Renal hyperfiltration related to diabetes mellitus and obesity in human disease
Bibliographic record
Abstract
High intraglomerular pressure is associated with renal hyperfiltration, leading to the initiation and progression of kidney disease in experimental models of diabetes mellitus (DM). In humans, hyperfiltration is observed in patients with type 1 and type 2 DM, and is also seen in patients with pre-diabetic conditions, such as the metabolic syndrome. From a mechanistic perspective, both vascular and tubular factors likely contribute to the pathogenesis of hyperfiltration. Until now, human studies have primarily focused on the use of medications that inhibit the renin angiotensin system to reduce efferent vasoconstriction and thereby improve hyperfiltration. More recent advances in the development of investigational adenosine antagonists and inhibitors of sodium glucose co-transport may help to elucidate tubular factors that contribute to afferent vasodilatation. In this review, we summarize available data from experimental and human studies of type 1 and type 2 DM and obesity to provide an overview of factors that contribute to the hyperfiltration state. We have focused on the renin angiotensin system, cyclooxygenase-2 system, nitric oxide, protein kinase C and endothelin as vascular determinants of hyperfiltration. We also discuss relevant tubular factors, since experimental models have suggested that inhibition of sodium-glucose cotransport may be renoprotective.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".