Comparative Analysis of Lamina Parameters in Nonglaucomatous Eyes With and Without Pseudoexfoliation Syndrome

초록

center dot OBJECTIVE: To compare lamina cribrosa (LC) parameters in nonglaucomatous eyes with pseudoexfoliation syndrome (PXFS) and healthy control eyes to assess structural alterations that may contribute to early glaucoma pathophysiology. center dot DESIGN: Retrospective, cross-sectional study. center dot PARTICIPANTS: Fifty eyes with non-glaucomatous PXFS and 50 healthy, age-matched control eyes. center dot METHODS: All participants underwent spectral-domain optical coherence tomography with enhanced-depth imaging. LC parameters were measured at the superior mid-peripheral, central, and inferior mid-peripheral regions. Comparisons between groups were assessed with paired t-tests for continuous data and the McNemar test for categorical data. Mixed effects model analysis was utilized to identify factors associated with lamina cribrosa thickness (LCT). center dot MAIN OUTCOME MEASURES: Measurements of LCT, lamina cribrosa curvature depth (LCCD), curvature index (LCCI), and prelaminar tissue thickness (PLTT). center dot RESULTS: Eyes with PXFS exhibited significantly thinner LCT than controls across all regions: superior mid-peripheral (163.85 +/- 39.13 mu m vs. 222.18 +/- 38.03 mu m), central (172.95 +/- 40.63 mu m vs. 220.17 +/- 51.77 mu m), and inferior mid-peripheral (165.58 +/- 35.18 mu m vs. 217.73 +/- 44.00 mu m). No significant differences were observed in PLTT, LCCD and LCCI between the 2 groups. Multivariable mixed effects model analysis identified the presence of PXFS (beta =-50.687) as an independent factor associated with LCT. center dot CONCLUSION: PXFS eyes show significant LC thinning, potentially representing early structural changes preceding glaucoma. Monitoring LC parameters in PXFS eyes may facilitate early detection of patients at risk of disease progression. (Am J Ophthalmol 2025;274: 163-170. (c) 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.)

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