An Alzheimer’s disease blood test study in a diverse community‐based population: Preliminary results from SEABIRD
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD) blood tests are needed to aid in the evaluation of dementia, but the performance of these tests should be evaluated 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 participant acceptability and validity (relative to amyloid PET) of a plasma amyloid‐β 42/40 test in a diverse, community‐based sample of older adults, and explore the impact of important factors (age, race, education, cognition, APOE genotype, and medical conditions) on the blood test’s performance. Method SEABIRD measured plasma amyloid‐β 42/40 in a diverse population of older adults in the Saint Louis, Missouri, USA area. Participants completed a blood collection, cognitive screening (AD8® dementia screening interview and Montreal Cognitive Assessment [MoCA]), and a survey about their study experience and perceptions of the blood test. A subset of participants completed additional blood collection for reproducibility, amyloid PET and MRI for validation of the blood test, and the Clinical Dementia Rating® (CDR) for validation of the cognitive screening measures. 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% (Table 1). Compared to the general population, SEABIRD participants were more likely to be female, aged 70‐79 years, and highly educated. They were less likely to report high cholesterol, diabetes, kidney disease, and stroke, and more likely to report depression. The receiver operating characteristic area under the curve for distinguishing cognitively unimpaired (CDR = 0) from cognitively impaired (CDR>0) individuals was 0.72 for the informant‐rated AD8, 0.46 for the self‐rated AD8, 0.70 for the MoCA, and 0.79 for the AD8/MoCA composite (Table 2). Survey results indicated that the blood test was well accepted (Figure 1), but it was perceived more positively by white and highly educated individuals. Conclusion A study of an AD blood test achieved rapid enrollment, broader diversity than typical AD cohorts, and high rates of participation. These results suggest that an AD blood test could be used for screening in a diverse clinical population.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".