Sahil Thakur

The cardiovascular–kidney–metabolic (CKM) syndrome reframes cardiovascular (CV), kidney, and metabolic disease as an integrated continuum, yet its management relies on reactive laboratory markers with substantial resource burdens. Retinal oculomics offers a noninvasive 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 CV end points, where retinopathy and quantitative vessel metrics are associated with incident stroke, CV mortality, and coronary heart disease. Similar associations are reported for new-onset hypertension, diabetes, and incident 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 CV 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 eGFR, KRT, 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 CV 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.

Nam D, et al. Clin Hypertens. 2026 ;32(1):e38. https://doi.org/10.5646/ch.2026.32.e38

Background: Although retinal imaging–based artificial intelligence (AI) tools have recently been introduced for cardiovascular disease (CVD) risk assessment, little is known about the distribution of these AI-derived scores across the full age spectrum or their associations with traditional cardiometabolic risk factors at different ages.Methods: We analyzed data from 138,745 participants who underwent routine health examinations at two health screening centers in Seoul, Korea. The AI-based retinal CVD risk score (Dr.Noon CVD), as well as anthropometric, hemodynamic, and metabolic indices and cardiometabolic disease status, were compared across ages 16 to 96 years. In a subgroup of 13,182 individuals who underwent coronary artery calcium scoring (CACS) by cardiac computed tomography, we evaluated the performance of the Dr.Noon CVD score in detecting CACS using receiver operating characteristic curve analysis.Results: Mean Dr.Noon CVD scores rose steadily with age from 14.2±2.9 (0, and an area under the curve of 0.82 (95% confidence interval, 0.81–0.83) for identifying significant calcification burden, defined as CACS >100.Conclusions: Dr.Noon CVD scores were consistently correlated with age, conventional risk factors, and CACS, suggesting a potential role in broad-based cardiovascular risk stratification and in guiding personalized prevention strategies.