Phosphorylated tau is more closely associated with the spatial extent of tauopathy than with tau load
Bibliographic record
Abstract
Abstract Background Recent evidence indicated that cognitive impairment is more closely associated with the spatial extent of tauopathy (SEOT) than with tau load. It remains unclear whether this is also true for other markers of Alzheimer’s disease (AD) severity, such as fluid levels of phosphorylated tau (pTau). Here, we compared the link between fluid pTau and the SEOT and tau load in the brain, as assessed by PET. Method We studied individuals across the aging and AD continuum or with non‐AD neurodegenerative diseases from the TRIAD and ADNI cohorts. TRIAD participants underwent [18F]MK6240 tau‐PET and CSF and plasma pTau 181, 217, and 231. ADNI participants were evaluated with [18F]AV1451 tau‐PET and CSF pTau181. We calculated standard uptake value ratios (SUVR) as a proxy of tau load, and the proportion of abnormal voxels as a measure of the SEOT. Abnormal voxels were those 2.5 SD higher than the mean of a group of young adults. Spearman’s correlations assessed the associations of pTau with SEOT and tau load in a temporal meta‐region of interest (ROI) and Braak‐like cumulative ROIs. Correlation coefficients were compared in R using the cocor package. Result We included 325 participants from TRIAD (mean [SD] age, 68 [9.8] years) and 417 from ADNI (mean [SD] age, 71.8 [9.0] years). All pTau biomarkers showed significant correlations with SEOT and tau load in all ROIs (Fig. 1A‐B). All pTau epitopes were more closely associated with SEOT than with tau load in Braak I‐V and I‐VI (Fig. 2). In TRIAD, this was also observed in Braak I‐III and I‐IV for CSF pTau 217 and 231, and in Braak I‐II for plasma pTau 231. In ADNI, this was also seen for CSF pTau181 in Braak I‐II and I‐III. Tau load did not correlate better with pTau in any ROI. Conclusion PTau biomarkers seem to inform better on SEOT than on tau load. There is a trend for better performance of SEOT as an imaging correlate of pTau when studying late Braak stages. Future studies should investigate the relationship between these measures and the clinicopathological progression of AD.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".