Use of Vitamin E and C Supplements for the Prevention of Cognitive Decline
Bibliographic record
Abstract
BACKGROUND: There are few studies of the association between the use of antioxidant vitamin supplements and the risk of Alzheimer's disease (AD). Cognitive decline is generally viewed as part of the continuum between normal aging and AD. OBJECTIVE: To evaluate whether the use of vitamin E and C supplements is associated with reduced risks of cognitive impairment, not dementia (CIND), AD, or all-cause dementia in a representative sample of older persons ≥65 years old. METHODS: Data from the Canadian Study of Health and Aging (1991-2002), a cohort study of dementia including 3 evaluation waves at 5-yearly intervals, were used. Exposure to vitamins E and C was self-reported at baseline in a risk factor questionnaire and/or in a clinical examination. RESULTS: The data set included 5269 individuals. Compared with those not taking vitamin supplements, the age-, sex-, and education-adjusted hazard ratios of CIND, AD, and all-cause dementia were, respectively, 0.77 (95% CI = 0.60-0.98), 0.60 (95% CI = 0.42-0.86), and 0.62 (95% CI = 0.46-0.83) for those taking vitamin E and/or C supplements. Results remained significant in fully adjusted models except for CIND. Similar results were observed when vitamins were analyzed separately. CONCLUSIONS: This analysis suggests that the use of vitamin E and C supplements is associated with a reduced risk of cognitive decline. Further investigations are needed to determine their value as a primary prevention strategy.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".