Urinary Peptidome May Predict Renal Function Decline in Type 1 Diabetes and Microalbuminuria
Bibliographic record
Abstract
One third of patients with type 1 diabetes and microalbuminuria experience an early, progressive decline in renal function that leads to advanced stages of chronic kidney disease and ESRD. We hypothesized that the urinary proteome may distinguish between stable renal function and early renal function decline among patients with type 1 diabetes and microalbuminuria. We followed patients with normal renal function and microalbuminuria for 10 to 12 yr and classified them into case patients (n = 21) with progressive early renal function decline and control subjects (n = 40) with stable renal function. Using liquid chromatography matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, we identified three peptides that decreased in the urine of patients with early renal function decline [fragments of alpha1(IV) and alpha1(V) collagens and tenascin-X] and three peptides that increased (fragments of inositol pentakisphosphate 2-kinase, zona occludens 3, and FAT tumor suppressor 2). In renal biopsies from patients with early nephropathy from type 1 diabetes, we observed increased expression of inositol pentakisphosphate 2-kinase, which was present in granule-like cytoplasmic structures, and zona occludens 3. These results indicate that urinary peptide fragments reflect changes in expression of intact protein in the kidney, suggesting new potential mediators of diabetic nephropathy and candidate biomarkers for progressive renal function decline.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".