09 Nov 2018

It seems blood tests for Aβ have moved off the wish list and are becoming reality—almost. Building a knowledge base for plasma measures as a proxy for brain amyloid, multiple methods applied to different research cohorts are now consistently linking low plasma Aβ42/40 to clinical, cognitive, and biomarker evidence of Alzheimer’s disease. At the 11th Clinical Trials on Alzheimer’s Disease conference, held October 24–27 in Barcelona, in presentation after presentation, researchers made the case that currently available blood tests are suitable pretests to reduce or replace amyloid PET scans for screening participants in clinical trials. This would save time and money, lighten the burden on volunteers, and expand access to therapeutic trials beyond the catchment areas of centers with PET capability. Current work to standardize the tests focuses on pre-analytic sample handling to minimize error. While several candidate assay platforms are vying for clinical samples to help them validate their product, at least one recruitment effort plans to incorporate multiple assays to compare and contrast their performances in real life.

In a keynote that opened the meeting, Randy Bateman of Washington University, St. Louis, recapped Aβ blood assay research over the last 18 months. It happened fast. Until 2017, plasma Aβ assays had low accuracy and yielded mixed results. A meta-analysis of more than two dozen studies found high variability and no significant difference between people with AD and healthy controls (Apr 2016 news on Olsson et al., 2016; Alzbiomarker). 

From his lab’s kinetics work measuring production and clearance of Aβ in people, Bateman knew half the peptide produced in the brain ended up in blood, and that brain amyloid deposition throttled this transfer. To find a blood signature of this change, his lab developed an ultrasensitive assay for blood Aβ based on immunoprecipitating and quantitating the peptides with mass spectrometry. The technique uncovered a subtle but reproducible signal, whereby the ratio of Aβ42/40 in blood nudged downward by 14.3 percent in people with brain amyloid (Ovod et al., 2017). Compared with what’s found in cerebrospinal fluid, blood Aβ concentrations were much lower, and the drop in amyloid-positive people less dramatic, but nonetheless, blood Aβ42/40 ratios were able to distinguish amyloid-positive and -negative people with 89 percent accuracy.

Since then, Bateman has validated the assay in two additional cohorts, including one with longitudinal data. Over time, blood Aβ follows a similar pattern as CSF Aβ, whereby the Aβ42/40 ratio drops early in the disease, both preceding and predicting brain amyloidosis. Among amyloid PET-negative, cognitively normal people, a lower Aβ42/40 ratio signaled who would become PET-positive during two to seven years of follow-up.

It’s Complicated. Rather than relying on a single cutoff value, schemes that combine plasma Aβ concentration, age, and APOE4 status could produce fine-grained predictions of amyloid PET positivity. [Courtesy of S. Schindler, Y. Li, and R. Bateman.]

The relationship between plasma Aβ42/40 and brain amyloid holds regardless of a person’s age or ApoE4 status, and when these three factors are combined, they predict amyloid status with 95 percent accuracy. This suggests blood measures could be as good as CSF or PET at detecting brain amyloid in the general population, Bateman claimed. For secondary prevention trials, a blood amyloid prescreen would halve the number of PET scans needed to identify amyloid-positive, asymptomatic participants, he said. For primary prevention studies seeking participants who are unlikely to be amyloid-positive, such as younger or ApoE4-negative people, prescreening with a blood test might reduce PET scans as much as 85 percent, he calculated.

Slicing the data another way, Bateman suggested that instead of instituting a single cutoff value for positivity or negativity, researchers might instead consider a range of probabilities based on a person’s Aβ42/40 concentration, age, and ApoE4 status combined. Inge Verberk of VU University Medical Center, Amsterdam, suggested this same approach at AAIC last summer (Verberk et al., 2018).

Since Bateman’s ultrasensitive blood Aβ assay development, the field is becoming crowded with options. They include an independent mass spec technique (Feb 2018 news), a fully automated ELISA system, and several other antibody-based detection systems (Verberk et al., 2018). Consistently, each assay points to lower Aβ42/40 ratios in blood as a specific and sensitive indicator of brain amyloid.

The newest entry is an enhanced immunoassay based on single molecule optimized array (Simoa) detection. It is already moving toward commercialization. Developed in Charlotte Teunissen’s lab at Amsterdam University Medical Center, the assay better distinguishes amyloid-positive from -negative than the commercially available Simoa assay from Quanterix (Aug 2018 conference news). Jeroen Vanbrabant, ADx Neurosciences, Gent, Belgium, told Alzforum that ADx has begun scale-up and commercial production of Teunissen’s assay. ADx scientists have replicated her lab’s results and have scaled up production to 100-kit batches. Going forward, Vanbrabant said they’ll continue to test and validate the assay, generate standard operating procedures for sample collection, storage, and analysis, and expand clinical data to compare the blood test with amyloid PET. Hugo Vanderstichele, also of ADx, said researchers can already obtain prototype kits.

