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Annotated abstract

Peripheral retinal haemorrhage density on ultra-widefield imaging as a novel biomarker for predicting diabetic retinopathy progression: a 2-year longitudinal study in Asians

bjophthalmol · 2026-05-12 · canonical JSON source

2 visible annotations · policy: published · automated confidence ≥ 75.00%

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Aims To evaluate whether predominantly peripheral lesions (PPLs) and other imaging biomarkers on ultra-widefield (UWF) photography can predict the progression of diabetic retinopathy (DR) in a multiethnic Asian cohort.Methods This was a prospective, longitudinal cohort study involving 282 participants (528 eyes) with diabetes and either no DR or non-proliferative DR, recruited from the Singapore National Eye Centre between July 2017 and May 2021. Participants underwent annual UWF colour fundus photography and systemic evaluations over a 2-year period. Images were graded at a centralised reading centre. An automated lesion detection algorithm was used for objective quantification of microaneurysms and retinal haemorrhages. Multivariate regression analysis was conducted to identify independent predictors of DR progression, adjusting for systemic and ocular risk factors. The primary outcome was DR progression, defined as a ≥2 step worsening on the Diabetic Retinopathy Severity Scale or development of proliferative DR within 2 years.Results The 2-year progression rate of DR was 9.85%. Independent predictors of progression included increased peripheral retinal haemorrhage density (OR=3.09; 95% CI 1.03 to 9.22; p=0.044), presence of PPLs (OR=4.00; 95% CI 1.07 to 15.0; p=0.040) and higher baseline diastolic blood pressure (OR=1.04; 95% CI 1.01 to 1.08; p=0.015). Eyes with PPLs had a 1.6-fold higher risk of progression compared with eyes with predominantly central lesions (15.3% vs 9.3%).Conclusion Peripheral biomarkers on UWF imaging, including peripheral retinal haemorrhage density and PPLs, are independent predictors of DR progression. These findings support the clinical utility of peripheral retinal assessment and automated artificial intelligence-based imaging tools in DR risk stratification and management.