Urinary adenosine excretion in type 1 diabetes
Bibliographic record
Abstract
In experimental models of diabetes, augmented sodium-glucose cotransport-2 (SGLT2) activity diminishes sodium (Na+) delivery at the macula densa. As a result, less vasoconstrictive adenosine is generated, leading to afferent arteriolar vasodilatation and hyperfiltration. The measurement and significance of urinary adenosine in humans has not been examined extensively in states of renal hemodynamic impairment like that of diabetes. Our aim was to validate a method for urine adenosine quantification in humans and perform an exploratory post hoc analysis to determine whether urinary adenosine levels change dynamically in response to natriuresis in patients with type 1 diabetes (T1D) before and after treatment with the SGLT2 inhibitor (SGLT2i) empagliflozin. We hypothesized that SGLT2i, which reduces renal hyperfiltration through increased Na+delivery to the macula densa, would increase urinary adenosine excretion. Urine adenosine corrected for creatinine was measured using our validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) method in 40 healthy participants and 40 patients with T1D. In the T1D cohort, measurements were performed during clamped euglycemic and hyperglycemic conditions before and following 8 wk of SGLT2i therapy. Urinary adenosine was detectable in healthy subjects (0.32 ± 0.11 µmol/mmol Cr) and patients with T1D. In response to SGLT2i, urine adenosine increased during clamped hyperglycemia (0.40 ± 0.11 vs. 0.45 ± 0.12 µmol/mmol Cr, P = 0.005). Similar trends were observed during clamped euglycemia ( P = 0.08). In conclusion, SGLT2i increases urinary adenosine excretion under clamped hyperglycemic conditions in patients with T1D. The potentially protective role of SGLT2i against glomerular hyperfiltration and its mediation by adenosine in diabetes merits further study.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".