Efficacy and outcomes of dapagliflozin in diabetic nephropathy: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Diabetic nephropathy is one of the most prevalent consequences of diabetes and is associated with increased morbidity and mortality in diabetic patients. The pathogenesis of Diabetic Nephropathy is very complex and is still not fully understood, resulting in poor therapeutic outcomes. Dapagliflozin is thought to be associated with decreased progression of diabetic nephropathy. However, fewer trials have been conducted for the role of dapagliflozin in human patients with diabetic nephropathy. This is the first systematic review and meta-analysis where we aim to generate appropriate evidence regarding the safety and efficacy of dapagliflozin in diabetic nephropathy. METHODS: We searched the databases like PubMed, MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL) and Embase for studies published from inception to November 2023. Bias assessment was done using risk of bias tool for randomized controlled trial (RCT) and Newcastle-Ottawa scale for non-randomized studies. Synthesis of the data was done using RevMan 5.4 software. Efficacy was assessed in terms of lowering HbA1c level, serum creatinine, and urine albumin-to-creatinine ratio. Statistical heterogeneity was measured by using the I squared test and the results were reported using forest plots. RESULTS: Two studies were selected, 1 RCT and 1 comparative study including 296 patients. The dapagliflozin-treated group showed significant improvements in lowering serum creatinine [Standardized mean difference (SMD): -0.56 (-1.08, -0.04), P < .00001] and HbA1c [SMD: -0.84 (-1.07, -0.61), P = .03] when compared to the control group. But there was no discernible change in urine albumin-to-creatinine ratio [SMD: -1.45 (-3.33, 0.42), P = .13] after dapagliflozin therapy. Hypoglycemia [risk ratio: 0.26 (0.08-0.82), P = .02] was considerably lower in the dapagliflozin treatment group than in the control group in terms of safety. CONCLUSION: This meta-analysis showed that dapagliflozin was more effective in treating diabetic nephropathy while having fewer side effects. Our results support to the endeavor of conducting bigger, multicentre RCTs that assess dapagliflozin's efficacy and delineate its unfavorable risk profile.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".