Booij BB, Lindahl T, Wetterberg P, Skaane NV, Sæbø S, Feten G, Rye PD, Kristiansen LI, Hagen N, Jensen M, Bårdsen K, Winblad B, Sharma P, Lönneborg A.
A gene expression pattern in blood for the early detection of Alzheimer's disease.
J Alzheimers Dis. 2011;23(1):109-19.
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Several groups have already reported that various subgroups of blood cells seem to differ between AD patients and cognitively normal subjects either by number, function, or overall gene expression. So, to me, it is not a big surprise that the team working with Anders Lönneborg found 1,239 genes that have a changed expression profile in the blood of AD patients compared to non-demented controls, and that these profiles can be used to classify AD and non-AD in independent test sets with very high agreement with the clinical diagnosis. One has to really appreciate that the authors made a considerable effort to collect a larger number of samples from various sites than what is often used in many studies that look for new molecular biomarkers for AD. This is clearly the strength of this study.
The genes, of which roughly 1,000 only change expression less than 20 percent up or down, are also able to distinguish between samples from AD and Parkinson’s disease patients, and also predict with high accuracy conversion to AD of a small set of patients suffering from mild cognitive impairment. The challenges of finding solid peripheral biomarkers for AD become, however, quite evident in the accompanying paper by the same group (Rye et al., 2010), where the authors narrow these 1,239 genes down to a set of 96 based on a screen in another independent sample set using a different platform. By doing so, they lose a lot of the reproducibility of correctly classifying AD and non-AD in three more cohorts. Still, it is remarkable that the predictability was not entirely lost, and this indicates that some of the selected genes could be suitable future biomarkers upon further validation.
The accuracy of classifying AD and non-AD based on gene expression profiles in blood cells is similar to reports based on levels of several soluble proteins in the blood (e.g., Ray et al., 2007; Buerger et al., 2009; Soares et al., 2009; O'Bryant et al., 2010). Whether one approach is superior to the other cannot be determined, however. Both face similar challenges, including sample number and quality, and the accuracy of clinical diagnosis to which a new test will be compared. Also, these two approaches may not be directly comparable; for example, the whole genome expression analysis is a shotgun approach for a specific subset of cells. On the other hand, a multiplex assay with only a few hundred detectable secreted proteins in the blood takes a snapshot of an entire biological system in which the measured proteins represent the integrated response of all cells that communicate with each other within or across tissues. So it's not really to be expected that the gene expression analysis by Rye et al. would find the soluble proteins that were reported earlier, as stated by the authors. A closer look reveals, however, that the genomic and the proteomic studies overlap in some biological pathways that are represented by the identified proteins or genes, for example, those involving TNF or cell adhesion. It will be interesting to combine the two approaches and to find out whether they could actually complement each other. In addition, it should be tested whether the identified pathways, genes, or proteins are associated with the currently accepted molecular biomarkers of AD in CSF—Aβ and tau—or other neuropathological alterations, or what their biological role is in AD. This could be relevant for both establishing a simple blood test for the early diagnosis of AD and identifying novel drug targets for AD.
Rye PD, Booij BB, Grave G, Lindahl T, Kristiansen L, Andersen HM, Horndalsveen PO, Nygaard HA, Naik M, Hoprekstad D, Wetterberg P, Nilsson C, Aarsland D, Sharma P, Lönneborg A.
A novel blood test for the early detection of Alzheimer's disease.
J Alzheimers Dis. 2011;23(1):121-9.
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Classification and prediction of clinical Alzheimer's diagnosis based on plasma signaling proteins.
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A serum protein-based algorithm for the detection of Alzheimer disease.
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