，HbA1c、尿白蛋白、脉压、空腹血糖和收缩压是最强的二分类DR锚点。个体水平通路分析识别出糖代谢-肾功能与糖代谢-血流动力学为最清晰的中介信号。结果表明，视网膜图谱表型可助力优先筛选系统通路，同时保留图像支持与同主体中介的区别。
原文摘要 · Abstract (English)
Diabetic retinopathy (DR) is a local retinal lesion process and a visible manifestation of systemic microvascular injury. Modern retinal AI can grade images accurately, but often leaves unanswered how local lesion evidence, retinal vascular structure, and systemic disease pathways are connected. This paper introduces \emph{Causal-RetiGraph}, a compact biomedical informatics framework that links retinal graph phenotypes with NHANES-anchored pathway modelling. The retinal-image fold constructs an interpretable $X1234$ phenotype from vessel maps, lesion evidence, image embeddings, and AutoMorph biomarkers through spatial $X_{12}$ and Jacobian $X_{34}$ branches. The NHANES fold models systemic exposures, covariates, a same-subject retinal mediator family $R^*$, and downstream outcome families. $X1234$ is used for retinal support and pathway prioritisation, while $R^*$ is used for participant-level pathway summaries. On the retinal fold, $X1234$ achieves 0.9055 binary DR accuracy and 0.9711 AUROC, with graded DR QWK of 0.8312. The results show that lesion and biomarker streams improve contextual retinal representation under scarce and imbalanced data. In NHANES, HbA1c, urine albumin, pulse pressure, fasting glucose, and systolic blood pressure are the strongest binary DR anchors. Participant-level pathway analysis identifies glycaemic--renal and glycaemic--haemodynamic pathways as the clearest mediator-style signals. These results suggest that retinal graph phenotypes can help prioritise systemic pathways in DR while preserving the distinction between image-derived support and same-subject mediation.
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