Evidence for widespread, severe brain copper deficiency in Alzheimer's dementia
Bibliographic record
Abstract
Datasets comprising simultaneous measurements of many essential metals in Alzheimer's disease (AD) brain are sparse, and available studies are not entirely in agreement. To further elucidate this matter, we employed inductively-coupled-plasma mass spectrometry to measure post-mortem levels of 8 essential metals and selenium, in 7 brain regions from 9 cases with AD (neuropathological severity Braak IV-VI), and 13 controls who had normal ante-mortem mental function and no evidence of brain disease. Of the regions studied, three undergo severe neuronal damage in AD (hippocampus, entorhinal cortex and middle-temporal gyrus); three are less-severely affected (sensory cortex, motor cortex and cingulate gyrus); and one (cerebellum) is relatively spared. Metal concentrations in the controls differed among brain regions, and AD-associated perturbations in most metals occurred in only a few: regions more severely affected by neurodegeneration generally showed alterations in more metals, and cerebellum displayed a distinctive pattern. By contrast, copper levels were substantively decreased in all AD-brain regions, to 52.8-70.2% of corresponding control values, consistent with pan-cerebral copper deficiency. This copper deficiency could be pathogenic in AD, since levels are lowered to values approximating those in Menkes' disease, an X-linked recessive disorder where brain-copper deficiency is the accepted cause of severe brain damage. Our study reinforces others reporting deficient brain copper in AD, and indicates that interventions aimed at safely and effectively elevating brain copper could provide a new experimental-therapeutic approach.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".