Lipoprotein(a), Interleukin-6 inhibitors, and atherosclerotic cardiovascular disease: Is there an association?
Bibliographic record
Abstract
Background and aims: Lipoprotein(a) [Lp(a)] and interleuking-6 (IL-6), an inflammation biomarker, have been established as distinct targets of the residual atherosclerotic cardiovascular disease (ASCVD) risk. We aimed to investigate the association between them, and the potential clinical implications in ASCVD prevention. Methods: A literature search was conducted in PubMed until December 31st, 2022, using relevant keywords. Results: Elevated lipoprotein(a) [Lp(a)] levels constitute the most common inherited lipid disorder associated with ASCVD. Although Lp(a) levels are mostly determined genetically by the LPA gene locus, they may be altered by acute conditions of stress and chronic inflammatory diseases. Considering its resemblance with low-density lipoproteins, Lp(a) is involved in atherosclerosis, but it also exerts oxidative, thrombotic, antifibrinolytic and inflammatory properties. The cardiovascular efficacy of therapies lowering Lp(a) by >90% is currently investigated. On the other hand, interleukin (IL)-1b/IL-6 pathway also plays a pivotal role in atherosclerosis and residual ASCVD risk. IL-6 receptor inhibitors [IL-6(R)i] lower Lp(a) by 16-41%, whereas ongoing trials are investigating their potential anti-atherosclerotic effect. The Lp(a)-lowering effect of IL-6(R)i might be attributed to the inhibition of the IL-6 response elements in the promoter region of the LPA gene. Conclusions: Although the effect of IL-6(R)i on Lp(a) levels is inferior to that of available Lp(a)-lowering therapies, the dual effect of the former on both inflammation and apolipoprotein (a) synthesis may prove of equal or even greater significance when it comes ASCVD outcomes. More trials are required to establish IL-6(R)i in ASCVD prevention and elucidate their interplay with Lp(a) as well as its clinical significance.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".