. Cerebrospinal fluid proteomic biomarkers for Alzheimer's disease. Ann Neurol. 2007 Feb;61(2):120-9. PubMed.

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  1. The stringent requirements for a diagnosis of definite Alzheimer disease (AD) make it unlikely that the measurement of a single biomarker in isolation can provide a sufficiently accurate antemortem test for AD. The gold standard of postmortem diagnosis requires documentation of multiple biomarkers (plaques, tangles, neuronal loss, etc.) in a characteristic density and distribution throughout the brain. Furthermore, these markers need to be interpreted in light of other factors, such as the person's age at death and whether he or she was demented during life. Neuropathologists would be hard-pressed to make a diagnosis of definite AD if they were restricted to observing just one element of AD neuropathology and blinded to the clinical history. Is it reasonable to expect that an antemortem biomarker test with similar restrictions will perform any better?

    Alzheimer diagnosis is a Bayesian process, with information from the clinical history, physical examinations, lab tests, and brain imaging contributing to the probability of an accurate diagnosis. These and other considerations led us to study the cerebrospinal fluid (CSF) proteome as a means of identifying multiplexed antemortem markers for AD. Following this line of reasoning, if our panel of 23 biomarkers is further validated in future studies and implemented on a platform suitable for clinical use, it may be best utilized in conjunction with other diagnostic tests or as a supplement to them.

    Our use of CSF from autopsy-proven AD cases is one of the distinguishing features of our proteomic study. Autopsy confirmation of diagnosis in all of our controls would have been desirable; however, CSF from autopsied normal controls is not readily available. One of the factors that has impeded progress in this field is the lack of availability of large numbers of CSF specimens from well-characterized controls and cases at different stages of dementia and dementia prodromes. The collection of CSF in initiatives such as ADNI should help overcome this bottleneck, as will the sharing of CSF specimens among the various groups carrying out proteomic and other types of biomarker studies.

    We are aware that some laboratories pursue a "complexity reduction" step prior to CSF analysis such as the depletion of albumin. We performed a large number of experiments to study the capacity of commercially available solutions to carry out such depletions. In each case, we observed that the kits were unable to reproducibly deplete the specified proteins without altering other proteins of potential interest. While depletion strategies may help access lower abundance proteins (perhaps including tau and Aβ42), they may compromise other components of the proteome. It’s far from clear that the only proteins of relevance to AD diagnosis are those that are brain-derived. As such, we chose not to remove or ignore any proteins in CSF regardless of whether they were peripheral or central in origin. We studied intact CSF and were gratified to find, for example, changes in specific albumin fragments that correlated with an AD diagnosis.

    While proteomic methods are becoming increasingly sophisticated, the current reality is that there is no single technology available that can visualize all of the proteins present in a given sample equally well. Several special classes of proteins (e.g., low abundance, small molecular weight, high isoelectric point, etc.) can be well-studied but often at the expense of less effectively characterizing other classes of proteins. In considering these trade-offs, we pursued a method that we believed would give us the best overall quantitative picture of the CSF proteome relative to AD diagnosis. The fact that Aβ42 and various tau isoforms did not emerge from this analysis doesn't mean that they are unimportant markers. To some extent this may reflect limits in current proteomics technology, but a more positive interpretation is that proteomics provides complementary information to that which is obtained through hypothesis-driven biomarker studies.

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