Circulating Leptin and Bone Mineral Density in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association between circulating leptin and bone mineral density (BMD) in patients with rheumatoid arthritis (RA). METHODS: One-hundred thirty postmenopausal women with RA were assessed for body mass index (BMI), disease characteristics, history of drug use, rheumatoid factor, and erythrocyte sedimentation rate (ESR). BMD (g/cm(2)) was determined in the hip and spine by DEXA. Serum leptin concentrations were measured by ELISA. Spearman's correlation coefficients (rho) were determined between BMD and leptin and other variables. A multiple regression analysis was used to adjust for confounders. RESULTS: Patients' serum leptin levels varied widely (range 2-128 ng/ml). Thirty-three patients (25%) had osteoporosis. Higher levels of leptin correlated significantly with BMD in the lumbar spine (rho = 0.17, p = 0.04) and total hip (rho = 0.21, p = 0.01). The variables that were negatively correlated with BMD were age, duration of menopause, and ESR. After adjustment for confounders, leptin was no longer associated with BMD. In the multivariate model, factors that remained associated with BMD in the total hip were age (p = 0.021) and BMI (p = 0.003); and the factors that remained associated with BMD in the lumbar spine were BMI (p = 0.03) and ESR (p = 0.01). CONCLUSION: No relevant association was found between circulating leptin levels and BMD in patients with RA in this cross-sectional study. Followup studies are needed to evaluate whether abnormal leptin levels confer a risk for fractures due to osteoporosis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".