Sodium–Glucose Cotransporter-2 Inhibitors and the Risk for Diabetic Ketoacidosis
Bibliographic record
Abstract
BACKGROUND: Sodium-glucose cotransporter-2 (SGLT-2) inhibitors could increase the risk for diabetic ketoacidosis (DKA). OBJECTIVE: To assess whether SGLT-2 inhibitors, compared with dipeptidyl peptidase-4 (DPP-4) inhibitors, are associated with an increased risk for DKA in patients with type 2 diabetes. DESIGN: Population-based cohort study; prevalent new-user design between 2013 and 2018. (ClinicalTrials.gov: NCT04017221). SETTING: Electronic health care databases from 7 Canadian provinces and the United Kingdom. PATIENTS: 208 757 new users of SGLT-2 inhibitors were matched by using time-conditional propensity scores to 208 757 recipients of DPP-4 inhibitors. MEASUREMENTS: Cox proportional hazards models estimated site-specific hazard ratios (HRs) with 95% CIs of DKA comparing receipt of SGLT-2 inhibitors with receipt of DPP-4 inhibitors, which were pooled by using random-effects models. Secondary analyses were stratified by molecule, age, sex, and prior receipt of insulin. RESULTS: Overall, 521 patients were diagnosed with DKA during 370 454 person-years of follow-up (incidence rate per 1000 person-years, 1.40 [95% CI, 1.29 to 1.53]). Compared with DPP-4 inhibitors, SGLT-2 inhibitors were associated with an increased risk for DKA (incidence rate, 2.03 [CI, 1.83 to 2.25] versus 0.75 [CI, 0.63 to 0.89], respectively; HR, 2.85 [CI, 1.99 to 4.08]). Molecule-specific HRs were 1.86 (CI, 1.11 to 3.10) for dapagliflozin, 2.52 (CI, 1.23 to 5.14) for empagliflozin, and 3.58 (CI, 2.13 to 6.03) for canagliflozin. Age and sex did not modify the association; prior receipt of insulin appeared to decrease the risk. LIMITATIONS: There was unmeasured confounding and no laboratory data were available for the majority of patients, and molecule-specific analyses were conducted at a limited number of sites. CONCLUSION: SGLT-2 inhibitors were associated with an almost 3-fold increased risk for DKA, with molecule-specific analyses suggesting a class effect. PRIMARY FUNDING SOURCE: Canadian Institutes of Health Research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".