Pharmacological Targeting of the Atherogenic Dyslipidemia Complex: The Next Frontier in CVD Prevention Beyond Lowering LDL Cholesterol
Bibliographic record
Abstract
Notwithstanding the effectiveness of lowering LDL cholesterol, residual CVD risk remains in high-risk populations, including patients with diabetes, likely contributed to by non-LDL lipid abnormalities. In this Perspectives in Diabetes article, we emphasize that changing demographics and lifestyles over the past few decades have resulted in an epidemic of the "atherogenic dyslipidemia complex," the main features of which include hypertriglyceridemia, low HDL cholesterol levels, qualitative changes in LDL particles, accumulation of remnant lipoproteins, and postprandial hyperlipidemia. We briefly review the underlying pathophysiology of this form of dyslipidemia, in particular its association with insulin resistance, obesity, and type 2 diabetes, and the marked atherogenicity of this condition. We explain the failure of existing classes of therapeutic agents such as fibrates, niacin, and cholesteryl ester transfer protein inhibitors that are known to modify components of the atherogenic dyslipidemia complex. Finally, we discuss targeted repurposing of existing therapies and review promising new therapeutic strategies to modify the atherogenic dyslipidemia complex. We postulate that targeting the central abnormality of the atherogenic dyslipidemia complex, the elevation of triglyceride-rich lipoprotein particles, represents a new frontier in CVD prevention and is likely to prove the most effective strategy in correcting most aspects of the atherogenic dyslipidemia complex, thereby preventing CVD events.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".