Vascular pathology drives the association of plasma GFAP with gray matter atrophy and cognitive decline in amyloid‐negative individuals
Bibliographic record
Abstract
Abstract Background Amyloid (Aβ) pathology potentiates the association between GFAP, a biomarker for reactive astrogliosis, and neurodegeneration, while it remains unclear whether GFAP is associated with neurodegeneration without Aβ abnormalities. Preclinical studies suggest vascular pathology may activate glial cells, triggering deleterious effects. Here, we tested the hypothesis that, similar to Aβ pathology, vascular pathology may also potentiate the effects of plasma GFAP on neurodegeneration and cognitive decline in Aβ‐negative individuals. Method We assessed 324 cognitively impaired (CDR < 0) Aβ‐negative (Centiloid < 24) participants from a memory clinic cohort (BICWALZS), with available CDR global, MMSE, plasma GFAP, clinical assessment of peripheral vascular risk factors [PVPs: hypertension (HTN), diabetes (DBT), and dyslipidemia (DLP)], FLAIR and T1‐based volumetrics. Participants were divided into two groups according to their WMH status: Fazekas 1 (Fz1; n = 203) and Fazekas 2‐3 (Fz2‐3; n = 121). Group differences were analyzed using ANCOVA. Associations were assessed using linear regressions, and the contribution of PVPs to the effects of biomarkers was accessed through multicollinearity analysis accounting for age, sex, and years of education. Result Individuals in the Fz2‐3 group showed more hippocampal atrophy (Figure 1A, 1B; p = 0.0115), plasma NfL levels (1C; p = 0.0020), no changes in plasma GFAP levels (Figure 1D) and lower MMSE score (Figure 1E). In the Fz2‐3 group, hippocampal atrophy (Table 1; β: ‐0.179, p = 0.0440) and cognitive decline (Table 1; β: ‐0.194, p = 0.0390) was associated with plasma GFAP. No abnormalities or associations between biomarkers were found in the Fz1 group. Among the PVPs evaluated, HTN and DBT presented a stronger effect in the association of plasma GFAP in hippocampal atrophy (R 2 : 0.2089; p = 0.0001) and cognitive decline (R 2 : 0.1707; p < 0.0001), respectively (Figure 3). Conclusion Our study found that plasma GFAP is strongly linked to neurodegeneration and MMSE decline in Aβ‐negative individuals with high vascular burden. HTN and DBT, prevalent in the elderly, were the main contributors. This highlights vascular pathology as a key driver of neuroinflammation‐related neurodegeneration, underscoring the importance of managing these conditions to prevent brain atrophy.
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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 | 0.002 |
| 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 | 0.001 | 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".