ArticlemedRxiv : the preprint server for health sciences2025
Substituting Blood-Based Biomarkers for Imaging Measures in Alzheimer's Disease Studies: Implications for Sample Size and Bias.
Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Blood-based biomarkers for Alzheimer's disease (AD) pathology are appealing options in large population-based studies due to their low cost, minimal invasiveness, and feasibility of collection in non-clinical settings. Despite these benefits, blood-based biomarkers have lower test-retest reliability than neuroimaging measures like amyloid positron emission tomography (amyloid-PET) Centiloids; trade-offs in power and bias remain unexplored. Methods: We use data from Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) studies, which include both amyloid-PET and blood-based measures, to assess differences in statistical power, required sample size, and bias when replacing a neuroimaging measure with a blood-based measure. We use simulations parameterized based on these studies to show potential implications of using plasma p-tau181 or p-tau217, blood-based AD biomarkers, in place of Centiloids from amyloid-PET, when the biomarker is either the exposure or the outcome in an analysis of interest. Results: We demonstrated that substituting amyloid-PET Centiloids with a blood-based measure of p-tau can substantially reduce power, requiring 3 to 6 times the sample size to achieve 80% power compared to amyloid-PET. In addition, using a blood-based biomarker as the exposure can introduce significant regression dilution bias, attenuating estimated associations. Conclusions: Due to their lower cost and ease of collection compared with neuroimaging, blood-based biomarkers facilitate AD pathology measures on larger, more diverse samples with longitudinal follow-up. Consideration of the sample sizes they necessitate and their potential for bias is critical for the design and interpretation of studies employing these biomarkers.
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