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Cluster Analysis and Genotype–Phenotype Assessment of Geographic Atrophy in Age-Related Macular Degeneration

Age-Related Eye Disease Study 2 Report 25

      Purpose

      To explore whether phenotypes in geographic atrophy (GA) secondary to age-related macular degeneration can be separated into 2 or more partially distinct subtypes and if these have different genetic associations. This is important because distinct GA subtypes associated with different genetic factors might require customized therapeutic approaches.

      Design

      Cluster analysis of participants within a controlled clinical trial, followed by assessment of phenotype–genotype associations.

      Participants

      Age-Related Eye Disease Study 2 participants with incident GA during study follow-up: 598 eyes of 598 participants.

      Methods

      Phenotypic features from reading center grading of fundus photographs were subjected to cluster analysis, by k-means and hierarchical methods, in cross-sectional analyses (using 15 phenotypic features) and longitudinal analyses (using 14 phenotypic features). The identified clusters were compared by 4 pathway-based genetic risk scores (complement, extracellular matrix, lipid, and ARMS2). The analyses were repeated in reverse (clustering by genotype and comparison by phenotype).

      Main Outcome Measures

      Characteristics and quality of cluster solutions, assessed by Calinski-Harabasz scores, unexplained variance, and consistency; and genotype–phenotype associations, assessed by t test.

      Results

      In cross-sectional phenotypic analyses, k-means identified 2 clusters (labeled A and B), whereas hierarchical clustering identified 4 clusters (C-F); cluster membership differed principally by GA configuration but in few other ways. In longitudinal phenotypic analyses, k-means identified 2 clusters (G and H) that differed principally by smoking status but in few other ways. These 3 sets of cluster divisions were not similar to each other (r ≤ 0.20). Despite adequate power, pairwise cluster comparison by the 4 genetic risk scores demonstrated no significant differences (P > 0.05 for all). In clustering by genotype, k-means identified 2 clusters (I and J). These differed principally at ARMS2, but no significant genotype–phenotype associations were observed (P > 0.05 for all).

      Conclusions

      Phenotypic clustering resulted in GA subtypes defined principally by GA configuration in cross-sectional analyses, but these were not replicated in longitudinal analyses. These negative findings, together with the absence of significant phenotype–genotype associations, indicate that GA phenotypes may vary continuously across a spectrum, rather than consisting of distinct subtypes that arise from separate genetic causes.

      Abbreviations and Acronyms:

      AMD (age-related macular degeneration), AREDS2 (Age-Related Eye Disease Study 2), BCVA (best-corrected visual acuity), CART (classification and regression tree), DHA (docosahexaenoic acid), EPA (eicosapentaenoic acid), GA (geographic atrophy), IQR (interquartile range), LASSO (least absolute shrinkage and selection operator), OCT (optical coherence tomograph), RPE (retinal pigment epithelium), RPD (reticular pseudodrusen), SNP (single nucleotide polymorphism)
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      References

        • Ammar M.J.
        • Hsu J.
        • Chiang A.
        • et al.
        Age-related macular degeneration therapy: a review.
        Curr Opin Ophthalmol. 2020; 31: 215-221
        • Liao D.S.
        • Grossi F.V.
        • El Mehdi D.
        • et al.
        Complement C3 inhibitor pegcetacoplan for geographic atrophy secondary to age-related macular degeneration: a randomized phase 2 trial.
        Ophthalmology. 2020; 127: 186-195
        • Jaffe G.J.
        • Westby K.
        • Csaky K.G.
        • et al.
        C5 inhibitor avacincaptad pegol for geographic atrophy due to age-related macular degeneration: a randomized pivotal phase 2/3 trial.
        Ophthalmology. 2021; 128: 576-586
        • Guymer R.H.
        • Wu Z.
        • Hodgson L.A.B.
        • et al.
        Subthreshold nanosecond laser intervention in age-related macular degeneration: the LEAD randomized controlled clinical trial.
        Ophthalmology. 2019; 126: 829-838
        • Nebbioso M.
        • Lambiase A.
        • Cerini A.
        • et al.
        Therapeutic approaches with intravitreal injections in geographic atrophy secondary to age-related macular degeneration: current drugs and potential molecules.
        Int J Mol Sci. 2019; 20: 1693
        • Biarnes M.
        • Colijn J.M.
        • Sousa J.
        • et al.
        Genotype- and phenotype-based subgroups in geographic atrophy secondary to age-related macular degeneration: the EYE-RISK Consortium.
        Ophthalmol Retina. 2020; 4: 1129-1137
        • Keenan T.D.
        • Agron E.
        • Domalpally A.
        • et al.
        Progression of geographic atrophy in age-related macular degeneration: AREDS2 report number 16.
        Ophthalmology. 2018; 125: 1913-1928
        • Grassmann F.
        • Harsch S.
        • Brandl C.
        • et al.
        Assessment of novel genome-wide significant gene loci and lesion growth in geographic atrophy secondary to age-related macular degeneration.
        JAMA Ophthalmol. 2019; 137: 867-876
        • Keenan T.D.L.
        The hitchhiker’s guide to cluster analysis: multi pertransibunt et augebitur scientia.
        Ophthalmol Retina. 2020; 4: 1125-1128
        • Chew E.Y.
        • Clemons T.
        • et al.
        • Age-Related Eye Disease Study 2 Research Group
        The Age-Related Eye Disease Study 2 (AREDS2): study design and baseline characteristics (AREDS2 report number 1).
        Ophthalmology. 2012; 119: 2282-2289
        • Danis R.P.
        • Domalpally A.
        • Chew E.Y.
        • et al.
        Methods and reproducibility of grading optimized digital color fundus photographs in the Age-Related Eye Disease Study 2 (AREDS2 report number 2).
        Invest Ophthalmol Vis Sci. 2013; 54: 4548-4554
        • Sunness J.S.
        • Bressler N.M.
        • Tian Y.
        • et al.
        Measuring geographic atrophy in advanced age-related macular degeneration.
        Invest Ophthalmol Vis Sci. 1999; 40: 1761-1769
        • Keenan T.D.L.
        • Chen Q.
        • Peng Y.
        • et al.
        Deep learning automated detection of reticular pseudodrusen from fundus autofluorescence images or color fundus photographs in AREDS2.
        Ophthalmology. 2020; 127: 1674-1687
        • Fritsche L.G.
        • Igl W.
        • Bailey J.N.
        • et al.
        A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants.
        Nat Genet. 2016; 48: 134-143
        • Christian Hennig
        • Marina Meila
        • Fionn Murtagh
        • Roberto Rocci
        Handbook of Cluster Analysis. CRC Press (Taylor & Francis Group), New York2015
        • Calinski T.
        • Harabasz J.
        A dendrite method for cluster analysis.
        Commun Stat. 1974; 3: 1-27
        • Mantel N.
        The detection of disease clustering and a generalized regression approach.
        Cancer Res. 1967; 27: 209-220
        • Keenan T.D.
        • Agron E.
        • Domalpally A.
        • et al.
        Progression of geographic atrophy in age-related macular degeneration: AREDS2 report number 16.
        Ophthalmology. 2018; 125: 1913-1928
        • Sadda S.R.
        • Guymer R.
        • Holz F.G.
        • et al.
        Consensus definition for atrophy associated with age-related macular degeneration on OCT: Classification of Atrophy report 3.
        Ophthalmology. 2018; 125: 537-548