Association between aluminum in drinking water and incident Alzheimer’s disease in the Canadian Study of Health and Aging cohort
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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) | 0.000 | 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 | 0.000 |
| 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 it