Relationship of Weight, Height, and Body Mass Index With Fracture Risk at Different Sites in Postmenopausal Women: The Global Longitudinal Study of Osteoporosis in Women (GLOW)
Bibliographic record
Abstract
Low body mass index (BMI) is a well-established risk factor for fracture in postmenopausal women. Height and obesity have also been associated with increased fracture risk at some sites. We investigated the relationships of weight, BMI, and height with incident clinical fracture in a practice-based cohort of postmenopausal women participating in the Global Longitudinal study of Osteoporosis in Women (GLOW). Data were collected at baseline and at 1, 2, and 3 years. For hip, spine, wrist, pelvis, rib, upper arm/shoulder, clavicle, ankle, lower leg, and upper leg fractures, we modeled the time to incident self-reported fracture over a 3-year period using the Cox proportional hazards model and fitted the best linear or nonlinear models containing height, weight, and BMI. Of 52,939 women, 3628 (6.9%) reported an incident clinical fracture during the 3-year follow-up period. Linear BMI showed a significant inverse association with hip, clinical spine, and wrist fractures: adjusted hazard ratios (HRs) (95% confidence intervals [CIs]) per increase of 5 kg/m(2) were 0.80 (0.71-0.90), 0.83 (0.76-0.92), and 0.88 (0.83-0.94), respectively (all p < 0.001). For ankle fractures, linear weight showed a significant positive association: adjusted HR per 5-kg increase 1.05 (1.02-1.07) (p < 0.001). For upper arm/shoulder and clavicle fractures, only linear height was significantly associated: adjusted HRs per 10-cm increase were 0.85 (0.75-0.97) (p = 0.02) and 0.73 (0.57-0.92) (p = 0.009), respectively. For pelvic and rib fractures, the best models were for nonlinear BMI or weight (p = 0.05 and 0.03, respectively), with inverse associations at low BMI/body weight and positive associations at high values. These data demonstrate that the relationships between fracture and weight, BMI, and height are site-specific. The different associations may be mediated, at least in part, by effects on bone mineral density, bone structure and geometry, and patterns of falling.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".