Last reviewed · How we verify
NCT05402579: DaNGER
Diabetic Ketoacidosis From New SGLT2i: Can Genomics Estimate Risk
trial testing Genomic analysis in Diabetes Type 2 in 63 participants. Completed in 20 January 2025.
20 January 2025
Quick facts
| Lead sponsor | Mount Sinai Hospital, Canada |
|---|---|
| Status | Completed |
| Study type | OBSERVATIONAL |
| Enrollment | 63 |
| Start date | 29 July 2022 |
| Primary completion | 20 January 2025 |
| Estimated completion | 20 January 2025 |
| Sites | 2 locations across Canada |
Drugs / interventions tested
- Genomic analysis
Conditions studied
- Diabetes Type 2 — all drugs for Diabetes Type 2 →
- DKA — all drugs for DKA →
- Diabetic Ketoacidosis — all drugs for Diabetic Ketoacidosis →
Sponsor
Mount Sinai Hospital, Canada
Who can join
Adults 18 to 100, any sex, with Diabetes Type 2 or DKA. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
Sodium glucose co-transporter 2 (SGLT2) inhibitors have revolutionized care for people living with type 2 diabetes mellitus (T2DM). They reduce a person's risk of heart failure, renal failure, myocardial infarction, stroke, cardiovascular mortality, and potentially all-cause mortality. Remarkably, some of these benefits also extend to people who do not have T2DM. While the benefits of SGLT2 inhibitors are impressive, there is one life-threatening side effect associated with their use: diabetic ketoacidosis (DKA). The ability to predict which patients are at highest risk of DKA is needed to sufficiently mitigate this risk. Moreover, considering the impressive benefits of SGLT2 inhibitors, identifying patients at the lowest risk of SGLT2 inhibitor-associated DKA is also important so that providers do not overestimate risk in those who stand to benefit most. Advances in genomic technologies and related analyses have provided unprecedented opportunities to bring genomics-driven precision medicine initiatives to the forefront of clinical research. Leading these developments has been the progress made by genome-wide association studies (GWAS) due to decreasing genotyping costs, and consequently, the ability to routinely study large numbers of patients. These approaches allow for systematic screening of the genome in an unbiased manner and have accelerated the discovery of genetic variants and novel biological processes that contribute to the development of adverse treatment outcomes. By using innovative approaches, which harness large cohorts of population controls, sample size limitations that are associated with rare adverse drug reactions such as SGLT2 inhibitor-associated DKA can be overcome. The DANGER study represents a highly innovative new direction wherein partnership among basic science researchers and computational biologists will lead to the application of genomic techniques to identify genetic variants that may be associated with SGLT2 inhibitor-associated DKA.
Publications & conference data
No peer-reviewed publications indexed yet for this trial. Completed trials usually publish results within 12-18 months.
Verify or expand the search:
- PubMed search for NCT05402579
- Europe PMC full search
- ASCO Meeting Library
- ESMO Meeting Library
- bioRxiv preprints
- medRxiv preprints
- Google Scholar
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Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT05402579 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by Mount Sinai Hospital, Canada
- Last refreshed: 4 December 2025
Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT05402579.
Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing