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Atherosclerotic coronary plaque regression from lipid-lowering therapies: A meta-analysis and meta-regression. | LitMetric

Background: Studies reporting collective and comprehensive data on plaque regression of different lipid-lowering therapies (LLTs) are limited.

Objectives: We evaluated plaque regression of LLTs based on multiple markers and performed subgroup analyses based on LLT type and post-treatment LDL-C levels.

Methods: A literature search was performed to identify studies assessing plaque regression from LLTs. The following LLTs groups were included: High-intensity statin (HIS), HIS+ eicosapentaenoic acid (EPA), HIS + ezetimibe, Low-intensity statin (LIS), LIS + EPA, LIS + Ezetimibe, and PCSK9 inhibitors. Our primary outcomes were change in percent atheroma volume (PAV). Secondary outcomes included mean differences in total atheroma volume (TAV), lumen, plaque, and vessel volumes, fibrous cap thickness (FCT), and lipid arc (LA). Subgroup analyses were performed on LLT type and post-treatment LDL-C levels. Meta-regression was performed to control for covariates.

Results: We identified 51 studies with 9,113 adults (22 % females). LLTs reduced PAV levels (-1.10 % [-1.63, -0.56], < 0.01), with significant reduction observed with HIS, LIS + ezetimibe, LIS + EPA, and PCSK9 inhibitors. LLTs reduced TAV levels (-5.84 mm3 [-8.64 to -3.04] < 0.01), mainly driven by HIS (-7.60 mm3 [-11.89, -3.31] < 0.01). LLTs reduced plaque volume and LA and increased FCT.

Conclusion: The plaque regression associated with LLTs is observed to be mainly driven by HIS, reducing both TAV and PAV. This suggest that HIS is the most effective LLT for plaque regression.

Unstructured Abstract: We evaluated plaque regression of LLTs from 51 studies. We found that while reduction of PAV (-1.10 % [-1.63, -0.56], < 0.01) were present across different LLT types, reduction of TAV (-5.84 mm3 [-8.64 to -3.04] < 0.01) was mainly driven by HIS (-7.60 mm3 [-11.89, -3.31] < 0.01). These results suggest that HIS is the most effective LLT for plaque regression.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10966153PMC
http://dx.doi.org/10.1016/j.ajpc.2024.100645DOI Listing

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