Back to the Future: Glomerular Hyperfiltration and the Diabetic Kidney
Bibliographic record
Abstract
Observations that early changes in renal hemodynamics beget later kidney damage was a seminal scientific discovery that ultimately led to development of the only class of agents, inhibitors of the renin-angiotensin system (ACE inhibitors and angiotensin receptor blockers), approved by regulatory agencies for the treatment of diabetic kidney disease (DKD) (1,2). The first drugs in this class were approved more than two decades ago, and the concept of glomerular hyperfiltration as a targetable mechanism for DKD goes back almost four decades. It is rather ironic that looking backward in this direction may move the field forward in the future. Despite tremendous efforts of the clinical and translational research community and enormous amounts of scientific evidence discovered about various disease mechanisms, no others have successfully translated to a new treatment for DKD. Processes like fibrosis and inflammation undoubtedly contribute in major ways to the pathogenesis of DKD, but many attempts to translate these mechanisms to therapeutic targets have not yet succeeded (3–7). This consequence is likely due to a number of issues in the design and conduct of clinical trials, lack of validated DKD biomarkers, and barriers in the regulatory and business domains (8). Interest has resurged in renal hemodynamics because this DKD mechanism is targetable by strategies already in hand. In addition to the effects of renin-angiotensin system inhibition to decompress the glomerulus by releasing efferent arteriolar vasoconstriction, the sodium–glucose cotransporter 2 inhibitor class of oral hypoglycemic agents may reduce glomerular hyperfiltration by another mechanism. These agents increase distal tubular delivery of solute, specifically sodium chloride to the macula densa, and thereby reduce afferent arteriolar vasodilation via tubuloglomerular feedback (Fig. 1) (9 …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 0.001 |
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 teacher head, 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".