Age-related Eye Disease Studies Supplements and Genetic Risk Score Are Crucial Determinants of Intestinal Microbial Alterations in Advanced Age-Related Macular Degeneration.
Neda Dadgar, Kevin Fung, Scott McClintic, Christina Metea, Victor Llorenç, Mohamed Saleh, Yukiko K Nakamura, Cody Jahrig, Lee Kiang, Cathleen Janowitz, Sean Davin, Ariel Balter, Kim-Anh Le Cao, Lisa Karstens, Tammy M Martin
Abstract
Open AccessObjective: Determine whether an intestinal microbial signature is associated with advanced age-related macular degeneration (AMD); investigate the relationship between the microbiota and AMD genetic risk, intestinal immunoglobulin-A (IgA), and Age-Related Eye Disease Studies (AREDS) supplementation. Design: Case-control study. Methods: Fecal 16S rRNA sequencing, DESeq2 differential abundance, and IgA-sequencing. Subjects: Advanced AMD and age-similar non-AMD control subjects. Main Outcome Measures: Differential abundance plots using DESeq2, α and β diversity, and impact of AREDS supplementation and genetic risk on AMD microbiota. Results: In 85 advanced AMD compared with 49 healthy control subjects' intestinal microbiota, exploratory partial least-squares-discriminant analysis (PLS-DA) showed that gut microbiome composition was able to predict AMD with moderate confidence (cross validation error rates, 0.28-0.36) with the potential for overfitting. A higher AMD genetic risk score was associated with lower gut microbial diversity (P = 0.0086; Spearman r = -0.3), a finding confirmed by multiple linear regression with confounding covariates, whereas AREDS supplementation was associated with increased gut bacterial diversity (coefficient, 2.64; P < 0.05). Differential abundance plots showed increased Proteobacteria and many differentially abundant genera (including Prevotella, Desulfovibrio, Oscillospira, and Ruminococcaceae) in AMD versus controls. Flow cytometry and IgA-sequencing suggested increased IgA-coating of gut bacteria in the age-related maculopathy susceptibility 2 (ARMS2) gene risk genotype, including higher IgA indices for Prevotella. These findings are hypothesis-generating and require functional validation. Predicted metabolic pathways (via piphillin) that differed between AMD and controls included lipid metabolism and xenobiotic processing by cytochrome P450; these findings are inferred and require confirmation by metabolomic studies. Conclusions: The intestinal microbiome is able to predict advanced AMD via PLS-DA. AREDS supplementation and genetic risk are crucial determinants of the AMD microbiome, which interacts with gut immunity by increasing IgA binding to certain bacteria. Understanding how the gut microbiome and its metabolites interact with gut immunity and host genetics will allow us to further investigate the microbiome to find potentially novel therapeutic targets in AMD. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.