Validation of the plasma phosphorylated tau quantified using NUcleic acid Linked Immuno‐Sandwich Assay (NULISA) for the detection of Alzheimer’s disease pathology
Bibliographic record
Abstract
Abstract Background Blood‐based biomarkers have been revolutionizing the detection, diagnosis and screening of Alzheimer’s disease (AD). Antibody‐based immunoassays are powerful tools to investigate pathological changes indicated by blood‐based biomarkers and have been studied extensively in AD research. A novel proteomic technology ‐ NUcleic acid Linked Immuno‐Sandwich Assay (NULISA) – was developed to improve the sensitivity of traditional proximity ligation assays and offer a comprehensive outlook for protein biomarkers in neurodegenerative diseases. Due to the relative novelty of the NULISA technology in quantifying AD plasma biomarkers, validation through comparisons with more established methods is required. Method In this present study, we assessed 397 participants from the Translational Biomarkers in Aging and Dementia (TRIAD) cohort where participants had plasma measurements of p‐tau181, p‐tau217 and p‐tau231 from both NULISA and other established immunoassays. Participants also underwent neuroimaging assessments including MRI, amyloid and tau positron emission tomography (PET). Result Our findings suggest an excellent agreement between plasma p‐tau variants quantified using different immunoassays and strong associations with PET signals in the brain (Fig. 1). As shown in Figure 2, similar to p‐tau217 quantified using Janssen and ALZpath immunoassays, plasma p‐tau217 NULISA shows excellent discriminative accuracy for abnormal amyloid‐PET (AUC = 0.918, 95%CI: 0.883 to 0.953, P < 0.0001) and abnormal tau‐PET status (AUC = 0.939; 95%CI: 0.909 to 0.969, P < 0.0001). It also presents the capability for differentiating tau‐PET staging (Table 1). Conclusion Validation of the NULISA CNS panel adds to the current analytical methods for AD diagnosis, screening, and staging, and could potentially expedite the development of a blood‐based biomarker panel.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".