The renal transcriptome of<i>db/db</i>mice identifies putative urinary biomarker proteins in patients with type 2 diabetes: a pilot study
Bibliographic record
Abstract
We sought to identify novel urinary biomarkers of kidney function in type 2 diabetes. We screened the renal transcriptome of db/db and db/m mice for differentially expressed mRNA transcripts that encode secreted proteins with human orthologs. Whether elevated urine levels of the orthologous proteins correlated with diminished glomerular filtration rate was tested in a cross-sectional study of n = 56 patients with type 2 diabetes. We identified 36 putative biomarker genes in db/db kidneys: 31 upregulated and 5 downregulated. Urinary protein levels of six selected candidates (endothelin-1, lipocalin-2, transforming growth factor-β, growth and differentiation factor-15, interleukin-6, and macrophage chemoattractant protein-1) were elevated in type 2 diabetic patients with subnormal glomerular filtration rate (i.e., <90 ml·min(-1)·1.73 m(-2)), independent of microalbuminuria, age, sex, race, and use of angiotensin-converting enzyme inhibitors and angiotensin receptor antagonists. In contrast, urinary levels of fibroblast growth factor were not increased. A composite variable of urine albumin and any of the six candidate markers was associated with subnormal estimated glomerular filtration rate more closely than albumin alone. In addition, urinary endothelin-1, growth and differentiation factor-15, and interleukin-6 were associated with a marker of proximal tubule damage, N-acetyl-β-d-glucosaminidase activity. These results suggest that gene expression profiling in diabetic mouse kidney can complement existing proteomic-based approaches for renal biomarker discovery in humans.
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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 | 0.000 |
| 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) | 0.001 | 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".