Comparison of plasma amyloid, tau, and astrocyte biomarkers to identify AD pathophysiology
Bibliographic record
Abstract
Abstract Background Although it has been already demonstrated that plasma amyloid‐β (Aβ), phosphorylated tau (p‐tau), and glial fibrillar protein (GFAP) can predict with high accuracy Alzheimer's disease (AD) pathophysiology, no previous study has compared their performance in the same set of individuals. Here, we compare the performance of plasma Aβ42/40, p‐tau, GFAP, and NfL against Aβ and tau PET across the AD spectrum. Method We used the ROC curve to test the predictive performance of Simoa plasma Aβ42/40, p‐tau (at threonine 181 and 231), NfL, and GFAP to identify Aβ and Pau PET positivity in 138 cognitive unimpaired (CU) and 87 cognitive impaired (CI) from the McGill TRIAD cohort. Pearson correlation and linear regression tested the association between markers. Result We showed that plasma p‐tau231, p‐tau181, GFAP, and NfL correlated with each other (Figure 1), while Aβ42/40 did not. In CU, voxel‐wise linear regressions (Figure 2A) showed that p‐tau231, p‐tau181, and GFAP concentrations were significantly associated with Aβ‐PET (Figure 2A). While for Tau‐PET (Figure 2B), there was a significant association only with p‐tau231 and p‐tau181 (Figure 2B). P‐tau231 outperformed the other plasma biomarkers to identify both Aβ‐ and Tau‐PET positivity (AUC 0.877 and 0.796, respectively) in CU individuals. In CI, Aβ‐ and Tau‐PET were significantly associated with p‐tau231, p‐tau181, and GFAP, whereas NfL was only associated with Tau‐PET. The discriminative accuracy of GFAP in identifying both Aβ‐PET and Tau positivity (AUC 0.936 and 0.944, respectively) outperformed the other plasma biomarkers in CI individuals (Figure 3, Table 1). Conclusion We showed that plasma p‐tau231, a novel biomarker of early AD, best depicted AD pathophysiology in CU individuals. Interestingly, GFAP, an astrocyte reactivity marker, was better associated with brain Aβ and tau pathologies than plasma p‐tau and Aβ markers in CI individuals. Our results highlight that the performance of the novel plasma biomarkers of amyloid, tau and neuroinflammation to detect brain AD pathophysiology is disease stage specific.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".