C-peptide as a Therapy for Kidney Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
C-peptide has intrinsic biological activity and may be renoprotective. We conducted a systematic review to determine whether C-peptide had a beneficial effect on renal outcomes. MEDLINE, EMBASE, and the Cochrane Central Databases were searched for human and animal studies in which C-peptide was administered and renal endpoints were subsequently measured. We identified 4 human trials involving 74 patients as well as 18 animal studies involving 35 separate experiments with a total of 641 animals. In humans, the renal effects of exogenously delivered C-peptide were only studied in type 1 diabetics with either normal renal function or incipient nephropathy. Pooled analysis showed no difference in GFR (mean difference, -1.36 mL/min/1.73 m2, p = 0.72) in patients receiving C-peptide compared to a control group, but two studies reported a reduction in glomerular hyperfiltration (p<0.05). Reduction in albuminuria was also reported in the C-peptide group (p<0.05). In diabetic rodent models, C-peptide led to a reduction in GFR (mean difference, -0.62 mL/min, p<0.00001) reflecting a partial reduction in glomerular hyperfiltration. C-peptide also reduced proteinuria (mean difference, -186.25 mg/day, p = 0.05), glomerular volume (p<0.00001), and mesangial matrix area (p<0.00001) in diabetic animals without affecting blood pressure or plasma glucose. Most studies were relatively short-term in duration, ranging from 1 hour to 3 months. Human studies of sufficient sample size and duration are needed to determine if the beneficial effects of C-peptide seen in animal models translate into improved long-term clinical outcomes for patients with chronic kidney disease. (PROSPERO CRD42014007472).
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.023 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".