Sex impacts the association of plasma Glial Fibrillary Acidic Protein with neurodegeneration in Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background Growing evidence suggests an increased prevalence of Alzheimer’s disease (AD) in females compared to males. Elucidating how disease biomarkers correlate in females and males is critical to understanding the basis of sex differences in AD. Our aim was to evaluate sex‐related differences in the association of plasma Glial fibrillary acidic protein (GFAP) levels, a biomarker of astrocyte reactivity, with downstream neurodegeneration in Alzheimer’s disease (AD) pathophysiology. Method We cross‐sectionally assessed participants from TRIAD cohorts. Unpaired t‐test compare the difference in plasma GFAP levels between females and males. We performed linear regression with an interaction term for sex to compare the association of plasma GFAP with neurodegeneration measured with hippocampal volume (HCV) between cognitively impaired females and males. Result We assessed 308 participants (MCI = 63, AD = 45 CN = 200, mean age = 69.8 (8)). Females showed significantly higher GFAP levels than males (Fig 1). We found a significant interaction term and strong correlation between plasma GFAP and neurodegeneration (HCV atrophy) (R‐squared = 0.13; P = 0.003) only in females. No association was found between GFAP and AD biomarkers in females or males without cognitive impairment (Fig 2). Conclusion Our results suggest astrocyte reactivity is highly associated with neurodegeneration, a process known to play a key role in AD pathophysiology, in females than males. This may have important implications for elucidating the basis of the higher prevalence of AD in females. Yet, these results were generated with a limited number of subjects and using a cross‐sectional design. Therefore, replication in larger, independent longitudinal datasets is needed to better understand the association of GFAP levels, sex, and AD pathophysiology.
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 | 0.001 | 0.002 |
| 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".