Moxon Joseph V, Liu Dawei, Wong Gerard, Weir Jacquelyn M, Behl-Gilhotra Ratnesh, Bradshaw Barbara, Kingwell Bronwyn A, Meikle Peter J, Golledge Jonathan
The Vascular Biology Unit, Queensland Research Centre for Peripheral Vascular Disease, James Cook University, Townsville, Queensland, Australia.
Circ Cardiovasc Genet. 2014 Feb;7(1):71-9. doi: 10.1161/CIRCGENETICS.113.000343. Epub 2014 Jan 21.
Currently, the relationship between circulating lipids and abdominal aortic aneurysm (AAA) is unclear. We conducted a lipidomic analysis to identify serum lipids associated with AAA presence. Secondary analyses assessed the ability of models incorporating lipidomic features to improve stratification of patient groups with and without AAA beyond traditional risk factors.
Serum lipids were profiled via liquid chromatography tandem mass spectrometry analysis of serum from 161 patients with AAA and 168 controls with peripheral artery disease. Binary logistic regression was used to identify AAA-associated lipids. Classification models were created based on a combination of (1) traditional risk factors only or (2) lipidomic features and traditional risk factors. Model performance was assessed using receiver operator characteristic curves. Three diacylglycerols and 7 triacylglycerols were associated with AAA. Combining lipidomic features with traditional risk factors significantly improved stratification of AAA and peripheral artery disease groups when compared with traditional risk factors alone (mean area under the receiver operator characteristic curve [95% confidence interval], 0.760 [0.756-0.763] and 0.719 [0.716-0.723], respectively; P<0.05).
A group of linoleic acid containing triacylglycerols and diacylglycerols were significantly associated with AAA presence. Inclusion of lipidomic features in multivariate analyses significantly improved prediction of AAA presence when compared with traditional risk factors alone.
目前，循环脂质与腹主动脉瘤（AAA）之间的关系尚不清楚。我们进行了脂质组学分析，以确定与AAA存在相关的血清脂质。二次分析评估了纳入脂质组学特征的模型在传统风险因素之外改善AAA患者组和非AAA患者组分层的能力。
通过液相色谱串联质谱分析法对161例AAA患者和168例外周动脉疾病对照者的血清进行脂质谱分析。采用二元逻辑回归来识别与AAA相关的脂质。基于（1）仅传统风险因素或（2）脂质组学特征与传统风险因素的组合创建分类模型。使用受试者工作特征曲线评估模型性能。三种二酰甘油和七种三酰甘油与AAA相关。与仅使用传统风险因素相比，将脂质组学特征与传统风险因素相结合显著改善了AAA组和外周动脉疾病组的分层（受试者工作特征曲线下平均面积[95%置信区间]分别为0.760[0.756 - 0.763]和0.719[0.716 - 0.723]；P<0.05）。
一组含亚油酸的三酰甘油和二酰甘油与AAA的存在显著相关。与仅使用传统风险因素相比，在多变量分析中纳入脂质组学特征显著改善了AAA存在的预测。
