Re: Dietary Supplements and Cancer Prevention: Balancing Potential Benefits Against Proven Harms
Bibliographic record
Abstract
Martínez et al. (1) recommended against vitamin D supplements for reducing the risk of cancer. However, the evidence that vitamin D reduces the risk of cancer is very strong despite reports to the contrary. There are several types of evidence: ecological, case–control, cohort, and randomized controlled trials. Each ap- proach has its strengths and limitations. The strengths of the ecological approach include the large number of cases and the large number of data sets available for such studies. The limitations include assessing the role of confounding factors, but many cancer risk–modifying factors are included in most recent ecological studies. A recent review of ecological studies found strong support for solar ultraviolet-B in reducing the risk of 15 types of cancer, with weaker support for another nine types of cancer (2). No factor other than vitamin D production has been proposed to explain the link. Case–control studies have the strength of determining serum 25-hydroxyvitamin D [25(OH)D] concentrations near the time of cancer diagnosis. Although there is a concern that the disease state may affect serum 25(OH)D concentration, there does not seem to be evidence to support this concern. Case–control studies have found the strongest inverse associations between serum 25(OH)D concentration and breast and colorectal cancer incidence (3). Cohort studies are perceived to be the strongest observational approach. The advantage is that the risk-modifying factors are determined before disease outcome. However, a little-recognized disadvantage is that a single blood collection at the time of enrollment in the cohort study is used to determine serum 25(OH)D concentrations, and this value loses predictive ability with increasing follow-up time (3). A recent analysis of the regression coefficient for two serum 25(OH)D concentration measurements for a cohort as a function of interval found a decrease of −0.020/year for intervals ranging from 1 to 14 years (4). In cohort studies of breast and colorectal cancer, the relative risks increased toward unity at a rate of 0.03/year to 0.05/year (3), whereas the hazard ratios for all-cause mortality rate increased at a rate of 0.017/year (4). In addition, only cohort studies that find direct relationships between serum 25(OH)D concentration and cancer incidence rates, such as for pancreatic and prostate cancer, are mentioned in References 66 and 68 (1). Many randomized-controlled trials such as the Women’s Health Initiative used only 400 IU/day vitamin D3. However, a reanalysis of the Women’s Health Initiative restricted to women who had not taken vitamin D or calcium (CaD) supplements before enrollment found that “CaD statistically significantly decreased the risk of total cancer, total breast cancer, and invasive breast cancers by 14–20% and nonsignificantly reduced the risk of colorectal cancer by 17%.” (5). Marshall et al. recently reported that for those with low-risk prostate cancer, supplementing with 4000 IU/day vitamin D3 led to biopsy-assessed tumor regression in 55% of case patients (6). When all the evidence regarding solar ultraviolet-B and vitamin D is evaluated using the criteria for causality in a biological system proposed by AB Hill, the evidence is found to be strong for several types of cancer (2,7). The author receives funding from the UV Foundation (McLean, VA), Bio-Tech Pharmacal (Fayetteville, AR), the Vitamin D Council (San Luis Obispo, CA), and the Vitamin D Society (Canada).
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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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.089 | 0.090 |
| Insufficient payload (model declined to judge) | 0.012 | 0.012 |
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".