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Contains fulltext : 240741.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 235008.pdf (Publisher’s version ) (Open Access) PURPOSE: Age-related macular degeneration (AMD) is a common multifactorial disease in the elderly with a prominent genetic basis. Many risk variants have been identified, but the interpretation remains challenging. We investigated the genetic distribution of AMD-associated risk variants in a large European consortium, calculated attributable and pathway-specific genetic risks, and assessed the influence of lifestyle on genetic outcomes. DESIGN: Pooled analysis of cross-sectional data from the European Eye Epidemiology Consortium. PARTICIPANTS: Seventeen thousand one hundred seventy-four individuals 45 years of age or older participating in 6 population-based cohort studies, 2 clinic-based studies, and 1 case-control study. METHODS: Age-related macular degeneration was diagnosed and graded based on fundus photographs. Data on genetics, lifestyle, and diet were harmonized. Minor allele frequencies and population-attributable fraction (PAF) were calculated. A total genetic risk score (GRS) and pathway-specific risk scores (complement, lipid, extra-cellular matrix, other) were constructed based on the dosage of SNPs and conditional β values; a lifestyle score was constructed based on smoking and diet. MAIN OUTCOME MEASURES: Intermediate and late AMD. RESULTS: The risk variants with the largest difference between late AMD patients and control participants and the highest PAFs were located in ARMS2 (rs3750846) and CHF (rs570618 and rs10922109). Combining all genetic variants, the total genetic risk score ranged from -3.50 to 4.63 and increased with AMD severity. Of the late AMD patients, 1581 of 1777 (89%) showed a positive total GRS. The complement pathway and ARMS2 were by far the most prominent genetic pathways contributing to late AMD (positive GRS, 90% of patients with late disease), but risk in 3 pathways was most frequent (35% of patients with late disease). Lifestyle was a strong determinant of the outcome in each genetic risk category; unfavorable lifestyle increased the risk of late AMD at least 2-fold. CONCLUSIONS: Genetic risk variants contribute to late AMD in most patients. However, lifestyle factors have a strong influence on the outcome of genetic risk and should be a strong focus in patient management. Genetic risks in ARMS2 and the complement pathway are present in most late AMD patients but are mostly combined with risks in other pathways.

Objective In the current study we aimed to identify metabolites associated with age-related macular degeneration (AMD) by performing the largest metabolome association analysis in AMD to date. In addition, we aimed to determine the effect of AMD-associated genetic variants on metabolite levels, and aimed to investigate associations between the identified metabolites and activity of the complement system, one of the main AMD-associated disease pathways. Design Case-control assocation analysis of metabolomics data. Subjects 2,267 AMD cases and 4,266 controls from five European cohorts. Methods Metabolomics was performed using a high-throughput H-NMR metabolomics platform, which allows the 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/C3 ratio) were investigated using linear regression. Main Outcome Measures Metabolites associated with AMD Results We identified 60 metabolites that were significantly associated with AMD, including increased levels of large and extra-large HDL subclasses and decreased levels of VLDL, amino acids and citrate. Out of 52 AMD-associated genetic variants, seven variants were significantly associated 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, LIPC) with metabolites belonging to the large and extra-large HDL subclasses. In addition, 57 out of 60 metabolites were significantly associated with complement activation levels, and these associations were 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, while 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. Abbreviations AMDAge-related macular degenerationGWASGenome-wide association studyHDLHigh density lipoproteinVLDLVery low density lipoproteinNMRNuclear magnetic resonanceACMEAverage casual effect estimatesOROdds ratiopFDRFalse discovery rate corrected p-valueCIConfindence intervalCVDCardiovascular diseasesPCAPrincipal component analysisSDStandard deviationBMIBody mass indexFDRFalse discovery rateEUGENDAEuropean Genetic DatabaseRSRotterdam StudyALIENORAntioxydants, LIpides Essentiels, Nutrition et maladies OculaiResCORRBICombined Ophthalmic Research Rotterdam BiobankMARSMünster Age and Retina Study. For all metabolite abbreviations please see supplementary table 2

Contains fulltext : 202086.pdf (Publisher’s version ) (Closed access)

