SGLT-2 inhibitors or GLP-1 receptor agonists for adults with type 2 diabetes: a clinical practice guideline
Bibliographic record
Abstract
CLINICAL QUESTION: What are the benefits and harms of sodium-glucose cotransporter 2 (SGLT-2) inhibitors and glucagon-like peptide 1 (GLP-1) receptor agonists when added to usual care (lifestyle interventions and/or other diabetes drugs) in adults with type 2 diabetes at different risk for cardiovascular and kidney outcomes? CURRENT PRACTICE: Clinical decisions about treatment of type 2 diabetes have been led by glycaemic control for decades. SGLT-2 inhibitors and GLP-1 receptor agonists are traditionally used in people with elevated glucose level after metformin treatment. This has changed through trials demonstrating atherosclerotic cardiovascular disease (CVD) and chronic kidney disease (CKD) benefits independent of medications' glucose-lowering potential. RECOMMENDATIONS: The guideline panel issued risk-stratified recommendations concerning the use of SGLT-2 inhibitors or GLP-1 receptor agonists in adults with type 2 diabetes• Three or fewer cardiovascular risk factors without established CVD or CKD: Weak recommendation against starting SGLT-2 inhibitors or GLP-1 receptor agonists.• More than three cardiovascular risk factors without established CVD or CKD: Weak recommendation for starting SGLT-2 inhibitors and weak against starting GLP-1 receptor agonists.• Established CVD or CKD: Weak recommendation for starting SGLT-2 inhibitors and GLP-1 receptor agonists.• Established CVD and CKD: Strong recommendation for starting SGLT-2 inhibitors and weak recommendation for starting GLP-1 receptor agonists.• For those committed to further reducing their risk for CVD and CKD outcomes: Weak recommendation for starting SGLT-2 inhibitors rather than GLP-1 receptor agonists. HOW THIS GUIDELINE WAS CREATED: An international panel including patients, clinicians, and methodologists created these recommendations following standards for trustworthy guidelines and using the GRADE approach. The panel applied an individual patient perspective. THE EVIDENCE: A linked systematic review and network meta-analysis (764 randomised trials included 421 346 participants) of benefits and harms found that SGLT-2 inhibitors and GLP-1 receptor agonists generally reduce overall death, and incidence of myocardial infarctions, and end-stage kidney disease or kidney failure (moderate to high certainty evidence). These medications exert different effects on stroke, hospitalisations for heart failure, and key adverse events in different subgroups. Absolute effects of benefit varied widely based on patients' individual risk (for example, from five fewer deaths in the lowest risk to 48 fewer deaths in the highest risk, for 1000 patients treated over five years). A prognosis review identified 14 eligible risk prediction models, one of which (RECODe) informed most baseline risk estimates in evidence summaries to underpin the risk-stratified recommendations. Concerning patients' values and preferences, the recommendations were supported by evidence from a systematic review of published literature, a patient focus group study, a practical issues summary, and a guideline panel survey. UNDERSTANDING THE RECOMMENDATION: We stratified the recommendations by the levels of risk for CVD and CKD and systematically considered the balance of benefits, harms, other considerations, and practical issues for each risk group. The strong recommendation for SGLT-2 inhibitors in patients with CVD and CKD reflects what the panel considered to be a clear benefit. For all other adults with type 2 diabetes, the weak recommendations reflect what the panel considered to be a finer balance between benefits, harms, and burdens of treatment options. Clinicians using the guideline can identify their patient's individual risk for cardiovascular and kidney outcomes using credible risk calculators such as RECODe. Interactive evidence summaries and decision aids may support well informed treatment choices, including shared decision making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".