Furosemide and Serum Protein-Bound Uremic Toxin Concentrations in Patients With CKD
Bibliographic record
Abstract
Introduction Furosemide is commonly prescribed to patients with CKD but may impair the kidney's excretion of protein-bound uraemic toxins (PBUTs) via the organic anion transporters 1 and 3 (OAT1/OAT3). We evaluated the association between the furosemide prescription (status and dose level) and the serum concentrations of free OAT1/3-inhibiting uraemic toxins (UTs) in patients with CKD. Methods We included 2,342 patients with CKD (stages 2–5) from the CKD-REIN cohort and with centralized serum UT assay data at baseline. The UTs were assayed using liquid chromatography - tandem mass spectrometry. The OAT1/3-inhibiting UTs identified in a literature review included indoxyl sulphate (IS), kynurenine (Kyn), p-cresyl sulphate (PCS), and indole-3-acetic acid (IAA). Multiple linear regression was used to assess each PBUT or their sum ( Σ UTs free ) as the dependent variable. Results Patients prescribed furosemide (n=799, 34%) were older and had a lower estimated glomerular filtration rate, a higher C-reactive protein concentration, more comorbidities and more concomitant medications than patients not prescribed furosemide. After adjustment for potential confounders, patients prescribed >120 mg furosemide had significantly higher serum concentrations of Σ UTs free (+19.1%), IS (+31.9%), Kyn (+9.3%), PCS (+29.3%) and IAA (+16.9%) than patients not prescribed furosemide. Using a smooth function to model the association between the furosemide dose level and PBUTs, we observed (for Σ UTs free and each free UT) a steep increase between 80 and 100 mg and then a high plateau. Conclusion In patients with CKD, furosemide (particularly at a dose level >100 mg) is independently associated with higher serum free PBUT concentrations. Our findings suggested that drug-UT competition contributes to PBUT accumulation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".