Mapping the metabolic reprogramming induced by sodium-glucose cotransporter 2 inhibition
Bibliographic record
Abstract
Diabetes is associated with increased risk for kidney disease, heart failure, and mortality. Sodium-glucose cotransporter 2 inhibitors (SGLT2i) prevent these adverse outcomes; however, the mechanisms involved are not clear. We generated a roadmap of the metabolic alterations that occur in different organs in diabetes and in response to SGLT2i. In vivo metabolic labeling with 13C-glucose in normoglycemic and diabetic mice treated with or without dapagliflozin, followed by metabolomics and metabolic flux analyses, showed that, in diabetes, glycolysis and glucose oxidation are impaired in the kidney, liver, and heart. Treatment with dapagliflozin failed to rescue glycolysis. SGLT2 inhibition increased glucose oxidation in all organs; in the kidney, this was associated with modulation of the redox state. Diabetes was associated with altered methionine cycle metabolism, evident by decreased betaine and methionine levels, whereas treatment with SGLT2i increased hepatic betaine along with decreased homocysteine levels. mTORC1 activity was inhibited by SGLT2i along with stimulation of AMPK in both normoglycemic and diabetic animals, possibly explaining the protective effects against kidney, liver, and heart diseases. Collectively, our findings suggest that SGLT2i induces metabolic reprogramming orchestrated by AMPK-mTORC1 signaling with common and distinct effects in various tissues, with implications for diabetes and aging.
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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".