Old but (Unfortunately) Not Forgotten—The Alarming Use of Outdated Sulfonylureas (InHypo-DM Study)
Bibliographic record
Abstract
While sulfonylureas (SUs) are known to induce hypoglycemia, their low cost and ease-of-use has made them a mainstay therapy for T2DM. Second-generation SUs (SGSUs), compared to first-generation SUs (FGSUs), have been successful at reducing these events. Pragmatic evidence substantiating this association remains limited; this study leverages data from the population-based InHypo-DM study to describe the real-world patterns of SU use and impact on hypoglycemia incidence in Canada. A validated questionnaire (InHypo-DMPQ) was administered online to a nationwide panel consisting of adults with SU-treated T2DM. Questions related to respondents’ past hypoglycemia events, as well as socio-demographic and clinical traits. Negative binomial regression (NBR) was used to test the effect of SU type (FGSUs (chlorpropamide/tolbutamide) vs. SGSUs (glyburide/gliclazide/glimepiride)) on the annual rate of any hypoglycemia. A directed acyclic graph was constructed to identify the adjustment set. Of the 255 adults with SU-treated T2DM (56% male, mean age: 53.1 (SD: 14.4) years), 10.6% were on FGSUs (chlorpropamide: 7.5%, tolbutamide: 3.1%) and 89.5% on SGSUs (glyburide: 27.5%, gliclazide: 54.9%, glimepiride: 7.1%). Annualized crude event frequencies revealed that those on FGSUs, vs. SGSUs, were 1.57 times as likely to have ≥1 event; this group also experienced an average of 2.70 more events/person-year. Based on the NBR, FGSUs vs. SGSUs was associated with a 2.76 (95% CI: 1.07-7.11, p=0.035) factor increase in the rate of hypoglycemia, adjusting for drug coverage, T2DM duration, income, and clinician type. These results confirm that FGSUs induce a dangerous, real-world risk for hypoglycemia. It exposes a worrying trend for the continued use of outdated SUs for T2DM, despite the availability of safer alternatives in Canada. Our study serves as a pressing call-to-action to ensure that clinicians provide the safest and most effective therapeutic management for patients with T2DM. Disclosure N.H. Au: None. A. Ratzki-Leewing: None. B.L. Ryan: None. S. Mequanint: None. J.E. Black: None. S.M. Reichert: Other Relationship; Self; Novo Nordisk Inc., Sanofi, Abbott, AstraZeneca. Advisory Panel; Self; Servier. Speaker's Bureau; Self; Eli Lilly and Company. Other Relationship; Self; Boehringer Ingelheim Pharmaceuticals, Inc.. Speaker's Bureau; Self; Merck & Co., Inc., Janssen Pharmaceuticals, Inc.. J.B. Brown: None. S. Harris: Advisory Panel; Self; Novo Nordisk A/S, Sanofi, Merck, AstraZeneca, Amgen Inc., Lilly/Boehringer Ingelheim, Abbott, Janssen. Consultant; Self; Novo Nordisk A/S, Sanofi, Merck, AstraZeneca, Lilly/Boehringer Ingelheim, Abbott, Janssen. Research Support; Self; Novo Nordisk A/S, Sanofi, Merck, Abbott, AstraZeneca, Janssen. Other Relationship; Self; CIHR, CDA, The Lawson Foundation.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".