Amyloid-beta modulates the association between neurofilament light chain and brain atrophy in Alzheimer’s disease
Bibliographic record
Abstract
Neurofilament light chain (NFL) measurement has been gaining strong support as a clinically useful neuronal injury biomarker for various neurodegenerative conditions. However, in Alzheimer's disease (AD), its reflection on regional neuronal injury in the context of amyloid pathology remains unclear. This study included 83 cognitively normal (CN), 160 mild cognitive impairment (MCI), and 73 AD subjects who were further classified based on amyloid-beta (Aβ) status as positive or negative (Aβ+ vs Aβ-). In addition, 13 rats (5 wild type and 8 McGill-R-Thy1-APP transgenic (Tg)) were examined. In the clinical study, reduced precuneus/posterior cingulate cortex and hippocampal grey matter density were significantly associated with increased NFL concentrations in cerebrospinal fluid (CSF) or plasma in MCI Aβ+ and AD Aβ+. Moreover, AD Aβ+ showed a significant association between the reduced grey matter density in the AD-vulnerable regions and increased NFL concentrations in CSF or plasma. Congruently, Tg rats recapitulated and validated the association between CSF NFL and grey matter density in the parietotemporal cortex, entorhinal cortex, and hippocampus in the presence of amyloid pathology. In conclusion, reduced grey matter density and elevated NFL concentrations in CSF and plasma are associated in AD-vulnerable regions in the presence of amyloid positivity in the AD clinical spectrum and amyloid Tg rat model. These findings further support the NFL as a neuronal injury biomarker in the research framework of AD biomarker classification and for the evaluation of therapeutic efficacy in clinical trials.
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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 | 0.000 | 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".