Association of Four Genetic Loci with Uric Acid Levels and Reduced Renal Function: The J-SHIPP Suita Study
Bibliographic record
Abstract
BACKGROUND: Recent genome-wide association studies have identified several genetic variants as susceptibility loci for serum uric acid (UA) levels. We also identified a common nonsense mutation, W258X, responsible for renal hypouricemia. Here, we investigated clinical implications of these genetic variants by cross-sectional and longitudinal genetic epidemiological analysis. METHODS: The study enrolled 5,165 Japanese subjects aged 64 ± 12 years from the general population. Clinical parameters were obtained from the personal health records, evaluated at medical checkups. RESULTS: Serum UA levels were significantly different between the SLC22A12 rs11231825 (CC/CT/TT: 4.5 ± 1.6, 5.0 ± 1.4, 5.3 ± 1.4 mg/dl; p = 7.6 × 10(-20)), SLC2A9 rs1014290 (TT/TG/GG: 4.9 ± 1.4, 5.1 ± 1.4, 5.3 ± 1.4 mg/dl; p = 3.1 × 10(-11)) and ABCG2 rs2231142 (TT/TG/GG: 5.3 ± 1.5, 5.2 ± 1.4, 5.1 ± 1.4 mg/dl; p = 2.0 × 10(-5)) genotypes. During 9.4 years of follow-up, 87 new cases of hyperuricemia were diagnosed. Multiple logistic regression analysis identified the accumulation of risk alleles as a significant determinant of future development of hyperuricemia (OR = 7.94; 95% CI: 1.97-53.6). In contrast, subjects with nonsense mutation predominantly showed lower UA levels (XX/XW/WW: 1.3 ± 1.7, 3.6 ± 1.0, 5.2 ± 1.4 mg/dl; p = 9.3 × 10(-82)). However, these subjects showed significantly reduced renal function (β = -0.111; p < 0.001) independently of possible covariates. CONCLUSION: Accumulation of risk genotypes was an independent risk factor for future development of hyperuricemia. Genetically developed hypouricemia was an independent risk factor for decreased renal function.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".