Low Levels of Serum Uric Acid Increase the Risk of Low Bone Mineral Density in Young Male Patients with Ankylosing Spondylitis
Bibliographic record
Abstract
OBJECTIVE: Uric acid (UA) has antiosteoporotic effects in postmenopausal women. This study investigated the association between serum UA levels and bone mineral density (BMD) in young male patients with ankylosing spondylitis (AS). METHODS: One hundred fifty patients who fulfilled the modified New York criteria for the classification of AS were analyzed. All patients were male and under 50 years of age. BMD, serum UA concentrations, clinical variables, and radiographic progression were assessed. The associations between UA and BMD at the lumbar spine and hip were evaluated using multiple linear regression analysis. Multivariate logistic regression analyses were performed to identify risk factors associated with low BMD. RESULTS: Mean serum UA concentration in the 150 patients with AS was 5.5 ± 1.3 mg/dl. BMD at the lumbar spine, but not at the total hip and femoral neck, increased with increasing serum UA tertiles (p = 0.033). The significant positive association between serum UA and BMD at the lumbar spine remained after adjustment for confounding factors (β = 0.185, p = 0.014, adjusted R(2) = 0.310). Multiple logistic regression analyses showed that lower UA concentrations (OR 4.02, 95% CI 1.34-12.3) and body mass index and increased erythrocyte sedimentation rate were independently associated with the risk of low BMD. CONCLUSION: Lower serum UA levels are associated with lower BMD in young male patients with AS. UA may be a novel predictive marker or therapeutic target in patients with AS.
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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 | 0.001 |
| 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".