Impact of Dapagliflozin Therapy on Renal Protection and Kidney Morphology in Patients With Uncontrolled Type 2 Diabetes Mellitus
Bibliographic record
Abstract
BACKGROUND: We examined whether the sodium-glucose cotransporter-2 inhibitor (SGLT2i) dapagliflozin can improve urine albumin-to-creatinine ratio (UACR) associated with a reduction in body weight or body fat in patients with type 2 diabetes mellitus (T2DM). METHODS: We prospectively recruited T2DM patients having inadequate glycemic control (hemoglobin A1c (HbA1c) > 7.0%) not on SGLT2i therapy. We treated the patients with add-on dapagliflozin treatment or intensification of non-SGLT2 inhibitor therapies for 6 months. We measured UACR, urine N-acetyl-β-glucosaminidase (uNAG), and body composition including total body fat mass (TBFM) as assessed by bioelectrical impedance analysis. We also investigated changes in length and radiation attenuation properties of the kidneys and abdominal fat area using computed tomography. RESULTS: We enrolled 62 patients with a mean HbA1c of 8.0%. The HbA1c and fasting blood glucose were significantly decreased in both the dapagliflozin-group and non-SGLT2i-group, with no significant difference between the two groups. Dapagliflozin treatment, but not non-SGLT2i treatment, significantly decreased UACR and uNAG. The changes in UACR and uNAG were significantly greater in the dapagliflozin group compared with the non-SGLT2i group. Dapagliflozin treatment, but not non-SGLT2i treatment, significantly decreased the body weight, TBFM, and abdominal fat area and significantly increased kidney length and radiation attenuation. The percentage change in UACR was significantly correlated with changes in TBFM, but not with body weight. By multivariate logistic regression analysis, dapagliflozin treatment was significantly associated with the improvement of UACR. CONCLUSIONS: Add-on treatment with dapagliflozin exhibited significant renoprotective effects, with improvement of UACR and uNAG and increased kidney length and radiation attenuation in patients with uncontrolled T2DM.
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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 | 0.000 |
| 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) | 0.001 | 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".