Clin J Am Soc Nephrol. 2026 Aug 19. doi: 10.2215/CJN.0000001224. Online ahead of print.
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.
PMID:42616587 | DOI:10.2215/CJN.0000001224