[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82918-en":3,"doc-seo-82918-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82918,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Causal RetiGraph Cross Cohort Retinal Support and Same Subject Pathway Analysis for Diabetic Retinopathy","Diabetic retinopathy (DR) is a retinal manifestation of systemic microvascular injury, yet current retinal AI often fails to clarify how local lesion evidence and vascular structure connect to systemic disease pathways. Causal-RetiGraph is a compact biomedical informatics framework linking retinal graph phenotypes with NHANES-anchored pathway modeling. It builds an interpretable phenotype X1234 from vessel maps, lesion evidence, embeddings, and AutoMorph biomarkers, and uses R* for participant-level pathway summaries. On retinal data it achieves 0.9055 accuracy and 0.9711 AUROC with QWK 0.8312, while NHANES analysis prioritizes glycaemic–renal and glycaemic–haemodynamic mediating signals.","Causal-RetiGraph: Cross-Cohort Retinal Support and Same-Subject Pathway Analysis for Diabetic  \nRetinopathy  \nInam Ullah∗ University of Southampton Southampton, United Kingdom [i1n23@soton.ac.uk](i1n23@soton.ac.uk)  \nImran Razzak Shoaib Jameel  \nMohamed bin Zayed University of Artificial Intelligence University of Southampton  \nUnited Arab Emirates Southampton, United Kingdom  \n[imran.razzak@mbzuai.ac.ae](imran.razzak@mbzuai.ac.ae) [M.S.Jameel@southampton.ac.uk](M.S.Jameel@southampton.ac.uk)  \narXiv :2607 .05204v 1 [ cs .CV] 6 Jul 2026  \n∗ Corresponding author: [i1n23@soton.ac.uk](i1n23@soton.ac.uk)  \nAbstract—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 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 X12 and Jacobian X34 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 mediatorstyle signals. These results suggest that retinal graph phenotypescan help prioritise systemic pathways in DR while preserving the distinction between image-derived support and same-subject mediation.  \nIndex Terms—Retinal Imaging, Causal Learning, Explainable AI, Oculomics, Biomarkers, Cardiovascular Diseases  \nI. INTRODUCTION  \nDiabetes is a systemic metabolic disorder in which impaired insulin production or insulin action leads to chronic hyperglycaemia and progressive vascular injury. Over time, persistent glycaemic burden interacts with blood-pressure stress, dyslipidaemia, inflammation, renal dysfunction and disease duration, producing both macrovascular and microvascular complications [1, 2] . Diabetic retinopathy, diabetic nephropathy and diabetic neuropathy are among the major microvascular complications of diabetes, and they share several upstream mechanisms related to endothelial dysfunction, capillary damage and metabolic stress [3, 4, 5, 6] . DR is therefore not only  \nan isolated ocular disease; it is a local retinal expression of systemic diabetic microvascular burden [7] .  \nThe retina is particularly important because it provides a non-invasive view of the human microcirculation. Unlike many vascular beds, the retinal circulation can be repeatedly imaged using colour fundus photography, allowing vessel structure, lesion burden and microvascular remodelling tobe measured at scale. This has led to growing interest in retinal biomarkers and oculomics, where retinal imaging is used to study ocular, cardiovascular, renal and systemic health [8] . Recent retinal biomarker roadmaps emphasise that retinal imaging may support cardiovascular and systemic disease assessment when image-derived biomarkers are standardised and linked to interpretable physiology [9] . Reviews of DR imaging similarly highlight the importance of vascular calibre, vessel density, tortuosity, fractal dimension and multimodal retinal measurements for early DR characterisation a","cbCaigf2yc21wvXN","https://ap.wps.com/l/cbCaigf2yc21wvXN","pdf",9714711,3,1,10,"English","en",105,"# Introduction\n## Motivation: systemic microvascular burden and retinal imaging\n## Challenges in DR modeling\n## Interpretability needs and visible lesion evidence\n## Proposed workflow and objectives","[{\"question\":\"What problem does Causal-RetiGraph address in diabetic retinopathy research?\",\"answer\":\"It targets the gap where retinal AI can grade images accurately but does not explain how local lesion evidence and vascular structure relate to systemic disease pathways.\"},{\"question\":\"How does the framework build the retinal representation used for DR prediction and support?\",\"answer\":\"It constructs an interpretable phenotype X1234 from vessel maps, lesion evidence, image embeddings, and AutoMorph biomarkers using spatial and Jacobian branches.\"},{\"question\":\"What role does NHANES play in the pathway analysis?\",\"answer\":\"NHANES 2005–2008 provides a systemic anchoring source to model exposures, covariates, a same-subject mediator family R*, and downstream outcome families for cross-cohort pathway prioritization.\"}]",1784183942,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"causal-retigraph-cross-cohort-retinal-support-and-same-subject-pathway-analysis-for-diabetic-retinopathy","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/causal-retigraph-cross-cohort-retinal-support-and-same-subject-pathway-analysis-for-diabetic-retinopathy/82918/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-24","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does Causal-RetiGraph address in diabetic retinopathy research?","Question",{"text":75,"@type":76},"It targets the gap where retinal AI can grade images accurately but does not explain how local lesion evidence and vascular structure relate to systemic disease pathways.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the framework build the retinal representation used for DR prediction and support?",{"text":80,"@type":76},"It constructs an interpretable phenotype X1234 from vessel maps, lesion evidence, image embeddings, and AutoMorph biomarkers using spatial and Jacobian branches.",{"name":82,"@type":73,"acceptedAnswer":83},"What role does NHANES play in the pathway analysis?",{"text":84,"@type":76},"NHANES 2005–2008 provides a systemic anchoring source to model exposures, covariates, a same-subject mediator family R*, and downstream outcome families for cross-cohort pathway prioritization.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":22,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]