Lipidomic-Based Algorithms Can Enhance Prediction of Obstructive Coronary Artery Disease

IF 3.6 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Thomai Mouskeftara, Olga Deda, Theodoros Liapikos, Eleftherios Panteris, Efstratios Karagiannidis, Andreas S. Papazoglou and Helen Gika*, 
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Abstract

Lipidomics emerges as a promising research field with the potential to help in personalized risk stratification and improve our understanding on the functional role of individual lipid species in the metabolic perturbations occurring in coronary artery disease (CAD). This study aimed to utilize a machine learning approach to provide a lipid panel able to identify patients with obstructive CAD. In this posthoc analysis of the prospective CorLipid trial, we investigated the lipid profiles of 146 patients with suspected CAD, divided into two categories based on the existence of obstructive CAD. In total, 517 lipid species were identified, from which 288 lipid species were finally quantified, including glycerophospholipids, glycerolipids, and sphingolipids. Univariate and multivariate statistical analyses have shown significant discrimination between the serum lipidomes of patients with obstructive CAD. Finally, the XGBoost algorithm identified a panel of 17 serum biomarkers (5 sphingolipids, 7 glycerophospholipids, a triacylglycerol, galectin-3, glucose, LDL, and LDH) as totally sensitive (100% sensitivity, 62.1% specificity, 100% negative predictive value) for the prediction of obstructive CAD. Our findings shed light on dysregulated lipid metabolism’s role in CAD, validating existing evidence and suggesting promise for novel therapies and improved risk stratification.

Abstract Image

基于血脂组学的算法可加强对阻塞性冠状动脉疾病的预测
脂质组学是一个前景广阔的研究领域,有望帮助进行个性化风险分层,并提高我们对单个脂质种类在冠状动脉疾病(CAD)代谢紊乱中的功能作用的认识。本研究旨在利用机器学习方法提供一个能够识别阻塞性冠状动脉疾病患者的血脂面板。在这项前瞻性 CorLipid 试验的事后分析中,我们调查了 146 名疑似 CAD 患者的血脂谱,根据是否存在阻塞性 CAD 将患者分为两类。共鉴定出 517 种脂质,最后对其中的 288 种脂质进行了定量,包括甘油磷脂、甘油三酯和鞘磷脂。单变量和多变量统计分析显示,阻塞性 CAD 患者血清脂质体之间存在显著差异。最后,XGBoost 算法确定了 17 种血清生物标志物(5 种鞘磷脂、7 种甘油磷脂、1 种三酰甘油、galectin-3、葡萄糖、低密度脂蛋白和低密度脂蛋白胆固醇)对预测阻塞性 CAD 完全敏感(灵敏度 100%、特异性 62.1%、阴性预测值 100%)。我们的研究结果揭示了脂质代谢失调在 CAD 中的作用,验证了现有证据,为新型疗法和改进风险分层带来了希望。
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来源期刊
Journal of Proteome Research
Journal of Proteome Research 生物-生化研究方法
CiteScore
9.00
自引率
4.50%
发文量
251
审稿时长
3 months
期刊介绍: Journal of Proteome Research publishes content encompassing all aspects of global protein analysis and function, including the dynamic aspects of genomics, spatio-temporal proteomics, metabonomics and metabolomics, clinical and agricultural proteomics, as well as advances in methodology including bioinformatics. The theme and emphasis is on a multidisciplinary approach to the life sciences through the synergy between the different types of "omics".
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