Neuroinflammation is associated with the rising of early Alzheimer’s disease pathology in amyloid‐negative elderly
Bibliographic record
Abstract
Abstract Background Amyloid‐β (Aβ) and tau are the two well‐recognised pathological hallmarks of Alzheimer’s disease (AD). In the recent decades, increasing evidence supports neuroinflammation as one of the earliest pathomechanistic alterations throughout the AD continuum. However, little is known about the spatial and temporal patterns of neuroinflammatory processes based on AD pathological status. Furthermore, it also remains elusive how longitudinal change of neuroinflammation affects cerebral amyloid and tau load in the trajectory of AD. Method We examined a total number of 122 subjects (mean age= 65.6 years, 66.4% women, 33.1% APOEɛ4 carriers) from the TRIAD cohort at McGill University Research Centre for Studies in Aging. Neuroinflammation, cerebral Aβ load and tau deposition were assessed with positron emission tomography (PET) radiotracers [11C]PBR28, [18F]AZD4694 ([18F]NAV4694) and [18F]MK6240 respectively. Amyloid‐β positivity was determined by a cortical composite [18F]NAV4694 SUVR threshold of 1.55, based on previously published method. Voxelwise analyses were performed to evaluate the relationships between cerebral amyloid load and neuroinflammation. A subgroup of 42 subjects who had underwent two‐year follow‐up PET scans were used to study how the longitudinal change of neuroinflammation affects other AD hallmarks. Result At early stages of amyloid pathology, we found a positive linear relationship between neuroinflammation and amyloid load. Voxelwise analyses also revealed a stronger association between amyloid and neuroinflammation among the Aβ‐negative subjects. Longitudinally, the increase of neuroinflammation was linked to the accumulation of cerebral amyloid and tau in Aβ‐negative subjects. Conclusion Neuroinflammation was associated with amyloid at early stages of amyloid pathology. In addition, elevation of neuroinflammation was associated with the increase of amyloid and tau accumulation in Aβ‐negative subjects.
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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 | 0.001 |
| 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) | 0.001 | 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".