Identify biological Alzheimer’s disease using a novel nucleic acid–linked protein immunoassay
Bibliographic record
Abstract
Abstract Blood-based biomarkers have been revolutionizing the detection, diagnosis and screening of Alzheimer’s disease. Specifically, phosphorylated-tau variants (p-tau181, p-tau217 and p-tau231) are promising biomarkers for identifying Alzheimer’s disease pathology. Antibody-based assays such as single molecule arrays immunoassays are powerful tools to investigate pathological changes indicated by blood-based biomarkers and have been studied extensively in the Alzheimer’s disease research field. 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 120 protein biomarkers in neurodegenerative diseases. Due to the relative novelty of the NULISA technology in quantifying Alzheimer’s disease biomarkers, validation through comparisons with more established methods is required. The main objective of the current study was to determine the capability of p-tau variants quantified using NULISA for identifying abnormal amyloid-β and tau pathology. We assessed 397 participants [mean (standard deviation) age, 64.8 (15.7) years; 244 females (61.5%) and 153 males (38.5%)] from the Translational Biomarkers in Aging and Dementia (TRIAD) cohort where participants had plasma measurements of p-tau181, p-tau217 and p-tau231 from NULISA and single molecule arrays immunoassays. Participants also underwent neuroimaging assessments, including structural MRI, amyloid-PET and tau-PET. Our findings suggest an excellent agreement between plasma p-tau variants quantified using NULISA and single molecule arrays immunoassays. Plasma p-tau217 measured with NULISA shows excellent discriminative accuracy for abnormal amyloid-PET (area under the receiver operating characteristic curve = 0.918, 95% confidence interval = 0.883 to 0.953, P < 0.0001) and tau-PET (area under the receiver operating characteristic curve = 0.939; 95% confidence interval = 0.909 to 0.969, P < 0.0001). It also presents the capability for differentiating tau-PET staging. Validation of the NULISA-measured plasma biomarkers adds to the current analytical methods for Alzheimer’s disease 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".