. A blood-based signature of cerebrospinal fluid Aβ1-42 status. Sci Rep. 2019 Mar 11;9(1):4163. PubMed.


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  1. These data add to the currently emerging evidence for an AD signature being detectable in blood. It is interesting that Aβ42 is identified by the random forest model, which is in line with other recent evidence of Aβ reductions in blood and CSF. It is not clear whether the ratio of Aβ42/40 has been included in the model, though this is the biomarker modality under current strong investigation. One could even state that any final blood AD assay will include amyloid forms, especially given the established pathological relation.

    The other markers in the panel are somewhat more difficult to understand. It is not clear what their added value is on the AUC compared to a model containing only Aβ42 (besides age, gender, and ApoE), but replication should show if they are useful. Replication should also include different analysis methods, because the Rules-Based Medicine platform running on a Luminex platform is not a routine laboratory technology. Moreover, comparison to other dementias and comorbidities, e.g. cardiac diseases (ApoE), and diseases involving inflammation (eotaxin-3) should show if the observed signature in blood is specific. But even if the panel shows specificity when the models are correct for these confounders in epidemiological large cohorts, implementation of such a test becomes a challenge: Should we not use the test when comorbidities are present? And what to do if these are not clinical visible?

    The lack of added value of the metabolome may not be surprising, again in view of the disease-specificity of these molecules. A signature that includes brain specific proteins would probably be a format to use in widespread clinical implementation.

    View all comments by Charlotte Teunissen
  2. These data add to growing the evidence base that there are signals in plasma (and possibly in cellular blood elements) which are informative on the state of Aβ accumulation in the central nervous system. The technologies used in this report include the multiplexed immunoassays (Luminex xMAP) of Rules-Based Medicine (now owned by Myriad). Their machine-learning approach identified a combination of age, ApoE4 carriage, and the four plasma analytes chromogranin A (CGA), Aβ42, eotaxin 3 (CC chemokine ligand 26) and ApoE4, which delivered a reasonable predictor for CSF and PET Aβ status (AUCs high 70s to low 80s). The performance of this approach may not be as high as more recent techniques which utilize IP/MS or SIMOA (Ovod et al., 2017Nakamura et al., 2018; Verberk et al., 2018), but may be relatively easier to introduce into clinical practice.

    It appears that the CSF and plasma values of the brain-derived Aβ42 are in a more dynamic range than PET-Aβ signals at the lower cutpoint, when subjects are crossing the threshold from normality to abnormality. This is the window at which the field has to direct its attention for both primary and secondary prevention strategies. Hence the urgency for developing robust and high performance blood tests. Much progress has been made over the past two years. 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.


    . High performance plasma amyloid-β biomarkers for Alzheimer's disease. Nature. 2018 Feb 8;554(7691):249-254. Epub 2018 Jan 31 PubMed.

    . Plasma Amyloid as Prescreener for the Earliest Alzheimer Pathological Changes. Ann Neurol. 2018 Nov;84(5):648-658. Epub 2018 Oct 4 PubMed.

    View all comments by Colin Masters
  3. Most previous blood tests have predicted amyloid PET measurements, whereas in our study we predict CSF Aβ1-42, as these have been shown to change potentially five to 10 years before the amyloid PET signal reaches the critical threshold (Palmqvist et al., 2016). The study by Nakamura et al., 2018, is very interesting in this regard. In this study, the principal prediction was for PET amyloid, but they also showed a strong association between plasma Aβ1-42, normalized for plasma APP669-771 peptide, and CSF Aβ1-42 in a small subcohort (n=46).

    While the predictive performance of our model is lower, the cohort we used was substantially larger (n=358). Further, the protein concentrations measured in ADNI were from an ELISA-based methodology, which is potentially easier to translate into a clinical setting compared to the IP-MALDI-TOF-MS method employed by Nakamura et al.

    We hope that our work will complement other approaches to developing blood-based tests, including the development of more sensitive assays for Aβ1-42 such as those by Nakamura et al., 2018, Verberk et al., 2018, or Ovod et al., 2017, in the effort to develop a screening tool for the identification of individuals at risk of AD. This could be followed up by lumbar puncture and/or amyloid PET scan if a positive result was predicted.

    Our work adds further evidence that plasma-based tests for Alzheimer’s risk factors are a very promising line of research. Approaches based on machine learning are likely to be complementary to other developments in plasma assays. We are showing that there are other proteins, in addition to plasma Aβ1-42, that add useful information to the signal from plasma.

    While we used an “internal validation cohort” (comprising individuals who were not used to train the model) to test the utility of the model, we need to validate the work in an independent external cohort. We are looking to use the Australian Imaging Biomarker and Lifestyle (AIBL) study of aging. We are also currently conducting research to apply these AI techniques to predict other important biomarkers that are associated with the progression of Alzheimer’s disease (e.g., CSF pTau and Tau) and are investigating the role that polygenic risk scores may play. The results from this work will be presented at AD/PD 2019 at the end of March.


    . Cerebrospinal fluid analysis detects cerebral amyloid-β accumulation earlier than positron emission tomography. Brain. 2016 Apr;139(Pt 4):1226-36. Epub 2016 Mar 2 PubMed.

    . High performance plasma amyloid-β biomarkers for Alzheimer's disease. Nature. 2018 Feb 8;554(7691):249-254. Epub 2018 Jan 31 PubMed.

    . Plasma Amyloid as Prescreener for the Earliest Alzheimer Pathological Changes. Ann Neurol. 2018 Nov;84(5):648-658. Epub 2018 Oct 4 PubMed.

    . Amyloid β concentrations and stable isotope labeling kinetics of human plasma specific to central nervous system amyloidosis. Alzheimers Dement. 2017 Aug;13(8):841-849. Epub 2017 Jul 19 PubMed.

    View all comments by Benjamin Goudey

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  1. Deep Blue for Aβ Blood Test?