Diagnostic performance of Alzheimer’s disease blood biomarkers in a Brazilian cohort
Bibliographic record
Abstract
Blood-based biomarkers (BBMs) have emerged as promising tools to enhance Alzheimer's disease (AD) diagnosis. Despite two-thirds of dementia cases occurring in the Global South, research on BBMs has predominantly focused on populations from the Global North. This geographical disparity hinders our understanding of BBM performance in diverse populations. To address this, we evaluated the diagnostic properties of AD BBMs in a real-world memory clinic from Brazil, one of the largest countries in the Global South. We measured blood and cerebrospinal fluid (CSF) biomarkers - amyloid-β (Aβ)40, Aβ42, phosphorylated tau (p-tau) 217, neurofilament light (NfL) chain, and glial fibrillary acidic protein (GFAP) - in 59 individuals. Sample comprised 20 cognitively unimpaired (CU) individuals, 22 with AD dementia, and 17 with vascular dementia (VaD). We compared BBM levels across diagnostic groups and assessed their discriminative ability for AD. Notably, individuals with VaD and AD had lower educational levels (6.8±3.0) compared to CU individuals (61.4±6.6). Among the BBMs tested, plasma p-tau217 demonstrated the best performance, exhibiting high accuracy in differentiating CU from AD (AUC 0.96) and Aβ pathology (AUC 0.98). However, the ability of AD BBMs to distinguish between AD and VaD was lower than expected (AUC from 0.52 to 0.79), particularly when compared to studies from the Global North. Our findings highlight the potential utility of BBMs for AD diagnosis in real-world settings within the Global South. However, they also underscore the need for proper implementation and validation of these biomarkers within these populations to ensure accurate and reliable 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".