The Effects of Bazedoxifene on Bone, Glucose, and Lipid Metabolism in Postmenopausal Women With Type 2 Diabetes: An Exploratory Pilot Study
Bibliographic record
Abstract
BACKGROUND: Selective estrogen receptor modulators (SERMs) decrease homocysteine and cross-linking of pentosidine and reduce low-density lipoprotein cholesterol (LDL-C), and they are expected to improve bone quality and atherosclerosis. Therefore, the potential effects of bazedoxifene on bone (bone resorption, bone formation, and bone quality), as well as on glucose and lipid metabolism markers, were examined in Japanese postmenopausal women with type 2 diabetes mellitus (T2DM). METHODS: Eligible patients received 20 mg of bazedoxifene tablets once daily and were followed up for 12 weeks. Bone resorption markers including tartrate-resistant acid phosphatase 5b (TRACP-5b), bone formation markers and bone quality markers such as homocysteine and serum pentosidine, total cholesterol (TC), LDL-C, high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), and HbA1c were all measured. RESULTS: Twenty patients completed this study. All bone resorption markers decreased significantly 4 weeks after bazedoxifene treatment. In particular, TRACP-5b decreased significantly at 12 weeks (median percent change: -20.6%), and the minimum significant change (MSC) achievement rate of TRACP-5b was 65%. Bazedoxifene also decreased bone formation markers. However, bazedoxifene did not improve bone quality markers. LDL-C, HDL-C, and non-HDL-C were decreased, but TG was unchanged. Glucose metabolism was not changed after bazedoxifene treatment. In a subgroup analysis, the group of patients in whom the percent change in TRACP-5b exceeded the MSC had no change in pentosidine levels at 12 weeks. However, in the group of patients in whom the percent change in TRACP-5b did not exceed the MSC, pentosidine levels tended to increase. CONCLUSIONS: Bazedoxifene may improve bone resorption markers and LDL-C without affecting glucose metabolism in Japanese postmenopausal women with T2DM.
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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) | 0.001 | 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 | 0.001 | 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".