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Identification of SPARC-like 1 Protein as Part of a Biomarker Panel for Alzheimer's Disease in Cerebrospinal Fluid

Vafadar-Isfahani, Baharak; Ball, Graham; Coveney, Clare; Lemetre, Christophe; Boocock, David; Minthon, Lennart LU ; Hansson, Oskar LU ; Miles, Amanda Kathleen; Janciauskiene, Sabina M. and Warden, Donald, et al. (2012) In Journal of Alzheimer's Disease 28(3). p.625-636
Abstract
We have used proteomic fingerprinting to investigate diagnosis of Alzheimer's disease (AD). Samples of lumbar cerebrospinal fluid (CSF) from clinically-diagnosed AD cases (n = 33), age-matched controls (n = 20), and mild cognitive impairment (MCI) patients (n = 10) were used to obtain proteomic profiles, followed by bioinformatic analysis that generated a set of potential biomarkers in CSF samples that could discriminate AD cases from controls. The identity of the biomarker ions was determined using mass spectroscopy. The panel of seven peptide biomarker ions was able to discriminate AD patients from controls with a median accuracy of 95% (sensitivity 85%, specificity 97%). When this model was applied to an independent blind dataset from... (More)
We have used proteomic fingerprinting to investigate diagnosis of Alzheimer's disease (AD). Samples of lumbar cerebrospinal fluid (CSF) from clinically-diagnosed AD cases (n = 33), age-matched controls (n = 20), and mild cognitive impairment (MCI) patients (n = 10) were used to obtain proteomic profiles, followed by bioinformatic analysis that generated a set of potential biomarkers in CSF samples that could discriminate AD cases from controls. The identity of the biomarker ions was determined using mass spectroscopy. The panel of seven peptide biomarker ions was able to discriminate AD patients from controls with a median accuracy of 95% (sensitivity 85%, specificity 97%). When this model was applied to an independent blind dataset from MCI patients, the intensity of signals was intermediate between the control and AD patients implying that these markers could potentially predict patients with early neurodegenerative disease. The panel were identified, in order of predictive ability, as SPARC-like 1 protein, fibrinogen alpha chain precursor, amyloid-beta, apolipoprotein E precursor, serum albumin precursor, keratin type I cytoskeletal 9, and tetranectin. The 7 ion ANN model was further validated using an independent cohort of samples, where the model was able to classify AD cases from controls with median accuracy of 84.5% (sensitivity 93.3%, specificity 75.7%). Validation by immunoassay was performed on the top three identified markers using the discovery samples and an independent sample cohort which was from postmortem confirmed AD patients (n = 17). (Less)
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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Alzheimer's disease, biomarker, MALDI-MS, SPARCL1
in
Journal of Alzheimer's Disease
volume
28
issue
3
pages
625 - 636
publisher
IOS Press
external identifiers
  • wos:000300414500013
  • scopus:84863229780
ISSN
1387-2877
DOI
10.3233/JAD-2011-111505
language
English
LU publication?
yes
id
3ed912d8-ff3f-4d1f-b78f-ccc98271e080 (old id 2403269)
date added to LUP
2012-04-02 09:28:17
date last changed
2017-08-20 03:25:32
@article{3ed912d8-ff3f-4d1f-b78f-ccc98271e080,
  abstract     = {We have used proteomic fingerprinting to investigate diagnosis of Alzheimer's disease (AD). Samples of lumbar cerebrospinal fluid (CSF) from clinically-diagnosed AD cases (n = 33), age-matched controls (n = 20), and mild cognitive impairment (MCI) patients (n = 10) were used to obtain proteomic profiles, followed by bioinformatic analysis that generated a set of potential biomarkers in CSF samples that could discriminate AD cases from controls. The identity of the biomarker ions was determined using mass spectroscopy. The panel of seven peptide biomarker ions was able to discriminate AD patients from controls with a median accuracy of 95% (sensitivity 85%, specificity 97%). When this model was applied to an independent blind dataset from MCI patients, the intensity of signals was intermediate between the control and AD patients implying that these markers could potentially predict patients with early neurodegenerative disease. The panel were identified, in order of predictive ability, as SPARC-like 1 protein, fibrinogen alpha chain precursor, amyloid-beta, apolipoprotein E precursor, serum albumin precursor, keratin type I cytoskeletal 9, and tetranectin. The 7 ion ANN model was further validated using an independent cohort of samples, where the model was able to classify AD cases from controls with median accuracy of 84.5% (sensitivity 93.3%, specificity 75.7%). Validation by immunoassay was performed on the top three identified markers using the discovery samples and an independent sample cohort which was from postmortem confirmed AD patients (n = 17).},
  author       = {Vafadar-Isfahani, Baharak and Ball, Graham and Coveney, Clare and Lemetre, Christophe and Boocock, David and Minthon, Lennart and Hansson, Oskar and Miles, Amanda Kathleen and Janciauskiene, Sabina M. and Warden, Donald and Smith, A. David and Wilcock, Gordon and Kalsheker, Noor and Rees, Robert and Matharoo-Ball, Balwir and Morgan, Kevin},
  issn         = {1387-2877},
  keyword      = {Alzheimer's disease,biomarker,MALDI-MS,SPARCL1},
  language     = {eng},
  number       = {3},
  pages        = {625--636},
  publisher    = {IOS Press},
  series       = {Journal of Alzheimer's Disease},
  title        = {Identification of SPARC-like 1 Protein as Part of a Biomarker Panel for Alzheimer's Disease in Cerebrospinal Fluid},
  url          = {http://dx.doi.org/10.3233/JAD-2011-111505},
  volume       = {28},
  year         = {2012},
}