Incremental value of AI-derived retinal vascular parameters for predicting incident type 2 diabetes

To evaluate whether artificial intelligence (AI)-derived retinal vascular parameters provide incremental predictive value for incident type 2 diabetes (T2DM) beyond the American Diabetes Association Risk Test (ADART), and to assess external performance in an independent Chinese community-based cohort.

We analysed the UK Biobank (n = 63,968; 70:30 derivation/internal validation split) and the BRAVE cohort (n = 1159; external validation). Multivariable Cox proportional hazards models with/without AI-derived retinal parameters were constructed to predict the risk of incident T2DM. Model performance was assessed using Harrell's C-index with bootstrap confidence intervals, calibration, continuous net reclassification improvement (NRI) with event and non-event components, integrated discrimination improvement (IDI), and decision curve analysis.

Retinal arterial fractal dimension (FDa) and arterial tortuosity (atts) were independently associated with incident T2DM after adjustment for ADART. In the UK Biobank internal validation set, adding FDa and atts increased the C-index from 0.746 to 0.751 (delta = 0.005, 95 % CI 0.003 to 0.008); continuous NRI was 0.258, with event NRI 0.158 and non-event NRI 0.100. In BRAVE, the C-index increased from 0.618 to 0.631 (delta = 0.013, 95 % CI 0.009 to 0.016), supporting external validation of discrimination.

AI-derived retinal vascular parameters, particularly FDa and atts, provided measurable incremental predictive information beyond ADART for incident T2DM. These findings support the use of retinal AI biomarkers as complementary risk markers, although prospective validation with large sample sizes, recalibration in target populations, and cost-effectiveness assessment are required before clinical use.