Changes in plasma and urine metabolites associated with empagliflozin in patients with type 1 diabetes
Bibliographic record
Abstract
AIM: To examine the impact of the sodium-glucose co-transporter-2 inhibitor, empagliflozin, on plasma and urine metabolites in participants with type 1 diabetes. MATERIAL AND METHODS: Participants (n = 40, 50% male, mean age 24.3 years) with type 1 diabetes and without overt evidence of diabetic kidney disease had baseline assessments performed under clamped euglycaemia and hyperglycaemia, on two consecutive days. Participants then proceeded to an 8-week, open-label treatment period with empagliflozin 25 mg/day, followed by repeat assessments under clamped euglycaemia and hyperglycaemia. Plasma and urine metabolites were first grouped into metabolic pathways using MetaboAnalyst software. Principal component analysis was performed to create a representative value for each sufficiently represented metabolic group (false discovery rate ≤ 0.1) for further analysis. RESULTS: Of the plasma metabolite groups, tricarboxylic acid (TCA) cycle (P < .0001), biosynthesis of unsaturated fatty acids (P = .0045), butanoate (P < .0001), propanoate (P = .0053), and alanine, aspartate and glutamate (P < .0050) metabolites were increased after empagliflozin treatment under clamped euglycaemia. Of the urine metabolite groups, only butanoate metabolites (P = .0005) were significantly increased. Empagliflozin treatment also attenuated the increase in a number of urine metabolites observed with acute hyperglycaemia. CONCLUSIONS: Empagliflozin was associated with increased lipid and TCA cycle metabolites in participants with type 1 diabetes, suggesting a shift in metabolic substrate use and improved mitochondrial function. These effects result in more efficient energy production and may contribute to end-organ protection by alleviating local hypoxia and oxidative stress.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".