Does low-density lipoprotein cholesterol induce inflammation? If so, does it matter? Current insights and future perspectives for novel therapies
Bibliographic record
Abstract
BACKGROUND: Dyslipidemia and inflammation are closely interrelated contributors in the pathogenesis of atherosclerosis. Disorders of lipid metabolism initiate an inflammatory and immune-mediated response in atherosclerosis, while low-density lipoprotein cholesterol (LDL-C) lowering has possible pleiotropic anti-inflammatory effects that extend beyond lipid lowering. MAIN TEXT: Activation of the immune system/inflammasome destabilizes the plaque, which makes it vulnerable to rupture, resulting in major adverse cardiac events (MACE). The activated immune system potentially accelerates atherosclerosis, and atherosclerosis activates the immune system, creating a vicious circle. LDL-C enhances inflammation, which can be measured through multiple parameters like high-sensitivity C-reactive protein (hsCRP). However, multiple studies have shown that CRP is a marker of residual risk and not, itself, a causal factor. Recently, anti-inflammatory therapy has been shown to decelerate atherosclerosis, resulting in fewer MACE. Nevertheless, an important side effect of anti-inflammatory therapy is the potential for increased infection risk, stressing the importance of only targeting patients with high residual inflammatory risk. Multiple (auto-)inflammatory diseases are potentially related to/influenced by LDL-C through inflammasome activation. CONCLUSIONS: Research suggests that LDL-C induces inflammation; inflammation is of proven importance in atherosclerotic disease progression; anti-inflammatory therapies yield promise in lowering (cardiovascular) disease risk, especially in selected patients with high (remaining) inflammatory risk; and intriguing new anti-inflammatory developments, for example, in nucleotide-binding leucine-rich repeat-containing pyrine receptor inflammasome targeting, are currently underway, including novel pathway interventions such as immune cell targeting and epigenetic interference. Long-term safety should be carefully monitored for these new strategies and cost-effectiveness carefully evaluated.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".