Gout and the risk of type 2 diabetes among men with a high cardiovascular risk profile
Bibliographic record
Abstract
OBJECTIVE: Our objective was to evaluate the independent relation between a history of gout and the future risk of type 2 diabetes among men with a high cardiovascular risk profile. METHODS: We prospectively examined over a 6-yr period the relation between gout and the risk of incident type 2 diabetes in 11 351 male participants from the Multiple Risk Factor Intervention Trial (MRFIT). Incident diabetes was defined based on the American Diabetes Association (ADA) criteria for epidemiological studies. Cox proportional hazards regression was used to adjust for potential confounders. RESULTS: We documented 1215 new cases of type 2 diabetes. After adjusting for age, BMI, smoking, family history of type 2 diabetes, alcohol intake, dietary factors and presence of individual components of the metabolic syndrome, the multivariate relative risk (RR) for incident type 2 diabetes among men with gout at baseline, as compared with men without gout, was 1.34 (95% CI 1.09, 1.64). When we further adjusted for serum uric acid levels, the association remained significant (RR 1.26; 95% CI 1.02, 1.54). When we updated the status of gout annually during follow-up as a time-varying covariate, the association remained similar. The association also remained similar in our subgroup analyses by major covariates (P-values for interaction >0.16). CONCLUSIONS: These findings from men with a high cardiovascular risk profile suggest that men with gout are at a higher future risk of type 2 diabetes independent of other known risk factors. These data expand on well-established, cross-sectional associations between hyperuricaemia, gout and the metabolic syndrome, and extend the link to the future risk of type 2 diabetes.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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, 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".