Renal cell carcinoma risk associated with lower intake of micronutrients
Bibliographic record
Abstract
Kidney cancer incidence in African Americans (AA) is higher than among European Americans (EA); reasons for this disparity are not fully known. Dietary micronutrients may have a protective effect on renal cell carcinoma (RCC) development by inhibiting oxidative DNA damage and tumor growth. We evaluated whether any micronutrient associations differed by race in the US Kidney Cancer Study. 1142 EA and AA RCC cases and 1154 frequency-matched controls were enrolled in a population-based case-control study between 2002 and 2007. Dietary micronutrient intake was derived from an interviewer-administered diet history questionnaire. RCC risk associated with micronutrient intake was estimated using adjusted odds ratios from logistic regression comparing lower to highest quartiles of intake and sample weighting. Inverse associations with RCC risk were observed for α-carotene, β-carotene, lutein zeaxanthin, lycopene, vitamin A, folate, thiamin, vitamin C, α-tocopherol, β-tocopherol, γ-tocopherol, and selenium. A trend for β-cryptoxanthin was suggested among EA but not AA or the total sample (P-interaction = .04). Otherwise, findings did not differ by race, gender, age, or smoking status. The increase in RCC risk associated with lower micronutrient intake is similar within AA and EA populations. A diet rich in sources of micronutrients found in fruits, vegetables, and nuts may help to reduce the overall risk of RCC.
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 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 | 0.001 |
| 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 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".