Pupil response biomarkers for early detection and monitoring of Alzheimer's disease

Authors

• Shaun Frost
• Yogesan Kanagasingam
• Hamid R. Sohrabi, Edith Cowan UniversityFollow
• Pierrick Bourgeat
• Victor Villemagne
• Christopher Rowe
• S Lance Macaulay
• Cassandra Szoeke
• Kathryn Ellis
• David Ames
• Colin Masters
• Stephanie R. Rainey-Smith, Edith Cowan UniversityFollow
• Ralph N. Martins, Edith Cowan UniversityFollow

Document Type

Journal Article

Keywords

Alzheimer’s, aging, diagnosis, pupil, screening, vision

Publisher

Bentham Science Publishers

Faculty

Faculty of Health, Engineering and Science

School

School of Medical Sciences / Centre of Excellence for Alzheimer's Disease Research and Care

RAS ID

17171

Comments

Frost, S., Kanagasingam, Y., Sohrabi, H. R., Bourgeat, P., Villemagne, V., Rowe, C., Macaulay, S., Szoeke, C., Ellis, K., Ames, D., Masters, C., Rainey-Smith, S. R., & Martins, R. N. (2013). Pupil Response Biomarkers for Early Detection and Monitoring of Alzheimer's Disease. Current Alzheimer Research, 10(9), 931-939. Available here

Abstract

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 NineteenADandseventyhealthy control (HC) participants were recruited from the Australian Imaging, Biomarkers and Lifestyle (AIBL) Flagship Study of Ageing. Thepotential correlations betweenPFRparameters and1) 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 demonstratestatistically 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 withneocortical plaque burden in pre-clinical AD. Conclusions These PFR changes show potential as an adjunct for noninvasive, cost-effective screening for pre-clinical AD.