Relationships between obesity markers and bone parameters in community-dwelling older adults
Bibliographic record
Abstract
BACKGROUND: Osteoporosis is an age-related condition that can lead to fragility fractures and other serious consequences. The literature data on the impact of obesity on bone health are contradictory. The main reasons for this discrepancy could be the imperfect nature of the body mass index (BMI) as a marker of obesity, the metabolic status (inflammation and metabolically healthy obesity), and/or heterogeneity in bone variables and architecture or sex. AIMS: To examine the relationship between bone variables and three validated obesity criteria. METHODS: In this cross-sectional study, participants were classified as obese according to their BMI, waist circumference (WC), and fat mass (FM). Bone variables and architecture were assessed using dual-energy X-ray absorptiometry and peripheral quantitative computed tomography, respectively. RESULTS: One hundred sixty-eight adults aged 55 or over (men: 68%) were included. 48 (28%) participants were obese according to the BMI, with 108 (64%) according to the FM, and 146 (87%) according to the WC. Bone variables were positively correlated with WC and BMI (Pearson's r = 0.2-0.42). In men only, the obesity measures were negatively correlated with cortical bone density (Pearson's r = - 0.32 to - 0.19) and positively correlated with cortical bone area (Pearson's r = 0.22-0.39). CONCLUSION: Our findings indicate that independent of sex and obesity criteria, when significant, being obese seems to lead to higher bone parameters than being non-obese, except for cortical bone density. Thus, in the obese population, assessing cortical density might help the physician to identify bone alteration. Further researches are needed to confirm our findings.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".