Abstract Background Shared anatomical, histological and physiological pathways between the kidney and the eye are well documented, demonstrating that ocular manifestations serve as valuable prognostic indicators in chronic kidney disease (CKD), providing insights into disease severity and progression. Through non‐invasive imaging modalities such as retinal fundus photography, early retinal microvascular alterations indicative of CKD progression can be detected, enabling timely intervention and risk stratification. However, the conclusions drawn from the review primarily demonstrate a strong or independent association between glaucoma or retinopathy and CKD. Results and Conclusion Multiple shared pathophysiological events have been implicated in the pathogenesis in the alterations at eye and kidney including renin‐angiotensin‐aldosterone system. Patients with CKD are more likely to experience glaucoma, age‐related macular degeneration, cataracts, uremic optic neuropathy and retinopathy. To establish the role of ocular manifestations in predicting CKD progression, it is crucial to address the limitations of correlation and explore the underlying causality with further research on common disease pathogenesis. Additionally, specific methods for risk stratification based on retinal changes, the effectiveness of timely interventions, and the development of predictive tools combining ocular and renal data are of utmost importance research topics to enlighten the bidirectional causality. 

Unveiling the intricacies of chronic kidney disease: From ocular manifestations to therapeutic frontiers

Diabetic retinopathy Is associated with histologic severity of diabetic kidney disease and systemic inflammation

This study was developed and evaluated deep learning model for detecting chronic kidney disease (CKD) by retinal fundus images. This study included 42,963 clinical visits from 17,442 patients who underwent retinal fundus examination between October 19, 2006, and September 13, 2018, with estimated glomerular filtration rate (eGFR) measurements available within a 7-day interval of the imaging examination. We developed and compared three model configurations: using a single fundus image (Model A), combining a single image with demographic features (Model B), and integrating bilateral fundus images (Model C). We compared two base architectures, EfficientNet-B3 and EfficientNetV2-S, and evaluated the impact of different training strategies: a single model versus a 5-fold cross-validation (CV) ensemble. Model performance was assessed using the Area Under the Curve (AUC), sensitivity, specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV). Among all evaluated models, the bilateral-image model (Model C) utilizing the EfficientNet-B3 architecture with a 5-fold CV ensemble strategy demonstrated the best overall performance, achieving an AUC of 0.868, with a sensitivity of 0.792 and a specificity of 0.788 on an independent test set. The performance of this ensemble strategy was statistically superior to its single-model counterpart trained on the full dataset (AUC 0.850, p < 0.001). Among single models, Model B yielded the highest AUC (0.857) and sensitivity (0.794), while Model C offered the highest specificity (0.799), revealing a clinical trade-off between the different approaches. Furthermore, benchmarking against the newer EfficientNetV2-S architecture did not yield a performance benefit in this study. The study exhibited a superior performance in detecting advanced chronic kidney disease in patients with diabetes mellitus through retinal fundus image. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-025-21366-y.

pdf/prediction-of-advanced-chronic-kidney-disease-through-atvz5uga0pu6.pdf

Prediction of advanced chronic kidney disease through retinal fundus images by deep learning

Background Chronic kidney disease (CKD) is a significant global health concern, emphasizing the necessity of early detection to facilitate prompt clinical intervention. Leveraging the unique ability of the retina to offer insights into systemic vascular health, it emerges as an interesting, non-invasive option for early CKD detection. Integrating this approach with existing invasive methods could provide a comprehensive understanding of patient health, enhancing diagnostic accuracy and treatment effectiveness. Objectives The purpose of this review is to critically assess the potential of retinal imaging to serve as a diagnostic tool for CKD detection based on retinal vascular changes. The review tracks the evolution from conventional manual evaluations to the latest state-of-the-art in deep learning. Survey Methodology A comprehensive examination of the literature was carried out, using targeted database searches and a three-step methodology for article evaluation: identification, screening, and inclusion based on Prisma guidelines. Priority was given to unique and new research concerning the detection of CKD with retinal imaging. A total of 70 publications from 457 that were initially discovered satisfied our inclusion criteria and were thus subjected to analysis. Out of the 70 studies included, 35 investigated the correlation between diabetic retinopathy and CKD, 23 centered on the detection of CKD via retinal imaging, and four attempted to automate the detection through the combination of artificial intelligence and retinal imaging. Results Significant retinal features such as arteriolar narrowing, venular widening, specific retinopathy markers (like microaneurysms, hemorrhages, and exudates), and changes in arteriovenous ratio (AVR) have shown strong correlations with CKD progression. We also found that the combination of deep learning with retinal imaging for CKD detection could provide a very promising pathway. Accordingly, leveraging retinal imaging through this technique is expected to enhance the precision and prognostic capacity of the CKD detection system, offering a non-invasive diagnostic alternative that could transform patient care practices. Conclusion In summary, retinal imaging holds high potential as a diagnostic tool for CKD because it is non-invasive, facilitates early detection through observable microvascular changes, offers predictive insights into renal health, and, when paired with deep learning algorithms, enhances the accuracy and effectiveness of CKD screening.

