Uric Acid and Prevalence of Hypertension in a General Population of Japanese: ISSA-CKD Study
Bibliographic record
Abstract
BACKGROUND: There is uncertainty surrounding the causal relationship between serum uric acid and hypertension. The aim was to investigate the association between serum uric acid and prevalence of hypertension in a general population of Japanese. METHODS: This was a population-based cross-sectional study using health check-up data of the residents of the Iki City, Nagasaki Prefecture, Japan. A total of 7,484 participants aged 30 years or older were included in this study. Serum uric acid was classified into four groups: group 1 (< 357 µmol/L (< 6 mg/dL)), group 2 (357 - 415 µmol/L (6 - 6.9 mg/dL)), group 3 (416 - 475 µmol/L (7 - 7.9 mg/dL)) and group 4 (≥ 476 µmol/L (≥ 8 mg/dL)). Hypertension was defined as blood pressure (BP) levels of ≥ 140/90 mm Hg or use of BP lowering medications. RESULTS: Hypertension was observed among 3,467 participants (prevalence 46.3%). The prevalence of hypertension increased with elevation of serum uric acid levels: 42.8% in group 1, 55.0% in group 2, 57.6% in group 3 and 59.8% in group 4 (P < 0.001 for trend). This association was significant even after adjustment for other risk factors including age, sex, current smoking, current alcohol intake, obesity, diabetes, dyslipidemia, estimated glomerular filtration rate and proteinuria: odds ratios (95% confidence intervals) were 1.50 (1.28 - 1.77) for group 2, 1.58 (1.25 - 1.99) for group 3 and 1.89 (1.36 - 2.64) for group 4 compared with the reference group of group 1 (P < 0.001 for trend). CONCLUSIONS: Serum uric acid was clearly associated with prevalence of hypertension in a general population of Japanese.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".