Performance of plasma biomarkers for diagnosis and prediction of dementia in a Brazilian cohort
Bibliographic record
Abstract
Abstract Background Dementia is a growing concern throughout the developing world and is severely underdiagnosed among the Brazilian population. Despite remarkable progress in the biomarker field in recent years, local testing and validation of plasma biomarkers of AD and dementia is still lacking in Brazil and Latin America. Method In this longitudinal cohort study of 145 participants, the diagnostic performance of plasma biomarkers was assessed based on clinical diagnosis and CSF biomarker positivity. Follow‐up data of up to 4.7 years were used to determine biomarker performance in predicting diagnostic conversions. The study was conducted at the Memory Clinic at the D’Or Institute for Research and Education (IDOR) in Rio de Janeiro. Participants were volunteers referred to the service. All were native Brazilians, had Portuguese as their first language and 60+ years of age. They were diagnosed (DSM‐5 criteria) and underwent extensive psychiatric and laboratory assessments. Diagnoses outside the scope of the study were excluded. CSF biomarker data was available for 34% of the sample. Participants were categorized as cognitively normal controls (n = 49), amnestic mild cognitive impairment (aMCI; n = 29), Alzheimer’s disease (AD; n = 37), Lewy body dementia (n = 23), or vascular dementia (n = 7). Plasma samples collected at initial and follow‐up visits were tested for relevant biomarkers. Result Plasma Tau, Aβ40, Aβ42, NfL, GFAP, pTau231 and pTau181 were measured on the SIMOA HD‐X platform. Results were evaluated against clinical diagnosis and CSF biomarker status. Plasma NfL and GFAP could discriminate between all‐cause dementia and controls with ROC AUCs of 0.79 (95% CI: [0.70–0.87]) and 0.74 [0.65–0.83], respectively. Plasma pTau181 had good diagnostic performance discriminating clinical AD (AUC = 0.89 [0.82–0.96]), CSF‐biomarker+ aMCI/AD (AUC = 0.90 [0.82–0.98]), or CSF‐biomarker‐confirmed AD (AUC = 0.95 [0.89–1.00]) from controls. Conclusion In this first description of the plasma biomarker profile of a Brazilian dementia cohort, plasma pTau181 confirmed its potential as a clinically useful diagnostic tool. This study comprises an initial step towards local validation and adoption of dementia biomarkers in Brazil and Latin America.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".