The EYE-RISK Consortium = Soufiane Ajana1, Blanca Arango-Gonzalez2, Verena Arndt3, Vaibhav Bhatia4, Shomi S. Bhattacharya4, Marc Biarnés5, Anna Borrell5, Sebastian Bühren6, Sofia M. Calado4, Johanna M. Colijn7,8, Audrey Cougnard-Grégoire1, Sascha Dammeier2, Eiko K. de Jong9, Berta De la Cerda4, Cécile Delcourt1, Anneke I. den Hollander9,10, Francisco J. Diaz-Corrales4, Sigrid Diether2, Eszter Emri11, Tanja Endermann3, Lucia L. Ferraro5, Míriam Garcia5, Thomas J. Heesterbeek9, Sabina Honisch2, Carel B. Hoyng9, Eveline Kersten9, Ellen Kilger2, Caroline C.W. Klaver7,8,9, Hanno Langen12, Imre Lengyel11, Phil Luthert13, Cyrille Maugeais12, Magda Meester-Smoor7,8, Bénédicte M.J. Merle1, Jordi Monés5, Everson Nogoceke12, Tunde Peto14, Frances M. Pool15, Eduardo Rodríguez5, Marius Ueffing2,16, Karl U. Ulrich Bartz-Schmidt2,16, Elisabeth M. van Leeuwen7,8, Timo Verzijden7,8, Markus Zumbansen17. [1 Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, team LEHA, UMR 1219, Bordeaux, France. 2 Centre for Ophthalmology, Institute for Ophthalmic Research, Eberhard Karls University Tuebingen, University Clinic Tuebingen, Tuebingen, Germany. 3 Assay Development, AYOXXA Biosystems GmbH, Cologne, Germany. 4 Department of Regeneration and Cell Therapy, Andalusian Molecular Biology and Regenerative Medicine Centre (CABIMER), Seville, Spain. 5 Barcelona Macula Foundation, Barcelona, Spain. 6 Business Development, AYOXXA Biosystems GmbH, Cologne, Germany. 7 Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands. 8 Department of Ophthalmology, Erasmus Medical Center, Rotterdam, the Netherlands. 9 Department of Ophthalmology, Radboud university medical center, Nijmegen, the Netherlands. 10 Department of Human Genetics, Radboud university medical center, Nijmegen, the Netherlands. 11 Centre for Experimental Medicine, Queen's University Belfast, Belfast, United Kingdom. 12 Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland. 13 Institute of Ophthalmology, University College London, London, United Kingdom. 14 Centre for Public Health, Queen's University Belfast, Belfast, United Kingdom. 15 Ocular biology, UCL Institute of Opthalmology, London, United Kingdom. 16 Department of Ophthalmology, University Medical Centre Tübingen, Tuebingen, Germany. 17 Research and & Development, AYOXXA Biosystems GmbH, Cologne, Germany.]; International audience; PURPOSE: Genetic and epidemiologic studies have shown that lipid genes and High Density Lipoproteins (HDL) are implicated in age-related macular degeneration (AMD). We studied circulating lipid levels in relation to AMD in a large European dataset, and investigated whether this relationship is driven by certain sub fractions. DESIGN: (Pooled) analysis of cross-sectional data. PARTICIPANTS: 30,953 individuals aged 50+ participating in the E3 consortium; and 1530 individuals from the Rotterdam Study with lipid sub fraction data. METHODS: In E3, AMD features were graded per eye on fundus photographs using the Rotterdam Classification. Routine blood lipid measurements were available from each participant. Data on genetics, medication and confounders such as body mass index, were obtained from a common database. In a subgroup of the Rotterdam Study, lipid sub fractions were identified by the Nightingale biomarker platform. Random-intercepts mixed-effects models incorporating confounders and study site as a random-effect were used to estimate the associations. MAIN OUTCOME MEASURES: early, late or any AMD, phenotypic features of early AMD, lipid measurements. RESULTS: HDL was associated with an increased risk of AMD, corrected for potential confounders (Odds Ratio (OR) 1.21 per 1mmol/L increase (95% confidence interval[CI] 1.14-1.29); while triglycerides were associated with a decreased risk (OR 0.94 per 1mmol/L increase [95%CI 0.91-0.97]). Both were associated with drusen size, higher HDL raises the odds of larger drusen while higher triglycerides decreases the odds. LDL-cholesterol only reached statistical significance in the association with early AMD (p=0.045). Regarding lipid sub fractions: the concentration of extra-large HDL particles showed the most prominent association with AMD (OR 1.24 [95%CI 1.10-1.40]). The CETP risk variant (rs17231506) for AMD was in line with increased-HDL levels (p=7.7x10(-7)); but LIPC risk variants (rs2043085, rs2070895) were associated in an opposite way (p=1.0x10(-6) and 1.6x10(-4)). CONCLUSIONS: Our study suggests that HDL-cholesterol is associated with increased risk of AMD and triglycerides negatively associated. Both show the strongest association with early AMD and drusen. Extra-large HDL sub fractions seem to be drivers in the relation with AMD, variants in lipid genes play a more ambiguous role in this association. Whether systemic lipids directly influence AMD or represent lipid metabolism in the retina remains a question to be answered.