Impact of obesity and hypertriglyceridemia on gout development with or without hyperuricemia: A prospective study
Bibliographic record
Abstract
OBJECTIVE: Hyperuricemia is the most important risk factor for the development of gout; however, not all patients with hyperuricemia develop gout, and patients experiencing a gout attack are not necessarily found to have hyperuricemia. We hypothesized that the interactions between serum uric acid (sUA) and other potential metabolic comorbidities increase the risk of gout development. METHODS: A prospective study was conducted to link baseline metabolic profiles from the MJ Health Screening Center to gout outcomes extracted from the Taiwan National Health Insurance database. A Cox proportional hazards model was used to assess the metabolic risks for incident gout stratified by hyperuricemia status (sUA level >7 mg/dl or not). RESULTS: During a mean followup period of 6.45 years (261,500 person-years), 1,189 patients with clinical gout (899 men, 202 women ages >50 years, and 88 women ages ≤50 years) were identified among the 40,513 examinees. The multivariate adjusted hazard ratios (HRs) of hyperuricemia for gouty arthritis were 5.80 (95% confidence interval [95% CI] 4.93-6.81) in men and 4.37 (95% CI 3.38-5.66) in women. Hypertriglyceridemia (triglyceride level >150 mg/dl) was found as an independent risk factor, with HRs of 1.38 (95% CI 1.18-1.60) in men with hyperuricemia and 1.40 (95% CI 1.02-1.92) in men without hyperuricemia. General obesity (body mass index >27 kg/m(2) ) was independently associated with gout in older women, with HRs of 1.72 (95% CI 1.15-2.56) in women with hyperuricemia and 2.19 (95% CI 1.47-3.26) in women without hyperuricemia. CONCLUSION: General obesity in women and hypertriglyceridemia in men may potentiate an sUA effect for gout development. Further investigation is needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".