Effect of hyperglycemia and empagliflozin on markers of cardiorenal injury and inflammation in patients with type 1 diabetes
Bibliographic record
Abstract
AIMS: To investigate the effect of hyperglycemia and empagliflozin on cardiorenal injury and inflammation in patients with uncomplicated type 1 diabetes (T1D). METHODS: Serum cardiac (sST2, Gal-3, cTnT), kidney injury (KIM-1, NGAL), inflammatory (sTNFR1, sTNFR2), and hemodynamic (NT-proBNP, EPO) markers were assessed post-hoc in two separate T1D cohorts. The glycemic clamp trial (NCT02344602) evaluated 49 adults with T1D and 27 controls under euglycemic and acute hyperglycemic conditions. The crossover BETWEEN trial (NCT02632747) investigated empagliflozin 25 mg plus ramipril for 4 weeks compared to placebo-ramipril for 4 weeks in 30 adults with T1D. RESULTS: In the glycemic clamp study, hyperglycemia acutely increased levels of NT-proBNP (p = 0.0003) and sTNFR2 (p = 0.003). BETWEEN participants treated with empagliflozin exhibited a paradoxical subacute rise in NT-proBNP (p = 0.0147) compared to placebo, independent of hematocrit. Individuals with higher baseline levels of sST2 and sTNFR1 had greater empagliflozin-associated reductions in systolic blood pressure and greater activation of renin-angiotensin-aldosterone system (RAAS) mediators, whereas those with higher baseline levels of KIM-1 and sTNFR1 had greater glomerular filtration rate (GFR) dip. CONCLUSION: The protective mechanisms of SGLT2 inhibition on blood pressure, RAAS activation, and renal hemodynamics are apparent in the subset of people with uncomplicated T1D with adverse cardiorenal and inflammatory markers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".