Explainable artificial intelligence identifies an AQP4 polymorphism-based risk score associated with brain amyloid burden

Abstract

Aquaporin-4 (AQP4) is hypothesized to be a component of the glymphatic system, a pathway for removing brain interstitial solutes like amyloid-β (Aβ). Evidence exists that genetic variation of AQP4 impacts Aβ clearance, clinical outcome in Alzheimer's disease as well as sleep measures. We examined whether a risk score calculated from several AQP4 single-nucleotide polymorphisms (SNPs) is related to Aβ neuropathology in older cognitively unimpaired white individuals. We used a machine learning approach and explainable artificial intelligence to extract information on synergistic effects of AQP4 SNPs on brain amyloid burden from the ADNI cohort. From this information, we formulated a sex-specific AQP4 SNP-based risk score and evaluated it using data from the screening process of the A4 study. We found in both cohorts significant associations of the risk score with brain amyloid burden. The results support the hypothesis of an involvement of the glymphatic system, and particularly AQP4, in brain amyloid aggregation pathology. They suggest also that different AQP4 SNPs exert a synergistic effect on the build-up of brain amyloid burden.

Keywords: Alzheimer's disease; Amyloid-β; Aquaporin-4; Explainable artificial intelligence; Glymphatic system.