Multi-Omics Integration With Machine Learning Identified Early Diabetic Retinopathy, Diabetic Macula Edema and Anti-VEGF Treatment Response

• Published in: Translational vision science & technology 2024-12, Vol.13 (12), p.23
• Main Authors: Pang, Yuhui, Luo, Chaokun, Zhang, Qingruo, Zhang, Xiongze, Liao, Nanying, Ji, Yuying, Mi, Lan, Gan, Yuhong, Su, Yongyue, Wen, Feng, Chen, Hui
• Format: Article
• Language: English
• Subjects: Aged; Angiogenesis Inhibitors - therapeutic use; Aqueous Humor - chemistry; Aqueous Humor - drug effects; Aqueous Humor - metabolism; Biomarkers - analysis; Biomarkers - metabolism; Chromatography, High Pressure Liquid; Diabetes Mellitus, Type 2 - complications; Diabetes Mellitus, Type 2 - drug therapy; Diabetes Mellitus, Type 2 - metabolism; Diabetic Retinopathy - drug therapy; Diabetic Retinopathy - metabolism; Female; Humans; Lipidomics - methods; Machine Learning; Macular Edema - drug therapy; Macular Edema - metabolism; Male; Metabolomics - methods; Middle Aged; Multiomics; Prospective Studies; Retina; Vascular Endothelial Growth Factor A - antagonists & inhibitors; Vascular Endothelial Growth Factor A - metabolism
• ISSN: 2164-2591; 2164-2591
• DOI: 10.1167/tvst.13.12.23

Identify optimal metabolic features and pathways across diabetic retinopathy (DR) stages, develop risk models to differentiate diabetic macular edema (DME), and predict anti-vascular endothelial growth factor (anti-VEGF) therapy response. We analyzed 108 aqueous humor samples from 78 type 2 diabetes mellitus patients and 30 healthy controls. Ultra-high-performance liquid chromatography-high-resolution-mass-spectrometry detected lipidomics and metabolomics profiles. DME patients received ≥3 anti-VEGF treatments, categorized into strong and weak response groups. Machine learning (ML) screened prospective metabolic features, developing prediction models. Key metabolic features identified in the metabolomics and lipidomics datasets included n-acetyl isoleucine (odds ratio [OR] = 1.635), cis-aconitic acid (OR = 3.296), and ophthalmic acid (OR = 0.836) for DR. For early-DR, n-acetyl isoleucine (OR = 1.791) and decaethylene glycol (PEG-10) (OR = 0.170) were identified as key markers. L-kynurenine (OR = 0.875), niacinamide (OR = 0.843), and linoleoyl ethanolamine (OR = 0.941) were identified as significant indicators for DME. Trigonelline (OR = 1.441) and 4-methylcatechol-2-sulfate (OR = 1.121) emerged as predictors for strong response to anti-VEGF. Predictive models achieved R² values of 99.9%, 97.7%, 93.9%, and 98.4% for DR, early-DR, DME, and strong response groups in the calibration set, respectively, and validated well with R² values of 96.3%, 96.8%, 79.9%, and 96.3%. This research used ML to identify differential metabolic features from metabolomics and lipidomics datasets in DR patients. It implies that metabolic indicators can effectively predict early disease progression and potential weak responders to anti-VEGF therapy in DME eyes. The identified metabolic indicators may aid in predicting the early progression of DR and optimizing therapeutic strategies for DME.