SP427GLUCOSE-LOWERING DRUGS ADDED TO EXISTING THERAPIES AND RISKS OF MORTALITY AND CARDIOVASCULAR DISEASE IN TYPE 2 DIABETES: NETWORK META-ANALYSIS OF RANDOMIZED TRIALS
Bibliographic record
Abstract
INTRODUCTION AND AIMS: The optimal treatment strategy to reduce premature death and cardiovascular disease in type 2 diabetes is relatively uncertain. Recent trials have compared treatments with placebo and evaluated effects on mortality. However, few trials are available to compare different glucose-lowering therapies on mortality and cardiovascular outcomes. This network meta-analysis aimed to compare effects of glucose lowering drugs regardless of background therapy on preventing mortality and cardiovascular events and avoiding hypoglycemia for patients with type 2 diabetes. METHODS: We did a systematic review and random-effects network meta-analysis of randomized trials longer than 24 weeks comparing glucose-lowering drugs in addition to any background therapy among adults with type 2 diabetes. Electronic databases (CENTRAL, Medline, and Embase) were searched from inception to June 16, 2016. Outcomes were all-cause and cardiovascular mortality, myocardial infarction, stroke, heart failure, and hypoglycemia. RESULTS: 238 trials involving 187,134 patients were eligible. SGLT-2 inhibitors were more effective at reducing mortality than thiazolidinediones (odds ratio 0.71, 0.54-0.94), metformin (0.66, 0.44-0.98), sulfonylureas (0.61, 0.44-0.85), and basal insulin (0.39, 0.17-0.90) and were similarly effective to GLP-1 receptor agonists (0.83, 0.65-1.06). GLP-1 receptor agonists were significantly more effective at lowering mortality than sulfonylureas (0.73, 0.55-0.99). SGLT-2 inhibitors were more effective at preventing cardiovascular death than thiazolidinediones (0.67, 0.48-0.95), DPP-4 inhibitors (0.65, 0.49-0.87), and sulfonylureas (0.51, 0.30-0.88), and possibly more effective than GLP-1 receptor agonists (0.76, 0.57-1.00). No drug class other than SGLT-2 inhibitors reduced odds of cardiovascular death compared with placebo. No drug class was found to prevent stroke or myocardial infarction. SGLT-2 inhibitors were more effective than DPP-4 inhibitors, metformin and thiazolidinediones for preventing heart failure. All drug classes except SGLT-2 inhibitors incurred higher odds of hypoglycemia than placebo. CONCLUSIONS: SGLT-2 inhibitors appear to be the most effective and safest glucose lowering drug class to prevent all-cause and cardiovascular death in patients with type 2 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 | 0.028 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.049 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".