Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, Australia.
Arterioscler Thromb Vasc Biol. 2011 Nov;31(11):2723-32. doi: 10.1161/ATVBAHA.111.234096.
Traditional risk factors for coronary artery disease (CAD) fail to adequately distinguish patients who have atherosclerotic plaques susceptible to instability from those who have more benign forms. Using plasma lipid profiling, this study aimed to provide insight into disease pathogenesis and evaluate the potential of lipid profiles to assess risk of future plaque instability.
Plasma lipid profiles containing 305 lipids were measured on 220 individuals (matched healthy controls, n=80; stable angina, n=60; unstable coronary syndrome, n=80) using electrospray-ionisation tandem mass spectrometry. ReliefF feature selection coupled with an L2-regularized logistic regression based classifier was used to create multivariate classification models which were verified via 3-fold cross-validation (1000 repeats). Models incorporating both lipids and traditional risk factors provided improved classification of unstable CAD from stable CAD (C-statistic=0.875, 95% CI 0.874-0.877) compared with models containing only traditional risk factors (C-statistic=0.796, 95% CI 0.795-0.798). Many of the lipids identified as discriminatory for unstable CAD displayed an association with disease acuity (severity), suggesting that they are antecedents to the onset of acute coronary syndrome.
Plasma lipid profiling may contribute to a new approach to risk stratification for unstable CAD.
传统的冠心病（CAD）危险因素不能充分区分易发生不稳定斑块的患者与具有更良性形式的患者。本研究使用血浆脂质谱分析，旨在深入了解疾病发病机制，并评估脂质谱评估未来斑块不稳定风险的潜力。
采用电喷雾电离串联质谱法对 220 名个体（匹配的健康对照组，n=80；稳定型心绞痛，n=60；不稳定型冠状动脉综合征，n=80）的血浆脂质谱进行了 305 种脂质的测量。采用 ReliefF 特征选择和基于 L2 正则化逻辑回归的分类器创建多元分类模型，并通过 3 倍交叉验证（1000 次重复）进行验证。与仅包含传统危险因素的模型相比，包含脂质和传统危险因素的模型可改善不稳定 CAD 与稳定 CAD 的分类（C 统计量=0.875，95%CI 0.874-0.877）。确定为不稳定 CAD 具有鉴别性的许多脂质与疾病严重性呈关联，表明它们是急性冠状动脉综合征发病的前兆。
血浆脂质谱分析可能有助于为不稳定 CAD 提供一种新的风险分层方法。
