Reductions in glucose among postmenopausal women who use and do not use estrogen therapy
Bibliographic record
Abstract
OBJECTIVE: Among postmenopausal women who do not use estrogen therapy (ET), we have previously reported that intensive lifestyle modification (ILS) leads to increases in sex hormone-binding globulin (SHBG) and that such increases are associated with reductions in fasting plasma glucose (FPG) and 2-hour postchallenge glucose (2HG). Oral ET decreases FPG and increases 2HG while increasing both SHBG and estradiol (E2). It is unknown if ILS reduces glucose among ET users, if changes in SHBG and E2 might mediate any glucose decreases in ET users, and if these patterns differ from those in non-ET users. METHODS: We conducted a secondary analysis of postmenopausal women in the Diabetes Prevention Program who used ET at baseline and 1-year follow-up (n = 324) and who did not use ET at either time point (n = 382). Participants were randomized to ILS, metformin, or placebo administered at 850 mg BID. RESULTS: ET users were younger, more often white, and more likely to have had bilateral oophorectomy than non-ET users. Among ET users, ILS reduced FPG (P < 0.01) and 2HG (P < 0.01), and metformin reduced FPG (P < 0.01) but not 2HG (P = 0.56), compared with placebo. Associations between SHBG and total E2 with FPG and 2HG were not significant among women randomized to ILS or metformin. These patterns differed from those observed among women who did not use ET. CONCLUSIONS: We conclude that among glucose-intolerant ET users, interventions to reduce glucose are effective but possibly mediated through different pathways than among women who do not use ET.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".