Blood-based Biomarkers of Alzheimer’s Disease and Neurodegeneration in an Indigenous African Cohort using both SIMOA and NULISA Platforms
Bibliographic record
Abstract
Background: In low- and middle-income countries, Alzheimer's disease and related dementias (ADRD) constitute a growing public health burden. Indeed, the lack of awareness and easy screening tools, such as blood-based biomarkers, leaves many patients undiagnosed. In this study, we explored the core biomarkers of AD in an indigenous African cohort (VALIANT) to assess their relevance and potential utility to aid clinical diagnosis. Methods: Nigerian African older adults (n = 967; ≥50 years) participating in the VALIANT study completed a baseline cross-sectional evaluation with associated clinical diagnosis. We quantified phosphorylated tau (p-tau 217), glial fibrillary acidic protein (GFAP), neurofilament light (NfL), and amyloid beta (Aβ42 and Aβ40) levels in plasma with both the Single Molecule Assay (SIMOA, Quanterix) and Nucleic acid-Linked Immuno-Sandwich Assay (NULISA, Alamar) platforms. Results: < 0.05). These results were consistent across both SIMOA and NULISA platforms. Comparison between sexes showed higher levels of biomarkers in male participants across diagnostic groups. We identified a significant effect of apoE E4 proteotype on p-tau217 levels after adjusting for age and sex but no significant effect on the other AD biomarkers. Conclusion: This first application of cutting-edge plasma AD biomarker immunoassay using two ultrasensitive platforms in an indigenous African cohort showed good concordance and underscores the relevance and utility of blood-based biomarkers of AD in diverse populations. Additionally, sex differences could unveil biological distinctions inherent in the African 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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".