How Does Bone Quality Differ Between Healthy-weight and Overweight Adolescents and Young Adults?
Bibliographic record
Abstract
BACKGROUND: Overweight youth have greater bone mass than their healthy-weight peers but sustain more fractures. However, it is unclear whether and how excess body fat influences bone quality in youth. QUESTIONS/PURPOSES: We determined whether overweight status correlated with three-dimensional aspects of bone quality influencing bone strength in adolescent and young adult females and males. METHODS: We categorized males (n=103; mean age, 17 years) and females (n=85; mean age, 18 years) into healthy-weight and overweight groups. We measured lean mass (LM) and fat mass (FM) with dual-energy x-ray absorptiometry (DXA). We used high-resolution peripheral quantitative CT to assess the distal radius (7% site) and distal tibia (8% site). Bone quality measures included total bone mineral density (Tt.BMD), total area (Tt.Ar), trabecular bone volume fraction (BV/TV), trabecular number (Tb.N), separation (Tb.Sp), and thickness (Tb.Th). We used multiple regression to compare bone quality between healthy-weight and overweight adolescents adjusting for age, ethnicity, limb length, LM, and FM. RESULTS: Overweight males had higher (10%-21%) Tt.BMD, BV/TV, and Tb.N and lower Tb.Sp at the tibia and lower Tt.Ar at the radius than healthy-weight males. No differences were observed between overweight and healthy-weight females. LM attenuated the differences in bone quality between groups in males while FM negatively predicted Tt.BMD, BV/TV, Tb.N, and Tb.Th. CONCLUSIONS: Our data suggest overweight males have enhanced bone quality compared with healthy-weight males; however, when group differences are interpreted in the context of the mechanostat theory, it appears bone quality of overweight adolescents adapts to LM and not to greater FM.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".