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.
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http://dx.doi.org/10.3233/JAD-200578 | DOI Listing |
Diabetes Care
February 2025
Division of Blood Disorders and Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, GA.
Objective: The goal of this study was to assess the additive value of considering type 2 diabetes (T2D) polygenic risk score (PRS) in addition to family history for T2D prediction.
Research Design And Methods: Data were obtained from the All of Us (AoU) research database. First-degree T2D family history was self-reported on the personal family history health questionnaire.
JAMA
January 2025
Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
Importance: Chronic obstructive pulmonary disease (COPD) is often undiagnosed. Although genetic risk plays a significant role in COPD susceptibility, its utility in guiding spirometry testing and identifying undiagnosed cases is unclear.
Objective: To determine whether a COPD polygenic risk score (PRS) enhances the identification of undiagnosed COPD beyond a case-finding questionnaire (eg, the Lung Function Questionnaire) using conventional risk factors and respiratory symptoms.
Am J Hematol
January 2025
Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China.
Phenotypic age acceleration (PhenoAgeAccel) is a novel clinical aging indicator. This study was carried out to investigate the relationship between PhenoAgeAccel and the incidence of VTE, as well as to integrate PhenoAgeAccel with genetic susceptibility to improve risk stratification of VTE. The study included 394 041 individuals from the UK Biobank.
View Article and Find Full Text PDFBrain Commun
January 2025
Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap) at the University of Gothenburg, Mölndal 43139, Sweden.
Atrial fibrillation and heart failure have both been suggested to increase stroke and dementia risk. However, in observational studies, reversed causation and unmeasured confounding may occur. To mitigate these issues, this study aims to investigate if higher genetic risk for atrial fibrillation and heart failure increases dementia and stroke risk.
View Article and Find Full Text PDFFront Med (Lausanne)
January 2025
International Laboratory of Bioinformatics, AI and Digital Sciences Institute, Faculty of Computer Science, HSE University, Moscow, Russia.
Background: Polygenic risk score (PRS) prediction is widely used to assess the risk of diagnosis and progression of many diseases. Routinely, the weights of individual SNPs are estimated by the linear regression model that assumes independent and linear contribution of each SNP to the phenotype. However, for complex multifactorial diseases such as Alzheimer's disease, diabetes, cardiovascular disease, cancer, and others, association between individual SNPs and disease could be non-linear due to epistatic interactions.
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