Integrating Metabolomics, Genomics, and Disease Pathways in Age-Related Macular Degeneration: The EYE-RISK Consortium.


Journal

Ophthalmology
ISSN: 1549-4713
Titre abrégé: Ophthalmology
Pays: United States
ID NLM: 7802443

Informations de publication

Date de publication:
12 2020
Historique:
received: 20 12 2019
revised: 05 05 2020
accepted: 08 06 2020
pubmed: 20 6 2020
medline: 27 2 2021
entrez: 20 6 2020
Statut: ppublish

Résumé

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. Case-control association analysis of metabolomics data. Five European cohorts consisting of 2267 AMD patients and 4266 control participants. 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. Metabolites associated with AMD. 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. 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.

Identifiants

pubmed: 32553749
pii: S0161-6420(20)30561-3
doi: 10.1016/j.ophtha.2020.06.020
pii:
doi:

Substances chimiques

ABCA1 protein, human 0
ATP Binding Cassette Transporter 1 0
ApoE protein, human 0
Apolipoproteins E 0
CETP protein, human 0
Cholesterol Ester Transfer Proteins 0
LIPC protein, human 0
Lipase EC 3.1.1.3

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1693-1709

Investigateurs

Blanca Arango-Gonzalez (B)
Angela Armento (A)
Franz Badura (F)
Vaibhav Bhatia (V)
Shomi S Bhattacharya (SS)
Marc Biarnés (M)
Anna Borrell (A)
Sofia M Calado (SM)
Sascha Dammeier (S)
Berta De la Cerda (B)
Francisco J Diaz-Corrales (FJ)
Sigrid Diether (S)
Eszter Emri (E)
Tanja Endermann (T)
Lucia L Ferraro (LL)
Míriam Garcia (M)
Sabina Honisch (S)
Ellen Kilger (E)
Elod Kortvely (E)
Claire Lastrucci (C)
Hanno Langen (H)
Imre Lengyel (I)
Phil Luthert (P)
Jordi Monés (J)
Everson Nogoceke (E)
Tunde Peto (T)
Frances M Pool (FM)
Eduardo Rodriguez-Bocanegra (E)
Luis Serrano (L)
Jose Sousa (J)
Eric Thee (E)
Marius Ueffing (M)
Karl U Ulrich Bartz-Schmidt (KU)
Markus Zumbansen (M)

Informations de copyright

Copyright © 2020 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Auteurs

İlhan E Acar (İE)

Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.

Laura Lores-Motta (L)

Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.

Johanna M Colijn (JM)

Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands.

Magda A Meester-Smoor (MA)

Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands.

Timo Verzijden (T)

Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands.

Audrey Cougnard-Gregoire (A)

Université de Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, Team LEHA, Bordeaux, France.

Soufiane Ajana (S)

Université de Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, Team LEHA, Bordeaux, France.

Benedicte M J Merle (BMJ)

Université de Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, Team LEHA, Bordeaux, France.

Anita de Breuk (A)

Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.

Thomas J Heesterbeek (TJ)

Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.

Erik van den Akker (E)

Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands; Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, The Netherlands.

Mohamed R Daha (MR)

Department of Nephrology, Leiden University Medical Center, Leiden, The Netherlands.

Birte Claes (B)

Institute for Epidemiology and Social Medicine, University of Muenster, Muenster, Germany.

Daniel Pauleikhoff (D)

Augenzentrum, St. Franziskus Hospital, Münster, Germany.

Hans-Werner Hense (HW)

Institute for Epidemiology and Social Medicine, University of Muenster, Muenster, Germany.

Cornelia M van Duijn (CM)

Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands; Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, United Kingdom.

Sascha Fauser (S)

Department of Ophthalmology, University Hospital of Cologne, Cologne, Germany; Roche Pharma Research and Early Development, F. Hoffmann-La Roche, Ltd., Basel, Switzerland.

Carel B Hoyng (CB)

Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.

Cécile Delcourt (C)

Université de Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, Team LEHA, Bordeaux, France.

Caroline C W Klaver (CCW)

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.

Tessel E Galesloot (TE)

Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.

Anneke I den Hollander (AI)

Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands. Electronic address: Anneke.denhollander@radboudumc.nl.

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Classifications MeSH