Uromodulin in a Pathway Between Decreased Renal Urate Excretion and Albuminuria
Bibliographic record
Abstract
BACKGROUND: The mechanism explaining the inverse association between renal urate and albumin excretion remains unclear. First, we evaluated the impact of candidate variants in the main urate transporter genes (i.e., SLC2A9, SLC22A12, ABCG2) on the association between fractional excretion of uric acid (FEUA) and urinary albumin/creatinine ratio (uACR). Second, we examined uromodulin and sodium excretion as mediators of the association between FEUA and uACR. METHODS: We performed cross-sectional analysis of 737 French Canadians from the CARTaGENE cohort, a random sample of the Quebec population aged 40-69 years (a total of 20,004 individuals). Individuals with available genotyping and urinary data were obtained from a sub-study including gender-matched pairs with high and low Framingham Risk Score and vascular rigidity index. We further excluded individuals with an estimated glomerular filtration rate <60 ml/min/1.73 m2, glycosuria, and use of confounding medication. A spot urine sample was analyzed. Genotyping was performed using the Illumina Omni2.5-8 BeadChips. Genetic variants were analyzed using an additive model. RESULTS: Final analyses included 593 individuals (45.5% of men; mean age 54.3 ± 8.6). We observed an antagonistic interaction between rs13129697 variant of the SLC2A9 gene and FEUA tertiles on uACR (P = 0.002). Using the mediation analysis, uromodulin explained 32%, fractional excretion of sodium (FENa) 44%, and uromodulin together with FENa explained 70% of the inverse relationship between FEUA and uACR. Bootstrapping process confirmed the role of both mediators. CONCLUSIONS: Our data suggest that the association of albuminuria with decreased renal urate excretion may be modified by the transporter SLC2A9, and mediated by uromodulin and sodium handling.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".