The role of inflammation and the possibilities of inflammation reduction to prevent cardiovascular events
Bibliographic record
Abstract
Chronic systemic inflammation is a risk factor for cardiovascular (CV) disease (CVD). Whether this relationship extends to subclinical inflammation, quantified by values of circulating markers associated with inflammation in the high range of the normal interval, remains debatable. This narrative review evaluates evidence exploring this relationship. A review of pharmacological and non-pharmacological interventions, including diet and lifestyle strategies, supplements, nutraceuticals, and other natural substances aimed at reducing inflammation was also conducted, since few reviews have synthesized this literature. PubMed and EMBASE were used to search the literature and several well-studied triggers of inflammation [oxidized LDL, Lp(a), as well as C-reactive protein (CRP)/high-sensitivity CRP (hs-CRP)] were included to increase sensitivity and address the lack of existing reviews summarizing their influence in the context of inflammation. All resulting references were assessed. Overall, there is good data supporting associations between circulating hs-CRP and CV outcomes. However, the same was not seen in studies evaluating triggers of inflammation, such as oxidized LDL or Lp(a). There is also insufficient evidence showing treatments to target inflammation and lead to reductions in hs-CRP result in improvements in CV outcomes, particularly in those with normal baseline levels of hs-CRP. Regarding pharmacological interventions, statins, bempedoic acid, and apabetalone significantly reduce circulating hs-CRP, unlike PCSK-9 inhibitors. A variety of natural substances and vitamins were also evaluated and none reduced hs-CRP. Regarding non-pharmacological interventions, weight loss was strongly associated with reductions in circulating hs-CRP, whereas various dietary interventions and exercise regimens were not, unless accompanied by weight loss.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".