The existence of multiple competing assays presents a challenge for standardization and replication between labs and cohorts, issues the field has begun to address. One effort, spearheaded by Kaj Blennow, University of Gothenburg, Sweden, will generate a panel of 50 plasma samples and ship them to different labs by the end of the year. For CSF assays, pre-analytical sample handling was a nettlesome source of variability, and the situation is shaping up to be similar for blood. At CTAD, Tobias Bittner and colleagues at Roche presented an analysis of common aspects of sample handling—all the boring little details—that might muddle results in Roche’s automated Elecsys blood Aβ and tau immunoassays. The goal was to issue a recommended sample-handling protocol. Some details did not matter: The time of day blood was drawn, what plastic the tube was made of, up to five transfers from tube to tube, and up to three freeze-thaw cycles all did not affect results. But other little things did: Choice of anticoagulant, storage temperature, and how long samples sat around before being tested or frozen all affected detection of Aβ40, Aβ42, and tau. Filling tubes less than halfway led to loss of tau signal, the scientists found.

Vanderstichele agrees controlling pre-analysis is important. In agreement with Roche’s data, the ADx protocol calls for collection of EDTA plasma, and freezing samples within an hour. “The assays need strictly controlled collection and storage. Time and temperature are critical,” he said. Another assay, offered by Araclon Biotech-Grifols, Zaragoza, Spain, strictly requires people to fast overnight before giving a blood sample, said Ian Sherriff of Araclon.  

Araclon’s assay had its own, company-sponsored, session at CTAD, rolling out data from academic collaborators and multiple cohorts. Araclon’s Pedro Pesini reviewed its automated Aβ40 and Aβ42 assay, which the company sells as an in-house test service (Pérez-Grijalba et al., 2016). The assay distinguishes between free and total Aβ peptides, which are measured in undiluted or diluted plasma, respectively. Both correlate with other biomarkers of brain amyloid deposition, though the ratio of total Aβ42 to 40 (TP42/40) appears to correlate best.

Lower TP42/40 was associated with amyloid positivity and with accumulation of amyloid over time in cognitively healthy normal people in the Australian Imaging, Biomarker, and Lifestyle Flagship Study (Fandos et al., 2017). At CTAD, Victor Villemagne, University of Melbourne, Australia, showed data for the entire AIBL cohort spanning from healthy control to mild cognitive impairment to AD. He confirmed that the association with amyloid status held across the clinical spectrum. In AIBL, the relationship of blood Aβ to PET was similar to that of CSF Aβ42 to PET, with TP42/40 dropping logarithmically with increasing amyloid PET signal. The decrease in plasma ratio was linear over time, and faster in people who were accumulating amyloid.

Early warning? Modeling indicates that, in a healthy person who accumulates amyloid, TP42/40 drops below mean values a decade before he or she reaches the brain-wide cutoff for amyloid positivity (bottom panel). For amyloid-PET, the first signs of deposition come seven years before the threshold is reached. [Courtesy of V. Villemagne.]

Modeling the changes over time in healthy elderly controls, Villemagne estimated that blood Aβ values start to drop 10 to 11 years before a person crosses the threshold to PET positivity (see image above). By comparison, PET starts showing deposits in isolated brain regions about seven years before a person crosses the threshold for brain-wide amyloid positivity.

To illustrate the value of the blood test as a prescreen, Villemagne offered a hypothetical trial enrolling 150 amyloid PET-positive, cognitively normal people. For that, researchers expect to have to perform an average of 3.3 PET scans per recruitment. The blood test could reduce that to 1.4 scans per recruited person. At a cost of $5,000 per PET scan versus $140 for the blood test, that reduction would add up to a savings of $1.4 million dollars, he calculated.

At CTAD, Anne Fagan of Washington University, St. Louis, presented more recent data published this month by the Spanish group (Pérez-Grijalba et al., 2018). It indicates that TP42/40 is associated with FDG PET, amyloid PET, and risk of progression to AD dementia in a group of people with amnestic MCI. The AB255 study, sponsored by Araclon, draws volunteers from memory clinics in Spain, Italy, Sweden, and France. The investigators enrolled 228 people age 65–85, either cognitively normal or with amnestic cognitive impairment (aMCI), and followed them for two years.

At baseline, the average TP42/40 was lower in amnestic MCI than cognitively normal participants, and came with cortical hypometabolism suggestive of a high risk of AD. Indeed, a lower TP42/40 increased a person’s risk of progressing from aMCI to Alzheimer’s dementia over the next two years by 70 percent. In a subgroup, TP42/40 correlated with Aβ42 in CSF and inversely with PET-PIB. Finally, Fagan showed that TP42/40 separated amyloid PET-positive and -negative groups with a sensitivity and specificity similar to Bateman’s mass spec assay. Were there limitations to the Araclon assay? Sure. The values in the two groups overlap a fair amount, the effect size is modest, and there is not yet a universal cutoff for amyloid positivity, Fagan said. “Although I do not endorse one method over another, as they each have their strengths and weaknesses, the similarities of the results in both studies is exciting since it gives further support to the potential viability of the ratio as a useful biomarker,” she told Alzforum.

