Association between BMI, vitamin D, and estrogen levels in postmenopausal women using adjuvant letrozole: a prospective study
Bibliographic record
Abstract
Abstract Studies have suggested that women with elevated BMI or 25-OH vitamin D levels may derive less benefit from AIs versus tamoxifen. We prospectively investigated whether high BMI or 25-OH vitamin D levels were associated with higher estrogen levels in post-menopausal women receiving standard adjuvant letrozole (2.5 mg/day). Furthermore, we evaluated whether an increased dose of letrozole resulted in lower serum estrogens in women with BMI > 25 kg/m2. Correlation between entry BMI and day 29 serum biomarkers (estrogens, 25-OH vitamin D, insulin, CRP, leptin) was assessed in all patients. On day 29, participants with BMI > 25 kg/m2 switched to letrozole 5 mg/day for 4-weeks and blood was drawn upon completion of the study. The change in serum estrogen levels was assessed in these patients (BMI > 25 kg/m2). 112 patients completed days 1–28. The Pearson correlations of estradiol and estrone with BMI or serum 25-OH vitamin D levels were near zero (−0.04 to 0.07, p = 0.48–0.69). Similar results were obtained for correlation with markers of obesity (insulin, CRP, and leptin) with estradiol and estrone (−0.15 to 0.12; p = 0.11–0.82). Thirty-one patients (BMI > 25 kg/m2) completed the interventional component; Increasing the dose of letrozole did not further reduce estradiol or estrone levels (change 0.1 and 0.4 pmol/L respectively; p = 0.74 and 0.36). There was no observed association between markers of obesity (BMI, insulin, leptin, and CRP), serum 25-OH vitamin D levels and estradiol or estrone levels. Additionally, an increased dose of letrozole did not further reduce estradiol or estrone levels compared to the standard dose.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".