The Differential Influence of Immune, Endocytotic, and Lipid Metabolism Genes on Amyloid Deposition and Neurodegeneration in Subjects at Risk of Alzheimer’s Disease
Affiliations: [a]
Imperial College London, London, United Kingdom
| [b]
Dublin City University, Dublin, Ireland
| [c] School of Medicine, Cardiff University, Cardiff, United Kingdom
Correspondence:
[*]
Correspondence to:Dr. Paul Edison, MBBS, MRCP, MPhil, PhD, FRCP, FRCPI, Senior Clinical Lecturer, Imperial College London and Visiting Professor, Cardiff University, Neurology Imaging Unit, Imperial College London, 1st Floor, B Block, Hammersmith Hospital Campus, Du Cane Road, London. W12 0NN, UK. Tel.: +442083833725; Fax: +442033134320; E-mail: [email protected].
Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (https://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: https://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
Abstract: Background:Over 20 single-nucleotide polymorphisms (SNPs) are associated with increased risk of Alzheimer’s disease (AD). We categorized these loci into immunity, lipid metabolism, and endocytosis pathways, and associated the polygenic risk scores (PRS) calculated, with AD biomarkers in mild cognitive impairment (MCI) subjects. Objective:The aim of this study was to identify associations between pathway-specific PRS and AD biomarkers in patients with MCI and healthy controls. Methods:AD biomarkers ([18F]Florbetapir-PET SUVR, FDG-PET SUVR, hippocampal volume, CSF tau and amyloid-β levels) and neurocognitive tests scores were obtained in 258 healthy controls and 451 MCI subjects from the ADNI dataset at baseline and at 24-month follow up. Pathway-related (immunity, lipid metabolism, and endocytosis) and total polygenic risk scores were calculated from 20 SNPs. Multiple linear regression analysis was used to test predictive value of the polygenic risk scores over longitudinal biomarker and cognitive changes. Results:Higher immune risk score was associated with worse cognitive measures and reduced glucose metabolism. Higher lipid risk score was associated with increased amyloid deposition and cortical hypometabolism. Total, immune, and lipid scores were associated with significant changes in cognitive measures, amyloid deposition, and brain metabolism. Conclusion:Polygenic risk scores highlights the influence of specific genes on amyloid-dependent and independent pathways; and these pathways could be differentially influenced by lipid and immune scores respectively.