Does the relationship between TP42/40 and brain amyloid hold for subjective cognitive decline? Yes, sort of, said Agustin Ruiz of the Fundació ACE in Barcelona. His data came from the Fundació’s Healthy Brain Initiative (FACEHBI) (Rodriquez-Gomez et al., 2017), a study that recruits people who complain of memory issues yet score normal on cognitive tests. In those participants who also received florbetaben PET scans, the investigators once again found an inverse association between TP42/40 and PET positivity. However, the blood ratio discriminated less well here than in some other clinical cohorts, probably because FACEHBI included fewer people with brain amyloid, Ruiz said. Even so, an empirical cutoff that pinned amyloid positivity with 83 percent sensitivity and 59 percent specificity cut by half the number of people who would need PET scans for a trial targeting that early stage of AD, and still capture only PET-positive people.

Sure enough, blood tests are already wending their way into prescreening efforts. The Trial-Ready Cohort for Preclinical/Prodromal Alzheimer’s Disease (TRC PAD) project, run jointly by Reisa Sperling at Boston’s Brigham and Women’s Hospital, Jeffrey Cummings of the Cleveland Clinic Lou Ruvo Center for Brain Health in Las Vegas, and Paul Aisen at the University of Southern California Alzheimer’s Therapeutic Research Institute (ATRI), aims to include measures of plasma Aβ when it begins in-clinic screening to build its cohort early in 2019, said Gustavo Jimenez-Maggiora of USC.

Aisen confirmed that he is seeking funds to evaluate three plasma assays: the two immunoprecipitation/mass spectrometry methods reported by WashU and by Akinori Nakamura and Katsuhiko Yanagisawa at the National Center for Geriatrics and Gerontology in Aichi, Japan, and the Elecsys immunoassay. “The first stage would be comparative validation of the methods, as well as assessment of pre-processing approaches. The second stage would be incorporation of plasma Aβ ratio testing into our risk algorithm, to yield a highly accurate predictor of brain amyloid elevation,” Aisen wrote to Alzforum, adding “Ultimately, we hope a plasma assay may obviate the need for amyloid PET or lumbar puncture in the selection process for the trial-ready cohort.”—Pat McCaffrey

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References

News Citations

1. Meta-Analysis of 21 Years of Alzheimer's Fluid Biomarker Research 8 Apr 2016
2. Closing in on a Blood Test for Alzheimer’s? 2 Feb 2018
3. With Sudden Progress, Blood Aβ Rivals PET at Detecting Amyloid 23 Aug 2018

Biomarker Meta Analysis Citations

Paper Citations

1. Olsson B, Lautner R, Andreasson U, Öhrfelt A, Portelius E, Bjerke M, Hölttä M, Rosén C, Olsson C, Strobel G, Wu E, Dakin K, Petzold M, Blennow K, Zetterberg H. CSF and blood biomarkers for the diagnosis of Alzheimer's disease: a systematic review and meta-analysis. Lancet Neurol. 2016 Jun;15(7):673-84. Epub 2016 Apr 8 PubMed.
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4. Pérez-Grijalba V, Fandos N, Canudas J, Insua D, Casabona D, Lacosta AM, Montañés M, Pesini P, Sarasa M. Validation of Immunoassay-Based Tools for the Comprehensive Quantification of Aβ40 and Aβ42 Peptides in Plasma. J Alzheimers Dis. 2016 Sep 6;54(2):751-62. PubMed.
5. Fandos N, Pérez-Grijalba V, Pesini P, Olmos S, Bossa M, Villemagne VL, Doecke J, Fowler C, Masters CL, Sarasa M, AIBL Research Group. Plasma amyloid β 42/40 ratios as biomarkers for amyloid β cerebral deposition in cognitively normal individuals. Alzheimers Dement (Amst). 2017;8:179-187. Epub 2017 Sep 12 PubMed.
6. Pérez-Grijalba V, Romero J, Pesini P, Sarasa L, Monleón I, San-José I, Arbizu J, Martínez-Lage P, Munuera J, Ruiz A, Tárraga L, Boada M, Sarasa M. Plasma Aβ42/40 Ratio Detects Early Stages of Alzheimer's Disease and Correlates with CSF and Neuroimaging Biomarkers in the AB255 Study. J Prev Alzheimers Dis. 2019;6(1):34-41. PubMed.
7. Rodriguez-Gomez O, Sanabria A, Perez-Cordon A, Sanchez-Ruiz D, Abdelnour C, Valero S, Hernandez I, Rosende-Roca M, Mauleon A, Vargas L, Alegret M, Espinosa A, Ortega G, Guitart M, Gailhajanet A, Sotolongo-Grau O, Moreno-Grau S, Ruiz S, Tarragona M, Serra J, Martin E, Peleja E, Lomeña F, Campos F, Vivas A, Gomez-Chiari M, Tejero MA, Giménez J, Pesini P, Sarasa M, Martinez G, Ruiz A, Tarraga L, Boada M. FACEHBI: A Prospective Study of Risk Factors, Biomarkers and Cognition in a Cohort of Individuals with Subjective Cognitive Decline. Study Rationale and Research Protocols. J Prev Alzheimers Dis. 2017;4(2):100-108. PubMed.

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