Predicting Diabetic Retinopathy Using a Machine Learning Approach Informed by Whole-Exome Sequencing Studies.

Chong Yang SHE; Wen Ying FAN; Yun Yun LI; Yong TAO; Zu Fei LI

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Predicting Diabetic Retinopathy Using a Machine Learning Approach Informed by Whole-Exome Sequencing Studies.

Author: Chong Yang SHE ¹ ; Wen Ying FAN ² ; Yun Yun LI ² ; Yong TAO ¹ ; Zu Fei LI ³
Author Information

1. Department of Ophthalmology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100020, China.
2. Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing 100176, China.
3. Department of Otorhinolaryngology, Head and Neck Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100020, China.
Publication Type:Journal Article
Keywords: Diabetic retinopathy; Machine learning; Type 2 diabetes mellitus; Whole exome sequencing
MeSH: Diabetic Retinopathy/diagnosis*; Humans; Machine Learning; Male; Female; Polymorphism, Single Nucleotide; Middle Aged; Exome Sequencing; Aged; Adult; Pedigree; Diabetes Mellitus, Type 2/complications*; Genetic Predisposition to Disease; Mutation
From: Biomedical and Environmental Sciences 2025;38(1):67-78
CountryChina
Language:English
Abstract: OBJECTIVE:To establish and validate a novel diabetic retinopathy (DR) risk-prediction model using a whole-exome sequencing (WES)-based machine learning (ML) method.
METHODS:WES was performed to identify potential single nucleotide polymorphism (SNP) or mutation sites in a DR pedigree comprising 10 members. A prediction model was established and validated in a cohort of 420 type 2 diabetic patients based on both genetic and demographic features. The contribution of each feature was assessed using Shapley Additive explanation analysis. The efficacies of the models with and without SNP were compared.
RESULTS:WES revealed that seven SNPs/mutations ( rs116911833 in TRIM7, 1997T>C in LRBA, 1643T>C in PRMT10, rs117858678 in C9orf152, rs201922794 in CLDN25, rs146694895 in SH3GLB2, and rs201407189 in FANCC) were associated with DR. Notably, the model including rs146694895 and rs201407189 achieved better performance in predicting DR (accuracy: 80.2%; sensitivity: 83.3%; specificity: 76.7%; area under the receiver operating characteristic curve [AUC]: 80.0%) than the model without these SNPs (accuracy: 79.4%; sensitivity: 80.3%; specificity: 78.3%; AUC: 79.3%).
CONCLUSION:Novel SNP sites associated with DR were identified in the DR pedigree. Inclusion of rs146694895 and rs201407189 significantly enhanced the performance of the ML-based DR prediction model.