title: Systematic identification of risk factors for Alzheimer's disease through shared genetic architecture and electronic medical records.
abstract: Alzheimer's disease (AD) is one of the leading causes of death for older people in US with rapidly increasing incidence. AD irreversibly and progressively damages the brain, but there are treatments in clinical trials to potentially slow the development of AD. We hypothesize that the presence of clinical traits, sharing common genetic variants with AD, could be used as a non-invasive means to predict AD or trigger for administration of preventative therapeutics. We developed a method to compare the genetic architecture between AD and traits from prior GWAS studies. Six clinical traits were significantly associated with AD, capturing 5 known risk factors and 1 novel association: erythrocyte sedimentation rate (ESR). The association of ESR with AD was then validated using Electronic Medical Records (EMR) collected from Stanford Hospital and Clinics. We found that female patients and with abnormally elevated ESR were significantly associated with higher risk of AD diagnosis (OR: 1.85 [1.32-2.61], p=0.003), within 1 year prior to AD diagnosis (OR: 2.31 [1.06-5.01], p=0.032), and within 1 year after AD diagnosis (OR: 3.49 [1.93-6.31], p<0.0001). Additionally, significantly higher ESR values persist for all time courses analyzed. Our results suggest that ESR should be tested in a specific longitudinal study for association with AD diagnosis, and if positive, could be used as a prognostic marker.
authors: Div. of Systems Medicine, Dept. of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA. l3li@stanford.edu
authors: Li
authors: L
authors: Li
authors: David
authors: D
authors: Ruau
authors: Rong
authors: R
authors: Chen
authors: Susan
authors: S
authors: Weber
authors: Atul J
authors: AJ
authors: Butte
mesh_terms: D000368:Aged; D000544:Alzheimer Disease / Q000097:blood / Q000235:genetics*; D001799:Blood Sedimentation; D016022:Case-Control Studies; D015331:Cohort Studies; D019295:Computational Biology; D030541:Databases, Genetic / Q000706:statistics & numerical data; D057286:Electronic Health Records / Q000706:statistics & numerical data; D005260:Female; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D020641:Polymorphism, Single Nucleotide; D057285:Precision Medicine / Q000706:statistics & numerical data; D011379:Prognosis; D040641:Quantitative Trait Loci; D012307:Risk Factors