Comparing CKD populations with T1D and T2D: a perspective based on the FINE-ONE and FIDELITY populations
Bibliographic record
Abstract
Chronic kidney disease (CKD) is a common comorbidity of both type 1 diabetes (T1D) and type 2 diabetes (T2D) and is associated with increased mortality, end-stage kidney disease and cardiovascular disease risk. Despite standard-of-care treatment with renin-angiotensin system inhibitors added to blood pressure and glycaemic control, people with CKD and T1D have a residual risk of CKD progression. Advances in therapeutic management have been limited over the past 3 decades, especially compared with CKD in T2D, for which new treatment options have emerged in the last 5 years. In this review article we discuss the similarities and differences between T1D and T2D populations with CKD, including epidemiology, pathophysiology and clinical findings. Additionally, we explore the use of albuminuria as a potential bridging biomarker to extrapolate clinical evidence from one population to the other. This concept could offer a promising strategy to narrow the gap in treatment availability between these populations and address the unmet therapeutic need in people with CKD and T1D. The FINE-ONE trial is investigating the non-steroidal mineralocorticoid receptor antagonist finerenone in a population with CKD and T1D using the change in urine albumin:creatinine ratio from baseline over 6 months as its primary endpoint and bridging biomarker. Similarities between the populations from FINE-ONE trial and FIDELITY (a pooled dataset of individuals with CKD and T2D included in two large phase 3 clinical trials of finerenone) may inform the translation of clinical evidence on finerenone from people with CKD and T2D to those with CKD and T1D.
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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 | 0.028 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".