Renal physiology of glucose handling and therapeutic implications
Bibliographic record
Abstract
The rationale for using sodium-glucose cotransporter 2 (SGLT2) inhibitors in patients with type 2 diabetes (T2D) has evolved over the last decade. Due to the effects on glucosuria and body weight loss, SGLT2 inhibitors were originally approved for glycemic control in T2D. Since glucosuria is attenuated in chronic kidney disease (CKD) Stages 3-5, initial regulatory approval for SGLT2 inhibitor use was limited to patients with T2D and preserved estimated glomerular filtration rate. Over time, however, it has become increasingly apparent that these therapies have a variety of important pharmacodynamic and clinical effects beyond glycemic lowering, including antihypertensive and antialbuminuric properties, and the ability to reduce glomerular hypertension. Importantly, these sodium-related effects are preserved across CKD stages, despite attenuated glycemic effects, which are lost at CKD Stage 4. With the completion of cardiovascular (CV) outcome safety trials-EMPA-REG OUTCOME, CANVAS Program and DECLARE TIMI-58-in addition to reductions in CV events, SGLT2 inhibition consistently reduces hard renal endpoints. Importantly, these CV and renal effects are independent of glycemic control. Subsequent data from the recent CREDENCE trial-the first dedicated renal protection trial with SGLT-2 inhibition-demonstrated renal and CV benefits in albuminuric T2D patients, pivotal results that have expanded the clinical importance of these therapies. Ongoing trials will ultimately determine whether SGLT2 inhibition will have a role in renal protection in other clinical settings, including nondiabetic CKD and type 1 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".