Metabolic shifts in plasma amino acids and related metabolites in response to <scp>SGLT2</scp> inhibition and hyperglycemia in type 1 diabetes
Bibliographic record
Abstract
Regulated kidney function is dependent on maintaining efficient energy utilization. Our aim in this study was to determine the effects of acute, ambient hyperglycemia and sodium-glucose cotransporter-2 (SGLT2) inhibition on plasma amino acid metabolism in patients with type 1 diabetes (T1D). The ATIRMA trial, a single-arm study, evaluated the effects of 8 weeks of oral empagliflozin (25 mg/day) in 40 young adults with T1D. The study involved consecutive two-day assessments of clamped euglycemia and hyperglycemia at both baseline and post-treatment. MetaboAnalyst 6.0 categorized 35 metabolites into significant pathways, which were statistically compared using principal component analysis. Acute hyperglycemia induced changes to 10 metabolic pathways, including but not limited to increases in cysteine and methionine metabolism (0.52 ± 0.12, p < 0.0001), valine, leucine, and isoleucine biosynthesis (0.31 ± 0.10, p = 0.002); and nitrogen metabolism (0.11 ± 0.03, p = 0.003). Introduction of empagliflozin was associated with a decrease in adenine, and an increase in cysteine and methionine metabolism (0.31 ± 0.13, p = 0.02) when maintained under euglycemia and a decrease in nitrogen metabolism under hyperglycemia (-0.07 ± 0.04, p = 0.04). Our findings show that SGLT2 inhibition counteracts the hyperglycemia-induced changes in plasma amino acid metabolism, potentially improving energy efficiency and metabolic health, though more research is needed to confirm these metabolic effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".