Uric Acid and Insulin Sensitivity and Risk of Incident Hypertension
Bibliographic record
Abstract
BACKGROUND: Uric acid, insulin sensitivity, and endothelial dysfunction may be important in the development of hypertension. Corresponding circulating biomarkers are associated with risk of hypertension, but because these factors may be interrelated, whether they independently affect risk is unknown. METHODS: In 1496 women aged 32 to 52 years without hypertension at baseline, we prospectively analyzed the associations between fasting plasma levels of uric acid, insulin, triglycerides, the insulin sensitivity index, and 2 biomarkers associated with endothelial dysfunction (homocysteine and soluble intercellular adhesion molecule-1) and the odds of incident hypertension. Odds ratios were adjusted for standard risk factors and then for all biomarkers plus estimated glomerular filtration rate and total cholesterol level. Population-attributable risk was estimated for biomarkers significantly associated with hypertension. RESULTS: All the biomarkers were associated with incident hypertension after adjustment for standard hypertension risk factors. However, after simultaneously controlling for all the biomarkers, estimated glomerular filtration rate, and total cholesterol level, only uric acid and insulin levels were independently associated with incident hypertension. Comparing the highest and lowest quartiles of uric acid levels, the odds ratio was 1.89 (95% confidence interval, 1.26-2.82). A similar comparison yielded an odds ratio of 2.03 (95% confidence interval, 1.35-3.05) for insulin levels. Using an estimated basal incidence rate of 14.6 per 1000 annually, 30.8% of all hypertension occurring in young women annually is associated with uric acid levels of 3.4 mg/dL or greater (to convert to micromoles per liter, multiply by 59.485). For insulin levels of 2.9 microIU/mL or greater (to convert to picomoles per liter, multiply by 6.945), this proportion is 24.2%. CONCLUSIONS: Differences in uric acid and insulin levels robustly and substantially affect the risk of hypertension in young women. Measuring these biomarkers in clinical practice may identify higher-risk individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".