Serum urate and cardiovascular events in the DCCT/EDIC study
Bibliographic record
Abstract
In type 2 diabetes, hyperuricemia is associated with cardiovascular disease (CVD) and the metabolic syndrome (MetS), but associations in type 1 diabetes (T1D) have not been well-defined. This study examined the relationships between serum urate (SU) concentrations, clinical and biochemical factors, and subsequent cardiovascular events in a well-characterized cohort of adults with T1D. In 973 participants with T1D in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study (DCCT/EDIC), associations were defined between SU, measured once in blood collected 1997-2000, and (a) concurrent MetS and (b) incident 'any CVD' and major adverse cardiovascular events (MACE) through 2013. SU was higher in men than women [mean (SD): 4.47 (0.99) vs. 3.39 (0.97) mg/dl, respectively, p < 0.0001], and was associated with MetS features in both (men: p = 0.0016; women: p < 0.0001). During follow-up, 110 participants (11%) experienced "any CVD", and 53 (5%) a MACE. Analyzed by quartiles, SU was not associated with subsequent CVD or MACE. In women, SU as a continuous variable was associated with MACE (unadjusted HR: 1.52; 95% CI 1.07-2.16; p = 0.0211) even after adjustment for age and HbA1c (HR: 1.47; 95% CI 1.01-2.14; p = 0.0467). Predominantly normal range serum urate concentrations in T1D were higher in men than women and were associated with features of the MetS. In some analyses of women only, SU was associated with subsequent MACE. Routine measurement of SU to assess cardiovascular risk in T1D is not merited.Trial registration clinicaltrials.gov NCT00360815 and NCT00360893.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".