Estimated Lean Mass and Fat Mass Differentially Affect Femoral Bone Density and Strength Index but Are Not FRAX Independent Risk Factors for Fracture
Bibliographic record
Abstract
Although increasing body weight has been regarded as protective against osteoporosis and fractures, there is accumulating evidence that fat mass adversely affects skeletal health compared with lean mass. We examined skeletal health as a function of estimated total body lean and fat mass in 40,050 women and 3600 men age ≥50 years at the time of baseline dual-energy X-ray absorptiometry (DXA) testing from a clinical registry from Manitoba, Canada. Femoral neck bone mineral density (BMD), strength index (SI), cross-sectional area (CSA), and cross-sectional moment of inertia (CSMI) were derived from DXA. Multivariable models showed that increasing lean mass was associated with near-linear increases in femoral BMD, CSA, and CSMI in both women and men, whereas increasing fat mass showed a small initial increase in these measurements followed by a plateau. In contrast, femoral SI was relatively unaffected by increasing lean mass but was associated with a continuous linear decline with increasing fat mass, which should predict higher fracture risk. During mean 5-year follow-up, incident major osteoporosis fractures and hip fractures were observed in 2505 women and 180 men (626 and 45 hip fractures, respectively). After adjustment for fracture risk assessment tool (FRAX) scores (with or without BMD), we found no evidence that lean mass, fat mass, or femoral SI affected prediction of major osteoporosis fractures or hip fractures. Findings were similar in men and women, without significant interactions with sex or obesity. In conclusion, skeletal adaptation to increasing lean mass was positively associated with BMD but had no effect on femoral SI, whereas increasing fat mass had no effect on BMD but adversely affected femoral SI. Greater fat mass was not independently associated with a greater risk of fractures over 5-year follow-up. FRAX robustly predicts fractures and was not affected by variations in body composition.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".