Associations between long-term exposures to environmental factors and the development of amyotrophic lateral sclerosis: A matched case-control study
Bibliographic record
Abstract
ObjectiveAmyotrophic lateral sclerosis (ALS) is a motor neuron disease in which the causes remain unclear, particularly the contribution of environmental factors. Thus, we created a multifactorial database to investigate the association between long-term exposure to urbanization, air pollution, water pollution, and the development of ALS. ApproachA matched case-control study was conducted in New Brunswick, Canada from January 2003 to February 2021. Study population included 304 ALS patients and 1207 controls with their historical postal codes from the New Brunswick Citizen Database linked to medical records and spatial environmental datasets from the Canadian Urban Environmental Heath Research Consortium. We compared their environmental exposures at place of residence prior to disease onset via conditional logistic regression models. ResultsOf the common air pollutants investigated, odds of ALS was significantly higher with increased SO2 exposure (OR = 4.369, 1.190-16.044 [95% CI], per 1 ppb increase of SO2) in adjusted models. No significant associations were observed for the investigated urbanization and water pollution-related exposures. ConclusionsThis is the first study to rigorously identify a potential environmental cause of ALS. Our findings suggest an association between long-term exposure to air pollutants, particularly SO2, and the development of ALS. Revision of SO2 emission regulations may be required. ImplicationsThe identification of SO2 exposure as an ALS environmental risk factor opens new prospects for identifying ALS therapeutic targets. Utilizing a similar data linking workflow to pre-existing disease registries found within data centres should be conducted to further investigate environmental influences on complex diseases.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".