Prediagnostic Glycoprotein Acetyl Levels and Incident and Recurrent Flare Risk Accounting for Serum Urate Levels: A <scp>Population‐Based</scp>, Prospective Study and Mendelian Randomization Analysis
Bibliographic record
Abstract
OBJECTIVE: To prospectively investigate population-based metabolomics for incident gout and reproduce the findings for recurrent flares, accounting for serum urate. METHODS: We conducted a prediagnostic metabolome-wide analysis among 105,615 UK Biobank participants with nuclear magnetic resonance metabolomic profiling data (168 total metabolites) from baseline blood samples collected 2006-2010 in those without history of gout. We calculated hazard ratios (HRs) for incident gout, adjusted for gout risk factors, excluding and including serum urate levels, overall and according to fasting duration before sample collection. Potential causal effects were tested with 2-sample Mendelian randomization. Poisson regression was used to calculate rate ratios (RRs) for the association with recurrent flares among incident gout cases. RESULTS: ). Only GlycA remained associated with incident gout following urate adjustment (HR 1.52 [95% confidence interval (95% CI) 1.22-1.88] between extreme quintiles); the HR increased progressively with fasting duration before sample collection, reaching 4.01 (95% CI 1.36-11.82) for ≥8 hours of fasting. Corresponding HRs per SD change in GlycA levels were 1.10 (95% CI 1.04-1.17) overall and 1.54 (95% CI 1.21-1.96) for ≥8 hours of fasting. GlycA levels were also associated with recurrent gout flares among incident gout cases (RR 1.90 [95% CI 1.27-2.85] between extreme quintiles) with larger associations with fasting. Mendelian randomization corroborated a potential causal role for GlycA on gout risk. CONCLUSION: This prospective, population-based study implicates GlycA, a stable long-term biomarker reflecting neutrophil overactivity, in incident and recurrent gout flares (central manifestation from neutrophilic synovitis) beyond serum urate.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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".