Association between glial and synaptic fluid biomarkers and brain [18F]FDG‐PET signal
Bibliographic record
Abstract
Abstract Background Beyond the hypometabolic signature, indexed by [18F]FDG‐PET imaging, seen in the brain of Alzheimer’s disease (AD) patients, a transient brain glucose hypermetabolism has been recently identified in the early stages of AD. The cellular source of this dual metabolic response remains controversial, with glial cells gaining a lot of attention. In light of this, whether glial and synaptic fluid biomarkers are associated with [18F]FDG‐PET signal in the early stages of AD remains debatable. Thus, we evaluated the association of astrocyte, microglial and synaptic fluid biomarkers with [18F]FDG‐PET signal in an AD rat model. Method [18F]FDG‐PET imaging was conducted in ten‐month‐old (early amyloid stage) APP/PS1 (TgF344‐AD, n = 8‐17) and wild‐type (WT, n = 8‐15) rats. Glial (GFAP and sTREM2), synaptic (neurogranin), and amyloid biomarkers (Aß1‐40 and Aß1‐42) were quantified in the cerebrospinal fluid (CSF) and plasma through a multiplex immunoassay. T‐statistical maps of brain [18F]FDG‐PET and those fluid biomarkers were conducted at the voxel level using RMINC. Differences were considered statistically significant at p<0.05 (t>2). Result A large hypermetabolic cluster was identified in ten‐month‐old TgF344‐AD (Fig. 1AB, local maxima, t(13) = 4.28). We found positive correlations between CSF TREM2 and brain [18F]FDG‐PET signal (Fig. 2AC, local maxima, t(13) = 4.12). In addition, we identified a positive correlation between plasma GFAP and brain [18F]FDG‐PET signal (Fig. 2BC, local maxima, t(13) = 10.62). Plasma TREM2, CSF GFAP, and plasma neurogranin did not associate with brain glucose metabolism. Conclusion Our findings suggest that CSF TREM2 and plasma GFAP are associated with brain metabolism before amyloid load reaches a plateau. Stronger association of plasma GFAP, rather than CSF GFAP, with brain pathology corroborates cross‐sectional human studies. One could argue that [18F]FDG‐PET hypermetabolism identified is likely a response to astrocyte and microglial changes.
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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 | 0.001 | 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) | 0.001 | 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".