Sebastian Palmqvist, Noëlle Warmenhoven, Federica Anastasi, Andrea Pilotto, Shorena Janelidze, Pontus Tideman, Erik Stomrud, Niklas Mattsson-Carlgren, Ruben Smith, Rik Ossenkoppele, Kübra Tan, Anna Dittrich, Ingmar Skoog, Henrik Zetterberg, Virginia Quaresima, Chiara Tolassi
5 min
Global implementation of blood tests for Alzheimer's disease (AD) would be facilitated by easily scalable, cost-effective and accurate tests. In the present study, we evaluated plasma phospho-tau217 (p-tau217) using predefined biomarker cutoffs. The study included 1,767 participants with cognitive symptoms from 4 independent secondary care cohorts in Malmö (Sweden, n = 337), Gothenburg (Sweden, n = 165), Barcelona (Spain, n = 487) and Brescia (Italy, n = 230), and a primary care cohort in Sweden (n = 548). Plasma p-tau217 was primarily measured using the fully automated, commercially available, Lumipulse immunoassay. The primary outcome was AD pathology defined as abnormal cerebrospinal fluid Aβ42:p-tau181. Plasma p-tau217 detected AD pathology with areas under the receiver operating characteristic curves of 0.93-0.96. In secondary care, the accuracies were 89-91%, the positive predictive values 89-95% and the negative predictive values 77-90%. In primary care, the accuracy was 85%, the positive predictive values 82% and the negative predictive values 88%. Accuracy was lower in participants aged ≥80 years (83%), but was unaffected by chronic kidney disease, diabetes, sex, APOE genotype or cognitive stage. Using a two-cutoff approach, accuracies increased to 92-94% in secondary and primary care, excluding 12-17% with intermediate results. Using the plasma p-tau217:Aβ42 ratio did not improve accuracy but reduced intermediate test results (≤10%). Compared with a high-performing mass-spectrometry-based assay for percentage p-tau217, accuracies were comparable in secondary care. However, percentage p-tau217 had higher accuracy in primary care and was unaffected by age. In conclusion, this fully automated p-tau217 test demonstrates high accuracy for identifying AD pathology. A two-cutoff approach might be necessary to optimize performance across diverse settings and subpopulations.
The researchers aimed to validate a fully automated, commercially available blood test for Alzheimer’s disease (AD)—specifically, the plasma phospho-tau217 (p-tau217) immunoassay on the Lumipulse platform. Because current diagnostic methods like cerebrospinal fluid (CSF) analysis and PET scans are invasive or expensive, there is a critical need for scalable, accurate, and accessible blood-based biomarkers (BBMs) that can be used in both primary and secondary clinical care settings.
The study evaluated 1,767 participants across four secondary care cohorts (Sweden, Spain, and Italy) and one primary care cohort in Sweden. The researchers defined "AD pathology" as abnormal levels of Aβ42:p-tau181 in the CSF or a positive amyloid PET scan. They tested two diagnostic strategies:
They also assessed how demographic factors (age, sex), comorbidities (kidney disease, diabetes), and genetic markers (APOE genotype) influenced the test's performance.
The study found that the fully automated p-tau217 test is highly accurate for identifying AD pathology.
This research provides evidence that a standardized, automated blood test can reliably identify Alzheimer’s pathology in routine clinical settings. By moving diagnostic capability from specialized research labs to standard hospital platforms, this test could significantly increase access to early diagnosis. This is particularly important as new anti-amyloid treatments become available, which require early and accurate confirmation of AD pathology to be effective.
Sam: Rather than forcing a label onto every result, the test admits when it doesn't know. Did they validate this across different types of clinical settings?
Alex: They did. The study included over 1,700 participants, spread across four specialised memory clinics and one primary care setting. That last one is particularly important—it's the closest thing to a standard GP's surgery, which is exactly where this test would need to work if it's going to be widely adopted.
Sam: And what about people with other health conditions? Older patients often have things like kidney disease or diabetes. Could those interfere with the results?
Alex: The study suggests those conditions didn't meaningfully change the test's accuracy—which is a notable finding, because it's one of the practical concerns you'd have before rolling something like this out broadly. The one caveat the researchers did flag is that accuracy was slightly lower in participants aged 80 and older, so that age group may still benefit from additional follow-up testing.
Sam: So it's not a perfect tool for every single patient, but it holds up well across a wide range of people. How do researchers actually measure whether a diagnostic test is doing its job well?
Alex: They use a measure called the Area Under the Curve—AUC for short. Imagine you're grading a test on how well it can sort two groups: people who have the disease and people who don't. A perfect score means it gets every single case right. A score at the bottom of the scale means it's no better than a coin flip. The AUC is essentially a report card for that sorting ability. The researchers used it to confirm the blood test was performing at a level comparable to the more invasive methods.
Sam: And the two-cutoff approach helps protect that score—by setting aside the cases the test isn't confident about, the results it does commit to are more reliable.
Alex: Precisely. By setting a high bar for a positive result and a low bar for a negative one, they create a buffer zone. Anything in that buffer is held back from a firm diagnosis. It's a more cautious approach—but in medicine, caution in the face of genuine uncertainty is usually the right call.
Sam: This feels like a case where the methodology is as important as the finding itself. The test isn't just accurate—it's designed to know its own limits.
Alex: That's a fair summary. The core finding is that a blood-based test, run on equipment already present in many hospitals, can identify the biological hallmarks of Alzheimer's with accuracy that approaches the current gold standard—and it does so in a way that's honest about uncertainty rather than papering over it. The researchers suggest this could allow doctors to identify Alzheimer's pathology earlier, and more equitably, across the healthcare system. That matters most for patients who currently can't easily access a specialist or afford an expensive scan.
Sam: It's a step toward making a difficult diagnosis more straightforward—without cutting corners on accuracy.
Alex: That's exactly how the researchers frame it. Thanks for listening to ResearchPod.