Related Experiment Video
Updated: Jan 9, 2026
Author Spotlight: Overcoming Anti-VEGF Resistance Through Advanced Vascular Morphology Assessment in Choroidal Neovascularization
Published on: August 11, 2023
What Lies beneath Diabetic Macular Edema: Latent Phenotypic Clustering and Differential Treatment Responses to
Maria Vittoria Cicinelli1,2, Beatrice Leonardo1, Giacomo Maiucci1
1School of Medicine, Vita-Salute San Raffaele University, Milan, Italy.
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:
Three phenotypic clusters of DME were identified, each demonstrating distinct structural and functional characteristics: cluster 1 (29%, 95% confidence interval [CI]: 20.0%-38.4%), characterized by localized central IRF (mean 0.34 mm3, 32% in the 0-1 mm zone), moderate structural damage (EZ disruption: 13%), and better VA (mean logarithm of the minimum angle of resolution [LogMAR] 0.29); cluster 2 (49%, 95% CI: 39.6%-57.9%), with diffuse IRF (60% in the 3-6 mm zone), the highest IRF volume (mean: 3.33 mm3), 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.
