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Assessment of Vascular Function in Patients With Chronic Kidney Disease
Published on: June 16, 2014
The Eye as a Window to Cardio-Kidney-Metabolic Disease
Jung Tak Park1,2, Yong Yu Tan3,4, Ye Eun Ko1,5
1Department of Internal Medicine, College of Medicine, Institute of Kidney Disease Research, Yonsei University, Seoul, Korea.
Insights
Retinal oculomics, using AI, offers a non-invasive way to detect Cardiovascular-Kidney-Metabolic syndrome injury. Further research is needed to validate its use in advanced kidney disease and compare it with existing risk equations.
Area of Science:
- Ophthalmology
- Nephrology
- Cardiology
- Metabolic Diseases
- Artificial Intelligence
Background:
- Cardiovascular-Kidney-Metabolic (CKM) syndrome integrates three major disease categories but relies on resource-intensive lab markers.
- Retinal oculomics provides a non-invasive method to assess CKM syndrome, leveraging shared vulnerabilities between the retina and kidney to metabolic and hemodynamic stressors.
- Existing evidence primarily links retinal findings to cardiovascular outcomes, with emerging associations for hypertension, diabetes, and chronic kidney disease (CKD).
Purpose of the Study:
- To explore the potential of retinal oculomics, enhanced by artificial intelligence (AI), as a non-invasive tool for assessing Cardiovascular-Kidney-Metabolic (CKM) syndrome.
- To evaluate the current state of retinal AI in identifying CKM injury, particularly in cardiovascular and kidney disease contexts.
- To identify gaps in research and clinical application for retinal AI in CKM syndrome, especially concerning advanced CKD populations and direct comparison with established kidney risk equations.
Main Methods:
- Analysis of existing observational and retrospective studies on retinal features (retinopathy, vessel metrics) and their association with CKM-related endpoints.
- Review of studies employing AI for retinal image analysis, including systemic biomarker estimation, cardiovascular risk stratification, and CKD detection/prediction.
- Assessment of the evidence base for retinal AI, focusing on limitations such as reliance on cross-sectional surrogates, underrepresentation of advanced CKD populations, and lack of management-impact trials.
Main Results:
- Retinal features and quantitative vessel metrics show associations with cardiovascular endpoints like stroke and mortality.
- Retinal AI shows promise in systemic biomarker estimation and cardiovascular risk stratification, with some success in cross-sectional CKD detection.
- Current retinal AI models for kidney disease are limited, lacking direct comparison with kidney risk equations and targeting of key CKD progression markers like albuminuria or kidney replacement therapy.
Conclusions:
- Retinal oculomics, particularly with AI, presents a low-burden, integrative approach to assessing CKM injury, complementing conventional biomarkers.
- Significant gaps remain in validating retinal AI for advanced CKD, including algorithmic opacity, imaging standardization, and cost-effectiveness evaluations.
- Future research should address these limitations to enable retinal AI's role in screening, risk stratification, and monitoring within the CKM continuum, especially for advanced kidney disease.
Abstract:
The Cardiovascular-Kidney-Metabolic (CKM) syndrome reframes cardiovascular, kidney, and metabolic disease as an integrated continuum, yet its management relies on reactive laboratory markers with substantial resource burdens. Retinal oculomics offers a non-invasive window into this continuum, grounded in structural and functional parallels between the retina and kidney. Their shared vulnerability to metabolic and hemodynamic stressors allows the retina to reflect subclinical CKM injury. Evidence is strongest for cardiovascular endpoints, where retinopathy and quantitative vessel metrics are associated with incident stroke, cardiovascular mortality, and coronary heart disease. Similar associations are reported for new-onset hypertension, diabetes, and incident chronic kidney disease (CKD), although the CKD association attenuates after adjustment for conventional kidney markers. Artificial intelligence (AI) extends retinal analysis beyond categorical grading and predefined vessel metrics by learning latent features from fundus photographs. Systemic biomarker estimation and cardiovascular risk stratification are the most mature, whereas kidney-specific models remain confined to cross-sectional CKD detection and prediction of CKD development. However, retinal AI is supported by evidence that remains largely observational, retrospective, and dependent on cross-sectional surrogates. Albuminuria, sustained decline in estimated glomerular filtration rate, kidney replacement therapy, and kidney-related mortality have not been targeted. Advanced CKD, dialysis, and kidney transplant populations are underrepresented in development and validation cohorts. No retinal model has reported cardiovascular risk prediction in CKD or direct comparison with established kidney risk equations. Discrimination in these models falls in ethnically distinct cohorts, and calibration is infrequently reported. Management-impact trials have not been conducted, and cost-effectiveness remains unevaluated. Algorithmic opacity and imaging standardization remain unresolved. Once these gaps are addressed, retinal AI may support screening, risk stratification, progression monitoring, and treatment prioritization, shifting from screening in early CKM to complementary phenotyping in advanced CKD. Retinal AI would then complement conventional kidney biomarkers as an integrative, low-burden window into CKM injury.
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