Associations between neutrophils and amyloid deposition in the Alzheimer’s disease spectrum
Bibliographic record
Abstract
Abstract Background Neutrophils are key components of early innate immunity and contribute to uncontrolled systemic inflammation. Several studies have highlighted the link between systemic inflammation and the Alzheimer’s disease (AD) pathophysiology. In fact, experiments with animal models and studies with AD patients demonstrated the hyperactivation of neutrophils associated with AD pathology and cognitive decline. However, the comprehension of the inflammatory component in the AD spectrum is still uncomplete. We thus investigated whether the amount of systemic neutrophils is correlated with brain amyloid‐β in the AD spectrum Method The present study was conducted in a population of 170 individuals (115 cognitively unimpaired (CU) and 55 cognitively impaired (CI; 35 MCI and 20 AD) from the Translational Biomarkers in Aging and Dementia TRIAD cohort. The neutrophil relative values were assessed using the Automated Beckman DXH hematology Analyzer. Amyloid (Aβ) deposition was assessed with [18F]AZD4694 PET. A voxel‐based regression model evaluated the relationship between neutrophil count and Aβ PET, adjusted for age, sex, years of education and diagnosis. RFT was used to account for multiple comparisons. Result In the present study, a positive association was found between systemic neutrophil counts and brain Aβ load, where the associated regions were the anterior cingulate, cuneus, and occipital pole areas. Also, CI individuals showed significantly higher neutrophil counts as compared to the CU group. Conclusion Our findings review the link between the peripheral immune system and the central nervous system amyloid deposition. Further studies should investigate the use of neutrophil counts as biomarkers of AD.
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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) | 0.002 | 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".