Identifying amyloid pathology-related cerebrospinal fluid biomarkers for Alzheimer's disease in a multicohort study

• Authors: Yuk Yee Leung, University of Pennsylvania; Jon B. Toledo, University of Pennsylvania; Alexey Nefedov, University of Pennsylvania; Robi Polikar, Rowan University; Nandini Raghavan, Janssen Research & Development, LLC; Sharon X. Xie, University of Pennsylvania; Michael Farnum, Janssen Research & Development, LLC; Tim Schultz, Janssen Research & Development, LLC; Young Baek, University of Pennsylvania; Vivianna M. Van Deerlin, University of Pennsylvania; William Hu, Emory University; David M. Holtzman, Washington University in St. Louis; Anne M. Fagan, Washington University in St. Louis; Richard J. Perrin, Washington University in St. Louis; Murray Grossman, University of Pennsylvania; Holly D. Soares, Bristol-Meyer Squibb; Mitchel A. Kling, University of Pennsylvania; Matthew Mailman, Janssen Research & Development, LLC; Steven E. Arnold, University of Pennsylvania; Vaibhav Narayan, Janssen Research & Development, LLC; Virginia M-Y. Lee, University of Pennsylvania; Leslie M. Shaw, University of Pennsylvania; David Baker, Janssen Research & Development, LLC; Gayle M. Wittenberg, Janssen Research & Development, LLC; John Q. Trojanowski, University of Pennsylvania; Li-San Wang, University of Pennsylvania
• Journal Title: Alzheimer's and Dementia
• Volume: Volume 1, Number 3
• Publisher: Elsevier | 2015-09-01, Pages 339-348
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1016/j.dadm.2015.06.008
• Subjects: Biology, Neuroscience; Health Sciences, Pathology
• Keywords: Alzheimer’s disease; Amyloid beta; Biomarkers; Cerebrospinal fluid; Cognitive impairment; Dementia

Abstract

Introduction: The dynamic range of cerebrospinal fluid (CSF) amyloid β (Aβ1–42) measurement does not parallel to cognitive changes in Alzheimer's disease (AD) and cognitively normal (CN) subjects across different studies. Therefore, identifying novel proteins to characterize symptomatic AD samples is important. Methods: Proteins were profiled using a multianalyte platform by Rules Based Medicine (MAP-RBM). Due to underlying heterogeneity and unbalanced sample size, we combined subjects (344 AD and 325 CN) from three cohorts: Alzheimer's Disease Neuroimaging Initiative, Penn Center for Neurodegenerative Disease Research of the University of Pennsylvania, and Knight Alzheimer's Disease Research Center at Washington University in St. Louis. We focused on samples whose cognitive and amyloid status was consistent. We performed linear regression (accounted for age, gender, number of apolipoprotein E (APOE) e4 alleles, and cohort variable) to identify amyloid-related proteins for symptomatic AD subjects in this largest ever CSF–based MAP-RBM study. ANOVA and Tukey's test were used to evaluate if these proteins were related to cognitive impairment changes as measured by mini-mental state examination (MMSE). Results: Seven proteins were significantly associated with Aβ1–42 levels in the combined cohort (false discovery rate adjusted P < .05), of which lipoprotein a (Lp(a)), prolactin (PRL), resistin, and vascular endothelial growth factor (VEGF) have consistent direction of associations across every individual cohort. VEGF was strongly associated with MMSE scores, followed by pancreatic polypeptide and immunoglobulin A (IgA), suggesting they may be related to staging of AD. Discussion: Lp(a), PRL, IgA, and tissue factor/thromboplastin have never been reported for AD diagnosis in previous individual CSF–based MAP-RBM studies. Although some of our reported analytes are related to AD pathophysiology, other's roles in symptomatic AD samples worth further explorations.