Combination therapy with statin and fibrate in patients with dyslipidemia associated with insulin resistance, metabolic syndrome and type 2 diabetes mellitus
Bibliographic record
Abstract
INTRODUCTION: People with insulin resistance/metabolic syndrome (IR/MS) and/or type 2 diabetes mellitus (T2DM) have increased rates of cardiovascular disease (CVD) even when low-density lipoprotein cholesterol levels are at or near target levels. Contributors to this problem are the high triglyceride (TG) levels and low levels of high-density lipoprotein cholesterol (HDLC) that are commonly present in this population, even with statin therapy. AREAS COVERED: This review focuses on the use of a combination of statins with fibrates, which lower TG and raise HDLC concentrations and, therefore, have the potential to further lower rates of CVD more in people with IR/MS and/or T2DM. Treatment with this combination is uncommon because doctors and patients are fearful of muscle, liver and renal complications and because the evidence that the combination will actually reduce risk has been lacking. In this review, the authors examine the efficacy and safety of the statin-fibrate combination, particularly fenofibrate and simvastatin, the combination used in the ACCORD trial. EXPERT OPINION: The authors' opinion is that this combination of fenofibrate and statin is as safe as either drug alone and, in patients with significant dyslipidemia, is likely to reduce CVD. Concerns remain concerning fenofibrate-associated increases in serum creatinine levels and the significant heterogeneity in the reduction in CVD by the combination in women. A trial of statin + fenofibrate in people with IR/MS and/or T2DM who also have significant dyslipidemia is needed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".