Association between aluminum in drinking water and incident Alzheimer’s disease in the Canadian Study of Health and Aging cohort
Bibliographic record
Abstract
Epidemiological evidence linking aluminum in drinking water and Alzheimer's disease (AD) has been inconsistent, with previous studies often limited by small sample sizes. The present study addresses this issue using data from the Canadian Study of Health and Aging (CSHA), a prospective cohort of 10,263 subjects followed-up from 1991-1992 through 2001-2002. Participants' residential histories were linked to municipal drinking water sources in 35 Canadian municipalities to obtain ecologic pH, aluminum, fluoride, iron and silica concentrations in drinking water. Cox proportional hazards models were used to examine associations between aluminum and incident AD [Hazard Ratios (HRs), 95% confidence intervals (CIs)], adjusting for age, gender, history of stroke, education, and high blood pressure. A total of 240 incident AD cases were identified during follow-up of 3, 638 subjects derived from the CSHA cohort with complete data on all covariates. With categorical aluminum measurements, there was an increasing, but not statistically significant, exposure-response relationship (HR = 1.34, 95% CI 0.88-2.04, in the highest aluminum exposure category; p = 0.13 for linear trend). Similar results were observed using continuous aluminum measurements (HR=1.21, 95% CI 0.97-1.52, at the interquartile range of 333.8 μg/L; p = 0.09 for linear trend). In a subsample genotyped for ApoE-ε4, there was some evidence of an association between aluminum and AD (p = 0.03 for linear trend). Although a clear association between aluminum in drinking water and AD was not found, the linear trend observed in ApoE-ε4 subsample warrants further examination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".