Plasma biomarkers of tau for the differentiation between slow and fast tau‐PET accumulators
Bibliographic record
Abstract
Abstract Background Plasma markers of tau are currently being studied as proxies of cerebral neurofibrillary tangle (NFT) accumulation. Phosphorylated tau (pTau) and N‐terminal tau fragment (NTA) assays are associated with present and future tau‐PET load. Our aim was to investigate whether plasma markers could predict whether someone will be a slow or fast accumulator. Method We assessed 143 individuals [72 CU, 54 MCI, 17 AD] from the TRIAD cohort, with two available [18F]MK6240 tau‐PET scans and calculated the relative change (Δ[18F]MK6240) between baseline and follow‐up [mean follow‐up time: 2.1 ± 0.7 years]. We used tertiles to divide individuals as slow, medium and fast accumulators. Additionally, we measured baseline plasma pTau181, pTau217, pTau231 and NTA concentrations. We computed the effect size (Cohen’s d) and area under the curve (AUC) for each plasma marker for Δ[18F]MK6240 between slow and fast accumulators. Δ[18F]MK6240 was calculated in Braak stages I/II, III/IV and V/VI. Result We first observed that the highest effect size for Δ[18F]MK6240 in Braak I/II was depicted by pTau231. For Δ[18F]MK6240 in Braak III/IV and Braak V/VI, pTau217 presented the highest effect size (Figure 1). Moreover, AUC values were the highest, and highly similar, in ΔBraak I/II for pTau181, pTau217 and pTau231. For ΔBraak III/IV, pTau181 and pTau217 presented the highest values. Finally, AUC for ΔBraak V/VI, pTau231 and NTA had the highest values, which were also similar (Figure 2). Conclusion Plasma pTau biomarkers (181, 217 and 231) are great predictors of fast accumulation in early to middle Braak regions. For late Braak regions, fast accumulation was best predicted by pTau217 and NTA. Plasma markers are able to determine whether someone will be a fast accumulator in a stage‐specific manner. The currently available tau biofluid measures could be used in the clinical and clinical trial settings, as these are less invasive and cheaper than CSF or PET assessments. Especially in the recruitment phase, pTau217 could be used for screening individuals that are more likely to accumulate tau fast.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".