Exploring inflammation‐related protein expression and its relationship with TSPO PET in Alzheimer's disease
Bibliographic record
Abstract
INTRODUCTION: To understand the role of neuroinflammation in Alzheimer's disease (AD), we characterized immune-related proteins in central and peripheral biofluids. METHODS: Selection of participants from the Translational Biomarker of Aging and Dementia (TRIAD) cohort with available translocator protein (TSPO) positron emission tomography (PET), cerebrospinal fluid (CSF) (n = 97), and plasma (n = 165). Biofluid samples analyzed with Olink technology (368 inflammation proteins). RESULTS: Elevated proteins levels in CSF of TSPO-positive individuals were identified. Functional enrichment analysis of CSF proteins revealed processes implicated in AD (MAPK, ERK cascades, cytokine, and leukocyte signaling). Selected candidates (CXCL1 and TNFRSF11B) showed high correlation with each other in CSF and with TSPO PET signal, but weaker associations with amyloid and tau PET. No significantly changed proteins in plasma between TSPO groups were found. DISCUSSION: This explorative study identified two potential targets in CSF showing correlations with TSPO, amyloid and tau PET, suggesting a direct link between neuroinflammation, expression of these proteins and their potential implication in AD. HIGHLIGHTS: Several proteins are elevated in CSF of TSPO PET-positive individuals, linking them to neuroinflammation. Elevated CSF proteins were enriched in pathways such as MAPK, ERK, and cytokine signaling, linking them to the AD pathophysiology. Candidate proteins (CXCL1 and TNFRSF11B) correlated strongly with TSPO PET, particularly in brain regions known to be affected in AD. Although none of the plasma proteins remained significant after multiple comparisons correction when comparing their expression between TSPO groups, as done for CSF, candidate CSF proteins were found to correlate with plasmatic proteins, highlighting the complexity of the immune system.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".