Body mass and composition affect bone density in recently diagnosed inflammatory bowel disease: The Manitoba IBD cohort study
Bibliographic record
Abstract
BACKGROUND: This prospective study was undertaken to clarify the role of body mass and composition as a determinant of bone mineral density (BMD) in recently diagnosed inflammatory bowel disease (IBD). METHODS: A nested subgroup of 101 adult subjects of the population-based Manitoba IBD Cohort Study were enrolled. Baseline BMD and body composition were measured and repeated 2.3 +/- 0.3 years later. RESULTS: Greater weight, height, and body mass measurements were positively correlated with bone density at all sites (P < 0.01). Although both fat tissue and lean tissue showed positive relationships with BMD, lean tissue showed a much stronger correlation than fat tissue, especially for the total hip (r = 0.66, P < 0.001 versus r = 0.23, P < 0.05) and total body measurements (r = 0.59, P < 0.001 versus r = 0.04, P NS). Increase (or decrease) in hip bone density was strongly associated with an increase (or decrease) in all body mass variables (r = 0.49-0.54, P < 0.001). CONCLUSIONS: Measures of body mass are important determinants of baseline BMD in recently diagnosed IBD patients. Furthermore, change in body mass is correlated with change in BMD, especially at the total hip. Early optimization and maintenance of nutrition and body weight, particularly toward lean tissue mass, may play an important role in preventing IBD-related bone disease.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".