Characterization of the renal cortical transcriptome following Roux-en-Y gastric bypass surgery in experimental diabetic kidney disease
Bibliographic record
Abstract
INTRODUCTION: Roux-en-Y gastric bypass surgery (RYGB) reduces albuminuria and the long-term incidence of end-stage renal disease in patients with obesity and diabetes. Preclinical modeling in experimental diabetic kidney disease demonstrates that improvements in glomerular structure likely underpin these findings. RESEARCH DESIGN AND METHODS: In adult male Zucker diabetic fatty (ZDF) rats, we profiled the effect of RYGB on weight and metabolic control as well biochemical, structural and ultrastructural indices of diabetic renal injury. Furthermore, we sequenced the renal cortical transcriptome in these rats and used bioinformatic pathway analyses to characterize the transcriptional alterations governing the renal reparative response to RYGB. RESULTS: In parallel with improvements in weight and metabolic control, RYGB reduced albuminuria, glomerulomegaly, podocyte stress and podocyte foot process effacement. Pathway analysis of RYGB-induced transcriptomic changes in the renal cortex highlighted correction of disease-associated alterations in fibrosis, inflammation and biological oxidation pathways. RYGB reversed disease-associated changes in the expression of transforming growth factor (TGF)-β superfamily genes that strongly correlated with improvements in structural measures of glomerulopathy. CONCLUSIONS: Improved glomerular structure in ZDF rats following RYGB is underpinned by pathway level changes, including interruption of the TGF-β-driven early profibrotic programme. Our data provide an important layer of experimental support for clinical evidence demonstrating that RYGB arrests renal damage in patients with obesity and type 2 diabetes.
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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".