Teriparatide Increases Bone Formation in Modeling and Remodeling Osteons and Enhances IGF-II Immunoreactivity in Postmenopausal Women With Osteoporosis
Bibliographic record
Abstract
UNLABELLED: Transiliac bone biopsies were obtained from 55 women treated with teriparatide or placebo for 12-24 months. We report direct evidence that modeling bone formation at quiescent surfaces was present only in teriparatide-treated patients and bone formation at remodeling sites was higher with teriparatide than placebo. INTRODUCTION: Recombinant teriparatide [human PTH(1-34)], a bone formation agent for the treatment of osteoporosis when given once daily subcutaneously, increases biochemical markers of bone turnover and activation frequency in histomorphometry studies. MATERIALS AND METHODS: We studied the mechanisms underlying this bone-forming action of teriparatide at the basic multicellular unit by the appearance of cement lines, a method used to directly classify surfaces as modeling or remodeling osteons, and by the immunolocalization of IGF-I and IGF-II. Transiliac bone biopsies were obtained from 55 postmenopausal women treated with teriparatide 20 or 40 microg or placebo for 12-24 months (median, 19.8 months) in the Fracture Prevention Trial. RESULTS: A dose-dependent relationship was observed in modeling and mixed remodeling/modeling trabecular hemiosteons. Trabecular and endosteal hemiosteon mean wall thicknesses were significantly higher in both teriparatide groups than in placebo. There was a dose-dependent relationship in IGF-II immunoreactive staining at all bone envelopes studied. The greater local IGF-II presence after treatment with teriparatide may play a key role in stimulating bone formation. CONCLUSIONS: Direct evidence is presented that 12-24 months of teriparatide treatment induced modeling bone formation at quiescent surfaces and resulted in greater bone formation at remodeling sites, relative to placebo.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".