Metabo-endotypes of Age-related Macular Degeneration

Abstract

Purpose : Age-related Macular Degeneration (AMD) has a wide spectrum of both phenotypic and functional presentations. Yet, the contributing factors for this phenotypic variability remain not fully understood. Metabolomics reflects the downstream products of all the genetic transcription processes, making it appropriate to study multifactorial diseases like AMD. We hypothesize that there are distinct AMD endotypes (i.e. subtypes defined by functional or pathobiological mechanisms) that confer clinically meaningful differences. In this study, we aim to derive AMD endotypes based on metabolomics and correlate these endotypes with current AMD stage and retinal function.

Methods : Prospective, cross-sectional study including patients with any stage of AMD (n=196). All included participants had a complete ophthalmological exam and were imaged with color fundus photographs for AMD staging. Dark adaptation testing was performed with a 20 minutes protocol, and both rod intercept time (RIT) and area under the curve (AUDAC) were registered. Metabolomic profiling on fasting plasma samples was performed using ultra-performance liquid chromatography–mass spectrometry (LC-MS). Similarity Network Fusion (SNF) and spectral clustering were performed on the metabolomics and used to identify metabo-endotypes of AMD. Clinical differences across the metabo-endotypes were explored using one-way analysis of variance (ANOVA) for continuous variables and chi-squared test for categorical variables. Multivariable logistic regression models adjusted for covariates were then used to identify metabolomics drivers of each endotype.

Results : We identified 4 AMD endotypes using SNF and spectral clustering with metabolomics data. AMD stage (pval=0.002), RIT (pval=0.003), and AUDAC (pval=0.01) significantly differed across the endotypes. As shown in Figure 1, endotype membership tracked closely to RIT but appeared to be independent of the AMD stages. The most important drivers of the endotypes were amino acids (isoleucine, leucine, and valine metabolites), lipid metabolites (polyunsaturated and long-chain fatty acids), and nucleotides (purine metabolism).

Conclusions : Metabo-endotypes of AMD can be derived using metabolomics data and correlated with clinical and functional features. By interrogating the drivers of these metabo-endotypes, there is potential to better understand the pathophysiology behind AMD.

This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.

 

Figure 1. Mean and Std Error of RIT by A) AMD Stage and B) Metabo-endotypes.