Evaluating the potential of retinal photography in chronic kidney disease detection: a review

Hypertensive nephrosclerosis (HN) and diabetic nephropathy (DN) are the leading global causes of chronic kidney disease (CKD) and end-stage renal disease (ESRD). Both conditions share overlapping clinical and histopathological features, particularly in patients with concurrent hypertension and diabetes, making accurate differentiation challenging. This narrative review synthesizes current evidence on the pathophysiology, histopathology, diagnostic criteria, and therapeutic implications of HN and DN, with emphasis on areas of morphological and clinical overlap. HN is driven by chronic vascular injury from sustained hypertension, resulting in ischemic glomerulosclerosis, arteriolar hyalinosis, and interstitial fibrosis. DN, in contrast, is primarily mediated by hyperglycemia-induced metabolic and hemodynamic stressors, producing diffuse and nodular mesangial expansion, glomerular basement membrane thickening, and bilateral arteriolar hyalinosis. While certain histological features, such as Kimmelstiel-Wilson nodules and efferent arteriole involvement, favor DN, mixed lesions are common in long-standing type 2 diabetes with hypertension. Accurate differentiation is clinically significant, as it guides the application of disease-specific interventions such as intensive glycemic control and emerging pharmacotherapies for DN, versus targeted vascular risk reduction in HN. Future advances in biomarkers, imaging, and genomics hold promise for earlier, noninvasive diagnosis.

Hypertensive Nephrosclerosis: Pathological Changes and Overlap with Diabetic Nephropathy


 
 
 Up to 50–60% of patients with diabetes have non-diabetic kidney disease (NDKD) in the kidney biopsy. Diabetic retinopathy (DR) is a microvascular complication of diabetes frequently associated with diabetic nephropathy (DN). The objective of the current study is to investigate the kidney outcomes and survival in patients with biopsy diagnoses of DN and NDKD according to the presence of DR.
 
 
 
 Observational, multicenter and retrospective study of the pathological findings in renal biopsies from 832 consecutive patients with diabetes from 2002 to 2014 from 18 Nephrology departments. The association of DR with kidney replacement therapy (KRT) or survival was assessed by Kaplan Meier and Cox regression analyses.
 
 
 
 Out of 832 patients with diabetes and renal biopsy, 768 had a retinal examination and 221/768 (22.6%) had DR. During a follow-up of 10 years, 288/760 (37.9%) patients with follow-up data needed kidney replacement therapy (KRT) and 157/760 (20.7%) died. The incidence of KRT was higher among patients with DN (alone or with NDKD) and DR (103/175, 58.9%) than among patients without DR (88/216, 40.7%, P < 0.0001). The incidence of KRT was also higher among patients with only NDKD and DR than among those without DR (18/46, 39.1% vs 79/331, 23.9%, P < 0.0001). In multivariate analysis, DR or DN were independent risk factors for KRT (HR:2.48, CI:1.85–3.31, P < 0.001). DN (with or without DR) was also as an independent risk factor for mortality (HR:1.81, CI1.26–2.62 and p = :0.001).
 
 
 
 DR is associated with a higher risk of progression to kidney failure in patients with histological DN and in patients with NDKD.


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The coexistence of diabetic retinopathy and diabetic nephropathy is associated with worse kidney outcomes

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The coexistence of diabetic retinopathy and diabetic nephropathy is associated with worse kidney outcomes