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AAIC 2022 | Alzheimer’s disease plasma biomarkers across the spectrum: which perform best?

Pamela Lukasewicz Ferreira, PhD, University of Pittsburgh, Pittsburgh, PA, shares the findings of a comparative study, undertaken in order to establish the performance of different plasma biomarkers across the Alzheimer’s disease (AD) spectrum. Plasma Aβ42/40, phosphorylated tau (p-tau), and glial fibrillar protein (GFAP), and neurofilament light (NfL) were compared in their ability to predict Aβ- and tau-PET positivity in both cognitively unimpaired (CU) and cognitively impaired (CI) individuals. Firstly, it was shown that plasma p-tau231, p-tau181, GFAP, and NfL all correlated with each other, while Aβ42/40 did not. P-tau231 was the strongest predictor of both Aβ- and tau-PET positivity in the CU cohort, while GFAP outperformed the other markers in the CI patient group. While this set of plasma biomarkers were all known to predict AD pathology with high accuracy, this study demonstrates their comparative, disease stage specific performance for the first time. This interview took place at the Alzheimer’s Association International Conference (AAIC) 2022 in San Diego, CA.

Transcript (edited for clarity)

Like with the format compared to the analysis of plasma p-tau, amyloid, GFAP and NFL, to predict Aβ and tau positivity, we separate our groups by cognitively unimpaired and cognitively impaired because we want to evaluate if this was disease stage-specific. And one thing that we were very interested in was the addition of these biomarkers to the demographics that we use, age and sex. Will it improve the model that we use to predict Aβ positivity and tau positivity? Because sometimes, the addition of the biomarker will not predict better Aβ positivity, for example, than if you only use the demographics...

Like with the format compared to the analysis of plasma p-tau, amyloid, GFAP and NFL, to predict Aβ and tau positivity, we separate our groups by cognitively unimpaired and cognitively impaired because we want to evaluate if this was disease stage-specific. And one thing that we were very interested in was the addition of these biomarkers to the demographics that we use, age and sex. Will it improve the model that we use to predict Aβ positivity and tau positivity? Because sometimes, the addition of the biomarker will not predict better Aβ positivity, for example, than if you only use the demographics. Like, don’t have a significant difference.

So, we think about how in clinic is this relevant. So okay. So if I add NfL, for example, on the model, it’s better than if I use only the demographics? So, we did this and we compared the models, and what we found was that, for the cognitively unimpaired, if we add p-tau231 to our models, we can predict Aβ and tau positivity with a significant improvement on the model when we compare if we only use age and sex.

And for the cognitively impaired, we have this improvement with the GFAP, which is very interesting that we have the same biomarker for amyloid and tau positivity in the cognitively unimpaired and the same biomarker in the cognitive impaired. So, we think that it’s the biomarker going with the disease, and it’s more disease stage, then we use a different biomarker.

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