Article

Multivariate Protein Signatures of Pre-Clinical Alzheimer's Disease in the Alzheimer's Disease Neuroimaging Initiative (ADNI) Plasma Proteome Dataset

Priority Research Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, The University of Newcastle, Callaghan, New South Wales, Australia.
PLoS ONE (Impact Factor: 3.53). 04/2012; 7(4):e34341. DOI: 10.1371/journal.pone.0034341
Source: PubMed

ABSTRACT Recent Alzheimer's disease (AD) research has focused on finding biomarkers to identify disease at the pre-clinical stage of mild cognitive impairment (MCI), allowing treatment to be initiated before irreversible damage occurs. Many studies have examined brain imaging or cerebrospinal fluid but there is also growing interest in blood biomarkers. The Alzheimer's Disease Neuroimaging Initiative (ADNI) has generated data on 190 plasma analytes in 566 individuals with MCI, AD or normal cognition. We conducted independent analyses of this dataset to identify plasma protein signatures predicting pre-clinical AD.
We focused on identifying signatures that discriminate cognitively normal controls (n = 54) from individuals with MCI who subsequently progress to AD (n = 163). Based on p value, apolipoprotein E (APOE) showed the strongest difference between these groups (p = 2.3 × 10(-13)). We applied a multivariate approach based on combinatorial optimization ((α,β)-k Feature Set Selection), which retains information about individual participants and maintains the context of interrelationships between different analytes, to identify the optimal set of analytes (signature) to discriminate these two groups. We identified 11-analyte signatures achieving values of sensitivity and specificity between 65% and 86% for both MCI and AD groups, depending on whether APOE was included and other factors. Classification accuracy was improved by considering "meta-features," representing the difference in relative abundance of two analytes, with an 8-meta-feature signature consistently achieving sensitivity and specificity both over 85%. Generating signatures based on longitudinal rather than cross-sectional data further improved classification accuracy, returning sensitivities and specificities of approximately 90%.
Applying these novel analysis approaches to the powerful and well-characterized ADNI dataset has identified sets of plasma biomarkers for pre-clinical AD. While studies of independent test sets are required to validate the signatures, these analyses provide a starting point for developing a cost-effective and minimally invasive test capable of diagnosing AD in its pre-clinical stages.

0 Bookmarks
 · 
135 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Research into modeling the progression of Alzheimer's disease (AD) has made recent progress in identifying plasma proteomic biomarkers to identify the disease at the pre-clinical stage. In contrast with cerebral spinal fluid (CSF) biomarkers and PET imaging, plasma biomarker diagnoses have the advantage of being cost-effective and minimally invasive, thereby improving our understanding of AD and hopefully leading to early interventions as research into this subject advances. The Alzheimer's Disease Neuroimaging Initiative* (ADNI) has collected data on 190 plasma analytes from individuals diagnosed with AD as well subjects with mild cognitive impairment and cognitively normal (CN) controls. We propose an approach to classify subjects as AD or CN via an ensemble of classifiers trained and validated on ADNI data. Classifier performance is enhanced by an augmentation of a selective biomarker feature space with principal components obtained from the entire set of biomarkers. This procedure yields accuracy of 89% and area under the ROC curve of 94%.
    Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics; 09/2013
  • [Show abstract] [Hide abstract]
    ABSTRACT: The goal of this study was to identify a clinical biomarker signature of brain amyloidosis in the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) mild cognitive impairment (MCI) cohort. We developed a multimodal biomarker classifier for predicting brain amyloidosis using cognitive, imaging, and peripheral blood protein ADNI1 MCI data. We used CSF β-amyloid 1-42 (Aβ42) ≤192 pg/mL as proxy measure for Pittsburgh compound B (PiB)-PET standard uptake value ratio ≥1.5. We trained our classifier in the subcohort with CSF Aβ42 but no PiB-PET data and tested its performance in the subcohort with PiB-PET but no CSF Aβ42 data. We also examined the utility of our biomarker signature for predicting disease progression from MCI to Alzheimer dementia. The CSF training classifier selected Mini-Mental State Examination, Trails B, Auditory Verbal Learning Test delayed recall, education, APOE genotype, interleukin 6 receptor, clusterin, and ApoE protein, and achieved leave-one-out accuracy of 85% (area under the curve [AUC] = 0.8). The PiB testing classifier achieved an AUC of 0.72, and when classifier self-tuning was allowed, AUC = 0.74. The 36-month disease-progression classifier achieved AUC = 0.75 and accuracy = 71%. Automated classifiers based on cognitive and peripheral blood protein variables can identify the presence of brain amyloidosis with a modest level of accuracy. Such methods could have implications for clinical trial design and enrollment in the near future. This study provides Class II evidence that a classification algorithm based on cognitive, imaging, and peripheral blood protein measures identifies patients with brain amyloid on PiB-PET with moderate accuracy (sensitivity 68%, specificity 78%). © 2015 American Academy of Neurology.
    Neurology 01/2015; 84(7). DOI:10.1212/WNL.0000000000001231 · 8.30 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Cerebrospinal fluid, imaging and blood based biomarkers can provide critical information for dose selection, patient enrichment and supplementary evidence of disease modification in current clinical trials testing experimental therapeutics for Alzheimer's disease (AD). The current treatise provides examples of biomarker strategies utilized in AD clinical trial practice and highlights recent advances towards the identification of non-invasive approaches for enrichment and diagnosis.
    Drug Discovery Today Therapeutic Strategies 03/2014; DOI:10.1016/j.ddstr.2013.09.002

Full-text (3 Sources)

Download
35 Downloads
Available from
May 31, 2014