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P4‐118: Identification and Evaluation of a Novel CSF Peptide Signature That is Useful for Both Alzheimer’s Disease State Classification and for Predicting Future Disease Progression
Author(s) -
Llano Daniel,
Bundela Saurabh,
Mudar Raksha,
Devanarayan Viswanath
Publication year - 2016
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2016.06.2209
Subject(s) - alzheimer's disease neuroimaging initiative , disease , peptide , apolipoprotein e , biomarker , false discovery rate , analyte , proteomics , multiplex , medicine , cerebrospinal fluid , oncology , alzheimer's disease , computational biology , bioinformatics , biology , chemistry , chromatography , gene , genetics , biochemistry
Background: To determine if a multi-analyte cerebrospinal fluid (CSF) peptide signature can be used to differentiate Alzheimer Disease (AD) and normal aged controls (NL), and to determine if this same signature can also predict the future progression of MCI subjects to AD, cross-sectional analysis of CSF samples were done on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Methods: The profiles of 320 peptides from baseline samples of 287 subjects that were well-characterized clinically over a 3-6 year period were analyzed using a multiplex multiple reaction monitoring (MRM) panel developed by Caprion Proteomics in collaboration with the Biomarker Consortium Project Team. The raw data were quantile normalized and then log transformed prior to the statistical and predictive modeling. Peptides that were significant on their own were determined via the analysis of covariance, after adjusting for age and gender effects. The optimal combination of peptides (peptide signature) that differentiate AD and NL was developed using a multivariate regularization-based generalized linear model. Performance of this signature was first assessed via 20 iterations of five-fold cross-validation within the training set. This signaturewas then tested on an independent group ofMCI subjects to predict their progression to AD over three years. Results: The peptide most able to differentiate between AD vs. NL was found to be Apolipoprotein E. Other peptides, some of which are not classically associated with AD, such as heart fatty acid binding protein, and the neuronal pentraxin receptor, also differentiated the disease states. A 16-analyte signature was identified which differentiated AD vs. NL with an area under the ROC curve of 0.89, which was better than any combination of amyloid beta (1-42), tau and phospho-181 tau. This same signature also predicted the progression of an independent group of MCI subjects to AD, and an AD-like pattern on this signature resulted in a 3.8-fold faster progression to AD. Conclusions: These data suggest that multivariate peptide signatures from CSF predict MCI to AD progression, and point to potentially new roles for certain proteins not typically associated with AD.