Renal histology in diabetic nephropathy predicts progression to end-stage kidney disease but not the rate of renal function decline
Bibliographic record
Abstract
Abstract Background While histopathologic changes correlate with functional impairment in cross-sectional studies of diabetic nephropathy (DN), whether these findings predict future rate of kidney function loss remains uncertain. We thus sought to examine the relationship between kidney histopathology, incidence of end-stage kidney disease (ESKD), and rate of estimated glomerular filtration rate (eGFR) loss in DN. Methods In this longitudinal cohort study, we studied 50 adults diagnosed with biopsy-proven DN. We analyzed the histopathologic parameters of each patient’s kidney biopsy, as defined by the Renal Pathology Society classification system for DN, and tracked all available eGFR measurements post-biopsy. We additionally collected baseline clinical parameters (at the time of biopsy), including eGFR, albumin-to-creatinine ratio (ACR), and hemoglobin A 1c . Multivariable linear regression was used to assess the relationship between histologic and clinical parameters at the time of the biopsy and eGFR slope. Kaplan-Meier curves and Cox regression were used to evaluate the association between histologic and clinical parameters and ESKD incidence. Results Progression to ESKD was associated with worsening interstitial fibrosis score ( p = 0 . 05), lower baseline eGFR ( p = 0 . 02), higher ACR ( p = 0 . 001), and faster eGFR decline ( p < 0.001). The rate of eGFR decline did not associate with any histologic parameter. Baseline ACR was the only studied variable correlating with eGFR slope (rho = − 0 . 41). Conclusions Renal histology predicts ultimate progression to ESKD, but not the rate of progression. Future work is required to identify novel predictors of rapid functional decline in patients with diabetic nephropathy.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".