Menopause, postmenopausal hormone use and serum uric acid levels in US women – The Third National Health and Nutrition Examination Survey
Bibliographic record
Abstract
INTRODUCTION: Despite the substantial prevalence of gout in the ageing female population, female hormonal influence has not been comprehensively examined. We evaluated and quantified the potential independent association between menopause, postmenopausal hormone use and serum uric acid levels in a nationally representative sample of women. METHODS: Using data from 7662 women aged 20 years and older in the Third National Health and Nutrition Examination Survey (1988 to 1994), we examined the relation between menopause, postmenopausal hormone use and serum uric acid levels. We used multivariate linear regression to adjust for other risk factors for hyperuricaemia such as dietary factors, age, adiposity, alcohol use, renal function, hypertension and diuretic use. RESULTS: Menopause was associated with higher serum uric acid levels. After adjusting for covariates, serum uric acid levels among women with natural menopause and surgical menopause were greater than premenopausal women by 0.34 mg/dl (95% confidence interval [CI], 0.19 to 0.49) and 0.36 mg/dl (95% CI, 0.14 to 0.57), respectively. Current postmenopausal hormone use was associated with a lower serum uric acid level among postmenopausal women (multivariate difference, 0.24 mg/dl [95% CI, 0.11 to 0.36]). The serum uric acid levels increased with increasing age categories (crude difference between 20 to 29 years and 70 years and over = 1.03 mg/dl, p for trend < 0.001), but this increase was not present after adjusting for other covariates (p for trend = 0.66). CONCLUSIONS: These findings from a nationally representative sample of US women indicate that menopause is independently associated with higher serum uric acid levels, whereas postmenopausal hormone use is associated with lower uric acid levels among postmenopausal women. The age-associated increase in serum uric acid levels in women may be explained by menopause and other age-related factors.
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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".