Blood-Based Protein Biomarkers for Diagnosis of Alzheimer Disease

Archives of neurology (Impact Factor: 7.42). 07/2012; 69(10):1-8. DOI: 10.1001/archneurol.2012.1282
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OBJECTIVE To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD). DESIGN Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data. SETTING General community-based, prospective, longitudinal study of aging. PARTICIPANTS A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort. RESULTS A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, β2 microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD. Cross-validated accuracy measures from the AIBL cohort reached a mean (SD) of 85% (3.0%) for sensitivity and specificity and 93% (3.0) for the area under the receiver operating characteristic curve. A second validation using the ADNI cohort attained accuracy measures of 80% (3.0%) for sensitivity and specificity and 85% (3.0) for area under the receiver operating characteristic curve. CONCLUSIONS This study identified a panel of plasma biomarkers that distinguish individuals with AD from cognitively healthy control subjects with high sensitivity and specificity. Cross-validation within the AIBL cohort and further validation within the ADNI cohort provides strong evidence that the identified biomarkers are important for AD diagnosis.

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Available from: Noel G Faux, Oct 09, 2015
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    • "The last decade has seen the development of several panels of biomarkers in serum or plasma. Proteomics approaches have identified blood-based profiles or signatures that can distinguish between healthy controls, MCI, and AD patients [13] [14] [15] [16] and predict conversion from cognitive impairment to prodromal AD [17] [18]. Lipidomics approaches have also been incorporated into the search for AD biomarkers, and have revealed alterations in lipid metabolism pathways and lipid carrier proteins such as ApoE [19]. "
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    ABSTRACT: Abstract. Accurate blood-based biomarkers of Alzheimer’s disease (AD) could constitute simple, inexpensive, and non-invasive tools for the early diagnosis and treatment of this devastating neurodegenerative disease. We sought to develop a robust AD biomarker panel by identifying alterations in plasma metabolites that persist throughout the continuum of AD pathophysiology. Using a multicenter, cross-sectional study design, we based our analysis on metabolites whose levels were altered both in AD patients and in patients with amnestic mild cognitive impairment (aMCI), the earliest identifiable stage of AD. UPLC coupled to mass spectrometry was used to independently compare the levels of 495 plasma metabolites in aMCI (n = 58) and AD (n = 100) patients with those of normal cognition controls (NC, n = 93). Metabolite alterations common to both aMCI and AD patients were used to generate a logistic regression model that accurately distinguished AD from NC patients. The final panel consisted of seven metabolites: three amino acids (glutamic acid, alanine, and aspartic acid), one non-esterified fatty acid (22:6n-3, DHA), one bile acid (deoxycholic acid), one phosphatidylethanolamine [PE(36:4)], and one sphingomyelin [SM(39:1)]. Detailed analysis ruled out the influence of potential confounding variables, including comorbidities and treatments, on each of the seven biomarkers. The final model accurately distinguished AD from NC patients (AUC, 0.918). Importantly, the model also distinguished aMCI from NC patients (AUC, 0.826), indicating its potential diagnostic utility in early disease stages. These findings describe a sensitive biomarker panel that may facilitate the specific detection of early-stage AD through the analysis of plasma samples.
    Journal of Alzheimer's disease: JAD 02/2015; DOI:10.3233/JAD-142925 · 4.15 Impact Factor
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    • "In the initial Doecke et al. paper, ␤2M was shown to be significantly increased by 1.24 fold (p = 0.006) and was increased in AIBL, ADNI, and TARC datasets [18] [24]. Its relationship to other makers in the plasma proteome was a consistent feature in the Bayesian graphical network analysis. "
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    ABSTRACT: With different approaches to finding prognostic or diagnostic biomarkers for Alzheimer's disease (AD), many studies pursue only brief lists of biomarkers or disease specific pathways, potentially dismissing information from groups of correlated biomarkers. Using a novel Bayesian graphical network method, with data from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging, the aim of this study was to assess the biological connectivity between AD associated blood-based proteins. Briefly, three groups of protein markers (18, 37, and 48 proteins, respectively) were assessed for the posterior probability of biological connection both within and between clinical classifications. Clinical classification was defined in four groups: high performance healthy controls (hpHC), healthy controls (HC), participants with mild cognitive impairment (MCI), and participants with AD. Using the smaller group of proteins, posterior probabilities of network similarity between clinical classifications were very high, indicating no difference in biological connections between groups. Increasing the number of proteins increased the capacity to separate both hpHC and HC apart from the AD group (0 for complete separation, 1 for complete similarity), with posterior probabilities shifting from 0.89 for the 18 protein group, through to 0.54 for the 37 protein group, and finally 0.28 for the 48 protein group. Using this approach, we identified beta-2 microglobulin (β2M) as a potential master regulator of multiple proteins across all classifications, demonstrating that this approach can be used across many data sets to identify novel insights into diseases like AD.
    Journal of Alzheimer's disease: JAD 01/2015; 10(4). DOI:10.3233/JAD-141497 · 4.15 Impact Factor
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    • "For example, in the study by Hu and colleagues, several markers were significantly related to dementia status but in the opposite direction across cohorts despite the use of the same analytic platform. Additionally, several studies have examined the Alzheimer's Disease Neuroimaging Initiative (ADNI) proteomic database with different protein signatures reported [29] [30] [31]. The discrepant findings may be due to the approach employed as the ADNI cohort was utilized as the validation sample with the protein signatures being developed in other cohorts (i.e. "
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    ABSTRACT: The lack of readily available biomarkers is a significant hindrance toward progressing to effective therapeutic and preventative strategies for Alzheimer's disease (AD). Blood-based biomarkers have potential to overcome access and cost barriers and greatly facilitate advanced neuroimaging and cerebrospinal fluid biomarker approaches. Despite the fact that preanalytical processing is the largest source of variability in laboratory testing, there are no currently available standardized preanalytical guidelines. The current international working group provides the initial starting point for such guidelines for standardized operating procedures (SOPs). It is anticipated that these guidelines will be updated as additional research findings become available. The statement provides (1) a synopsis of selected preanalytical methods utilized in many international AD cohort studies, (2) initial draft guidelines/SOPs for preanalytical methods, and (3) a list of required methodological information and protocols to be made available for publications in the field to foster cross-validation across cohorts and laboratories.
    Alzheimer's and Dementia 09/2014; 11(5). DOI:10.1016/j.jalz.2014.08.099 · 12.41 Impact Factor
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