Prediagnostic Amino Acid Metabolites and Risk of Gout, Accounting for Serum Urate: Prospective Cohort Study and Mendelian Randomization
Bibliographic record
Abstract
Objective Our objective was to prospectively investigate prediagnostic population‐based metabolome for risk of hospitalized gout (ie, most accurate, severe, and costly cases), accounting for serum urate. Methods We conducted prediagnostic metabolome‐wide analyses among 249,677 UK Biobank participants with nuclear magnetic resonance metabolomic profiling (N = 168 metabolites, including eight amino acids) from baseline blood samples (2006–2010) without a history of gout. We calculated multivariable hazard ratios (HRs) for hospitalized incident gout, before and after adjusting for serum urate levels; we included patients with nonhospitalized incident gout in a sensitivity analysis. Potential causal effects were evaluated with two‐sample Mendelian randomization. Results Correcting for multiple testing, 107 metabolites were associated with incidence of hospitalized gout (n = 2,735) before urate adjustment, including glycine and glutamine (glutamine HR 0.64, 95% confidence interval [CI] 0.54–0.75, P = 8.3 × 10 −8 ; glycine HR 0.69, 95% CI 0.61–0.78, P = 3.3 × 10 −9 between extreme quintiles), and glycoprotein acetyls (HR 2.48, 95% CI 2.15–2.87, P = 1.96 × 10 −34 ). Associations remained significant and directionally consistent following urate adjustment (HR 0.83, 95% CI 0.70–0.98; HR 0.86, 95% CI 0.76–0.98; HR 1.41, 95% CI 1.21–1.63 between extreme quintiles), respectively; corresponding HRs per SD were 0.91 (95% CI 0.86–0.97), 0.94 (95% CI 0.91–0.98), and 1.10 (95% CI 1.06–1.14). Findings persisted when including patients with nonhospitalized incident gout. Mendelian randomization corroborated their potential causal role on hyperuricemia or gout risk; with change in urate levels of −0.05 mg/dL (95% CI −0.08 to −0.01) and −0.12 mg/dL (95% CI −0.22 to −0.03) per SD of glycine and glutamine, respectively, and odds ratios of 0.94 (95% CI 0.88–1.00) and 0.81 (95% CI 0.67–0.97) for gout. Conclusion These prospective findings with causal implications could lead to biomarker‐based risk prediction and potential supplementation‐based interventions with glycine or glutamine.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".