Effect of Rosiglitazone on Bone Quality in a Rat Model of Insulin Resistance and Osteoporosis
Bibliographic record
Abstract
OBJECTIVE: Rosiglitazone (RSG) is an insulin-sensitizing drug used to treat type 2 diabetes mellitus. The A Diabetes Outcome Progression Trial (ADOPT) shows that women taking RSG experienced more fractures than patients taking other type 2 diabetes drugs. These were not osteoporotic vertebral fractures but, rather, occurred in the limbs. The purpose of this study was to investigate how RSG treatment alters bone quality, which leads to fracture risk, using the Zucker fatty rat as a model. RESEARCH DESIGN AND METHODS: A total of 61 female 4-month-old rats were divided into six groups. One Sham group was a control and another was administered oral RSG 10 mg/kg/day. Four ovariectomized (OVX) groups were dosed as follows: controls, RSG 10 mg/kg, alendronate (ALN, injected at 0.7 mg/kg/week), and RSG 10 mg/kg plus ALN. After 12 weeks of treatment, bone quality was evaluated by mechanical testing. Microarchitecture, bone mineral density (BMD), cortical bone porosity, and bone remodeling were also measured. RESULTS: OVX RSG 10 mg/kg rats had lower vertebral BMD and compromised trabecular architecture versus OVX controls. Increased cortical bone porosity and decreased mechanical properties occurred in these rats. ALN treatment prevented decreased BMD and architectural and mechanical properties in the OVX model. Reduced bone formation, increased marrow adiposity, and excess bone resorption were observed in RSG-treated rats. CONCLUSIONS: RSG decreases bone quality. An unusual finding was an increase in cortical bone porosity induced by RSG, consistent with its effect on long bones of women. ALN, an inhibitor of bone resorption, enhanced mechanical strength and may provide an approach to partially counter the deleterious skeletal effects of RSG.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".