Amyloid‐dependent tau phosphorylation drives faster accumulation of tau aggregates in female
Bibliographic record
Abstract
Abstract Background The high prevalence of Alzheimer’s dementia in females have long puzzled researchers in the field. Despite similar amyloid levels, females show higher load of neurofibrillary tangles (NFTs). Previous literature proposed that amyloid‐β (Aβ) and phosphorylated tau (p‐tau) synergism accelerates biomarker abnormalities. However, it remains to be answered whether this synergism is the driving force behind faster tau progression in females. The overarching goal of the study wa to investigate whether amyloid‐β aggregates differentially impose tau hyperphosphorylation and neurofibrillary tangles formation in a sex‐specific manner. Method In this longitudinal study, we assessed 287 participants from TRIAD cohort at McGill University Research Centre for Studies in Aging. Cerebral Aβ and tau deposition were assessed with positron emission tomography (PET) radiotracers [18F]AZD4694 ([18F]NAV4694) and [18F]MK6240, respectively. Cerebrospinal fluid (CSF) p‐tau181 and p‐tau217 were also measured (analysed at the Clinical Neurochemistry Laboratory at the University of Gothenburg, Sweden). Regression and voxel‐based models with interaction terms were used to evaluate baseline tau load and NFT accumulation rate as a function of sex and baseline biomarkers (Aβ and p‐tau). Result We identified sex difference in the relationships between CSF p‐tau, Aβ and NFT (Fig 1). Specifically, voxelwise analyses demonstrated that female presented stronger positive correlations between CSF p‐tau and AD core biomarkers (Aβ and NFT). Furthermore, we discovered that Aβ and CSF ptau181 interactively potentiated tau accumulation in females but not males (Fig 2, Table 1). Together, the present results support that Aβ load imposes higher tau aggregation in females. Conclusion Aβ‐dependent tau phosphorylation was the key driver for faster NFT accumulation observed in female.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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".