Dyslipidemia in Type 2 Diabetes: What's the Role of Combination Drugs?
Bibliographic record
Abstract
Diabetes is a strong risk factor for cardiovascular (CV) events and death. It is associated with a two-to-four-fold increased risk of cardiovascular disease (CVD), causing up to 80% of deaths in people with diabetes. The control of blood glucose, blood pressure and lipids is among the 2003 Canadian Diabetes Guidelines’ highest-priority mandated measures for vascular protection. Those patients who are young, with shorter duration of diabetes, no other CVD risk factors and no other complications of diabetes may be considered at moderate risk (Table 1). The most common lipid abnormality in diabetes is high triglyceride (TG) and low highdensity lipoprotein (HDL) cholesterol (HDL-C) levels. Although low-density lipoprotein (LDL) cholesterol (LDL-C) may not be elevated, an increased proportion of LDL has been found to be of the more pro-atherogenic, small, dense variety. This lipid profile contributes to the high prevalence of metabolic syndrome in patients with Type 2 diabetes and shares many of its pathophysiological features. The primary abnormality of metabolic syndrome is the liver’s over-production of very low-density lipoprotein (VLDL), the major TG-carrying particles and precursor of the more cholesterolenriched LDL. The breakdown of TG by the enzyme called lipoprotein lipase may also be partially impaired, contributing to the high level of TG. The VLDL in the circulation interacts with HDL and LDL and exchanges their lipid contents through an enzyme called cholesterol-ester transfer protein. Excess TG in VLDL is transferred to both HDL and LDL in exchange for cholesterol; as a result, the excess TG-in HDL are broken down by an enzyme called hepatic lipase (HL), resulting in accelerated clearance of HDL and a low level of HDL-C. The excess TG in LDL is also broken down by HL, transforming them into small, dense LDL. Small, dense LDL is cleared more slowly by the liver through the LDL receptor, more readily enters arterial walls, induces more endothelial dysfunction and is more susceptible to oxidation. This vicious cycle creates the atherogenic triad of small, dense LDL, high TG and low HDL. Three classes of medications are commonly used to treat dyslipidemia in patients with diabetes: HMG-CoA reductase inhibitors (statins), fibric acid derivatives and niacin.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".