Loss of Glomerular Permselectivity in Type 2 Diabetes Associates With Progression to Kidney Failure
Bibliographic record
Abstract
We examined whether defects in glomerular size selectivity in type 2 diabetes are associated with progressive kidney disease. Glomerular filtration rate (GFR) and fractional clearances of dextrans of graded sizes were measured in 185 American Indians. The permselectivity model that best fit the dextran sieving data represented the glomerular capillary as being perforated by small restrictive pores and a parallel population of larger nonrestrictive pores characterized by ω0, the fraction of total filtrate volume passing through this shunt. The hazard ratio (HR) for kidney failure was expressed per 1-SD increase of ω0 by Cox regression after adjusting for age, sex, mean arterial pressure, HbA1c, GFR, and the urine albumin-to-creatinine ratio (ACR). Baseline mean ± SD age was 43 ± 10 years, HbA1c 8.9 ± 2.5%, GFR 147 ± 46 mL/min, and median (interquartile range) ACR 41 (11-230) mg/g. During a median follow-up of 17.7 years, 67 participants developed kidney failure. After adjustment, each 1-SD increment in ω0 was associated with a higher risk of kidney failure (HR 1.55 [95% CI 1.17, 2.05]). Enhanced transglomerular passage of test macromolecules was associated with progression to kidney failure, independent of albuminuria and GFR, suggesting that mechanisms associated with impaired glomerular permselectivity are important determinants of progressive kidney disease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".