Glycosuria-mediated urinary uric acid excretion in patients with uncomplicated type 1 diabetes mellitus
Bibliographic record
Abstract
Plasma uric acid (PUA) is associated with metabolic, cardiovascular, and renal abnormalities in patients with type 2 diabetes but is less well understood in type 1 diabetes (T1D). Our aim was to compare PUA levels and fractional uric acid excretion (FEUA) in patients with T1D vs. healthy controls (HC) during euglycemia and hyperglycemia. PUA, FEUA, blood pressure (BP), glomerular filtration rate (GFR-inulin), and effective renal plasma flow (ERPF-paraaminohippurate) were evaluated in patients with T1D (n = 66) during clamped euglycemia (glucose 4-6 mmol/l) and hyperglycemia (9-11 mmol/l), and in HC (n = 41) during euglycemia. To separate the effects of hyperglycemia vs. increased glycosuria, parameters were evaluated during clamped euglycemia in a subset of T1D patients before and after sodium glucose cotransporter 2 (SGLT2) inhibition for 8 wk. PUA was lower in T1D vs. HC (228 ± 62 vs. 305 ± 75 μmol/l, P < 0.0001). In T1D, hyperglycemia further decreased PUA (228 ± 62 to 199 ± 65 μmol/l, P < 0.0001), which was accompanied by an increase in FEUA (7.3 ± 3.8 to 11.6 ± 6.7, P < 0.0001). In T1D, PUA levels correlated positively with SBP (P = 0.029) and negatively with ERPF (P = 0.031) and GFR (P = 0.028). After induction of glycosuria with SGLT2 inhibition while maintaining clamped euglycemia, PUA decreased (P < 0.0001) and FEUA increased (P < 0.0001). PUA is lower in T1D vs. HC and positively correlates with SBP and negatively with GFR and ERPF in T1D. Glycosuria rather than hyperglycemia increases uricosuria in T1D. Future studies examining the effect of uric acid-lowering therapies should account for the impact of ambient glycemia, which causes an important uricosuric effect.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".