DR KONDA HARSHITHA,DR NITHYA R,DR NIRUMAL KHUMAR,DR. RANJITH MARI
DOI: https://doi.org/Introduction: Parkinson’s disease (PD) is a progressive neurodegenerative disorder associated with motor and non-motor symptoms. Retinal nerve fiber layer (RNFL) thinning has been suggested as a potential biomarker for PD, but its diagnostic utility remains uncertain. This study aims to compare RNFL thickness in PD patients and healthy controls and explore correlations with disease severity, visual function, and comorbid conditions.
Material and Methods: A comparative cross-sectional study was conducted, including 80 PD patients and 80 healthy controls. Demographic and clinical characteristics were recorded. Optical coherence tomography (OCT) was used to measure RNFL thickness in the superior, inferior, nasal, and temporal quadrants. Pearson’s correlation was used to assess the relationship between RNFL thickness, disease severity, and visual function parameters. The diagnostic value of RNFL thinning was analyzed using receiver operating characteristic (ROC) curves.
Results: PD patients exhibited significantly reduced RNFL thickness compared to controls across all quadrants (p < 0.05), with the most pronounced thinning in the inferior (-11.4 µm, p = 0.0008) and nasal (-7.4 µm, p = 0.0021) quadrants. Inferior and nasal quadrant thinning correlated significantly with disease severity (r = 0.305, p = 0.018; r = 0.478, p = 0.0009, respectively). Patients with diabetes and hypertension had greater RNFL thinning than those without comorbidities. The ROC curve analysis showed limited diagnostic value for RNFL thickness alone (AUC = 0.12). No significant correlations were found between RNFL thickness and visual function parameters.
Conclusion: RNFL thickness is significantly reduced in PD patients, with inferior and nasal quadrants showing the most pronounced thinning. RNFL thinning correlates with disease severity but has limited diagnostic value as a standalone biomarker. Comorbid conditions further exacerbate RNFL loss. Future studies should explore multimodal imaging approaches to improve early diagnosis and disease monitoring.
