Evidence that iron accelerates Alzheimer’s pathology: a CSF biomarker study
Bibliographic record
Abstract
Objective To investigate whether cerebrospinal fluid (CSF) ferritin (reporting brain iron) is associated with longitudinal changes in CSF β-amyloid (Aβ) and tau. Methods Mixed-effects models of CSF Aβ1-42 and tau were constructed using data from 296 participants who had baseline measurement of CSF ferritin and annual measurement of CSF tau and Aβ1-42 for up to 5 years. Results In subjects with biomarker-confirmed Alzheimer’s pathology, high CSF ferritin (>6.2 ng/mL) was associated with accelerated depreciation of CSF Aβ1-42 (reporting increased plaque formation; p=0.0001). CSF ferritin was neither associated with changes in CSF tau in the same subjects, nor longitudinal changes in CSF tau or Aβ1-42 in subjects with low baseline pathology. In simulation modelling of the natural history of Aβ deposition, which we estimated to occur over 31.4 years, we predicted that it would take 12.6 years to reach the pathology threshold value of CSF Aβ from healthy normal levels, and this interval is not affected by CSF ferritin. CSF ferritin influences the fall in CSF Aβ over the next phase, where high CSF ferritin accelerated the transition from threshold preclinical Aβ levels to the average level of Alzheimer’s subjects from 18.8 to 10.8 years. Conclusions Iron might facilitate Aβ deposition in Alzheimer’s and accelerate the disease process.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".