The Validation of Multifactor Model of Plasma Aβ42 and Total-Tau in Combination With MoCA for Diagnosing Probable Alzheimer Disease
Bibliographic record
Abstract
Alzheimer’s disease (AD) has an insidious onset and heterogeneous clinical symptoms. The well-accepted biomarkers for clinical diagnosis of AD includes β-amyloid (Aβ) deposition and pathologic tau level within cerebral spinal fluid (CSF) and imaging AD pathology like positive emission tomography (PET) imaging of the amyloid-binding agent Pittsburgh compound B (PET-PiB). However, the high expense and invasive nature of these methods highly limit their wide usage in clinic practice. Therefore, it is imperious to develop less expensive and invasive methods, and plasma biomarkers are the premium targets. In the current study, we utilized a single-blind comparison method, all the probable AD cases met the core clinical NIA-AA criteria, and validated by PET-PiB. We used ultra-sensitive immunomagnetic reduction (IMR) assays to measure plasma Aβ42 and total-tau (t-tau) levels, in combination with different variables including Aβ42 × t-tau value, Montreal Cognitive Scale (MoCA), and mini-mental state examination (MMSE). We used logistic regression to analyze the effect of all these variables in the algorism. Our results showed that (1) plasma Aβ42 and t-tau are efficient biomarkers for AD diagnosis using IMR platform, while Aβ42 × t-tau value is more efficient for discriminating control and AD; (2) in the control group，Aβ42 level and age demonstrated strong negative correlation; Aβ42 × t-tau value and age demonstrated significant negative correlation; (3) in the AD group, t-tau level and MMSE score demonstrated strong negative correlation; (4) using the model that Aβ42, Aβ42 × t-tau, and MoCA as the variable to generate ROC curve, cut-off value = 0.48, sensitivity = 0.973, specificity = 0.982, AUC = 0.986, offered better categorical efficacy, sensitivity, specificity, and AUC. The multi-factor model of plasma Aβ42 and t-tau in combine with MoCA can be a viable model separate health and AD subjects in clinical practice.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".