Zebardast, N. | bioRxiv Science

bioRxiv2025

Pyruvate and Related Energetic Metabolites Modulate Resilience Against High Genetic Risk for Glaucoma

A glaucoma polygenic risk score (PRS) can effectively identify disease risk, but some individuals with high PRS do not develop glaucoma. Factors contributing to this resilience remain unclear. Using 4,658 glaucoma cases and 113,040 controls in a cross-sectional study of the UK Biobank, we investigated whether plasma metabolites enhanced glaucoma prediction and if a metabolomic signature of resilience in high-genetic-risk individuals existed. Logistic regression models incorporating 168 NMR-based metabolites into PRS-based glaucoma assessments were developed, with multiple comparison corrections applied. While metabolites weakly predicted glaucoma (Area Under the Curve=0.579), they offered marginal prediction improvement in PRS-only-based models (P=0.004). We identified a metabolomic signature associated with resilience in the top glaucoma PRS decile, with elevated glycolysis-related metabolites--lactate (P=8.8E-12), pyruvate (P=1.9E-10), and citrate (P=0.02)--linked to reduced glaucoma prevalence. These metabolites combined significantly modified the PRS-glaucoma relationship (Pinteraction=0.011). Higher total resilience metabolite levels within the highest PRS quartile corresponded to lower glaucoma prevalence (Odds Ratiohighest vs. lowest total resilience metabolite quartile=0.71, 95% Confidence Interval=0.64-0.80). As pyruvate is a foundational metabolite linking glycolysis to tricarboxylic acid cycle metabolism and ATP generation, we pursued experimental validation for this putative resilience biomarker in a human-relevant Mus musculus glaucoma model. Dietary pyruvate mitigated elevated intraocular pressure (P=0.002) and optic nerve damage (P<0.0003) in Lmx1bV265D mice. These findings highlight the protective role of pyruvate-related metabolism against glaucoma and suggest potential avenues for therapeutic intervention.

genomics↗

bioRxiv2023

An artificial intelligence method for phenotyping of OCT scans using unsupervised and self-supervised deep learning

Artificial intelligence (AI) has been increasingly used to analyze optical coherence tomography (OCT) images to better understand physiology and genetic architecture of ophthalmic diseases. However, to date, research has been limited by the inability to transfer OCT phenotypes from one dataset to another. In this work, we propose a new AI method for phenotyping and clustering of OCT-derived retinal layer thicknesses using unsupervised and self-supervised methods in a large clinical dataset using glaucoma as a model disease and subsequently transfer our phenotypes to a large biobank. The model includes a deep learning model, manifold learning, and a Gaussian mixture model. We also propose a correlation analysis for the performance evaluation of our model based on Pearson correlation coefficients. Our model was able to identify clinically meaningful OCT phenotypes and successfully transfer phenotypes from one dataset to another. Overall, our results will contribute to stronger research methodologies for future research in OCT imaging biomarkers, augment testing of OCT phenotypes in multiple datasets, and ultimately improve our understanding of pathophysiology and genetic architecture of ocular diseases.

bioinformatics↗