The DOI matches its Crossref record; 258 of the 258 references it cites were found in a registry or an index.
Targeting inflammation in atherosclerosis — from experimental insights to the clinic
Open accessAbstract
Atherosclerosis, a dominant and growing cause of death and disability worldwide, involves inflammation from its inception to the emergence of complications. Targeting inflammatory pathways could therefore provide a promising new avenue to prevent and treat atherosclerosis. Indeed, clinical studies have now demonstrated unequivocally that modulation of inflammation can forestall the clinical complications of atherosclerosis. This progress pinpoints the need for preclinical investigations to refine strategies for combatting inflammation in the human disease. In this Review, we consider a gamut of attractive possibilities for modifying inflammation in atherosclerosis, including targeting pivotal inflammatory pathways such as the inflammasomes, inhibiting cytokines, manipulating adaptive immunity and promoting pro-resolution mechanisms. Along with lifestyle measures, pharmacological interventions to mute inflammation could complement traditional targets, such as lipids and hypertension, to make new inroads into the management of atherosclerotic risk. The contribution of inflammation to atherosclerosis is substantial, and is just beginning to be understood. In this Review, Soehnlein and Libby discuss how inflammation promotes atherosclerosis and its consequences, and how such processes could be targeted therapeutically. The potential pitfalls of targeting immune processes — namely the increased potential for infections — are also discussed, along with ways to modulate cardiovascular therapies in time and space to make them more effective.
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Self-citation
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Metadata
- Authors
- Soehnlein, O., & Libby, P.
- Venue
- Nature Reviews Drug Discovery
- Published
- 2021-05-11
- Volume
- 20
- Issue
- 8
- Pages
- 589-610
- ISSN
- 1474-1776 (ISSN-L)
- 1474-1784
- OpenAlex
- W3163123861
- Cited by
- 1,019
- References
- 258
- FWCI
- 14.42
- Type
- journal article
- Language
- en
- Open access
- bronze
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
