Association of plasma carnitine levels with bone mineral density and recent osteoporotic fracture
Bibliographic record
Abstract
Background: The carnitine system may play an essential role in bone metabolism. However, existing epidemiological studies on the association between carnitine and bone mineral density (BMD) are still controversial. No human study has examined the association of carnitine and osteoporotic fracture. The objective of this research was to examine the association of carnitine levels with BMD and recent osteoporotic fracture. Methods: We used cross-sectional and case-control studies to examine the associations of carnitine levels with BMD and recent osteoporotic fracture. The cross-sectional study identified 135 participants aged ≥45 years from the Second Hospital of Jilin University. The case-control study identified 44 recent fracture cases and 88 healthy controls aged 50 and older. Multivariable linear regression models were used to test the associations of carnitine with BMD, and conditional logistic regression models were used to analyze the association between carnitine levels and fracture. We used targeted metabolomics technology to measure 27 types of plasma carnitine levels. Results: > 0.05). In the case-control study, 23 participants (52.3%) were diagnosed with hip fracture. Greater total carnitine levels were negatively associated with the risk of osteoporotic fractures (adjusted odds ratio: 0.43, 95% confidence interval: 0.22-0.85). The magnitude of the associations was comparable for hip and non-hip fractures. Conclusion: Carnitine was not associated with BMD but was negatively associated with osteoporotic fracture. The low carnitine levels among fracture cases may be due to the post-fracture inflammatory and catabolic stress.
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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) | 0.002 | 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".