Association between Allopurinol and Cardiovascular Events and All-Cause Mortality in Diabetes—A Population-Based Cohort Study
Bibliographic record
Abstract
Higher uric acid (UA) is associated with cardiovascular events and mortality. Allopurinol, a UA-lowering therapy, may reduce risk of these outcomes. Despite the high prevalence of elevated UA in diabetes, the association between allopurinol and cardiovascular events and mortality in diabetes is unclear. A population-based cohort was constructed using administrative data in Ontario, Canada. Subjects with diabetes entered on receipt of a new prescription for allopurinol after age 66 (April 1/2002-March 31/2012) and were followed until a composite of all-cause mortality, stroke, myocardial infarction or revascularization. A Cox proportional hazards model was used for each sex, with time-varying allopurinol exposure modeled as yes/no, dose categories and cumulative dose. Over a median [IQR] follow-up time of 4.7[1.8-7.8] years, the composite outcome occurred in 16,262/23,103 males and 10,566/15,313 females. Allopurinol exposure was associated with a reduction in the composite outcome in a dose-response manner but there was no cumulative dose effect (Table 1). Any allopurinol exposure and higher allopurinol doses were associated with reduced cardiovascular events and mortality in a large diabetes cohort. Potential mechanisms include an acute reduction of oxidative stress and endothelial dysfunction. Table 1: Hazard Ratios by Sex.Males (n=23,103)Females (n=15,313)Allopurinol ExposureUnadjusted HRAdjusted HRUnadjusted HRAdjusted HRExposed time v. unexposed time0.82 (0.79, 0.84)0.77 (0.75, 0.80)0.88 (0.85, 0.92)0.81 (0.78, 0.84)Dose categories0mg---->0 and ≤100mg1.03 (0.99, 1.08)0.84 (0.80, 0.88)1.(1.01, 1.12)0.86 (0.81, 0.90)>100 and ≤200mg0.82 (0.78, 0.85)0.75 (0.72, 0.78)0.83 (0.79, 0.87)0.76 (0.72, 0.80)>200mg0.71 (0.68, 0.74)0.75 (0.72, 0.78)0.78 (0.73, 0.82)0.81 (0.77, 0.86)Cumulative Dose (per 100g increase)0.99 (0.98, 1.00)1.00 (0.99, 1.01)0.99 (0.98, 1.01)0.99 (0.98, 1.00) Disclosure A. Weisman: None. G.A. Tomlinson: None. L. Lipscombe: None. B.A. Perkins: Advisory Panel; Self; Boehringer Ingelheim GmbH. Research Support; Self; Boehringer Ingelheim GmbH, Novo Nordisk Inc.. Advisory Panel; Self; Novo Nordisk Inc., Abbott. Speaker's Bureau; Self; Abbott, Janssen Pharmaceuticals, Inc.. Advisory Panel; Self; Insulet Corporation. Speaker's Bureau; Self; Insulet Corporation, Dexcom, Inc.. G.A. Hawker: None.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".