CSF complement 3 and factor H are staging biomarkers in Alzheimer's disease.

• Type of Material: Article
• Authors: William Hu, Emory University; Kelly D. Watts, Emory University; Prashant Tailor, Emory University; Trung P. Nguyen, Emory University; Jennifer C. Howell, Emory University; Raven C. Lee, Emory University; Nicholas Seyfried, Emory University; Marla Gearing, Emory University; Chadwick Hales, Emory University; Allan Levey, Emory University; James Lah, Emory University; Eva K. Lee, Georgia Institute of Technology
• Language: English
• Date: 2016
• Publisher: BioMed Central
• Publication Version: Final Published Version
• Copyright Statement: © Hu et al. 2016. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1186/s40478-016-0277-8
• Title of Journal or Parent Work: Acta Neuropathologica Communications
• ISSN: 2051-5960
• Volume: 4
• Issue: 1
• Start Page: 14
• End Page: 14
• Grant/Funding Information: ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from several associations and corporations. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. See the article for additional funding information.; Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904).; This study has been supported by the Viretta Brady Discovery Fund, U01 AG042856, the American Federation for Aging Research, K23 AG016976, and Emory University.
• Keywords: Replication; Machine learning; C3; Tau; Diagnosis; FH; Amyloid beta
• Research Categories: Biology, Neuroscience; Health Sciences, Medicine and Surgery; Chemistry, Biochemistry

Abstract

INTRODUCTION: CSF levels of established Alzheimer's disease (AD) biomarkers remain stable despite disease progression, and non-amyloid non-tau biomarkers have the potential of informing disease stage and progression. We previously identified complement 3 (C3) to be decreased in AD dementia, but this change was not found by others in earlier AD stages. We hypothesized that levels of C3 and associated factor H (FH) can potentially distinguish between mild cognitive impairment (MCI) and dementia stages of AD, but we also found their levels to be influenced by age and disease status. RESULTS: We developed a biochemical/bioinformatics pipeline to optimize the handling of complex interactions between variables in validating biochemical markers of disease. We used data from the Alzheimer's Disease Neuro-imaging Initiative (ADNI, n = 230) to build parallel machine learning models, and objectively tested the models in a test cohort (n = 73) of MCI and mild AD patients independently recruited from Emory University. Whereas models incorporating age, gender, APOE ε4 status, and CSF amyloid and tau levels failed to reliably distinguish between MCI and mild AD in ADNI, introduction of CSF C3 and FH levels reproducibly improved the distinction between the two AD stages in ADNI (p < 0.05) and the Emory cohort (p = 0.014). Within each AD stage, the final model also distinguished between fast vs. slower decliners (p < 0.001 for MCI, p = 0.007 for mild AD), with lower C3 and FH levels associated with more advanced disease and faster progression. CONCLUSIONS: We propose that CSF C3 and FH alterations may reflect stage-associated biomarker changes in AD, and can complement clinician diagnosis in diagnosing and staging AD using the publically available ADNI database as reference.