Microglial activation impacts amyloid‐β effects on reactive astrogliosis
Bibliographic record
Abstract
Abstract Background Glial reactivity is a key phenomenon in Alzheimer’s disease (AD) and is closely associated with amyloid‐ß (Aß) pathology. Although compelling experimental data suggest that microglial activation modulates reactive astrogliosis, it remains to be elucidated whether microglial activation influences the association of Aß pathology with reactive astrogliosis in the living AD human brain. Here, we tested the association of microglial activation and Aß pathology with reactive astrogliosis in individuals across the aging and AD clinical spectrum. Method We studied 101 participants (62 cognitively unimpaired [CU], 26 with mild cognitive impairment [MCI], and 13 with AD dementia) from the Translational Biomarkers in Aging and Dementia (TRIAD) cohort. Individuals had available positron emission tomography (PET) for Aß ([18F]AZD4694) and microglial activation ([11C]PBR28), as well as magnetic resonance imaging. We further assessed reactive astrogliosis with plasma glial fibrillary acidic protein (GFAP). Linear regression analyses were used to investigate the associations between Aß, microglial activation and GFAP. Result Demographic characteristics of the study population are reported in Table 1. Regression analyses revealed a significant positive association between Aß PET burden and plasma GFAP levels in microglial activation‐positive but not in microglial activation‐negative individuals (Fig. 1A). A significant interaction between continuous values of Aß PET burden and [11C]PBR28 PET uptake on plasma GFAP levels (Fig. 1B) supported that microglial activation affects the association of Aß pathology with reactive astrogliosis. Analysis of variance further confirmed that the model with the interaction term was the most adequate to describe the association of Aß PET and microglial activation PET with plasma GFAP (P = 0.007). In additional analyses investigating the topography of the observed findings, we found that higher Aß PET burden was associated with higher plasma GFAP levels only in the presence of microglial activation positivity across Aß‐vulnerable cortical brain regions (Fig. 1C). Conclusion Our results suggest that microglial activation impacts Aß‐dependent reactive astrogliosis in the living AD brain. This can help to better understand the complementary roles of glial cells in neurodegenerative diseases, as well as provide insights for the development of novel therapeutic strategies for AD targeting the interplay between Aß and glial reactivity.
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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 | 0.000 |
| 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".