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What Lies beneath Diabetic Macular Edema: Latent Phenotypic Clustering and Differential Treatment Responses to Intravitreal Therapies

Year: 2025

Type: article

Abstract: Purpose: To identify latent phenotypic subgroups of diabetic macular edema (DME) using artificial intelligence-based OCT metrics and evaluate whether treatment responses to anti-VEGF and dexamethasone (DEX) therapies differ across these phenotypic clusters. Methods: Retrospective study including 114 eyes (82 patients) with treatment-naïve DME. Quantitative OCT metrics, including intraretinal fluid (IRF) and subretinal fluid volumes, IRF % distribution within central 0-1, 1-3, and 3-6 mm, hyperreflective foci counts, and ellipsoid zone (EZ) % disruption, were analyzed before and after treatment. Main Outcome Measures: Gaussian finite mixture modeling was used to identify distinct DME subgroups. Changes in visual acuity (VA) and OCT parameters following anti-VEGF or DEX therapy were analyzed using linear and generalized linear mixed-effects models, with false discovery rate correction applied to account for multiple comparisons. Results: ), significant structural disruption (EZ disruption: 46%), and the poorest VA (mean LogMAR: 0.63); and cluster 3 (22%, 95% CI: 13.9%-31.2%), showing intermediate fluid levels and minimal structural damage (EZ disruption: 0.5%). Anti-VEGF therapy led to the greatest VA improvement in cluster 2 (-31.5%, standard deviation: 28.6). Pairwise contrasts showed no significant VA differences between DEX and anti-VEGF in cluster 1 (-26.6%, 95% CI: -64.7 to 11.6) or in cluster 3 (-12.4%, 95% CI: -58.2 to 33.4), although the direction of effect suggested a trend toward greater improvement with DEX. In contrast, cluster 2 showed a nonsignificant difference favoring anti-VEGF (+25.0%, 95% CI: -4.6 to 54.6). For central subfield thickness, DEX achieved a significantly greater reduction than anti-VEGF in cluster 3 (-20.9%, 95% CI: -37.0 to -4.9) and was also associated with a relative increase in peripheral IRF distribution in cluster 3 (+26.7%, 95% CI: 6.5 to 46.9), supporting phenotype-dependent treatment effects. Conclusions: Latent heterogeneity in DME presentations may influence treatment responses. Artificial intelligence-derived spectral-domain OCT metrics could support tailored therapeutic approaches to optimize patient outcomes. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Source: Ophthalmology Science

DOI: 10.1016/j.xops.2025.100975