Nanostructured High-Entropy Yolk-Shell Oxides Platform for Efficient Metabolic Profiling of Diabetic Retinopathy

Area of Science:

• Biomedical Engineering
• Analytical Chemistry
• Ophthalmology

Background:

• Diabetic retinopathy (DR) is a major cause of blindness globally, with diagnosis relying on subjective interpretation of retinal images.
• Current diagnostic methods for DR are challenging and require specialized expertise.

Purpose of the Study:

• To develop a novel nanoenabled metabolomics strategy for accurate DR discrimination.
• To establish a high-throughput and precise platform for noninvasive clinical screening of DR.

Main Methods:

• Utilized high-entropy yolk-shell metal oxides as a novel inorganic laser desorption/ionization mass spectrometry (LDI MS) matrix.
• Obtained serum metabolic fingerprints from DR patients and healthy controls.
• Applied machine learning (Random Forest model) for data analysis and DR differentiation.

Main Results:

• Achieved an area under the curve (AUC) exceeding 0.99 for DR group discrimination using the LDI MS metabolomics approach.
• Developed a compact five-metabolite panel (DR-D5) that retained over 93% diagnostic accuracy.
• Identified key metabolic perturbations linked to DR pathogenesis.

Conclusions:

• The nanoenabled metabolomics strategy provides a highly accurate and efficient method for DR detection.
• This integrated workflow demonstrates significant potential for noninvasive clinical screening of diabetic retinopathy.
• The developed platform facilitates precise metabolite detection and data-driven modeling for disease diagnosis.