Routine health measurements – such as body weight, blood pressure, and cholesterol levels – could be used to accurately predict a person’s risk of developing retinopathy, offering the potential for more personalised screening.1

A prospective cohort study from China published in Diabetes & Metabolic Syndrome,1 followed 2,447 adults who did not have retinopathy at the start of the study. Participants represented a mix of metabolic health states, including normal glucose levels, prediabetes, and diabetes.

Over the course of the follow-up period, 5.9% of participants developed retinopathy, which can lead to vision loss if left untreated. Researchers found that several common clinical measures were strongly associated with future retinopathy risk, including body mass index (BMI), waist-to-hip ratio, triglyceride levels, blood pressure (both systolic and diastolic), history of hypertension, and ethnicity.

These findings suggest that retinopathy risk is closely tied to broader metabolic health, not just blood sugar levels. Using these factors, the research team developed two predictive models, known as nomograms. One used baseline data alone, while the other incorporated both baseline and follow-up information.

The more comprehensive model showed significantly better predictive performance. Importantly, it could also identify at-risk individuals without generating excessive false positives.

The findings could help clinicians identify patients who may benefit from earlier or more frequent eye exams. Because the model relies on widely available clinical data, it could be easily integrated into routine care without requiring specialised testing.

Reference available at mivision.com.au.