Genome-wide characterization of 54 urinary metabolites reveals molecular impact of kidney function
Bibliographic record
Abstract
Abstract Dissecting the genetic mechanisms underlying urinary metabolite concentrations can provide molecular insights into kidney function and open possibilities for causal assessment of urinary metabolites with risk factors and disease outcomes. Proton nuclear magnetic resonance metabolomics provides a high-throughput means for urinary metabolite profiling, as widely applied for blood biomarker studies. Here we report a genome-wide association study meta-analysed for 3 European cohorts comprising 8,026 individuals, covering both people with type 1 diabetes and general population settings. We identified 52 associations ( p <9.3×10 -10 ) for 19 of 54 studied metabolite concentrations. Out of these, 32 were not reported previously for relevant urinary or blood metabolite traits. Subsequent two-sample Mendelian randomization analysis suggests that estimated glomerular filtration rate (eGFR) causally affects 13 urinary metabolite concentrations whereas urinary ethanolamine, an initial precursor for phosphatidylcholine and phosphatidylethanolamine, was associated with higher eGFR lending support for a potential protective role.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".