Switching from Conventional Fibrates to Pemafibrate Has Beneficial Effects on the Renal Function of Diabetic Subjects with Chronic Kidney Disease
Bibliographic record
Abstract
BACKGRUOUND: Fibrates have renal toxicity limiting their use in subjects with chronic kidney disease (CKD). However, pemafibrate has fewer toxic effects on renal function. In the present analysis, we evaluated the effects of pemafibrate on the renal function of diabetic subjects with or without CKD in a real-world clinical setting. METHODS: We performed a sub-analysis of data collected during a multi-center, prospective, observational study of the effects of pemafibrate on lipid metabolism in subjects with type 2 diabetes mellitus complicated by hypertriglyceridemia (the PARM-T2D study). The participants were allocated to add pemafibrate to their existing regimen (ADD-ON), switch from their existing fibrate to pemafibrate (SWITCH), or continue conventional therapy (CTRL). The changes in estimated glomerular filtration rate (eGFR) over 52 weeks were compared among these groups as well as among subgroups created according to CKD status. RESULTS: Data for 520 participants (ADD-ON, n=166; SWITCH, n=96; CTRL, n=258) were analyzed. Of them, 56.7% had CKD. The eGFR increased only in the SWITCH group, and this trend was also present in the CKD subgroup (P<0.001). On the other hand, eGFR was not affected by switching in participants with severe renal dysfunction (G3b or G4) and/or macroalbuminuria. Multivariate analysis showed that being older and a switch from fenofibrate were associated with elevation in eGFR (both P<0.05). CONCLUSION: A switch to pemafibrate may be associated with an elevation in eGFR, but to a lesser extent in patients with poor renal function.
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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 | 0.000 | 0.000 |
| 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".