Bone Structure and Volumetric BMD in Overweight Children: A Longitudinal Study
Bibliographic record
Abstract
The effect of excess body fat on bone strength accrual is not well understood. Therefore, we assessed bone measures in healthy weight (HW) and overweight (OW) children. Children (9-11 yr) were classified as HW (n = 302) or OW (n = 143) based on body mass index. We assessed total (ToD) and cortical (CoD) volumetric BMD and bone area, estimates of bone strength (bone strength index [BSI]; stress-strain index [SSIp]), and muscle cross-sectional area (CSA) at the distal (8%), midshaft (50%), and proximal (66%) tibia by pQCT. We used analysis of covariance to compare bone outcomes at baseline and change over 16 mo. At baseline, all bone measures were significantly greater in OW compared with HW children (+4-15%; p <or= 0.001), with the exception of CoD at the 50% and 66% sites. Over 16 mo, ToA increased more in the OW children, whereas there was no difference for change in BSI or ToD between groups at the distal tibia. At the tibial midshaft, SSIp was similar between groups at baseline when adjusted for muscle CSA, but low when adjusted for body fat in the OW group. At both sites, bone strength increased more in OW because of a greater increase in bone area. Changes in SSIp were associated with changes in lean mass (r = 0.70, p < 0.001) but not fat mass. In conclusion, although OW children seem to be at an advantage in terms of absolute bone strength, bone strength did not adapt to excess body fat. Rather, bone strength was adapted to the greater muscle area in OW children.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".