Identification of the Metabolic Signature of Aging Retina

This study sheds light on the molecular mechanisms underlying retinal aging by identifying distinct metabolic profiles and pathways. These findings provide a valuable foundation for developing future clinical applications in diagnosing, identifying, and treating age-related retinal degeneration. Translational relevance: This study sheds light on novel metabolic profiles and biomarkers in aging retinas, potentially paving the way for targeted interventions in preventing, diagnosing, and treating age-related retinal degeneration and other retinal diseases.

Retinal samples were collected from both young (two months) and aging (14 months) mice to conduct an unbiased metabolic profiling. Liquid chromatography-tandem mass spectrometry analysis was conducted to screen for the metabolic biomarkers and altered signaling pathways associated with retinal aging.

This study aims to explore the metabolic signature of aging retina and identify the potential metabolic biomarkers for the diagnosis of retinal aging.

We identified 166 metabolites differentially expressed between young and aged retinas using a threshold of orthogonal projection to latent structures discriminant analysis variable importance in projection >1 and P < 0.05. These metabolites were significantly enriched in several metabolic pathways, including purine metabolism, citrate cycle, phenylalanine, tyrosine and tryptophan biosynthesis, glycerophospholipid metabolism, and alanine, aspartate and glutamate metabolism. Among these significantly enriched pathways, glycerophospholipid metabolites emerged as promising candidates for retinal aging biomarkers. We assessed the potential of these metabolites as biomarkers through an analysis of their sensitivity and specificity, determined by the area under the receiver-operating characteristic (ROC) curves. Notably, the metabolites like PC (15:0/22:6), PC (17:0/14:1), LPC (P-16:0), PE (16:0/20:4), and PS (17:0/16:1) demonstrated superior performance in sensitivity, specificity, and accuracy in predicting retinal aging. Conclusions: This study sheds light on the molecular mechanisms underlying retinal aging by identifying distinct metabolic profiles and pathways. These findings provide a valuable foundation for developing future clinical applications in diagnosing, identifying, and treating age-related retinal degeneration. Translational relevance: This study sheds light on novel metabolic profiles and biomarkers in aging retinas, potentially paving the way for targeted interventions in preventing, diagnosing, and treating age-related retinal degeneration and other retinal diseases.