Best practice for treating dyslipidaemia in patients with diabetes based on current international guidelines
Bibliographic record
Abstract
PURPOSE OF REVIEW: Dyslipidaemia is a major modifiable risk factor for atherosclerotic cardiovascular disease (ASCVD) in type 2 diabetes. We provide an in-context overview of recent trials of lipid-lowering pharmacotherapies and of recommendations from international guidelines for managing dyslipidaemia in patients with diabetes. RECENT FINDINGS: Clinical trials have demonstrated that patients with diabetes derive greater benefits from ezetimibe and proprotein convertase subtilisin-kexin type 9 inhibitors owing to the higher absolute ASCVD risk compared with patients without diabetes. Pure eicosapentaenoic acid ethyl ester therapy should be considered in high risk patients with diabetes and hypertriglyceridaemia who have well controlled low-density lipoprotein cholesterol on statin therapy. International guidelines from USA, Canada and Europe have been updated to support a more intensive approach to treating dyslipidaemia in diabetes. SUMMARY: Dyslipidaemia should be identified and treated intensively as part of overall diabetes management to reduce ASCVD risk. Although lifestyle modifications and statin therapy remain the cornerstone of management, add-on therapies should be strongly considered depending on the absolute risk of ASCVD and the degree of dyslipidaemia.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".