Single cell transcriptomic analysis of SGLT2 expression supports an indirect or off-target role for the cardioprotective benefits of empagliflozin in heart failure
Bibliographic record
Abstract
Sodium-glucose cotransporter 2 inhibitors (SGLT2i), such as empagliflozin, have shown remarkable benefits in reducing cardiovascular events and mortality in patients with heart failure irrespective of diabetes. Because of the magnitude of the benefits and broad application in both heart failure with reduced and preserved ejection fraction, there have been concerted efforts to identify a mechanism for the observed benefits. One hypothesis is that SGLT2i act directly on the heart. Given empagliflozin's high specificity to SGLT2, we reasoned that SGLT2 expression would be a requirement for cells to respond to treatment via the expected drug target. Here, we present a comprehensive transcriptomic analysis of SLC5A2, which encodes SGLT2 at the single cell level in multiple datasets from healthy and HF donors, confirming its expression in a subset of kidney epithelial cells but minimal expression in other cell types. This was true irrespective of developmental stage, disease state, sequencing method or depth, and species. Therefore, it is likely that the cardioprotective benefits of SGLT2i cannot be explained by "canonical" interactions with SGLT2.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".