Trp64Arg Polymorphism of the <i>ADRB3</i> Gene Predicts Hyperuricemia Risk in a Population from Southern Spain
Bibliographic record
Abstract
Objective. To study the role of Trp64Arg polymorphism of the ADRB3 gene in the risk of developing hyperuricemia in 1051 subjects from southern Spain, with a followup of 6 years. The inclusion of plasma levels of uric acid as a diagnostic criterion to define the metabolic syndrome is under discussion. Genes responsible for insulin resistance could contribute to the development of hyperuricemia. Previous cross-sectional studies have suggested ADRB3 as a possible candidate gene in the development of hyperuricemia and insulin resistance. Methods. A prospective, population-based, cohort study of 1051 persons examined in 1997–98 and reassessed at a second examination 6 years later. The metabolic phenotype was assessed at baseline and again at the followup. Insulin resistance was measured by homeostasis model assessment. The Trp64Arg polymorphism of ADRB3 was detected by real-time polymerase chain reaction. Subjects were considered normouricemic if their serum uric acid levels were ≤7 mg/dl for men or ≤ 6 mg/dl for women. Results. Carriers of the Arg64 allele who were normouricemic at baseline had a higher risk of developing hyperuricemia 6 years later (p = 0.017, OR 2.3, 95% CI 1.1–4.6). Multivariate logistic regression analysis showed that the OR of having hyperuricemia at the 6-year followup was significantly associated with the Arg64 allele, after adjusting for age, weight gain, baseline levels of triglycerides, serum uric acid, and insulin resistance (OR 3.1, 95% CI 1.3–7.1). Conclusion. Trp64Arg polymorphism of the ADRB3 gene predicted the risk of developing hyperuricemia in this adult population.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".