Alzheimer’s disease blood tests of amyloid‐β 42/40, p‐tau217, p‐tau181 and p‐tau205 ratios in a diverse community‐based population: Preliminary results from SEABIR
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD) blood tests that can identify and quantify amyloid plaques, tau tangles, and cognitive and clinical decline are needed in clinical practice, including primary care. However, these tests require validation in diverse groups and real‐world settings. The goals of the Study to Evaluate Amyloid in Blood and Imaging Related to Dementia (SEABIRD) were to enroll 1120 participants to determine the accuracy and validity of blood amyloid‐β (Aβ) and phosphorylated tau (p‐tau) tests in a diverse, community‐based sample of older adults compared with amyloid PET, and to explore the impact of key factors (age, race, years of education, cognition, APOE genotype, and medical conditions) on validity estimates. Method SEABIRD measured blood plasma Aβ42/40 ratio and ratios of phosphorylated to non‐phosphorylated p‐tau217, p‐tau181, and p‐tau205 (%p‐tau) in a diverse population of older adults. In addition, cognitive screening with the AD8® dementia screen and Montreal Cognitive Assessment (MoCA) were obtained. Approximately 25% of participants were selected for amyloid PET and CDR as a reference standard. Result Of the 859 participants enrolled in this ongoing study, 20.6% self‐identified as Black or African American and the percentage of APOE ε4 carriers was 30.2%, as expected for a population‐based sample (Table 1). For a subset of 76 participants with completed amyloid PET and all blood measures, plasma Aβ42/40 had ROC‐AUC with amyloid PET status of 0.85, %p‐tau217 of 0.97, %p‐tau181 of 0.83, and %p‐tau205 of 0.81 (Figure 1). Spearman correlations with amyloid PET SUVR were ‐0.59 for plasma Aβ42/40, 0.69 for p‐tau217 ratio, 0.43 for p‐tau181 ratio and 0.48 for p‐tau205 ratio. Conclusion These preliminary results demonstrate that AD blood tests have similar accuracies in a diverse population‐based sample to those reported in AD research cohorts. These findings suggest that rapid screening and diagnosis of amyloid pathology with AD blood tests is possible in a broader population with higher comorbid disease burden and greater diversity than typical AD research cohorts. Screening with AD blood tests could accelerate clinical trial recruitment and increase the representativeness of clinical trial populations, which may improve the generalizability of clinical trial results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".