Key result
A regression model based on a panel of serum long non-coding RNAs detected diabetic retinopathy with 75% accuracy and an AUC of 0.74, outperforming a model based on demographic data.
Why the study?
Many diabetic patients are missed by current screening methods for diabetic retinopathy, prompting the validation of previously identified serum lncRNA markers for early detection.
Does a serum lncRNA expression panel accurately detect diabetic retinopathy in patients with diabetes compared to demographic models?
Cross-Sectional (n=317)
Masked
No
Does a serum lncRNA expression panel accurately detect diabetic retinopathy in patients with diabetes compared to demographic models?
A serum panel of long non-coding RNAs demonstrated moderate accuracy (AUC 0.74) in detecting diabetic retinopathy, outperforming models based solely on clinical demographics.
Introduction: Diabetic retinopathy (DR) is the most common chronic complication of diabetes, the leading cause of vision impairments in working-aged adults, and a significant cause of reduced quality of life for diabetic patients. Diabetic patients are recommended to have regular screening in order to catch DR at an early enough stage for effective management. However, due to a variety of factors, many patients can still fall through the cracks with the current screening methods. Methods: Several long non-coding RNAs (lncRNAs), essential regulators of physiological and pathological processes, were previously identified by us as potential markers for DR phenotypes. In this study, we used a significantly larger sample set to validate our panel of lncRNAs. We also explored the possibility of creating a statistical model to detect DR from serum samples using the expression profiles of these lncRNAs. Results: Our regression models, based solely on lncRNA expression data, demonstrated the ability to adequately detect DR and potentially predict it. Models based solely on lncRNA expression performed equally or better compared to models with additional patient information. The models showed promising performance, suggesting that serum lncRNA expression profiles could serve as reliable markers for DR detection. Discussion: Further longitudinal studies are necessary to validate the model's capability to predict retinopathy in diabetic patients not yet diagnosed with DR. Nevertheless, our findings indicate that this lncRNA panel may offer a viable option for a simple, accessible, and convenient blood-based screening test for DR.
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Wang et al. (2025) conducted a cross-sectional in Diabetic retinopathy (n=317). Serum long non-coding RNA expression panel vs. Demographic data was evaluated on Detection of diabetic retinopathy (AUC 0.74). A regression model based on a panel of serum long non-coding RNAs detected diabetic retinopathy with 75% accuracy and an AUC of 0.74, outperforming a model based on demographic data.
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