INTRODUCTION: A screening process that could provide early and accurate diagnosis or prognosis for Alzheimer's disease (AD) would enable earlier intervention, and enable current and future treatments to be more effective. Ocular pathology and changes to vision and ocular function are being investigated for early detection and monitoring of AD. OBJECTIVE: To explore the relationship between pupil flash response (PFR) parameters, AD and brain amyloid plaque burden. METHODS: Nineteen AD and seventy healthy control (HC) participants were recruited from the Australian Imaging, Biomarkers and Lifestyle (AIBL) Flagship Study of Ageing. The potential correlations between PFR parameters and 1) AD and 2) brain amyloid plaque burden in the HC group (as a pre-clinical feature of AD), were investigated in this study. RESULTS: Our results demonstrate statistically significant relationships between PFR parameters, neocortical plaque burden and AD. A logistical model combining PFR parameters provided AD-classification performance with sensitivity 84.1%, specificity 78.3% and area under the curve 89.6%. Furthermore, some of the AD specific PFR parameters were also associated with neocortical plaque burden in pre-clinical AD. CONCLUSIONS: These PFR changes show potential as an adjunct for noninvasive, cost-effective screening for pre-clinical AD.
INTRODUCTION: A screening process that could provide early and accurate diagnosis or prognosis for Alzheimer's disease (AD) would enable earlier intervention, and enable current and future treatments to be more effective. Ocular pathology and changes to vision and ocular function are being investigated for early detection and monitoring of AD. OBJECTIVE: To explore the relationship between pupil flash response (PFR) parameters, AD and brain amyloid plaque burden. METHODS: Nineteen AD and seventy healthy control (HC) participants were recruited from the Australian Imaging, Biomarkers and Lifestyle (AIBL) Flagship Study of Ageing. The potential correlations between PFR parameters and 1) AD and 2) brain amyloid plaque burden in the HC group (as a pre-clinical feature of AD), were investigated in this study. RESULTS: Our results demonstrate statistically significant relationships between PFR parameters, neocortical plaque burden and AD. A logistical model combining PFR parameters provided AD-classification performance with sensitivity 84.1%, specificity 78.3% and area under the curve 89.6%. Furthermore, some of the AD specific PFR parameters were also associated with neocortical plaque burden in pre-clinical AD. CONCLUSIONS: These PFR changes show potential as an adjunct for noninvasive, cost-effective screening for pre-clinical AD.
Authors: Jeremiah K H Lim; Qiao-Xin Li; Zheng He; Algis J Vingrys; Vickie H Y Wong; Nicolas Currier; Jamie Mullen; Bang V Bui; Christine T O Nguyen Journal: Front Neurosci Date: 2016-11-17 Impact factor: 4.677
Authors: Shaun Frost; Liam Robinson; Christopher C Rowe; David Ames; Colin L Masters; Kevin Taddei; Stephanie R Rainey-Smith; Ralph N Martins; Yogesan Kanagasingam Journal: J Ophthalmol Date: 2017-08-16 Impact factor: 1.909