Highly Specific and Ultrasensitive Plasma Test Detects Abeta(1-42) and Abeta(1-40) in Alzheimer’s Disease
Bibliographic record
Abstract
Abstract BACKGROUND Plasma biomarkers that reflect specific amyloid beta (Abeta) proteoforms are essential to monitor treatment effects of Alzheimer’s disease (AD) therapies. Our aim was to develop and validate ready-to-use Simoa ‘Amyblood’ assays that measure full length Abeta1-42 and Abeta1-40 and compare their performance with two commercial assays. METHODS Linearity, intra- and inter-assay %CV were compared between Amyblood, Quanterix Simoa triplex, and Euroimmun ELISA. Sensitivity and selectivity were assessed for Amyblood and the Quanterix triplex. Clinical performance was assessed in CSF biomarker confirmed AD (n=43, 68±6 years) and controls (n=42, 62±5 years). RESULTS Prototype and Amyblood showed similar calibrator curves and differentiation (20 AD vs 20 controls, p<0.001). Amyblood, Quanterix triplex, and ELISA showed similar linearity (96%-122%) and intra-assay %CVs (≤3.1%). A minor non-specific signal was measured with Amyblood of +2.4 pg/mL Abeta1-42 when incubated with 60 pg/mL Abeta1-40. A substantial non-specific signal of +24.7 pg/mL Abetax-42 was obtained when 40 pg/mL Abeta3-42 was measured with the Quanterix triplex. Selectivity for Abeta1-42 at physiological Abeta1-42 and Abeta1-40 concentrations was 125% for Amyblood and 163% for Quanterix. Amyblood and Quanterix ratios (p<0.001) and ELISA Abeta1-42 concentration (p=0.025) could differentiate AD from controls. CONCLUSIONS We successfully developed and upscaled a prototype to the Amyblood assays with similar technical and clinical performance as the Quanterix triplex and ELISA, but better specificity and selectivity than the Quanterix triplex assay. These results suggest leverage of this specific assay for monitoring treatment response in trials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".