The prospects of using cerebrospinal fluid biomarkers to diagnose Alzheimer disease may have just gotten a bit brighter. In this month’s Annals of Neurology online, Kelvin Lee and colleagues at Cornell University, New York, report that “random forest,” a type of multivariate statistical analysis, has identified a suite of peptide markers that can distinguish AD patients from controls with a high degree of accuracy. If confirmed in larger sample sets, the finding could form the basis of a much-needed diagnostic test.
Currently, diagnosis of AD requires a combination of brain imaging, expert neuropsychological testing, and good, old-fashioned review of patient history. But a definitive diagnosis of AD does not come until after death, when brain tissues can be examined for pathological hallmarks such as amyloid plaques and neurofibrillary tangles. While this situation may be tolerable at present, the availability of a reliable and early diagnostic test will take on new importance once treatments become available that can prevent or slow the progression of the disease.
Because the cerebrospinal fluid (CSF) is in direct contact with the brain, analysis of the fluid is an attractive means of charting the rise and fall of molecules that might serve as AD biomarkers. Many labs have already taken a protein/proteomic tack, identifying several likely candidates, including amyloid-β and phospho-tau (see ARF related news story), and proteomic profiles (see, for example, Carrette et al., 2003; Puchades et al., 2003), but most samples studied have been from unconfirmed AD cases. This study differs by combining multivariate analysis with a data set comprising antemortem samples from patients that were confirmed to have AD postmortem. It also uses control samples taken from patients with other brain disorders to help weed out AD-specific variables from those that reflect more general changes going on in the brain.
First author Erin Finehout and colleagues used two dimensional electrophoresis (2DE) followed by time-of-flight mass spectroscopy to quantify CSF peptides in samples from 34 AD patients and 34 controls. The latter included nine healthy volunteers, 10 Parkinson patients, and 15 subjects with various other neurological diseases. From 1,938 2DE spots, an initial random forest analysis correctly classified only 26 of the 34 AD samples—the random forest algorithm is borrowed from the machine learning branch of computer science and has been shown to be particularly useful for analysis of proteomic data sets (see review by Izmirlian, 2004) The researchers then started felling the less statistically significant spots from the analysis one by one, until a copse of just 23 spots remained. This correctly classified 32 each of the AD samples and controls—a predicted error rate of 5.9 percent. On a second validation set of samples (10 AD, 18 non-AD) the biomarker profile was not as accurate, correctly classifying nine of the AD and only 15 of the non-AD samples. Overall, based on the two data sets, random forest analysis of the 23 spots had a slightly higher classification error rate of 8.3 percent. “Nonetheless, this multivariate statistical study represents the largest cohort of pathologically characterized antemortem CSF samples used in an AD proteomic biomarker study to date and suggests the possibility of eventually developing clinically relevant diagnostic assays based on CSF proteomic analysis,” write the authors.
The mass spec analysis showed that some of the spots contained more than one protein. Eighteen spots yielded peptides that matched 21 known proteins, while in five of the spots no known protein could be identified. Notable by their absence were Aβ, tau, and phospho-tau. Finehout and colleagues classified the 21 detected proteins into four major categories: Aβ transport (vitamin D-binding protein; albumins 1, 2 and 3; ApoE; ApoJ 1, 2 and 3; transthyretin 1 and 2; retinol binding protein); inflammation (immunoglobulin light and heavy chains; complement component 3; plasminogen; fibrin β); proteolytic enzyme inhibition (α-1-antitrypsin 1 and 2; ProSAAS), and neuronal membrane proteins (contactin; neuronal pentraxin receptor). Reasonable arguments can be made for the involvement of many of these proteins in AD pathology. The Aβ transporters may help regulate Aβ flux in the brain—albumin polymorphisms have also been linked to AD in a Japanese cohort (see Alzgene database)—while ApoE and J have been strongly linked to AD (see ARF related news story and ARF news story). Plasminogen may accelerate Aβ degradation (see Melchor et al., 2003), while the complement component 3 and the immunoglobulins play key roles in inflammation, which may be a major pathological process in the AD brain (see Forum poster review by Keith Crutcher). Interestingly, Maja Puchades and colleagues at Goteburg University, Sweden, previously identified α-1-antitrypsin in the CSF or AD patients.
“The method presented here has shown promising results,” write the authors. The analysis of a bigger and more diverse set of samples may help get a more accurate estimate of error prediction and may also help to identify profiles that correspond with different stages of the disease.—Tom Fagan
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