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 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 | 0.000 |
| 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) | 0.000 | 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".