June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
Identifying Key Drivers Regulating the Metabolites Network of Age-related Macular Degeneration
Author Affiliations & Notes
  • Tianxiao Huan
    University of Massachusetts Chan Medical School, Worcester, Massachusetts, United States
  • Mark Daly
    Massachusetts General Hospital and Broad Institute, Cambridge, Massachusetts, United States
  • Johanna M Seddon
    University of Massachusetts Chan Medical School, Worcester, Massachusetts, United States
  • Footnotes
    Commercial Relationships   Tianxiao Huan None; Mark Daly None; Johanna Seddon Laboratoires THEA, Code C (Consultant/Contractor), Gemini Therapeutics, Inc and Apellis , Code I (Personal Financial Interest)
  • Footnotes
    Support  Supported by NIH R01-EY011309, R01- EY028602, American Macular Degeneration Foundation, Northampton, MA; The Macular Degeneration Center of Excellence, University of Massachusetts Medical School, Department of Ophthalmology and Visual Sciences, Worcester, MA (JS). Worcester Foundation, Worcester, MA (TH).
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 1184 – A0038. doi:
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    • Get Citation

      Tianxiao Huan, Mark Daly, Johanna M Seddon; Identifying Key Drivers Regulating the Metabolites Network of Age-related Macular Degeneration. Invest. Ophthalmol. Vis. Sci. 2022;63(7):1184 – A0038.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Many circulating metabolites including lipids and fatty acids may be involved in the pathogenesis of age-related macular degeneration (AMD). However, conflicting results about their association with AMD have been reported and the relationship between metabolites and AMD remains largely unknown. Here we aim to provide an integrated perspective on the relationship and genetic basis of metabolites and AMD by leveraging the results of published genome-wide association studies (GWAS).

Methods : We curated published GWAS of human serum and plasma metabolites which identified quantitative trait loci for 300 metabolites (mQTLs). Summary statistics of AMD GWAS were derived from the International AMD Genomics Consortium. Two-sample Mendelian randomization (MR) was used to infer causal associations between metabolites and AMD using mQTLs as instrument variables.

Results : We built a metabolites network of AMD by cross-linking different metabolites with their shared SNP associations with AMD (P<5e-8). We found that four AMD risk loci near LIPC, CETP, APOE and MMP9 (PMID 20385826, 20888482, 21139980, 21447678) showed high pleiotropic effects on metabolites and each locus was associated with 20 metabolites on average. Some metabolites were related to multiple AMD risk loci, but the direction of association between metabolites and AMD conflicted at different loci. For example, rs17231506 in CETP was positively correlated with AMD (P=1.7e-8) and extra-large sized HDL (xl-HDL, P=6.7e-15), suggesting that xl-HDL was positively correlated with AMD. However, rs2070895 in LIPC was negatively correlated with AMD (P=1.8e-11) but positively correlated with xl-HDL (P=1.7e-9), suggesting that xl-HDL was negatively correlated with AMD. Results point to key regulatory genes that may balance levels of metabolites in AMD. MR analysis also showed that increased levels of Lyso-phosphatidylethanolamine (Beta=0.46, P=1.1e-18), docosatetranoic acid (Beta=1.4, P=3.5e-15), and m-HDL (Beta=0.67, P=6.9e-5) were associated with increased risk of AMD, and increased level of LDL, VLDL, and IDL (Beta=-0.37, P=4.6e-6) were associated with decreased risk of AMD.

Conclusions : Results provide evidence supporting potentially causal effects of many circulating metabolites on AMD, and pinpoint key regulators which may balance the metabolites network underlying AMD pathogenesis. Therefore, these findings may support new targets for AMD therapies.

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

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