The sodium/glucose cotransporter 2 inhibitor Empagliflozin inhibits long QT 3 late sodium currents in a mutation specific manner
Bibliographic record
Abstract
Background Sodium/glucose cotransporter 2 inhibitors (SGLT2is) like empagliflozin have demonstrated cardioprotective effects in patients with or without diabetes. SGLT2is have been shown to selectively inhibit the late component of cardiac sodium current (late I Na ). Induction of late I Na is the primary mechanism in the pathophysiology of congenital long QT syndrome type 3 (LQT3) gain-of-function mutations in the SCN5A gene encoding Nav1.5. We investigated empagliflozin's effect on late I Na in thirteen known LQT3 mutations located in distinct regions of the channel. Methods The whole-cell patch-clamp technique was used to investigate the effect of empagliflozin on late I Na in recombinantly expressed Nav1.5 channels containing different LQT3 mutations. Molecular modeling of human Nav1.5 and simulations in a mathematical model of human ventricular myocytes were used to extrapolate our experimental results to excitation-contraction coupling. Results Empagliflozin selectively inhibited late I Na in LQT3 mutations in the inactivation gate region of Nav1.5, without affecting peak current or channel kinetics. In contrast, empagliflozin inhibited both peak and late I Na in mutations in the S4 voltage-sensing regions, altered channel gating, and slowed recovery from inactivation. Empagliflozin had no effect on late/peak I Na or channel kinetics in channels with mutations in the putative empagliflozin binding region. Simulation results predict that empagliflozin may have a desirable therapeutic effect in LQT3 mutations in the inactivation gate region. Conclusions Empagliflozin selectively inhibits late I Na , without affecting channel kinetics, in LQT3 mutations in the inactivation gate region. Empagliflozin may thus be a promising precision medicine approach for patients with specific LQT3 mutations.
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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 | 0.001 |
| 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".