The CRH‐Transgenic Cushingoid Mouse Is a Model of Glucocorticoid‐Induced Osteoporosis
Bibliographic record
Abstract
ABSTRACT Glucocorticoids (GCs) have unparalleled anti‐inflammatory and immunosuppressive properties, which accounts for their widespread prescription and use. Unfortunately, a limitation to GC therapy is a wide range of negative side effects including Cushing's syndrome, a disease characterized by metabolic abnormalities including muscle wasting and osteoporosis. GC‐induced osteoporosis occurs in 30% to 50% of patients on GC therapy and thus, represents an important area of study. Herein, we characterize the molecular and physiologic effects of GC‐induced osteoporosis using the Cushing's mouse model, the corticotropin releasing hormone (CRH) transgenic mouse (CRH‐Tg). The humeri, femurs, and tibias from wild‐type (WT) and CRH‐Tg male mice, aged 13 to 14 weeks old were subjected to multiple bone tests including, micro–computed tomography (μCT), static and dynamic histomorphometry, strength testing, and gene expression analyses. The CRH‐Tg mice had a 38% decrease in cortical bone area, a 35% decrease in cortical thickness, a 16% decrease in trabecular thickness, a sixfold increase in bone adiposity, a 27% reduction in osteoid width, a 75% increase in bone‐resorbing osteoclast number/bone surface, a 34% decrease in bone formation rate, and a 40% decrease in bone strength compared to WT mice. At the gene expression level, CRH‐Tg bone showed significantly increased osteoclast markers and decreased osteoblast markers, whereas CRH‐Tg muscle had increased muscle atrophy gene markers compared to WT mice. Overall, the CRH‐Tg mouse model aged to 14 weeks recapitulated many features of osteoporosis in Cushing's syndrome and thus, represents a useful model to study GC‐induced osteoporosis and interventions that target the effects of GCs on the skeleton. © 2017 The Authors. JBMR Plus is published by Wiley Periodicals, Inc. on behalf of the American Society for Bone and Mineral Research.
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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) | 0.005 | 0.001 |
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".