İlhan E Acar1, Laura Lores-Motta1, Johanna M Colijn2, Magda A Meester-Smoor2, Timo Verzijden2, Audrey Cougnard-Gregoire3, Soufiane Ajana3, Benedicte M J Merle3, Anita de Breuk1, Thomas J Heesterbeek1, Erik van den Akker4, Mohamed R Daha5, Birte Claes6, Daniel Pauleikhoff7, Hans-Werner Hense6, Cornelia M van Duijn8, Sascha Fauser9, Carel B Hoyng1, Cécile Delcourt3, Caroline C W Klaver10, Tessel E Galesloot11, Anneke I den Hollander12. 1. Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands. 2. Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands. 3. Université de Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, Team LEHA, Bordeaux, France. 4. Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands; Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, The Netherlands. 5. Department of Nephrology, Leiden University Medical Center, Leiden, The Netherlands. 6. Institute for Epidemiology and Social Medicine, University of Muenster, Muenster, Germany. 7. Augenzentrum, St. Franziskus Hospital, Münster, Germany. 8. Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands; Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, United Kingdom. 9. Department of Ophthalmology, University Hospital of Cologne, Cologne, Germany; Roche Pharma Research and Early Development, F. Hoffmann-La Roche, Ltd., Basel, Switzerland. 10. Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands. 11. Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands. 12. Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands. Electronic address: Anneke.denhollander@radboudumc.nl.
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.
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 AMDpatients 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.
Authors: I Erkin Acar; Esther Willems; Eveline Kersten; Jenneke Keizer-Garritsen; Else Kragt; Bjorn Bakker; Tessel E Galesloot; Carel B Hoyng; Sascha Fauser; Alain J van Gool; Yara T E Lechanteur; Elod Koertvely; Everson Nogoceke; Jolein Gloerich; Marien I de Jonge; Laura Lorés-Motta; Anneke I den Hollander Journal: J Pers Med Date: 2021-11-25
Authors: Ines Lains; Kevin Mendez; Archana Nigalye; Raviv Katz; Vivian Paraskevi Douglas; Rachel S Kelly; Ivana K Kim; John B Miller; Demetrios G Vavvas; Liming Liang; Jessica Lasky-Su; Joan W Miller; Deeba Husain Journal: Metabolites Date: 2022-01-01
Authors: Matteo Stravalaci; Mariantonia Ferrara; Varun Pathak; Francesca Davi; Barbara Bottazzi; Alberto Mantovani; Reinhold J Medina; Mario R Romano; Antonio Inforzato Journal: Front Pharmacol Date: 2022-01-07 Impact factor: 5.810
Authors: Kevin M Mendez; Janice Kim; Inês Laíns; Archana Nigalye; Raviv Katz; Shrinivas Pundik; Ivana K Kim; Liming Liang; Demetrios G Vavvas; John B Miller; Joan W Miller; Jessica A Lasky-Su; Deeba Husain Journal: Metabolites Date: 2021-03-21