The Impact of Sex in Tau‐related AD pathology
Bibliographic record
Abstract
Abstract Background Studies suggest that sex influences the deposition of tau in the human brain and impacts on the relationship of tau with AD‐related outcomes. The aim of this study is to evaluate the extent to which sex influences PET‐Tau related outcomes and whether this effect is related to specific tau PET tracers. Method We assessed 456 participants from the HEAD study (mean age = 66.07 ± 13.05). All individuals had available Tau‐PET with Flortaucipir and MK6240, Aβ‐PET and a subset had plasma p ‐Tau 217 ( n = 352). We conducted a linear regression analysis between sex and PET Tau to evaluate the direct influence of sex on PET Tau SUVR. Next, we assessed whether sex affected the relationship between Tau PET (in BRAAK I‐II, III‐IV, V‐VI regions) and Aβ‐PET or plasma p ‐Tau 217 by adding an interaction term in the associations. All analyses were corrected by age, clinical classification, and amyloid burden. Result No significant differences were observed between male and female SUVR levels in either Flortaucipir and MK6240 tracers (Figure 1). However, when assessing the relationship between Aβ‐PET and Tau, a statistically significant interaction with sex was seen between MK6240 in BRAAK III‐IV (β = 0.160, p = 0.020 ; Figure 2) and BRAAK V‐VI regions (β = 0.196, p = 0.008; Figure 2), with women having a stronger association. Furthermore, we found an interaction between sex and p ‐tau217 on Tau PET SUVR in BRAAK regions I‐II using MK6240 or Flortaucipir (β = ‐0.184, p = 0.014; β = ‐0.266, p = 0.002; Figure 3) indicating that women present a weaker relationship between p ‐tau217 and Tau PET. Conclusion Our results did not show any differences between men and women in Tau‐PET SUVR uptake across tau tracers. However, we found that sex affected how Aβ‐PET and plasma p ‐tau217 were associated with Tau‐PET uptake. Further studies are needed to elucidate the underpinnings of the link between sex and the association between these biomarkers.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".