The neuroinflammatory marker sTNFR2 relates to worse cognition and tau in women across the Alzheimer's disease spectrum
Bibliographic record
Abstract
Abstract Introduction Despite women showing greater Alzheimer's disease (AD) prevalence, tau burden, and immune/neuroinflammatory response, whether neuroinflammation impacts cognition differently in women versus men and the biological basis of this impact remain unknown. We examined sex differences in how cerebrospinal fluid (CSF) neuroinflammation relates to cognition across the aging–mild cognitive impairment (MCI)–AD continuum and the mediating role of phosphorylated tau (p‐tau) versus other AD biomarkers. Methods Participants included 284 individuals from the Alzheimer's Disease Neuroimaging Initiative study. CSF neuroinflammatory markers included interleukin‐6, tumor necrosis factor α, soluble tumor necrosis factor receptor 2 (sTNFR2), and chitinase‐3‐like protein 1. AD biomarkers were CSF p‐tau181 and amyloid beta1‐42 levels and magnetic resonance imaging measures of hippocampal and white matter hyperintensity volumes. Results We found a sex‐by‐sTNFR2 interaction on Mini‐Mental State Examination and Clinical Dementia Rating‐Sum of Boxes. Higher levels of sTNFR2 related to poorer cognition in women only. Among biomarkers, only p‐tau181 eliminated the female‐specific relationships between neuroinflammation and cognition. Discussion Women may be more susceptible than men to the adverse effects of sTNFR2 on cognition with a potential etiological link with tau to these effects.
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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 | 0.001 |
| 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) | 0.002 | 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".