Machine learning for comprehensive prediction of high risk for Alzheimer’s disease based on chromatic pupilloperimetry

• Published in: Scientific reports Vol. 12; no. 1; p. 9945
• Main Authors: Lustig-Barzelay, Yael, Sher, Ifat, Sharvit-Ginon, Inbal, Feldman, Yael, Mrejen, Michael, Dallasheh, Shada, Livny, Abigail, Schnaider Beeri, Michal, Weller, Aron, Ravona-Springer, Ramit, Rotenstreich, Ygal
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15-06-2022; Nature Publishing Group; Nature Portfolio
• Subjects: 692/308/53/2423; 692/699/375/132/1283; Aged; Alzheimer Disease - diagnosis; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Cognitive ability; Dendrites; Eye; Family medical history; Genetics; Humanities and Social Sciences; Humans; Latency; Learning algorithms; Light; Machine Learning; Magnetic resonance imaging; Melanopsin; Middle Aged; multidisciplinary; Neurodegenerative diseases; Optic nerve; Photic Stimulation - methods; Pupil - physiology; Reflex, Pupillary - physiology; Retina; Retinal ganglion cells; Retinal Ganglion Cells - physiology; Rod Opsins - physiology; Science; Science (multidisciplinary); Visual field
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-13999-0

Currently there are no reliable biomarkers for early detection of Alzheimer's disease (AD) at the preclinical stage. This study assessed the pupil light reflex (PLR) for focal red and blue light stimuli in central and peripheral retina in 125 cognitively normal middle age subjects (45–71 years old) at high risk for AD due to a family history of the disease (FH+), and 61 age-similar subjects with no family history of AD (FH−) using Chromatic Pupilloperimetry coupled with Machine Learning (ML). All subjects had normal ophthalmic assessment, and normal retinal and optic nerve thickness by optical coherence tomography. No significant differences were observed between groups in cognitive function and volumetric brain MRI. Chromatic pupilloperimetry-based ML models were highly discriminative in differentiating subjects with and without AD family history, using transient PLR for focal red (primarily cone-mediated), and dim blue (primarily rod-mediated) light stimuli. Features associated with transient pupil response latency (PRL) achieved Area Under the Curve Receiver Operating Characteristic (AUC-ROC) of 0.90 ± 0.051 (left-eye) and 0.87 ± 0.048 (right-eye). Parameters associated with the contraction arm of the rod and cone-mediated PLR were more discriminative compared to parameters associated with the relaxation arm and melanopsin-mediated PLR. Significantly shorter PRL for dim blue light was measured in the FH+ group in two test targets in the temporal visual field in right eye that had highest relative weight in the ML algorithm (mean ± standard error, SE 0.449 s ± 0.007 s vs. 0.478 s ± 0.010 s, p = 0.038). Taken together our study suggests that subtle focal changes in pupil contraction latency may be detected in subjects at high risk to develop AD, decades before the onset of AD clinical symptoms. The dendrites of melanopsin containing retinal ganglion cells may be affected very early at the preclinical stages of AD.