Keenan TDL, Oden NL, Agrón E, Clemons TE, Henning A, Fritsche LG, Wong WT, Chew EY. Cluster analysis and genotype-phenotype assessment of geographic atrophy in age-related macular degeneration: AREDS2 Report 25.
Ophthalmol Retina 2021;
5:1061-1073. [PMID:
34325054 DOI:
10.1016/j.oret.2021.07.006]
[Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 06/25/2021] [Accepted: 07/20/2021] [Indexed: 11/16/2022]
Abstract
PURPOSE
To explore whether phenotypes in geographic atrophy (GA) secondary to age-related macular degeneration (AMD) can be separable into two or more partially distinct subtypes and if these have different genetic associations. This is important since the discovery of 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
AREDS2 participants with incident GA during study follow-up: 598 eyes of 598 participants (median age 75.7y).
METHODS
Phenotypic features from reading center grading of fundus photographs were subjected to cluster analysis, by both k-means and hierarchical methods, in cross-sectional analyses (using 15 phenotypic features assessed principally at GA emergence) and longitudinal analyses (using 14 phenotypic features). In pre-specified hypothesis tests, identified clusters were compared by four pathway-based genetic risk scores (complement, extracellular matrix, lipid, and ARMS2). The analyses were repeated in reverse, i.e., 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; genotype-phenotype associations, assessed by t test.
RESULTS
In cross-sectional phenotypic analyses, k-means identified two clusters (labeled A, B), while hierarchical clustering identified four (C-F); A-E membership differed principally by GA configuration but in relatively few other ways. In longitudinal phenotypic analyses, k-means identified two clusters (G, H), which differed principally by smoking status but in relatively few other ways. These three sets of cluster divisions were not similar to each other (r ≤ 0.20). Despite adequate power, pairwise cluster comparison by the four genetic risk scores demonstrated no significant differences (p>0.05 for all). In clustering by genotype, k-means identified two clusters (I/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 etiologies.
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