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001030442 1001_ $$0P:(DE-Juel1)133864$$aBeer, Simone$$b0$$eCorresponding author$$ufzj
001030442 245__ $$aExplainable artificial intelligence identifies an AQP4 polymorphism-based risk score associated with brain amyloid burden
001030442 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2024
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001030442 500__ $$aData collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012), as well as by the A4 study (a4study.org).
001030442 520__ $$aAquaporin-4 (AQP4) is hypothesized to be a component of the glymphatic system, a pathway for removing brain interstitial solutes like amyloid-β (Aβ). Evidence exists that genetic variation of AQP4 impacts Aβ clearance, clinical outcome in Alzheimer’s disease as well as sleep measures. We examined whether a risk score calculated from several AQP4 single-nucleotide polymorphisms (SNPs) is related to Aβ neuropathology in older cognitively unimpaired white individuals. We used a machine learning approach and explainable artificial intelligence to extract information on synergistic effects of AQP4 SNPs on brain amyloid burden from the ADNI cohort. From this information, we formulated a sex-specific AQP4 SNP-based risk score and evaluated it using data from the screening process of the A4 study. We found in both cohorts significant associations of the risk score with brain amyloid burden. The results support the hypothesis of an involvement of the glymphatic system, and particularly AQP4, in brain amyloid aggregation pathology. They suggest also that different AQP4 SNPs exert a synergistic effect on the build-up of brain amyloid burden.
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001030442 7001_ $$0P:(DE-Juel1)131679$$aElmenhorst, David$$b1$$ufzj
001030442 7001_ $$0P:(DE-Juel1)166265$$aBischof, Gerard N.$$b2$$ufzj
001030442 7001_ $$0P:(DE-HGF)0$$aRamirez, Alfredo$$b3
001030442 7001_ $$0P:(DE-Juel1)131672$$aBauer, Andreas$$b4$$ufzj
001030442 7001_ $$0P:(DE-Juel1)177611$$aDrzezga, Alexander$$b5$$ufzj
001030442 773__ $$0PERI:(DE-600)1498414-3$$a10.1016/j.neurobiolaging.2024.08.002$$gVol. 143, p. 19 - 29$$p19 - 29$$tNeurobiology of aging$$v143$$x0197-4580$$y2024
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