Microglial Reactivity Correlates with Presynaptic Loss Independent of β‐Amyloid and Tau
Bibliographic record
Abstract
Objective Triggering receptor expressed on myeloid cells‐2 (TREM2) and progranulin (PGRN) are critical regulators of microglia activation and can be detected in cerebrospinal fluid (CSF). However, whether microglial reactivity is detrimental or neuroprotective for Alzheimer disease (AD) is still debatable. Methods We identified 663 participants with baseline β‐amyloid (Aβ) positron emission tomography (PET) and CSF biomarker data, including phosphorylated tau181 (p‐Tau181), soluble TREM2 (sTREM2), PGRN, and growth‐associated protein‐43 (GAP‐43). Among them, 254 participants had concurrent longitudinal CSF biomarkers. We used multivariate regression analysis to study the associations of CSF microglial biomarkers with Aβ PET, CSF p‐Tau181, and CSF GAP‐43 cross‐sectionally and longitudinally. A Chinese aging cohort's independent CSF samples (n = 65) were analyzed as a validation. Results Higher baseline levels of CSF microglial biomarkers were related to faster rates of CSF sTREM2 increase and CSF PGRN decrease. Elevated CSF p‐Tau181 was associated with higher levels of CSF microglial biomarkers and faster rates of CSF sTREM2 increase and CSF PGRN decrease. In both cohorts, higher Aβ burden was associated with attenuated CSF p‐Tau181 effects on CSF microglial biomarker increases. Independent of Aβ PET and CSF p‐Tau181 pathologies, higher levels of CSF sTREM2 but not CSF PGRN were related to elevated CSF GAP‐43 levels and faster rates of CSF GAP‐43 increase. Interpretation These findings suggest that higher Aβ burden may attenuate the p‐Tau‐associated microglial responses, and TREM2‐related microglial reactivity may independently correlate with GAP‐43‐related presynaptic loss. This study highlights the two‐edged role of microglial reactivity in AD and other neurodegenerative diseases. ANN NEUROL 2024;95:917–928
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".