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<p>The objective of this study was to identify plasma lipid signatures associated with plaque vulnerability. We retrospectively evaluated coronary plaque in 99 patients using optical coherence tomography (OCT) and quantified 489 plasma lipids. We identified intra- and inter-class crosstalk among ceramide (Cer)-phosphatidylinositol (PI)-esterified cholesterol (CE)-sphingomyelin (SM) (Cer-PI-CE-SM) in patients with thin-cap fibroatheroma (TCFA). CE-16:0, SM d18:1/16:1, and GM3 d18:1/22:0, emerged as potential markers of TCFA, correlating with the thinnest fibrous cap thickness and the presence of cholesterol crystallization. Compared to the clinical model (area under the curve [AUC] = 0.810), the AUC of the combined clinical-lipid model improved [AUC = 0.880, <i>p</i> = 0.032]. Calibration and decision curves demonstrated that the combined model exhibited superior diagnostic performance. We identified lipid molecules that are strongly correlated with plaque vulnerability, thus providing an option for the non-invasive identification of vulnerable plaques, which could potentially facilitate the tailored treatment for high-risk patients.</p>

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Identification of Plasma Lipidomic Signatures Associated with Coronary Plaque Vulnerability

  • 期刊论文

  • 发表日期：

    2025年6月20日

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  • Yanyan Gong,
  • Chen Zhao,
  • Lixin Jia,
  • Bokang Qiao,
  • Jinwei Tian,
  • Haichu Wen,
  • Yuan Wang,
  • Bo Yu,
  • Jie Du

摘要

The objective of this study was to identify plasma lipid signatures associated with plaque vulnerability. We retrospectively evaluated coronary plaque in 99 patients using optical coherence tomography (OCT) and quantified 489 plasma lipids. We identified intra- and inter-class crosstalk among ceramide (Cer)-phosphatidylinositol (PI)-esterified cholesterol (CE)-sphingomyelin (SM) (Cer-PI-CE-SM) in patients with thin-cap fibroatheroma (TCFA). CE-16:0, SM d18:1/16:1, and GM3 d18:1/22:0, emerged as potential markers of TCFA, correlating with the thinnest fibrous cap thickness and the presence of cholesterol crystallization. Compared to the clinical model (area under the curve [AUC] = 0.810), the AUC of the combined clinical-lipid model improved [AUC = 0.880, p = 0.032]. Calibration and decision curves demonstrated that the combined model exhibited superior diagnostic performance. We identified lipid molecules that are strongly correlated with plaque vulnerability, thus providing an option for the non-invasive identification of vulnerable plaques, which could potentially facilitate the tailored treatment for high-risk patients.