New insight into fat, muscle and bone relationship in women: determining the threshold at which body fat assumes negative relationship with bone mineral density.
Bibliographic record
Abstract
BACKGROUND: The aim was to investigate the relationships among lean mass (LM), fat mass (FM), and bone mineral density (BMD) in women stratified by body mass index (BMI) (BMI - normal-weight, overweight, obese) and to determine threshold at which body fat assumes negative relationship with BMD. METHODS: This was a cross-sectional study in 471 healthy Caucasian women, aged 18-67 years. BMD, LM, and FM were measured using dual-energy X-ray absorptiometry. Analysis of variance with Bonferroni corrections was used to test the BMI group differences. Linear regression was used to examine independent contributions of LM and FM on BMD of various skeletal sites (controlling for age and height). In overweight/obese women PROC LOESS plots were used to determine the inflection points at which either LM or FM relationship with BMD changes direction. Separate analyses in pre- and post-menopausal women were conducted as well. RESULTS: Spine and femoral neck BMD were not different among three BMI groups while total body, femur and radius BMD were statistically different (the highest in the obese group). Linear regression revealed that LM had significant positive association with BMD of various skeletal sites in all groups. FM showed a negative association with BMD of femoral neck and femur in normal-weight and spine in overweight women, but a positive association with radius in obese women. Inflection points showed that body fat between 33% and 38% assumed negative relationship with BMD for most skeletal sites in overweight and obese women. CONCLUSIONS: Although LM has strong positive relationship with BMD, FM above 33% in overweight/obese women is negatively related to BMD of most skeletal sites. Therefore, overweight/obesity after certain amount of FM, may not be a protective factor against osteoporosis in females. For clinical practice in women, it is important to maintain LM and keep FM accrual below ~30% body fat to maintain good skeletal health.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".