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

ABSTRACT:

Purpose

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.

Methods

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.

Results

Key metabolic features identified in the metabolomics and lipidomics datasets included n-acety

SUBMITTER: Pang Y 

PROVIDER: S-EPMC11645727 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature