Distinct inflammatory profiles in young‐onset versus late‐onset Alzheimer's disease
Bibliographic record
Abstract
INTRODUCTION: Neuroinflammation, a key player in Alzheimer's disease (AD) pathogenesis, may be differentially involved in young-onset (YOAD) compared to late-onset (LOAD) AD. METHODS: Using proximity extension assay technology, we examined 737 inflammatory markers in the CSF of 26 healthy controls (63.9 ± 8.7; 12♀), 57 patients with YOAD (60.8 ± 4.9 y/o; 40♀), and 33 with LOAD (76.6 ± 4.5 y/o; 18♀). We also assessed biomarkers of AD pathology (Aβ42, p-tau181, t-tau) and neurodegeneration (neurofilament light-chain [NfL]). RESULTS: Compared to controls, SCRN1 and MMP10 were increased in LOAD and YOAD, but 16 markers showed YOAD-specific increases. Forty-six markers were significantly associated with NfL. P-tau181 and t-tau mediated the association between inflammatory markers and NfL in YOAD. In LOAD we could not identify a direct or indirect relationship between neuroinflammation and neurodegeneration. DISCUSSION: Using a proteomics approach, we observed an exacerbation of neuroinflammatory changes and a differential contribution of neuroinflammation to AD pathology and neurodegeneration in YOAD compared to LOAD. HIGHLIGHTS: Olink's Proximity Extension Assay was used to compare the inflammatory profile of 26 healthy controls and 90 Alzheimer's disease (AD) patients. AD patients were further stratified into young-onset (YOAD, n = 57) and late-onset (LOAD, n = 33) AD. Cerebrospinal fluid (CSF) levels of MMP10 and SCRN1 were increased in both YOAD and LOAD, but 16 proteins were only increased in YOAD. Tau mediated the association between inflammatory markers and neurodegeneration in YOAD. Neuroinflammation may be differentially involved in the pathogenesis of YOAD compared to LOAD.
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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".