Acar, İE;
Lores-Motta, L;
Colijn, JM;
Meester-Smoor, MA;
Verzijden, T;
Cougnard-Gregoire, A;
Ajana, S;
... EYE-RISK Consortium; + view all
(2020)
Integrating Metabolomics, Genomics, and Disease Pathways in Age-Related Macular Degeneration: The EYE-RISK Consortium.
Ophthalmology
10.1016/j.ophtha.2020.06.020.
(In press).
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Abstract
PURPOSE: The current study aimed to identify metabolites associated with age-related macular degeneration (AMD) by performing the largest metabolome association analysis in AMD to date, as well as aiming to determine the effect of AMD-associated genetic variants on metabolite levels and investigate associations between the identified metabolites and activity of the complement system, one of the main AMD-associated disease pathways. DESIGN: Case-control association analysis of metabolomics data. PARTICIPANTS: Five European cohorts consisting of 2267 AMD patients and 4266 control participants. METHODS: Metabolomics was performed using a high-throughput proton nuclear magnetic resonance metabolomics platform, which allows quantification of 146 metabolite measurements and 79 derivative values. Metabolome-AMD associations were studied using univariate logistic regression analyses. The effect of 52 AMD-associated genetic variants on the identified metabolites was investigated using linear regression. In addition, associations between the identified metabolites and activity of the complement pathway (defined by the C3d-to-C3 ratio) were investigated using linear regression. MAIN OUTCOME MEASURES: Metabolites associated with AMD. RESULTS: We identified 60 metabolites that were associated significantly with AMD, including increased levels of large and extra-large high-density lipoprotein (HDL) subclasses and decreased levels of very low-density lipoprotein (VLDL), amino acids, and citrate. Of 52 AMD-associated genetic variants, 7 variants were associated significantly with 34 of the identified metabolites. The strongest associations were identified for genetic variants located in or near genes involved in lipid metabolism (ABCA1, CETP, APOE, and LIPC) with metabolites belonging to the large and extra-large HDL subclasses. Also, 57 of 60 metabolites were associated significantly with complement activation levels, independent of AMD status. Increased large and extra-large HDL levels and decreased VLDL and amino acid levels were associated with increased complement activation. CONCLUSIONS: Lipoprotein levels were associated with AMD-associated genetic variants, whereas decreased essential amino acids may point to nutritional deficiencies in AMD. We observed strong associations between the vast majority of the AMD-associated metabolites and systemic complement activation levels, independent of AMD status. This may indicate biological interactions between the main AMD disease pathways and suggests that multiple pathways may need to be targeted simultaneously for successful treatment of AMD.
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