A Blood-Based Screening Tool for Alzheimer's Disease That Spans Serum and Plasma: Findings from TARC and ADNI

Department of Neurology, F. Marie Hall Institute for Rural and Community Health, Texas Tech University Health Sciences Center, Lubbock, Texas, United States of America.
PLoS ONE (Impact Factor: 3.23). 12/2011; 6(12):e28092. DOI: 10.1371/journal.pone.0028092
Source: PubMed


There is no rapid and cost effective tool that can be implemented as a front-line screening tool for Alzheimer's disease (AD) at the population level.
To generate and cross-validate a blood-based screener for AD that yields acceptable accuracy across both serum and plasma.
Analysis of serum biomarker proteins were conducted on 197 Alzheimer's disease (AD) participants and 199 control participants from the Texas Alzheimer's Research Consortium (TARC) with further analysis conducted on plasma proteins from 112 AD and 52 control participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The full algorithm was derived from a biomarker risk score, clinical lab (glucose, triglycerides, total cholesterol, homocysteine), and demographic (age, gender, education, APOE*E4 status) data.
Alzheimer's disease.
11 proteins met our criteria and were utilized for the biomarker risk score. The random forest (RF) biomarker risk score from the TARC serum samples (training set) yielded adequate accuracy in the ADNI plasma sample (training set) (AUC = 0.70, sensitivity (SN) = 0.54 and specificity (SP) = 0.78), which was below that obtained from ADNI cerebral spinal fluid (CSF) analyses (t-tau/Aβ ratio AUC = 0.92). However, the full algorithm yielded excellent accuracy (AUC = 0.88, SN = 0.75, and SP = 0.91). The likelihood ratio of having AD based on a positive test finding (LR+) = 7.03 (SE = 1.17; 95% CI = 4.49-14.47), the likelihood ratio of not having AD based on the algorithm (LR-) = 3.55 (SE = 1.15; 2.22-5.71), and the odds ratio of AD were calculated in the ADNI cohort (OR) = 28.70 (1.55; 95% CI = 11.86-69.47).
It is possible to create a blood-based screening algorithm that works across both serum and plasma that provides a comparable screening accuracy to that obtained from CSF analyses.

Download full-text


Available from: Robert Clinton Barber, Oct 04, 2015
31 Reads
  • Source
    • "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]. "
    [Show abstract] [Hide abstract]
    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
  • Source
    • "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. "
    [Show abstract] [Hide abstract]
    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
  • Source
    • "This study represented the first major support for the notion that an AD biomarker profile could yield excellent accuracy; however, enthusiasm waned when the findings did not crossvalidate on an independent assay platform [19]. Despite this initial setback, other groups have continued to identify promising signals in peripheral blood, suggesting that a bloodbased AD screen may be on the horizon [20] [21] [22] [23] [24] [25] [26] [27] [28] [29]. Recently, data from well-characterized international cohorts have yielded additional candidate biomarkers and panels [25] [30]. "
    [Show abstract] [Hide abstract]
    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
Show more