Assessing the Risk for Gout With Sodium–Glucose Cotransporter-2 Inhibitors in Patients With Type 2 Diabetes
Bibliographic record
Abstract
Background: Hyperuricemia is common in patients with type 2 diabetes mellitus and is known to cause gout. Sodium-glucose cotransporter-2 (SGLT2) inhibitors prevent glucose reabsorption and lower serum uric acid levels. Objective: To compare the rate of gout between adults prescribed an SGLT2 inhibitor and those prescribed a glucagon-like peptide-1 (GLP1) receptor agonist. Design: Population-based new-user cohort study. Setting: A U.S. nationwide commercial insurance database from March 2013 to December 2017. Patients: Persons with type 2 diabetes newly prescribed an SGLT2 inhibitor were 1:1 propensity score matched to patients newly prescribed a GLP1 agonist. Persons were excluded if they had a history of gout or had received gout-specific treatment previously. Measurements: The primary outcome was a new diagnosis of gout. Cox proportional hazards regression was used to estimate hazard ratios (HRs) of the primary outcome and 95% CIs. Results: The study identified 295 907 adults with type 2 diabetes mellitus who were newly prescribed an SGLT2 inhibitor or a GLP1 agonist. The gout incidence rate was lower among patients prescribed an SGLT2 inhibitor (4.9 events per 1000 person-years) than those prescribed a GLP1 agonist (7.8 events per 1000 person-years), with an HR of 0.64 (95% CI, 0.57 to 0.72) and a rate difference of -2.9 (CI, -3.6 to -2.1) per 1000 person-years. Limitation: Unmeasured confounding, missing data (namely incomplete laboratory data), and low baseline risk for gout. Conclusion: Adults with type 2 diabetes prescribed an SGLT2 inhibitor had a lower rate of gout than those prescribed a GLP1 agonist. Sodium-glucose cotransporter-2 inhibitors may reduce the risk for gout among adults with type 2 diabetes mellitus, although future studies are necessary to confirm this observation. Primary Funding Source: Brigham and Women's Hospital.
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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 | 0.000 | 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 | 0.000 |
| 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".