Empagliflozin Is Associated with Increased Plasma Lipid Metabolites in Type 1 Diabetes
Bibliographic record
Abstract
Background: Sodium glucose cotransporter-2 (SGLT2) inhibition reduces the risk of cardiorenal complications in people with diabetes, possibly by altering energy substrate pathways. It has been hypothesized that SGLT2 inhibitors improve mitochondrial efficiency and may induce a shift towards increased lipid utilization as an energy substrate. In this exploratory, post-hoc analysis, we investigated the effects of SGLT2 inhibition on plasma lipid and tricarboxylic acid (TCA) cycle metabolites in patients with type 1 diabetes (T1D). Methods: In the ATIRMA trial (NCT01392560), patients with T1D were assessed under clamped euglycemia and hyperglycemia at baseline and after 8 weeks of empagliflozin treatment. Plasma samples from the ATIRMA trial were analyzed for lipid and TCA cycle metabolites using the ZipChip method. Results: Of the 15 lipid metabolites, 5 increased during clamped euglycemia in response to empagliflozin (Figure) while 1 increased after treatment during clamped hyperglycemia. Of the 3 TCA cycle metabolites, 2 increased during clamped euglycemia in response to empagliflozin. None of the metabolites decreased significantly after empagliflozin treatment.Figure: Plasma concentrations of (A.) 2-ketoglutaric acid, (B.) succinic acid, (C.) arachidonic acid, (D.) α-lanolenic acid, (E.) ethylmalonic acid, (F.) linoleic acid, and (G.) oleic acid during clamped euglycemia and hyperglycemia at baseline and post 8 weeks treatment with empagliflozin. Data shown are medians ± Q1/Q3. Succinic acid levels significantly increased after treatment during clamped euglycemia.Conclusions: In patients with T1D, SGLT2 inhibition increased plasma TCA cycle metabolite levels, suggesting an impact on mitochondrial function. Lipid metabolite levels were also increased after SGLT2 inhibition, suggesting a possible increase in beta oxidation. Further work is needed to determine if these changes contribute to cardiorenal protection with SGLT2 inhibitors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".