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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".