Gout and risk of non-alcoholic fatty liver disease
Bibliographic record
Abstract
OBJECTIVES: To investigate the association between gout and non-alcoholic fatty liver disease (NAFLD). METHODS: The study subjects were participants in a health-screening programme at Chang Gung Memorial Hospital from 2000 to 2006. Subjects were classified into eight groups based on serum urate (SU) level and gout status (≤ 4.9, 5.0-6.9, 7.0-8.9, and ≥ 9.0 mg/dL, without and with gout). The association between gout and NAFLD was assessed by multiple logistic regression. RESULTS: Among a total of 54 325 subjects, 1930 (3.6%) had gout and 6169 (11.3%) had NAFLD. The prevalence of NAFLD was significantly higher in subjects with gout (23.1%, n = 445) than in those without gout (10.9%, n = 5724, p < 0.001). Among subjects with NAFLD, the severity of NAFLD was higher in gout patients. Gout was associated with an increased risk for NAFLD [odds ratio (OR) 1.42, 95% confidence interval (CI) 1.25-1.60, p < 0.001], after adjustment for age, sex, presence of metabolic syndrome, and low estimated glomerular filtration rate (eGFR). With SU ≤ 4.9 mg/dL in the absence of gout as reference, the ORs (95% CI) for NAFLD, after adjustment for age, sex, presence of metabolic syndrome, and low eGFR, were, respectively, 2.16 (1.94-2.41), 3.98 (3.55-4.46), and 5.99 (5.19-6.90) for SU levels 2-4 in those without gout and 2.61 (1.39-4.91), 2.87 (2.04-4.04), 4.53 (3.70-5.56), and 6.31 (5.12-7.77) for SU levels 1-4 in those with gout. CONCLUSIONS: There was an independent association between gout and the risk for NAFLD. In addition, there was a dose-response relationship between SU and NAFLD in subjects with and without gout.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".