Vitamin C and Copper Intake Associated With Cognitive Function in Older Adults
Bibliographic record
Abstract
Oxidative stress has been linked to the development of depression and anxiety as well as cognitive decline in older adults. Vitamins and minerals that have antioxidant properties or serve as cofactors can improve the oxidant-antioxidant balance in the body and lead to a reduction in oxidative stress and inflammation. We aimed to study antioxidant and antioxidant cofactor intake from diet in the older population in relation to mental and cognitive health. A cross-sectional study was conducted that included 181 men and women aged 60–80 years. Individuals diagnosed with dementia, Alzheimer's, or other neurological disorders were excluded. Dietary information was obtained using a 3-day diet record and food frequency questionnaire. Mental and cognitive health were assessed using Geriatric Depression Scale, Geriatric Anxiety Inventory, Montreal Cognitive Assessment (MoCA), and Digit Span test. Partial Pearson Correlation analyses were performed using SPSS software. Our findings indicate that vitamin B1 [r = .18, p < .05], vitamin C [r = .24, p < .001], vitamin D [r = .15, p < .05], and zinc [r = .15, p < .05] were positively correlated with Total Digit Span score, after controlling for antioxidant supplementation and other covariates such as age, education, economic status, etc. However, after further controlling for daily caloric intake, only vitamin C remained significantly associated with Total Digit Span score [r = .18, p < .05], and copper was inversely associated with MoCA scores [r = -.18, p < .05]. No other associations were found between the other variables. Our findings suggest that higher vitamin C and lower copper intake from foods, are associated with cognitive performance among older adults. Further studies are needed to better understand the role of vitamin C and copper intake in cognitive function of older adults. None.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".