Sodium-Glucose Cotransporter 2 Inhibitors Preceding ST-Segment Elevation Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: While sodium-glucose cotransport 2 receptor inhibitors (SGLT2i) improve post infarction cardiovascular outcomes, limited understanding exists on how these agents influence pathophysiology preceding myocardial infarction. OBJECTIVES: The objective of this study was to explore how proteins are differentially regulated in patients on and not on an SGLT2i preceding ST-segment elevation myocardial infarction (STEMI). METHODS: Between June 2021 and October 2023, blood was collected at the time of arterial sheath insertion from consecutive STEMI patients. We then identified patients with diabetes and created propensity-matched pairs of patients on and not on SGLT2i prior to STEMI (SGLT2i+ and SGLT2i-). Serum was separated, and following immunodepletion and enzymatic digestion, liquid chromatography-tandem mass spectrometry was performed to identify differentially regulated proteins between the 2 SGLT2i groups. RESULTS: Of the 560 STEMI patients, 149 eligible patients had diabetes distributed by pre-existing SGLT2i use as: SGLT2i+ (n = 35) and SGLT2i- (n = 114). Both SGLT2i groups were comparable in their presenting demographics and reperfusion strategies, except for higher proportion of insulin use in SGLT2i+ patients. Thirty-three SGLT2i+/SGLT2i- propensity-matched pairs were created from which 21 differentially expressed proteins were identified; dominantly noted was up-regulation of proteins involved in heme-scavenging and nitric oxide transport in patients on SGLT2i+ compared with SGLT2i preceding STEMI. CONCLUSIONS: SGLT2i appears to predominantly associate with up-regulation of heme-scavenging and nitric oxide, and plausibly through a related reduction in infarct size also associates with the observed related improvement in post infarction heart failure.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".