Dr. Jonathan Lin was awarded the 29 th Retina Society Fellowship Research Award for his work at Stanford University integrating electronic health record data and proteomics data to discover novel molecular markers of diabetic retinopathy. This work was conducted under the mentorship of Dr. Vinit Mahajan and Dr. Prithvi Mruthyunjaya.
Can you tell our readers about your study?
I am honored to receive the 29 th Retina Society Fellowship Research Award. I am grateful to my research mentors and our collaborators, Dr. Nima Aghaeepour and Dr. Samson Mataraso, without whom this study would not have been possible. There is a clinical need to improve our understanding of the cellular and molecular mechanisms contributing to the pathobiology of diabetic retinopathy, a leading cause of blindness.
One approach for novel biological discovery in the modern era is to use omics, which queries the specific genes, gene transcripts, proteins, and metabolites that are increased or decreased in disease compared to the healthy state. Modern proteomics platforms now enable us to measure the levels of thousands of proteins in extremely small volumes of ocular biofluids. Nonetheless, determining which of the hundreds of significantly dysregulated proteins are drivers of disease is difficult.
Here, we sought to determine whether incorporating electronic health record data into proteomics analysis could improve our ability to identify protein biomarkers that are reproducible and clinically relevant.
We performed an integrated analysis of proteomics and electronic health record data using multimodal AI to identify differences between patients with or without diabetic retinopathy. By using this sophisticated approach, we identified protein biomarkers that are linked to specific disease features, as well as others that are most linked to the clinical information from the electronic health record.
What are your next steps?
These findings not only serve as a proof of concept highlighting the value of incorporating electronic health record data in proteomics analysis but also provide a foundation for future mechanistic studies. We are excited about applying these analytical pipelines for studying other retinal neurodegenerative diseases.