Plasma p‐tau217 outperforms [<sup>18</sup>F]FDG‐PET in identifying biological Alzheimer’s disease in atypical and early‐onset dementia
Bibliographic record
Abstract
Abstract Background Biomarkers promise to significantly improve the differential diagnosis of Alzheimer’s disease (AD). Plasma biomarkers, such as phosphorylated tau (p‐tau), have shown potential in diagnosing AD with high accuracy. Unlike the widely‐used [18 F]FDG‐PET diagnostic biomarker in clinical practice, plasma p‐tau is specific to AD and can provide an affordable and scalable diagnostic tool. Method We conducted a retrospective analysis of individuals with atypical dementia, and/or early‐onset dementia cases, who were assessed at a specialized memory clinic. All participants underwent measurements of CSF Aβ42, p‐tau181, and total tau levels, as well as brain [18F]FDG‐PET scans and plasma p‐tau217 measurement. The [18F]FDG‐PET data was visually examined by two nuclear medicine experts to determine whether they were compatible with AD. CSF biomarker results were categorized as either AD biomarker positive or negative. Contingency analysis was performed to assess the relationships between PET scan interpretation and fluid biomarkers (plasma p‐tau217 and CSF p‐tau181 and Aβ42). CSF biomarker and amyloid‐PET were treated as the reference standard. Result 81 subjects with atypical dementia had CSF AD biomarker evaluation, [18F]FDG‐PET rating, and plasma p‐tau217 assessment. Both [18F]FDG‐PET and plasma p‐tau217 had high levels of agreement with reference standard AD biomarkers ([18F]FDG‐PET: 71%; plasma p‐tau217: 81%). Although both biomarkers had similar specificity for AD ([18F]FDG‐PET:70%, plasma p‐tau217:70%), plasma p‐tau217 had higher sensitivity for abnormal AD (97%). Overall accuracy was also higher for plasma p‐tau217 (AUC=84%, 95%CI = 0.75‐ 0.93). The same pattern of results was observed when using amyloid‐PET as the reference standard. Conclusion Our study provides evidence that plasma p‐tau217 has excellent diagnostic performance for AD in individuals with early‐onset or atypical dementia evaluated in specialized settings. Nevertheless, the topographical information from [18F]FDG‐PET may give complementary information.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".