Do SGLT2 Inhibitors Improve Cardio-Renal Outcomes in Patients With Type II Diabetes Mellitus: A Systematic Review
Bibliographic record
Abstract
Diabetes mellitus (DM) is associated with dreadful changes in the cardiovascular and renal systems, causing increased morbidity and mortality. Sodium-glucose cotransport-2 (SGLT2) inhibitors belong to the oral hypoglycemic group of drugs believed to reduce these events by various mechanisms in DM. We performed a systematic review to determine the effectiveness of SGLT2 inhibitors in reducing cardiovascular and renal complications and address safety concerns in participants with type 2 diabetes mellitus (T2DM). We explored PubMed, PubMed Central, Medical Literature Analysis and Retrieval System Online (MEDLINE), Cochrane library, and ResearchGate for randomized controlled trials and observational studies done on the advantages of SGLT2 inhibitors in the prevention or reduction of worsening cardiovascular and renal changes in T2DM. Studies were screened for the quality assessment using the Cochrane risk-of-bias assessment tool and Newcastle-Ottawa scale. We screened 5615 articles, out of which 22 articles with 7,02,977 diabetes mellitus patients treated with SGLT2 inhibitors were used for the systematic review after meticulously filtering articles based on inclusion and exclusion criteria. The trials included one of the following drugs - empagliflozin, dapagliflozin, canagliflozin, and luseogliflozin. SGLT2 inhibitors significantly reduced the risk of heart failure (HF), frequency of hospitalizations due to HF, all-cause mortality, cardiovascular mortality, and nonfatal myocardial infarction. Renal outcomes showed a significant lowering of risk of acute kidney failure, progression of chronic kidney disease, renal mortality, and improvement in urinary albumin creatinine ratio. We noticed an initial worsening of the estimated glomerular filtration rate followed by stabilizing and reaching the baseline on long-term treatment, especially in end-stage renal failure patients. The review showed that SGLT2 inhibitors have adverse reactions similar to that of a placebo, with a slight increase in treatable genital mycotic and urinary tract infections but no evidence of diabetic ketoacidosis, fractures, and amputations. According to the available data, SGLT2 inhibitors can significantly prevent or reduce cardiovascular diseases and kidney abnormalities in patients with type 2 diabetes mellitus with tolerable safety outcomes.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".