Brain TSPO expression is associated with plasma pTau181 & pTau231 across the AD spectrum
Bibliographic record
Abstract
Abstract Background Microglial activation is an important component of the immune response in the brain of AD patients and has been shown to follow a similar propagation pattern to tau tangle accumulation. However, it is unclear to what extent cerebral microglial activation is associated with biofluid concentrations of phosphorylated tau (pTau). The objective was to investigate whether concentrations of pTau181 and pTau231 are correlated with microglial activation indexed by [11C]PBR28 PET across the AD spectrum. Method The present study was conducted in a population of 130 individuals from the Translational Biomarkers of Aging and Dementia (TRIAD) cohort: 20 young cognitively unimpaired, 65 cognitively unimpaired elderlies, 31 with mild cognitive impairment and 14 with AD dementia. Participants were evaluated with blood biomarkers and [11C]PBR28 PET. Plasma pTau181 and pTau231 concentrations were quantified using a Simoa assay. Microglial activation was assessed via [11C]PBR28 PET, standardized uptake value ratios (SUVRs) were calculated between 0 and 90 minutes post‐injection, using cerebellum grey matter as the reference region. Voxel‐based regression models evaluated the relationship between plasma pTau181 and pTau231 with neuroinflammation as assessed by PET, correcting for age and sex. Result In the present study, positive correlations were found between concentrations of both pTau181 and pTau231 and neuroinflammation indexed by PET‐imaging. Voxel‐level analyses indicated that these associations prenominated in regions of the default mode network including the precuneus, posterior cingulate cortex and medial prefrontal cortex. These regional associations survived corrections for multiple comparisons. Conclusion Peripheral phosphorylated tau is associated with neuroinflammation in brain regions associated with AD. Our findings highlight the importance of neuroinflammation in the pathogenesis 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".