Association between plasma inflammatory biomarkers and [<sup>18</sup>F]FDG‐PET signal in a transgenic amyloid rat model
Bibliographic record
Abstract
Abstract Background Neuroinflammation is thought to play an important role in the pathogenesis of Alzheimer’s disease (AD). It has recently been demonstrated that [18F]FDG positron emission tomography (PET) signal is sensitive to central inflammatory changes in AD. However, it is unknown whether blood cytokines, which are important signaling molecules that regulate the peripheral inflammatory process, are associated with the cerebral [18F]FDG‐PET signal. In this work, we aimed to investigate whether plasma cytokines are associated with brain glucose metabolism in a transgenic amyloid rat model. Method Brain metabolism of 4‐ and 10‐month‐old wild‐type (WT) and APP/PS1 rats (TgF344‐AD) were assessed with [18F]FDG‐PET imaging. Plasma inflammatory markers (IFN‐γ, IL‐1α, IL‐1β, IL‐6, IL‐10 and TNF‐α) were measured by multiplex immunoassay. [18F]FDG‐SUVr was calculated with pons as the reference region. Correlation between inflammatory markers and [18F]FDG‐PET signal was conducted at the voxel level (RMINC; p<0.05 and t>2). Result At 4 months, IL‐10 levels significantly associate with [18F]FDG‐PET signal in the frontal and temporoparietal cortices (FCx and TPCx) and hippocampus (Fig1A), whereas other biomarkers present only small clusters associated with [18F]FDG‐PET signal (Fig1A). At 10 months, a positive association with [18F]FDG‐PET was found between IL‐1α and IL‐1β at FCx and TPCx, while IL‐10 showed a positive correlation at the TPCx (Fig1B). Additionality, IFN‐γ and TNF‐α presented small correlation clusters at the TPCx, and IL‐6 at the TPCx and CxF (Fig1B). Conclusion At the pre‐amyloid plaque stage, only an anti‐inflammatory cytokine, IL‐10, presented positive associations with brain glucose metabolism. However, multiple pro‐inflammatory cytokines correlated with brain glucose metabolism at the amyloid plaque stage. These findings suggest a dual peripheral inflammatory response impacting brain metabolism, which can be associated with amyloid species and deposition.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".