Biomarkers disclose associations between Neuroinflammation and Synaptic Depletion in AD.
Bibliographic record
Abstract
Abstract Background While in healthy conditions, synaptic function is sustained by the interplay between neurons and glial cells. The presence of Alzheimer’s disease (AD) pathophysiology might impose synaptic alterations via neuroinflammatory responses from microglia and astrocytes. Imaging and fluid biomarkers allow for testing this conceptual framework in living individuals. Here, we aim at uncovering the associations between neuroinflammatory and synaptic markers in aging and AD. Method Participants were recruited from the Translational Biomarker for aging and dementia cohort (TRIAD). We analyzed cognitively unimpaired young (CUY, N = 20), cognitively unimpaired older adults (CU; N = 48) individuals, patients with mild cognitive impairment (MCI; N = 24), AD dementia patients (ADD; N = 16) participants. The endpoints were CSF 14‐3‐3 sigma‐delta (ζδ), CSF Growth Associated Protein 43 (GAP43) and neurogranin as markers of synaptic dysfunction. We modeled associations between these synaptic biomarkers with astrocyte and microglial related inflammatory markers as well as tau, amyloid and neurodegeneration biomarkers. Result According to ROC analyses contrasting CSF biomarkers, 14‐3‐3 ζδ was able to discriminate amyloid‐ β pathology in cognitively impaired individuals (AUC = 0.88) (Figure 1) . The amyloid‐induced 14‐3‐3 ζδ increase was mediated by inflammatory astrogliosis measured by GFAP, whereas the tau induced 14‐3‐3 ζδ pathology was mediated by anti‐inflammatory cytokines (Figure 2) . CSF levels of GAP43 and neurogranin were positively associated with TSPO measured with [ 11 C]PBR28 (Figure 3.1) and GFAP (Figure 3.2) . [ 11 C]PBR28 cluster‐based SUVRs was positively correlated to concentrations of (A) Neurogranin (p = 5.8e‐08) and (B) GAP‐43(p = 6.8e‐10). Conclusion The present observations support the framework in which glia‐related neuroinflammatory responses contribute to synaptic alterations in carriers of AD pathophysiology. We will discuss the implications and limitations of this framework.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".