Effects of SGLT2 Inhibition on eGFR and Glomerular and Tubular Damage Markers in Japanese Patients With Type 2 Diabetes
Bibliographic record
Abstract
Background: Sodium-glucose cotransporter 2 (SGLT2) inhibitors are a new class of antihyperglycemic drugs that enhances insulin-independent urinary glucose excretion. Recent studies have suggested that SGLT2 inhibitors possess a renoprotective property in type 2 diabetes patients. However, evidence of the effects of SGLT2 inhibition on glomerular and tubular damage markers is lacking. The aim of this study was to examine the effect of SGLT2 inhibitors on renal function, especially on glomerular and tubular damage markers in patients with type 2 diabetes. Methods: We retrospectively analyzed data from 81 patients who used SGLT2 inhibitors. Next, we investigated whether treatment with SGLT2 inhibitors affected urinary damage markers including N-acetyl-beta-D-glucosaminidase (NAG), liver-type fatty acid-binding protein (L-FABP), type IV collagen, and beta2-microglobulin (beta2MG) in patients with type 2 diabetes. Results: In the retrospective study, SGLT2 inhibition reduced the estimated glomerular filtration rate (eGFR) at 4 and 12 weeks in a manner that was correlated with the baseline eGFR. In the longitudinal study, SGLT2 inhibition tended to increase the urinary damage marker levels with an accompanying decrease in eGFR after 1 month of use. The observed changes in eGFR and urinary damage markers were reversed at 3 months, even though both the HbA1c level and blood pressure were further improved. Conclusions: These results indicated that SGLT2 inhibition reduces the eGFR in a manner depending on the baseline eGFR levels and transiently increased the glomerular and tubular damage markers in patients with type 2 diabetes. J Endocrinol Metab. 2018;8(5):106-112 doi: https://doi.org/10.14740/jem531w
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
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".