The Utilization of Retinal Nerve Fiber Layer Thickness to Predict Cognitive Deterioration.

Our previous studies have shown that longitudinal reduction in retinal nerve fiber layer (RNFL) thickness is associated with cognitive deterioration. However, whether the combination of longitudinal reduction in RNFL thickness with baseline episodic memory performance can better predict cognitive deterioration remains unknown. Therefore, we set out to re-analyze the data obtained from our previous studies with 78 elderly adults (mean age 74.4 ± 3.83 years, 48.7% male) in the community over a 25-month period. The participants were categorized as either stable participants whose cognitive status did not change (n = 60) or converted participants whose cognitive status deteriorated (n = 18). A logistic regression analysis was applied to determine a conversion score for predicting the cognitive deterioration in the participants. We found that the area under the receiver operating characteristic curve (AUC) for the multivariable model was 0.854 (95% CI 0.762-0.947) using baseline story recall as a predictor, but the AUC increased to 0.915 (95% CI 0.849-0.981) with the addition of the longitudinal reduction of RNFL thickness in the inferior quadrant. The conversion score was significantly higher for the converted participants than the stable participants (0.59 ± 0.30 versus 0.12 ± 0.19, p <  0.001). Finally, the optimal cutoff value of the conversion score (0.134) was determined by the analysis of receiver operating characteristic curve, and this conversion score generated a sensitivity of 0.944 and a specificity of 0.767 in predicting the cognitive deterioration. These findings have established a system to perform a larger scale study to further test whether the longitudinal reduction in RNFL thickness could serve as a biomarker of Alzheimer's disease.