Interactive Effects of Empagliflozin and Hyperglycemia on Urinary Amino Acids in Individuals With Type 1 Diabetes
Bibliographic record
Abstract
Optimizing energy use in the kidney is critical for normal kidney function. Here, we investigate the effect of hyperglycemia and sodium-glucose cotransporter 2 (SGLT2) inhibition on urinary amino acid excretion in individuals with type 1 diabetes (T1D). The open-label ATIRMA trial assessed the impact of 8 weeks of 25 mg empagliflozin orally once per day in 40 normotensive normoalbuminuric young adults with T1D. A consecutive 2-day assessment of clamped euglycemia and hyperglycemia was evaluated at baseline and posttreatment visits. Principal component analysis was performed on urinary amino acids grouped into representative metabolic pathways using MetaboAnalyst. At baseline, acute hyperglycemia was associated with changes in 25 of the 33 urinary amino acids or their metabolites. The most significant amino acid metabolites affected by acute hyperglycemia were 3-hydroxykynurenine, serotonin, glycyl-histidine, and nicotinic acid. The changes in amino acid metabolites were reflected by the induction of four biosynthetic pathways: aminoacyl-tRNA; valine, leucine, and isoleucine; arginine; and phenylalanine, tyrosine, and tryptophan. In acute hyperglycemia, empagliflozin significantly attenuated the increases in aminoacyl-tRNA biosynthesis and valine, leucine, and isoleucine biosynthesis. Our findings using amino acid metabolomics indicate that hyperglycemia stimulates biosynthetic pathways in T1D. SGLT2 inhibition may attenuate the increase in biosynthetic pathways to optimize kidney energy metabolism.
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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 | 0.000 |
| 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".