In the March 11 Scientific Reports, researchers led by Noel Faux at IBM Research Australia in Southbank propose yet another blood test for Alzheimer’s disease, this one based on simple immunoassays. The scientists used machine learning to identify molecules in plasma that correlated with low Aβ42 in cerebrospinal fluid. Out of nearly 300 analytes, four proteins stood out. When combined with ApoE genotype, these predicted CSF Aβ status with 81 percent accuracy. Because the plasma proteins were measured by standard immunoassays, this test could be translated to the clinic quickly and cheaply, the authors claim.
- Machine learning finds four plasma proteins that correlate with CSF Aβ42.
- These proteins plus ApoE genotype predict amyloid positivity.
- This method uses standard immunoassays, hence may translate quickly to the clinic.
Colin Masters at the University of Melbourne, Australia, agreed. “The performance of this approach may not be as high as more recent techniques … but may be relatively easier to introduce into clinical practice,” he wrote to Alzforum (full comment below). Masters was not involved in the research.
Viable blood tests for AD sprang onto the scene in 2017, with the first report that mass spectrometry was accurate enough to detect a small drop in plasma Aβ42 in people who were accumulating brain amyloid (Jul 2017 conference news). Additional mass spec methods quickly followed, as well as highly sensitive antibody-based single-molecule arrays, aka Simoas (Feb 2018 news; Aug 2018 conference news; Nov 2018 conference news).
Adapting these sensitive techniques for routine clinical use may be expensive and technically challenging, however. Faux wondered if established immunoassay techniques could provide a simpler alternative. To test this, first author Benjamin Goudey used data from 358 participants in the Alzheimer’s Disease Neuroimaging Initiative. The cohort comprised 58 cognitively healthy controls, 198 people with amnestic mild cognitive impairment, and 102 people diagnosed with AD dementia. All had donated blood, which ADNI researchers analyzed using the Luminex xMap platform. This platform uses antibodies conjugated to beads to allow for the simultaneous capture and measurement of hundreds of different analytes.
Goudey and colleagues included Luminex plasma data from149 proteins and 138 metabolites in their analysis. First they assessed how well a person’s age and ApoE genotype predicted whether their CSF Aβ42 scores would fall above or below the threshold for AD. To this simple model they then added proteins, metabolites, or both. Proteins improved the predictive power, while metabolites slightly decreased it. Metabolites may add too much noise, the IBM scientists speculated.
Next, the authors used machine learning to find the smallest set of proteins that could equal the performance of the full complement. Four proteins, Aβ42, ApoE, chromogranin-A, and eotaxin 3, combined with ApoE genotype, predicted amyloid positivity with a sensitivity and specificity of 81 and 63 percent, respectively. For the first three proteins, low levels correlated with amyloid positivity; for eotaxin 3, high levels did. Chromogranin A is a synaptic protein previously associated with early AD (Mattsson et al., 2013; Duits et al., 2018). Eotaxin 3 is part of the innate immune system, and plasma levels rise in AD (Huber et al., 2016).
To test their model, the authors applied it to a separate cohort of 208 ADNI participants, 198 of whom had amnestic MCI and the remainder AD. Among the former, those flagged as amyloid-positive progressed to AD faster than those predicted to be amyloid-negative. Because this test cohort had not undergone lumbar puncture, Goudey correlated plasma test results with amyloid positivity as judged by a PET SUVR of 1.5 or higher by PiB or 1.11 or higher by florbetapir. Plasma and PET predictions agreed 80 percent of the time. The model performed better than age and ApoE genotype alone, which correlated with PET only 71 percent of the time.
Charlotte Teunissen at VU University, Amsterdam, noted that the identification of plasma Aβ42 as a marker of AD fits with other recent findings. “One could even state that any final blood AD assay will include amyloid forms,” she wrote to Alzforum (full comment below). She recommended that replication experiments assess what added value the other three plasma markers bring, and also test different analysis methods besides the Luminex platform.
The authors could not be reached for comment before publication of this story. In their paper, they acknowledged the need for replication of these results in independent cohorts and with different immunoassay platforms. In several previous cases, putative AD blood tests based on small studies have failed to repeat (Oct 2007 news; Jun 2013 webinar; Feb 2016 news).
It remains to be seen which of the many blood tests under development will advance to clinical practice (Feb 2019 news). “Much progress has been made over the past two years,” Masters wrote. “It is very pleasing to note that all the major players are cooperating in head-to-head performance testing under the auspices of the Alzheimer’s Association. We look forward to results in the coming months.”—Madolyn Bowman Rogers
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- Mattsson N, Insel P, Nosheny R, Zetterberg H, Trojanowski JQ, Shaw LM, Tosun D, Weiner M. CSF protein biomarkers predicting longitudinal reduction of CSF β-amyloid42 in cognitively healthy elders. Transl Psychiatry. 2013;3:e293. PubMed.
- Duits FH, Brinkmalm G, Teunissen CE, Brinkmalm A, Scheltens P, Van der Flier WM, Zetterberg H, Blennow K. Synaptic proteins in CSF as potential novel biomarkers for prognosis in prodromal Alzheimer's disease. Alzheimers Res Ther. 2018 Jan 15;10(1):5. PubMed.
- Huber AK, Giles DA, Segal BM, Irani DN. An emerging role for eotaxins in neurodegenerative disease. Clin Immunol. 2016 Sep 21; PubMed.
- Goudey B, Fung BJ, Schieber C, Faux NG, Alzheimer’s Disease Metabolomics Consortium, Alzheimer’s Disease Neuroimaging Initiative. A blood-based signature of cerebrospinal fluid Aβ1-42 status. Sci Rep. 2019 Mar 11;9(1):4163. PubMed.