Do Serum Urate–associated Genetic Variants Influence Gout Risk in People Taking Diuretics? Analysis of the UK Biobank
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine whether serum urate (SU)-associated genetic variants differ in their influence on gout risk in people taking a diuretic compared to those not taking a diuretic. METHODS: This research was conducted using the UK Biobank Resource (n = 359,876). Ten SU-associated single-nucleotide polymorphisms (SNP) were tested for their association with gout according to diuretic use. Gene-diuretic interactions for gout association were tested using a genetic risk score (GRS) and individual SNP by logistic regression adjusting for relevant confounders. RESULTS: After adjustment, use of a loop diuretic was positively associated with prevalent gout (OR 2.34, 95% CI 2.08-2.63), but thiazide diuretics were inversely associated with prevalent gout (OR 0.60, 95% CI 0.55-0.66). Compared with a lower GRS (< mean), a higher GRS (≥ mean) was positively associated with gout in those not taking diuretics (OR 2.63, 2.49-2.79), in those taking loop diuretics (OR 2.04, 95% CI 1.65-2.53), in those taking thiazide diuretics (OR 2.70, 2.26-3.23), and in those taking thiazide-like diuretics (OR 2.11, 95% CI 1.37-3.25). No nonadditive gene-diuretic interactions were observed. CONCLUSION: In people taking diuretics, SU-associated genetic variants contribute strongly to gout risk, with a similar effect to that observed in those not taking a diuretic. These findings suggest that the contribution of genetic variants is not restricted to people with "primary" gout, and that genetic variants can play an important role in gout susceptibility in the presence of other risk 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".