Identification of a simple and novel cut-point based CSF and MRI signature for predicting Alzheimer’s disease progression that reinforces the 2018 NIA-AA research framework

Abstract

The 2018 NIA-AA research framework proposes a classification system with beta-Amyloid deposition, pathologic Tau, and neurodegeneration (ATN) for the diagnosis and staging of Alzheimer’s Disease (AD). Data from the ADNI (AD neuroimaging initiative) database can be utilized to identify diagnostic signatures for predicting AD progression, and to determine the utility of this NIA-AA research framework. Profiles of 320 peptides from baseline cerebrospinal fluid (CSF) samples of 287 normal, mild cognitive impairment (MCI) and AD subjects followed over a 3-10 year period were measured via multiple reaction monitoring (MRM) mass spectrometry. CSF Aβ42, total-Tau (tTau), phosphorylated-Tau (pTau-181) and hippocampal volume were also measured. From these candidate markers, optimal diagnostic signatures with decision thresholds to separate AD and normal subjects were first identified via unbiased regression and tree-based algorithms. The best performing signature determined via cross-validation was then tested in an independent group of MCI subjects to predict future progression. This multivariate analysis yielded a simple diagnostic signature comprising CSF pTau-181 to Aβ42 ratio, MRI hippocampal volume and a novel PTPRN peptide, with a decision threshold on each marker. When applied to a separate MCI group at baseline, subjects meeting this signature criteria experience 4.3-fold faster progression to AD compared to a 2.2-fold faster progression using only conventional markers. This novel 4-marker signature represents an advance over the current diagnostics based on widely used marker, and is much easier to use in practice than recently published complex signatures. In addition, this signature reinforces the ATN construct from the 2018 NIA-AA research framework.DisclosuresViswanath Devanarayan is an employee of Charles River Laboratories, and as such owns equity in, receives salary and other compensation from Charles River Laboratories.Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.;Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

VGF in cerebrospinal fluid, when combined with conventional biomarkers, enhances prediction of conversion from mild cognitive impairment to Alzheimer’s Disease

“…Figures 1A-D recapitulates previous analyses by us and others [33, 47–49] showing that Aβ1-42, tTau, pTau-181 and HV are all significantly different in NL and AD subjects and that these values are intermediate for MCI subjects. For all four markers in Figures 1A-D, comparisons of the means between NL and AD groups reveal highly significant differences (p<0.0001 in all cases).…”

“…Methods and data used for this research are similar to those used in Devanarayan et al [33]. The ADNI database (adni.loni.usc.edu) utilized in this research was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD.…”

“…The focus of this work is on the VGF peptide fragment with sequence NSEPQDEGELFQGVDPR (“VGF.NSEP”) since it previously emerged as a strong predictor in a panel of peptides that predict MCI to AD conversion [20], though other VGF peptide fragments are also examined. Unlike our previous studies involving hypothesis-free approaches to identify optimal peptides to include in biomarker signatures [20, 33, 34], the current study was focused on the utility of VGF. Using data from two independent groups in the ADNI cohort: one group of AD and control subjects and a separate group of MCI subjects, it was found that VGF, when combined with conventional biomarkers, enhanced both the diagnostic accuracy of these markers and the ability of these markers to predict MCI to AD conversion.…”

VGF in Cerebrospinal Fluid Combined With Conventional Biomarkers Enhances Prediction of Conversion From MCI to AD

“…Methods and data used for this research are similar to those used in Devanarayan et al [33]. The ADNI database (adni.loni.usc.edu) utilized in this research was launched in 2003 as a publicprivate partnership, led by Principal Investigator Michael W. Weiner, MD.…”

“…CC-BY 4.0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under The copyright holder for this preprint (which was not this version posted January 7, 2019. ; https://doi.org/10.1101/512939 doi: bioRxiv preprint 5 studies involving hypothesis-free approaches to identify optimal peptides to include in biomarker signatures [20,33,34], the current study was focused on the utility of VGF. Using data from two independent groups in the ADNI cohort: one group of AD and control subjects and a separate group of MCI subjects, it was found that VGF, when combined with conventional biomarkers, enhanced both the diagnostic accuracy of these markers and the ability of these markers to predict MCI to AD conversion.…”