Blood Metal Levels and Amyotrophic Lateral Sclerosis Risk: A Prospective Cohort
Bibliographic record
Abstract
OBJECTIVE: Metals have been suggested as a risk factor for amyotrophic lateral sclerosis (ALS), but only retrospective studies are available to date. We compared metal levels in prospectively collected blood samples from ALS patients and controls, to explore whether metals are associated with ALS mortality. METHODS: A nested ALS case-control study was conducted within the prospective EPIC (European Prospective Investigation into Cancer and Nutrition) cohort. Cases were identified through death certificates. We analyzed metal levels in erythrocyte samples obtained at recruitment, as a biomarker for metal exposure from any source. Arsenic, cadmium, copper, lead, manganese, mercury, selenium, and zinc concentrations were measured by inductively coupled plasma-mass spectrometry. To estimate ALS risk, we applied conditional logistic regression models. RESULTS: The study population comprised 107 cases (65% female) and 319 controls matched for age, sex, and study center. Median time between blood collection and ALS death was 8 years (range = 1-15). Comparing the highest with the lowest tertile, cadmium (odds ratio [OR] = 2.04, 95% confidence interval [CI] = 1.08-3.87) and lead (OR = 1.89, 95% CI = 0.97-3.67) concentrations suggest associations with increased ALS risk. Zinc was associated with a decreased risk (OR = 0.50, 95% CI = 0.27-0.94). Associations for cadmium and lead remained when limiting analyses to noncurrent smokers. INTERPRETATION: This is the first study to compare metal levels before disease onset, minimizing reverse causation. The observed associations suggest that cadmium, lead, and zinc may play a role in ALS etiology. Cadmium and lead possibly act as intermediates on the pathway from smoking to ALS. ANN NEUROL 20209999:n/a-n/a.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".